From 0db442a5ce05a7a57a0ed330f19cc85db4155316 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:24:05 -0400
Subject: [PATCH 001/387] import epidatasets package

---
 DESCRIPTION            | 2 ++
 NAMESPACE              | 1 +
 R/epipredict-package.R | 2 +-
 3 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 6d1217587..889f2b11f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -31,6 +31,7 @@ Imports:
     cli,
     distributional,
     dplyr,
+    epidatasets,
     generics,
     ggplot2,
     glue,
@@ -66,6 +67,7 @@ Suggests:
 VignetteBuilder:
     knitr
 Remotes:
+    cmu-delphi/epidatasets,
     cmu-delphi/epidatr,
     cmu-delphi/epiprocess,
     dajmcdon/smoothqr
diff --git a/NAMESPACE b/NAMESPACE
index 23c5adeaf..f613231f7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -211,6 +211,7 @@ export(update_model)
 export(validate_layer)
 export(weighted_interval_score)
 import(distributional)
+import(epidatasets)
 import(epiprocess)
 import(parsnip)
 import(recipes)
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index 6460b65e4..fbc6ad744 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -10,6 +10,6 @@
 #' @importFrom checkmate assert assert_character assert_int assert_scalar
 #'             assert_logical assert_numeric assert_number assert_integer
 #'             assert_integerish assert_date assert_function assert_class
-#' @import epiprocess parsnip
+#' @import epiprocess parsnip epidatasets
 ## usethis namespace: end
 NULL

From 5c794392b00cb848e76b27fdc433e45000a265bf Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:32:54 -0400
Subject: [PATCH 002/387] replace state_census with epidatasets version

---
 NAMESPACE                 |   1 +
 R/data.R                  |  23 ------------------
 R/reexports-epidatasets.R |  17 +++++++++++++
 data-raw/state_census.R   |  11 ---------
 data/state_census.rda     | Bin 1274 -> 0 bytes
 man/state_census.Rd       |  50 +++++++++++++++++++++++++++-----------
 6 files changed, 54 insertions(+), 48 deletions(-)
 create mode 100644 R/reexports-epidatasets.R
 delete mode 100644 data-raw/state_census.R
 delete mode 100644 data/state_census.rda

diff --git a/NAMESPACE b/NAMESPACE
index f613231f7..73cc2c17b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -195,6 +195,7 @@ export(remove_frosting)
 export(remove_model)
 export(slather)
 export(smooth_quantile_reg)
+export(state_census)
 export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
diff --git a/R/data.R b/R/data.R
index 71e5bdcd3..ae00c2681 100644
--- a/R/data.R
+++ b/R/data.R
@@ -34,29 +34,6 @@
 #'   inclusive.
 "case_death_rate_subset"
 
-#' State population data
-#'
-#' Data set on state populations, from the 2019 US Census.
-#'
-#' @format Data frame with 57 rows (including one for the United States as a
-#'   whole, plus the District of Columbia, Puerto Rico Commonwealth,
-#'   American Samoa, Guam, the U.S. Virgin Islands, and the Northern Mariana,
-#'   Islands).
-#'
-#' \describe{
-#'   \item{fips}{FIPS code}
-#'   \item{name}{Full name of the state or territory}
-#'   \item{pop}{Estimate of the location's resident population in
-#'      2019.}
-#'   \item{abbr}{Postal abbreviation for the location}
-#' }
-#'
-#' @source United States Census Bureau, at
-#'   \url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.pdf},
-#'   \url{https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-puerto-rico-municipios.html},
-#'   and \url{https://www.census.gov/data/tables/2010/dec/2010-island-areas.html}
-"state_census"
-
 #' Subset of Statistics Canada median employment income for postsecondary graduates
 #'
 #' @format An [epiprocess::epi_df][epiprocess::as_epi_df] with 10193 rows and 8 variables:
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
new file mode 100644
index 000000000..9c5ebca2b
--- /dev/null
+++ b/R/reexports-epidatasets.R
@@ -0,0 +1,17 @@
+#' @inherit epidatasets::state_census description source references title
+#' @inheritSection epidatasets::state_census Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::state_census
+#'
+#' # works
+#' library(epipredict)
+#' state_census
+#'
+#' # fails
+#' data(state_census, package = "epipredict")
+#' @export
+delayedAssign("state_census", epidatasets::state_census)
diff --git a/data-raw/state_census.R b/data-raw/state_census.R
deleted file mode 100644
index 22dde5a41..000000000
--- a/data-raw/state_census.R
+++ /dev/null
@@ -1,11 +0,0 @@
-library(dplyr)
-library(tidyr)
-
-state_census <- covidcast::state_census %>%
-  select(STATE, NAME, POPESTIMATE2019, ABBR) %>%
-  rename(abbr = ABBR, name = NAME, pop = POPESTIMATE2019, fips = STATE) %>%
-  mutate(abbr = tolower(abbr)) %>%
-  as_tibble()
-
-
-usethis::use_data(state_census, overwrite = TRUE)
diff --git a/data/state_census.rda b/data/state_census.rda
deleted file mode 100644
index 1118db0d01236a12d67aac1d43ce2f87a694d6f0..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/man/state_census.Rd b/man/state_census.Rd
index eec13eb53..5afa590ec 100644
--- a/man/state_census.Rd
+++ b/man/state_census.Rd
@@ -1,28 +1,18 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/reexports-epidatasets.R
 \docType{data}
 \name{state_census}
 \alias{state_census}
 \title{State population data}
 \format{
-Data frame with 57 rows (including one for the United States as a
-whole, plus the District of Columbia, Puerto Rico Commonwealth,
-American Samoa, Guam, the U.S. Virgin Islands, and the Northern Mariana,
-Islands).
-
-\describe{
-\item{fips}{FIPS code}
-\item{name}{Full name of the state or territory}
-\item{pop}{Estimate of the location's resident population in
-2019.}
-\item{abbr}{Postal abbreviation for the location}
-}
+An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 57 rows and 4 columns.
 }
 \source{
-United States Census Bureau, at
+United States Census Bureau, Population Division, at
 \url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.pdf},
 \url{https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-puerto-rico-municipios.html},
 and \url{https://www.census.gov/data/tables/2010/dec/2010-island-areas.html}
+This data is public domain.
 }
 \usage{
 state_census
@@ -30,4 +20,36 @@ state_census
 \description{
 Data set on state populations, from the 2019 US Census.
 }
+\section{Data dictionary}{
+
+
+The data includes 57 regions (all US states, the United
+States as a whole, the District of Columbia, Puerto Rico Commonwealth,
+American Samoa, Guam, the U.S. Virgin Islands, and the Northern Mariana
+Islands) with columns:
+
+\describe{
+\item{fips}{2-digit FIPS code}
+\item{name}{Full name of the state or territory}
+\item{pop}{Estimate of the location's resident population in
+2019.}
+\item{abbr}{Postal abbreviation for the location}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::state_census
+
+# works
+library(epipredict)
+state_census
+
+# fails
+data(state_census, package = "epipredict")
+}
 \keyword{datasets}

From d289e810ea1b4b5c28061c686aab5c5e94b03fb0 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:35:14 -0400
Subject: [PATCH 003/387] replace grad_employ_subset with epidatasets version

---
 NAMESPACE                     |   1 +
 R/data.R                      |  29 ----------
 R/reexports-epidatasets.R     |  18 ++++++
 data-raw/grad_employ_subset.R | 106 ----------------------------------
 data/grad_employ_subset.rda   | Bin 8491 -> 0 bytes
 man/grad_employ_subset.Rd     |  71 ++++++++++++++++-------
 6 files changed, 69 insertions(+), 156 deletions(-)
 delete mode 100644 data-raw/grad_employ_subset.R
 delete mode 100644 data/grad_employ_subset.rda

diff --git a/NAMESPACE b/NAMESPACE
index 73cc2c17b..46d78511c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -166,6 +166,7 @@ export(flusight_hub_formatter)
 export(forecast)
 export(frosting)
 export(get_test_data)
+export(grad_employ_subset)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
diff --git a/R/data.R b/R/data.R
index ae00c2681..9c93506a3 100644
--- a/R/data.R
+++ b/R/data.R
@@ -33,32 +33,3 @@
 #'   June 7 is the average of the underlying data for June 1 through 7,
 #'   inclusive.
 "case_death_rate_subset"
-
-#' Subset of Statistics Canada median employment income for postsecondary graduates
-#'
-#' @format An [epiprocess::epi_df][epiprocess::as_epi_df] with 10193 rows and 8 variables:
-#' \describe{
-#'   \item{geo_value}{The province in Canada associated with each
-#'      row of measurements.}
-#'   \item{time_value}{The time value, a year integer in YYYY format}
-#'   \item{edu_qual}{The education qualification}
-#'   \item{fos}{The field of study}
-#'   \item{age_group}{The age group; either 15 to 34 or 35 to 64}
-#'   \item{num_graduates}{The number of graduates for the given row of characteristics}
-#'   \item{med_income_2y}{The median employment income two years after graduation}
-#'   \item{med_income_5y}{The median employment income five years after graduation}
-#' }
-#' @source This object contains modified data from the following Statistics Canada
-#' data table: \href{https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501}{
-#'  Characteristics and median employment income of longitudinal cohorts of postsecondary
-#'  graduates two and five years after graduation, by educational qualification and
-#'  field of study (primary groupings)
-#' }
-#'
-#' Modifications:
-#' * Only provincial-level geo_values are kept
-#' * Only age group, field of study, and educational qualification are kept as
-#'   covariates. For the remaining covariates, we keep aggregated values and
-#'   drop the level-specific rows.
-#' * No modifications were made to the time range of the data
-"grad_employ_subset"
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index 9c5ebca2b..d409baf9e 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -1,3 +1,21 @@
+#' @inherit epidatasets::grad_employ_subset description source references title
+#' @inheritSection epidatasets::grad_employ_subset Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::grad_employ_subset
+#'
+#' # works
+#' library(epipredict)
+#' grad_employ_subset
+#'
+#' # fails
+#' data(grad_employ_subset, package = "epipredict")
+#' @export
+delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
+
 #' @inherit epidatasets::state_census description source references title
 #' @inheritSection epidatasets::state_census Data dictionary
 #' @examples
diff --git a/data-raw/grad_employ_subset.R b/data-raw/grad_employ_subset.R
deleted file mode 100644
index ae063d22f..000000000
--- a/data-raw/grad_employ_subset.R
+++ /dev/null
@@ -1,106 +0,0 @@
-library(epipredict)
-library(epiprocess)
-library(cansim)
-library(dplyr)
-library(stringr)
-library(tidyr)
-
-# https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501
-statcan_grad_employ <- get_cansim("37-10-0115-01")
-
-gemploy <- statcan_grad_employ %>%
-  select(c(
-    "REF_DATE",
-    "GEO",
-    # "DGUID",
-    # "UOM",
-    # "UOM_ID",
-    # "SCALAR_FACTOR",
-    # "SCALAR_ID",
-    # "VECTOR",
-    # "COORDINATE",
-    "VALUE",
-    "STATUS",
-    # "SYMBOL",
-    # "TERMINATED",
-    # "DECIMALS",
-    # "GeoUID",
-    # "Hierarchy for GEO",
-    # "Classification Code for Educational qualification",
-    # "Hierarchy for Educational qualification",
-    # "Classification Code for Field of study",
-    # "Hierarchy for Field of study",
-    # "Classification Code for Gender",
-    # "Hierarchy for Gender",
-    # "Classification Code for Age group",
-    # "Hierarchy for Age group",
-    # "Classification Code for Status of student in Canada",
-    # "Hierarchy for Status of student in Canada",
-    # "Classification Code for Characteristics after graduation",
-    # "Hierarchy for Characteristics after graduation",
-    # "Classification Code for Graduate statistics",
-    # "Hierarchy for Graduate statistics",
-    # "val_norm",
-    # "Date",
-    "Educational qualification",
-    "Field of study",
-    "Gender",
-    "Age group",
-    "Status of student in Canada",
-    "Characteristics after graduation",
-    "Graduate statistics"
-  )) %>%
-  rename(
-    "geo_value" = "GEO",
-    "time_value" = "REF_DATE",
-    "value" = "VALUE",
-    "status" = "STATUS",
-    "edu_qual" = "Educational qualification",
-    "fos" = "Field of study",
-    "gender" = "Gender",
-    "age_group" = "Age group",
-    "student_status" = "Status of student in Canada",
-    "grad_charac" = "Characteristics after graduation",
-    "grad_stat" = "Graduate statistics"
-  ) %>%
-  mutate(
-    grad_stat = recode_factor(
-      grad_stat,
-      `Number of graduates` = "num_graduates",
-      `Median employment income two years after graduation` = "med_income_2y",
-      `Median employment income five years after graduation` = "med_income_5y"
-    ),
-    time_value = as.integer(time_value)
-  ) %>%
-  pivot_wider(names_from = grad_stat, values_from = value) %>%
-  filter(
-    # Drop aggregates for some columns
-    geo_value != "Canada" &
-      age_group != "15 to 64 years" &
-      edu_qual != "Total, educational qualification" &
-      # Keep aggregates for keys we don't want to keep
-      fos == "Total, field of study" &
-      gender == "Total, gender" &
-      student_status == "Canadian and international students" &
-      # Since we're looking at 2y and 5y employment income, the only
-      # characteristics remaining are:
-      # - Graduates reporting employment income
-      # - Graduates reporting wages, salaries, and commissions only
-      # For simplicity, keep the first one only
-      grad_charac == "Graduates reporting employment income" &
-      # Only keep "good" data
-      is.na(status) &
-      # Drop NA value rows
-      !is.na(num_graduates) & !is.na(med_income_2y) & !is.na(med_income_5y)
-  ) %>%
-  select(-c(status, gender, student_status, grad_charac, fos))
-
-nrow(gemploy)
-ncol(gemploy)
-
-grad_employ_subset <- gemploy %>%
-  as_epi_df(
-    as_of = "2022-07-19",
-    additional_metadata = list(other_keys = c("age_group", "edu_qual"))
-  )
-usethis::use_data(grad_employ_subset, overwrite = TRUE)
diff --git a/data/grad_employ_subset.rda b/data/grad_employ_subset.rda
deleted file mode 100644
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diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
index 46ba36913..fb5e8003c 100644
--- a/man/grad_employ_subset.Rd
+++ b/man/grad_employ_subset.Rd
@@ -1,34 +1,27 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/reexports-epidatasets.R
 \docType{data}
 \name{grad_employ_subset}
 \alias{grad_employ_subset}
 \title{Subset of Statistics Canada median employment income for postsecondary graduates}
 \format{
-An \link[epiprocess:epi_df]{epiprocess::epi_df} with 10193 rows and 8 variables:
-\describe{
-\item{geo_value}{The province in Canada associated with each
-row of measurements.}
-\item{time_value}{The time value, a year integer in YYYY format}
-\item{edu_qual}{The education qualification}
-\item{fos}{The field of study}
-\item{age_group}{The age group; either 15 to 34 or 35 to 64}
-\item{num_graduates}{The number of graduates for the given row of characteristics}
-\item{med_income_2y}{The median employment income two years after graduation}
-\item{med_income_5y}{The median employment income five years after graduation}
-}
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1445 rows and 7 columns.
 }
 \source{
-This object contains modified data from the following Statistics Canada
-data table: \href{https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501}{
-Characteristics and median employment income of longitudinal cohorts of postsecondary
-graduates two and five years after graduation, by educational qualification and
-field of study (primary groupings)
-}
+This object contains modified data adapted from
+Statistics Canada, \href{https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501}{
+Table 37-10-0115-01 Characteristics and median employment income of
+longitudinal cohorts of postsecondary graduates two and five years after
+graduation, by educational qualification and field of study
+(primary groupings)
+}. This does not constitute an endorsement by Statistics Canada of this product.
+
+The data is licensed under the terms of the
+\href{https://www.statcan.gc.ca/en/reference/licence}{Statistics Canada Open License}.
 
 Modifications:
 \itemize{
-\item Only provincial-level geo_values are kept
+\item Only provincial and territorial regions are kept
 \item Only age group, field of study, and educational qualification are kept as
 covariates. For the remaining covariates, we keep aggregated values and
 drop the level-specific rows.
@@ -39,6 +32,42 @@ drop the level-specific rows.
 grad_employ_subset
 }
 \description{
-Subset of Statistics Canada median employment income for postsecondary graduates
+Data set for all territories (aggregated) and all 10 provinces containing
+yearly income data for postsecondary graduates as reported by Statistics
+Canada, downloaded from the Statistics Canada website at
+www.statcan.gc.ca. This example data is a snapshot as of September 18,
+2024, and ranges from 2010 to 2017 (yearly).
+}
+\section{Data dictionary}{
+
+
+The data has columns:
+\describe{
+\item{geo_value}{The province in Canada associated with each
+row of measurements.}
+\item{time_value}{The time value, a year integer in YYYY format}
+\item{edu_qual}{The education qualification}
+\item{fos}{The field of study}
+\item{age_group}{The age group; either 15 to 34 or 35 to 64}
+\item{num_graduates}{The number of graduates for the given row of characteristics}
+\item{med_income_2y}{The median employment income two years after graduation}
+\item{med_income_5y}{The median employment income five years after graduation}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::grad_employ_subset
+
+# works
+library(epipredict)
+grad_employ_subset
+
+# fails
+data(grad_employ_subset, package = "epipredict")
 }
 \keyword{datasets}

From b04855aa1a599bbe41c3ce40f2b1db98de7e2623 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:43:18 -0400
Subject: [PATCH 004/387] replace case_death_rate_subset with
 epidatasets::covid_case_death_rates

---
 NAMESPACE                         |   1 +
 R/data.R                          |  35 ---------------
 R/reexports-epidatasets.R         |  18 ++++++++
 data-raw/case_death_rate_subset.R |  32 --------------
 data/case_death_rate_subset.rda   | Bin 212542 -> 0 bytes
 man/covid_case_death_rates.Rd     |  71 ++++++++++++++++++++++++++++++
 6 files changed, 90 insertions(+), 67 deletions(-)
 delete mode 100644 R/data.R
 delete mode 100644 data-raw/case_death_rate_subset.R
 delete mode 100644 data/case_death_rate_subset.rda
 create mode 100644 man/covid_case_death_rates.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 46d78511c..54aa672cb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -148,6 +148,7 @@ export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
 export(clean_f_name)
+export(covid_case_death_rates)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
diff --git a/R/data.R b/R/data.R
deleted file mode 100644
index 9c93506a3..000000000
--- a/R/data.R
+++ /dev/null
@@ -1,35 +0,0 @@
-#' Subset of JHU daily state cases and deaths
-#'
-#' This data source of confirmed COVID-19 cases and deaths
-#' is based on reports made available by the Center for
-#' Systems Science and Engineering at Johns Hopkins University.
-#' This example data ranges from Dec 31, 2020 to Dec 31, 2021,
-#' and includes all states.
-#'
-#' @format A tibble with 20,496 rows and 4 variables:
-#' \describe{
-#'   \item{geo_value}{the geographic value associated with each row
-#'       of measurements.}
-#'   \item{time_value}{the time value associated with each row of measurements.}
-#'   \item{case_rate}{7-day average signal of number of new
-#'       confirmed COVID-19 cases per 100,000 population, daily}
-#'   \item{death_rate}{7-day average signal of number of new confirmed
-#'       deaths due to COVID-19 per 100,000 population, daily}
-#' }
-#' @source This object contains a modified part of the
-#'   \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-#'   as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
-#'   This data set is licensed under the terms of the
-#'   \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-#'   by the Johns Hopkins University on behalf of its Center for Systems Science
-#'   in Engineering. Copyright Johns Hopkins University 2020.
-#'
-#' Modifications:
-#' * \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
-#'   These signals are taken directly from the JHU CSSE
-#'   \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
-#'   without changes. The 7-day average signals are computed by Delphi by
-#'   calculating moving averages of the preceding 7 days, so the signal for
-#'   June 7 is the average of the underlying data for June 1 through 7,
-#'   inclusive.
-"case_death_rate_subset"
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index d409baf9e..f7f76e60c 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -16,6 +16,24 @@
 #' @export
 delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
 
+#' @inherit epidatasets::covid_case_death_rates description source references title
+#' @inheritSection epidatasets::covid_case_death_rates Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::covid_case_death_rates
+#'
+#' # works
+#' library(epipredict)
+#' covid_case_death_rates
+#'
+#' # fails
+#' data(covid_case_death_rates, package = "epipredict")
+#' @export
+delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
+
 #' @inherit epidatasets::state_census description source references title
 #' @inheritSection epidatasets::state_census Data dictionary
 #' @examples
diff --git a/data-raw/case_death_rate_subset.R b/data-raw/case_death_rate_subset.R
deleted file mode 100644
index 4bf416dbf..000000000
--- a/data-raw/case_death_rate_subset.R
+++ /dev/null
@@ -1,32 +0,0 @@
-library(tidyverse)
-library(covidcast)
-library(epidatr)
-library(epiprocess)
-
-x <- covidcast(
-  data_source = "jhu-csse",
-  signals = "confirmed_7dav_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20201231, 20211231),
-  geo_values = "*"
-) %>%
-  fetch() %>%
-  select(geo_value, time_value, case_rate = value)
-
-y <- covidcast(
-  data_source = "jhu-csse",
-  signals = "deaths_7dav_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20201231, 20211231),
-  geo_values = "*"
-) %>%
-  fetch() %>%
-  select(geo_value, time_value, death_rate = value)
-
-case_death_rate_subset <- x %>%
-  full_join(y, by = c("geo_value", "time_value")) %>%
-  as_epi_df()
-
-usethis::use_data(case_death_rate_subset, overwrite = TRUE)
diff --git a/data/case_death_rate_subset.rda b/data/case_death_rate_subset.rda
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
new file mode 100644
index 000000000..c553e57bd
--- /dev/null
+++ b/man/covid_case_death_rates.Rd
@@ -0,0 +1,71 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/reexports-epidatasets.R
+\docType{data}
+\name{covid_case_death_rates}
+\alias{covid_case_death_rates}
+\title{JHU daily COVID-19 cases and deaths rates from all states}
+\format{
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 22176 rows and 4 columns.
+}
+\source{
+This object contains a modified part of the
+\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
+as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
+This data set is licensed under the terms of the
+\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
+by the Johns Hopkins University on behalf of its Center for Systems Science
+in Engineering. Copyright Johns Hopkins University 2020.
+
+Modifications:
+\itemize{
+\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
+These signals are taken directly from the JHU CSSE
+\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
+without changes. The 7-day average signals are computed by Delphi by
+calculating moving averages of the preceding 7 days, so the signal for
+June 7 is the average of the underlying data for June 1 through 7,
+inclusive.
+}
+}
+\usage{
+covid_case_death_rates
+}
+\description{
+This data source of confirmed COVID-19 cases and deaths is based on reports
+made available by the Center for Systems Science and Engineering at Johns
+Hopkins University, as downloaded from the CMU Delphi COVIDcast Epidata
+API. This example data is a snapshot as of March 20, 2024, and
+ranges from December 31, 2020 to December 31, 2021. It
+includes all states. It is used in the {epiprocess} correlation vignette.
+}
+\section{Data dictionary}{
+
+
+The data has columns:
+\describe{
+\item{geo_value}{the geographic value associated with each row
+of measurements.}
+\item{time_value}{the time value associated with each row of measurements.}
+\item{case_rate}{7-day average signal of number of new
+confirmed COVID-19 cases per 100,000 population, daily}
+\item{death_rate}{7-day average signal of number of new confirmed
+deaths due to COVID-19 per 100,000 population, daily}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::covid_case_death_rates
+
+# works
+library(epipredict)
+covid_case_death_rates
+
+# fails
+data(covid_case_death_rates, package = "epipredict")
+}
+\keyword{datasets}

From 7b6895d3bff8642792c241e3b44eedc0441f9dd5 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 11:43:53 -0400
Subject: [PATCH 005/387] replace all case_death_rate_subset references

---
 R/arx_classifier.R                            |  4 +-
 R/arx_forecaster.R                            |  4 +-
 R/autoplot.R                                  |  4 +-
 R/cdc_baseline_forecaster.R                   |  2 +-
 R/epi_recipe.R                                |  6 +--
 R/epi_workflow.R                              |  6 +--
 R/flatline_forecaster.R                       |  2 +-
 R/flusight_hub_formatter.R                    |  2 +-
 R/frosting.R                                  |  6 +--
 R/get_test_data.R                             |  4 +-
 R/layer_add_forecast_date.R                   |  2 +-
 R/layer_add_target_date.R                     |  2 +-
 R/layer_cdc_flatline_quantiles.R              |  8 +--
 R/layer_naomit.R                              |  2 +-
 R/layer_point_from_distn.R                    |  2 +-
 R/layer_predict.R                             |  2 +-
 R/layer_predictive_distn.R                    |  2 +-
 R/layer_quantile_distn.R                      |  2 +-
 R/layer_residual_quantiles.R                  |  2 +-
 R/layer_threshold_preds.R                     |  2 +-
 R/layers.R                                    |  2 +-
 R/make_grf_quantiles.R                        |  2 +-
 R/model-methods.R                             |  2 +-
 R/pivot_quantiles.R                           |  2 +-
 R/step_epi_naomit.R                           |  2 +-
 R/step_epi_shift.R                            |  2 +-
 R/step_epi_slide.R                            |  2 +-
 R/step_growth_rate.R                          |  6 +--
 R/step_lag_difference.R                       |  6 +--
 R/tidy.R                                      |  2 +-
 R/weighted_interval_score.R                   |  4 +-
 README.Rmd                                    |  4 +-
 README.md                                     |  4 +-
 _pkgdown.yml                                  |  2 +-
 man/Add_model.Rd                              |  2 +-
 man/add_epi_recipe.Rd                         |  2 +-
 man/add_frosting.Rd                           |  2 +-
 man/adjust_epi_recipe.Rd                      |  2 +-
 man/adjust_frosting.Rd                        |  2 +-
 man/arx_class_epi_workflow.Rd                 |  2 +-
 man/arx_classifier.Rd                         |  2 +-
 man/arx_fcast_epi_workflow.Rd                 |  2 +-
 man/arx_forecaster.Rd                         |  2 +-
 man/autoplot-epipred.Rd                       |  4 +-
 man/case_death_rate_subset.Rd                 | 49 -------------------
 man/cdc_baseline_forecaster.Rd                |  2 +-
 man/epi_recipe.Rd                             |  2 +-
 man/epi_workflow.Rd                           |  2 +-
 man/fit-epi_workflow.Rd                       |  2 +-
 man/flatline_forecaster.Rd                    |  2 +-
 man/flusight_hub_formatter.Rd                 |  2 +-
 man/frosting.Rd                               |  2 +-
 man/get_test_data.Rd                          |  4 +-
 man/grf_quantiles.Rd                          |  2 +-
 man/layer_add_forecast_date.Rd                |  2 +-
 man/layer_add_target_date.Rd                  |  2 +-
 man/layer_cdc_flatline_quantiles.Rd           |  8 +--
 man/layer_naomit.Rd                           |  2 +-
 man/layer_point_from_distn.Rd                 |  2 +-
 man/layer_predict.Rd                          |  2 +-
 man/layer_predictive_distn.Rd                 |  2 +-
 man/layer_quantile_distn.Rd                   |  2 +-
 man/layer_residual_quantiles.Rd               |  2 +-
 man/layer_threshold.Rd                        |  2 +-
 man/nested_quantiles.Rd                       |  2 +-
 man/predict-epi_workflow.Rd                   |  2 +-
 man/step_epi_naomit.Rd                        |  2 +-
 man/step_epi_shift.Rd                         |  2 +-
 man/step_epi_slide.Rd                         |  2 +-
 man/step_growth_rate.Rd                       |  6 +--
 man/step_lag_difference.Rd                    |  6 +--
 man/tidy.frosting.Rd                          |  2 +-
 man/update.layer.Rd                           |  2 +-
 man/weighted_interval_score.Rd                |  4 +-
 tests/testthat/test-bake-method.R             |  2 +-
 tests/testthat/test-blueprint.R               |  2 +-
 tests/testthat/test-epi_recipe.R              |  2 +-
 tests/testthat/test-epi_workflow.R            | 10 ++--
 tests/testthat/test-extract_argument.R        |  2 +-
 tests/testthat/test-frosting.R                |  6 +--
 tests/testthat/test-get_test_data.R           | 16 +++---
 tests/testthat/test-key_colnames.R            |  6 +--
 tests/testthat/test-layer_add_forecast_date.R |  2 +-
 tests/testthat/test-layer_add_target_date.R   |  2 +-
 tests/testthat/test-layer_naomit.R            |  2 +-
 tests/testthat/test-layer_predict.R           |  2 +-
 .../testthat/test-layer_residual_quantiles.R  |  2 +-
 tests/testthat/test-layer_threshold_preds.R   |  2 +-
 tests/testthat/test-population_scaling.R      |  8 +--
 tests/testthat/test-snapshots.R               |  2 +-
 vignettes/articles/smooth-qr.Rmd              |  2 +-
 vignettes/arx-classifier.Rmd                  |  6 +--
 vignettes/epipredict.Rmd                      |  2 +-
 vignettes/panel-data.Rmd                      |  6 +--
 vignettes/preprocessing-and-models.Rmd        | 10 ++--
 vignettes/update.Rmd                          |  4 +-
 96 files changed, 152 insertions(+), 201 deletions(-)
 delete mode 100644 man/case_death_rate_subset.Rd

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index ca6a3537b..71537a065 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -27,7 +27,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
@@ -97,7 +97,7 @@ arx_classifier <- function(
 #' @seealso [arx_classifier()]
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' arx_class_epi_workflow(jhu, "death_rate", c("case_rate", "death_rate"))
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 37c9aae86..219fecb0e 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -25,7 +25,7 @@
 #' @seealso [arx_fcast_epi_workflow()], [arx_args_list()]
 #'
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
 #' out <- arx_forecaster(
@@ -92,7 +92,7 @@ arx_forecaster <- function(
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-12-01"))
 #'
 #' arx_fcast_epi_workflow(
diff --git a/R/autoplot.R b/R/autoplot.R
index d35850fd6..50b7753cd 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -29,7 +29,7 @@ ggplot2::autoplot
 #' @name autoplot-epipred
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' r <- epi_recipe(jhu) %>%
@@ -70,7 +70,7 @@ ggplot2::autoplot
 #'
 #' # ------- Plotting canned forecaster output
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #' flat <- flatline_forecaster(jhu, "death_rate")
 #' autoplot(flat, .max_facets = 4)
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index 74af5e443..2bb075f9d 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -23,7 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' weekly_deaths <- case_death_rate_subset %>%
+#' weekly_deaths <- covid_case_death_rates %>%
 #'   select(geo_value, time_value, death_rate) %>%
 #'   left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
 #'   mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) %>%
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 88ba605cd..f2586e4c0 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -48,7 +48,7 @@ epi_recipe.default <- function(x, ...) {
 #' @examples
 #' library(dplyr)
 #' library(recipes)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-08-01") %>%
 #'   arrange(geo_value, time_value)
 #'
@@ -270,7 +270,7 @@ is_epi_recipe <- function(x) {
 #' library(dplyr)
 #' library(recipes)
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-08-01") %>%
 #'   arrange(geo_value, time_value)
 #'
@@ -354,7 +354,7 @@ update_epi_recipe <- function(x, recipe, ..., blueprint = default_epi_recipe_blu
 #' library(dplyr)
 #' library(workflows)
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index b059a81d0..c54420db4 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -20,7 +20,7 @@
 #' @importFrom generics augment
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset
+#' jhu <- covid_case_death_rates
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -84,7 +84,7 @@ is_epi_workflow <- function(x) {
 #' @name fit-epi_workflow
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
@@ -139,7 +139,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #' @name predict-epi_workflow
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset
+#' jhu <- covid_case_death_rates
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 55808b803..6048c4250 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -24,7 +24,7 @@
 #' @export
 #'
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
 #' out <- flatline_forecaster(jhu, "death_rate")
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index c91f738ae..3f7588b8e 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -57,7 +57,7 @@ abbr_to_location <- function(abbr) {
 #'
 #' @examples
 #' library(dplyr)
-#' weekly_deaths <- case_death_rate_subset %>%
+#' weekly_deaths <- covid_case_death_rates %>%
 #'   filter(
 #'     time_value >= as.Date("2021-09-01"),
 #'     geo_value %in% c("ca", "ny", "dc", "ga", "vt")
diff --git a/R/frosting.R b/R/frosting.R
index 8474edbdf..8060e8cb4 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -9,7 +9,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -128,7 +128,7 @@ update_frosting <- function(x, frosting, ...) {
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -268,7 +268,7 @@ new_frosting <- function() {
 #' wf <- epi_workflow() %>% add_frosting(f)
 #'
 #' # A more realistic example
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/get_test_data.R b/R/get_test_data.R
index 694e73b06..e37cab836 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -35,11 +35,11 @@
 #'   keys, as well other variables in the original dataset.
 #' @examples
 #' # create recipe
-#' rec <- epi_recipe(case_death_rate_subset) %>%
+#' rec <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14))
-#' get_test_data(recipe = rec, x = case_death_rate_subset)
+#' get_test_data(recipe = rec, x = covid_case_death_rates)
 #' @importFrom rlang %@%
 #' @export
 get_test_data <- function(
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 02395f960..7c47b0721 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -20,7 +20,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 9176fb593..a24b33a8b 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -21,7 +21,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 8d16ba32f..3a01d9915 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -56,14 +56,14 @@
 #'
 #' @examples
 #' library(dplyr)
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   # data is "daily", so we fit this to 1 ahead, the result will contain
 #'   # 1 day ahead residuals
 #'   step_epi_ahead(death_rate, ahead = 1L, skip = TRUE) %>%
 #'   recipes::update_role(death_rate, new_role = "predictor") %>%
 #'   recipes::add_role(time_value, geo_value, new_role = "predictor")
 #'
-#' forecast_date <- max(case_death_rate_subset$time_value)
+#' forecast_date <- max(covid_case_death_rates$time_value)
 #'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
@@ -71,7 +71,7 @@
 #'
 #' eng <- linear_reg(engine = "flatline")
 #'
-#' wf <- epi_workflow(r, eng, f) %>% fit(case_death_rate_subset)
+#' wf <- epi_workflow(r, eng, f) %>% fit(covid_case_death_rates)
 #' preds <- forecast(wf) %>%
 #'   select(-time_value) %>%
 #'   mutate(forecast_date = forecast_date)
@@ -91,7 +91,7 @@
 #'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
 #'     geom_line(aes(y = .pred), color = "orange") +
 #'     geom_line(
-#'       data = case_death_rate_subset %>% filter(geo_value %in% four_states),
+#'       data = covid_case_death_rates %>% filter(geo_value %in% four_states),
 #'       aes(x = time_value, y = death_rate)
 #'     ) +
 #'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 209a663b4..2b111a6f9 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -12,7 +12,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index f14008748..c433717bb 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -17,7 +17,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_predict.R b/R/layer_predict.R
index 6ca17ac24..b59be5f03 100644
--- a/R/layer_predict.R
+++ b/R/layer_predict.R
@@ -17,7 +17,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index b28e0c765..2b18fbf8e 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -21,7 +21,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 5f87ded29..f7bc9259d 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -23,7 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index eae151905..a9eb614f5 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -15,7 +15,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 56f8059ab..7b8ca0252 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -23,7 +23,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value < "2021-03-08", geo_value %in% c("ak", "ca", "ar"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/layers.R b/R/layers.R
index aa515a917..602476773 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -42,7 +42,7 @@ layer <- function(subclass, ..., .prefix = "layer_") {
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 253ea1ac7..e80637a1a 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -61,7 +61,7 @@
 #' # -- a more complicated task
 #'
 #' library(dplyr)
-#' dat <- case_death_rate_subset %>%
+#' dat <- covid_case_death_rates %>%
 #'   filter(time_value > as.Date("2021-10-01"))
 #' rec <- epi_recipe(dat) %>%
 #'   step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%
diff --git a/R/model-methods.R b/R/model-methods.R
index f3b374879..a575bd591 100644
--- a/R/model-methods.R
+++ b/R/model-methods.R
@@ -33,7 +33,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index c8601b4f6..a57d887b0 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -8,7 +8,7 @@
 #' @examples
 #' library(dplyr)
 #' library(tidyr)
-#' edf <- case_death_rate_subset[1:3, ]
+#' edf <- covid_case_death_rates[1:3, ]
 #' edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
 #'
 #' edf_nested <- edf %>% mutate(q = nested_quantiles(q))
diff --git a/R/step_epi_naomit.R b/R/step_epi_naomit.R
index d81ba398d..bfe8a4faa 100644
--- a/R/step_epi_naomit.R
+++ b/R/step_epi_naomit.R
@@ -8,7 +8,7 @@
 #'   of data loss.
 #' @export
 #' @examples
-#' case_death_rate_subset %>%
+#' covid_case_death_rates %>%
 #'   epi_recipe() %>%
 #'   step_epi_naomit()
 step_epi_naomit <- function(recipe) {
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 465d64e7f..0d3070e36 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -42,7 +42,7 @@
 #' @rdname step_epi_shift
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
diff --git a/R/step_epi_slide.R b/R/step_epi_slide.R
index 9714971fa..c5e09e3d1 100644
--- a/R/step_epi_slide.R
+++ b/R/step_epi_slide.R
@@ -32,7 +32,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
 #' rec <- epi_recipe(jhu) %>%
 #'   step_epi_slide(case_rate, death_rate,
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 06f8da4cf..99e17820c 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -32,13 +32,13 @@
 #' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_growth_rate(case_rate, death_rate)
 #' r
 #'
 #' r %>%
-#'   prep(case_death_rate_subset) %>%
-#'   bake(case_death_rate_subset)
+#'   prep(covid_case_death_rates) %>%
+#'   bake(covid_case_death_rates)
 step_growth_rate <-
   function(recipe,
            ...,
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index 39ae1ba59..2483b8b73 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -15,14 +15,14 @@
 #' @family row operation steps
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_lag_difference(case_rate, death_rate, horizon = c(7, 14)) %>%
 #'   step_epi_naomit()
 #' r
 #'
 #' r %>%
-#'   prep(case_death_rate_subset) %>%
-#'   bake(case_death_rate_subset)
+#'   prep(covid_case_death_rates) %>%
+#'   bake(covid_case_death_rates)
 step_lag_difference <-
   function(recipe,
            ...,
diff --git a/R/tidy.R b/R/tidy.R
index 61b298411..bfaf6ddb6 100644
--- a/R/tidy.R
+++ b/R/tidy.R
@@ -27,7 +27,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index cd67bbee9..48741de7d 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -44,13 +44,13 @@
 #'
 #' # Using some actual forecasts --------
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 #' preds <- flatline_forecaster(
 #'   jhu, "death_rate",
 #'   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 #' )$predictions
-#' actuals <- case_death_rate_subset %>%
+#' actuals <- covid_case_death_rates %>%
 #'   filter(time_value == as.Date("2021-12-01") + 7) %>%
 #'   select(geo_value, time_value, actual = death_rate)
 #' preds <- left_join(preds, actuals,
diff --git a/README.Rmd b/README.Rmd
index 36af14cd9..73cedbeaa 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -81,14 +81,14 @@ interfaces directly to Delphi's
 
 ```{r epidf, message=FALSE}
 library(epipredict)
-case_death_rate_subset
+covid_case_death_rates
 ```
 
 To create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
 
 ```{r make-forecasts, warning=FALSE}
 two_week_ahead <- arx_forecaster(
-  case_death_rate_subset,
+  covid_case_death_rates,
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
diff --git a/README.md b/README.md
index 9d912f7e6..561d00a1e 100644
--- a/README.md
+++ b/README.md
@@ -75,7 +75,7 @@ processed using
 
 ``` r
 library(epipredict)
-case_death_rate_subset
+covid_case_death_rates
 #> An `epi_df` object, 20,496 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
@@ -103,7 +103,7 @@ cases, we could use the following function.
 
 ``` r
 two_week_ahead <- arx_forecaster(
-  case_death_rate_subset,
+  covid_case_death_rates,
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c6df4c82d..ee8530733 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -122,6 +122,6 @@ reference:
       - clean_f_name
   - title: Included datasets
     contents:
-      - case_death_rate_subset
+      - covid_case_death_rates
       - state_census
       - grad_employ_subset
diff --git a/man/Add_model.Rd b/man/Add_model.Rd
index 17b65793c..641bd7676 100644
--- a/man/Add_model.Rd
+++ b/man/Add_model.Rd
@@ -72,7 +72,7 @@ properly.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/add_epi_recipe.Rd b/man/add_epi_recipe.Rd
index 0da2d55b3..b74267524 100644
--- a/man/add_epi_recipe.Rd
+++ b/man/add_epi_recipe.Rd
@@ -41,7 +41,7 @@ default blueprint to automatically handle \link[epiprocess:epi_df]{epiprocess::e
 library(dplyr)
 library(recipes)
 
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-08-01") \%>\%
   arrange(geo_value, time_value)
 
diff --git a/man/add_frosting.Rd b/man/add_frosting.Rd
index 94812cbe2..00b899b7c 100644
--- a/man/add_frosting.Rd
+++ b/man/add_frosting.Rd
@@ -27,7 +27,7 @@ Add frosting to a workflow
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/adjust_epi_recipe.Rd b/man/adjust_epi_recipe.Rd
index 7468c4ce2..0ed7148a3 100644
--- a/man/adjust_epi_recipe.Rd
+++ b/man/adjust_epi_recipe.Rd
@@ -55,7 +55,7 @@ illustrations of the different types of updates.
 library(dplyr)
 library(workflows)
 
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/adjust_frosting.Rd b/man/adjust_frosting.Rd
index c089b3443..3b855a9af 100644
--- a/man/adjust_frosting.Rd
+++ b/man/adjust_frosting.Rd
@@ -36,7 +36,7 @@ illustrations of the different types of updates.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index 713365f17..9f0aae6a1 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -48,7 +48,7 @@ may alter the returned \code{epi_workflow} object but can be omitted.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 
 arx_class_epi_workflow(jhu, "death_rate", c("case_rate", "death_rate"))
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index c7c2cf059..94503f3d3 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -49,7 +49,7 @@ that it estimates a class at a particular target horizon.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 
 out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
diff --git a/man/arx_fcast_epi_workflow.Rd b/man/arx_fcast_epi_workflow.Rd
index 4070a3337..c2e38218f 100644
--- a/man/arx_fcast_epi_workflow.Rd
+++ b/man/arx_fcast_epi_workflow.Rd
@@ -43,7 +43,7 @@ use \code{\link[=quantile_reg]{quantile_reg()}}) but can be omitted.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-12-01"))
 
 arx_fcast_epi_workflow(
diff --git a/man/arx_forecaster.Rd b/man/arx_forecaster.Rd
index d8c7671dc..ff820b8c8 100644
--- a/man/arx_forecaster.Rd
+++ b/man/arx_forecaster.Rd
@@ -41,7 +41,7 @@ This is an autoregressive forecasting model for
 that it estimates a model for a particular target horizon.
 }
 \examples{
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   dplyr::filter(time_value >= as.Date("2021-12-01"))
 
 out <- arx_forecaster(
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 27bfdf5f7..1025759b3 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -71,7 +71,7 @@ can simply call \code{autoplot()} on the original \code{epi_df}).
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 
 r <- epi_recipe(jhu) \%>\%
@@ -112,7 +112,7 @@ autoplot(wf, p, .max_facets = 4)
 
 # ------- Plotting canned forecaster output
 
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 flat <- flatline_forecaster(jhu, "death_rate")
 autoplot(flat, .max_facets = 4)
diff --git a/man/case_death_rate_subset.Rd b/man/case_death_rate_subset.Rd
deleted file mode 100644
index 119c8ee26..000000000
--- a/man/case_death_rate_subset.Rd
+++ /dev/null
@@ -1,49 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{case_death_rate_subset}
-\alias{case_death_rate_subset}
-\title{Subset of JHU daily state cases and deaths}
-\format{
-A tibble with 20,496 rows and 4 variables:
-\describe{
-\item{geo_value}{the geographic value associated with each row
-of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{case_rate}{7-day average signal of number of new
-confirmed COVID-19 cases per 100,000 population, daily}
-\item{death_rate}{7-day average signal of number of new confirmed
-deaths due to COVID-19 per 100,000 population, daily}
-}
-}
-\source{
-This object contains a modified part of the
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
-This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by the Johns Hopkins University on behalf of its Center for Systems Science
-in Engineering. Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
-These signals are taken directly from the JHU CSSE
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
-without changes. The 7-day average signals are computed by Delphi by
-calculating moving averages of the preceding 7 days, so the signal for
-June 7 is the average of the underlying data for June 1 through 7,
-inclusive.
-}
-}
-\usage{
-case_death_rate_subset
-}
-\description{
-This data source of confirmed COVID-19 cases and deaths
-is based on reports made available by the Center for
-Systems Science and Engineering at Johns Hopkins University.
-This example data ranges from Dec 31, 2020 to Dec 31, 2021,
-and includes all states.
-}
-\keyword{datasets}
diff --git a/man/cdc_baseline_forecaster.Rd b/man/cdc_baseline_forecaster.Rd
index cd3c4ed67..d32844372 100644
--- a/man/cdc_baseline_forecaster.Rd
+++ b/man/cdc_baseline_forecaster.Rd
@@ -38,7 +38,7 @@ This forecaster is meant to produce exactly the CDC Baseline used for
 }
 \examples{
 library(dplyr)
-weekly_deaths <- case_death_rate_subset \%>\%
+weekly_deaths <- covid_case_death_rates \%>\%
   select(geo_value, time_value, death_rate) \%>\%
   left_join(state_census \%>\% select(pop, abbr), by = c("geo_value" = "abbr")) \%>\%
   mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) \%>\%
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index d0105d1ec..9ef5eb288 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -59,7 +59,7 @@ columns present in an \code{epi_df}
 \examples{
 library(dplyr)
 library(recipes)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-08-01") \%>\%
   arrange(geo_value, time_value)
 
diff --git a/man/epi_workflow.Rd b/man/epi_workflow.Rd
index b29078d52..59e3d5c8f 100644
--- a/man/epi_workflow.Rd
+++ b/man/epi_workflow.Rd
@@ -33,7 +33,7 @@ this operates exactly like a \code{\link[workflows:workflow]{workflows::workflow
 and numerous examples, see there.
 }
 \examples{
-jhu <- case_death_rate_subset
+jhu <- covid_case_death_rates
 
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/fit-epi_workflow.Rd b/man/fit-epi_workflow.Rd
index 3dfa0029a..83b3b9f51 100644
--- a/man/fit-epi_workflow.Rd
+++ b/man/fit-epi_workflow.Rd
@@ -28,7 +28,7 @@ Fitting an \code{epi_workflow} involves two main steps, which are
 preprocessing the data and fitting the underlying parsnip model.
 }
 \examples{
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/flatline_forecaster.Rd b/man/flatline_forecaster.Rd
index 1803f1078..f70c05e0f 100644
--- a/man/flatline_forecaster.Rd
+++ b/man/flatline_forecaster.Rd
@@ -35,7 +35,7 @@ This forecaster is very similar to that used by the
 \href{https://covid19forecasthub.org}{COVID19ForecastHub}
 }
 \examples{
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   dplyr::filter(time_value >= as.Date("2021-12-01"))
 
 out <- flatline_forecaster(jhu, "death_rate")
diff --git a/man/flusight_hub_formatter.Rd b/man/flusight_hub_formatter.Rd
index b43bc0ac2..84f4e73e4 100644
--- a/man/flusight_hub_formatter.Rd
+++ b/man/flusight_hub_formatter.Rd
@@ -42,7 +42,7 @@ format for this forecast task is \href{https://github.com/cdcepi/FluSight-foreca
 }
 \examples{
 library(dplyr)
-weekly_deaths <- case_death_rate_subset \%>\%
+weekly_deaths <- covid_case_death_rates \%>\%
   filter(
     time_value >= as.Date("2021-09-01"),
     geo_value \%in\% c("ca", "ny", "dc", "ga", "vt")
diff --git a/man/frosting.Rd b/man/frosting.Rd
index a75f21b61..8534bc6d6 100644
--- a/man/frosting.Rd
+++ b/man/frosting.Rd
@@ -28,7 +28,7 @@ f <- frosting()
 wf <- epi_workflow() \%>\% add_frosting(f)
 
 # A more realistic example
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index b18685d89..b18c35ecb 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -56,9 +56,9 @@ values with more advanced techniques.
 }
 \examples{
 # create recipe
-rec <- epi_recipe(case_death_rate_subset) \%>\%
+rec <- epi_recipe(covid_case_death_rates) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
   step_epi_lag(case_rate, lag = c(0, 7, 14))
-get_test_data(recipe = rec, x = case_death_rate_subset)
+get_test_data(recipe = rec, x = covid_case_death_rates)
 }
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index e6852a55b..f6400edcf 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -85,7 +85,7 @@ predict(out, new_data = tib[1:5, ]) \%>\%
 # -- a more complicated task
 
 library(dplyr)
-dat <- case_death_rate_subset \%>\%
+dat <- covid_case_death_rates \%>\%
   filter(time_value > as.Date("2021-10-01"))
 rec <- epi_recipe(dat) \%>\%
   step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index e27f2bacd..2c044a3a3 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -37,7 +37,7 @@ model fitting, and postprocessing), an appropriate warning will be thrown.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index dc0d2f190..79ee62370 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -38,7 +38,7 @@ in the test data to get the target date.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/layer_cdc_flatline_quantiles.Rd b/man/layer_cdc_flatline_quantiles.Rd
index c3bc4f257..632fdb65e 100644
--- a/man/layer_cdc_flatline_quantiles.Rd
+++ b/man/layer_cdc_flatline_quantiles.Rd
@@ -85,14 +85,14 @@ adds them on to produce wider intervals as \code{ahead} increases.
 }
 \examples{
 library(dplyr)
-r <- epi_recipe(case_death_rate_subset) \%>\%
+r <- epi_recipe(covid_case_death_rates) \%>\%
   # data is "daily", so we fit this to 1 ahead, the result will contain
   # 1 day ahead residuals
   step_epi_ahead(death_rate, ahead = 1L, skip = TRUE) \%>\%
   recipes::update_role(death_rate, new_role = "predictor") \%>\%
   recipes::add_role(time_value, geo_value, new_role = "predictor")
 
-forecast_date <- max(case_death_rate_subset$time_value)
+forecast_date <- max(covid_case_death_rates$time_value)
 
 f <- frosting() \%>\%
   layer_predict() \%>\%
@@ -100,7 +100,7 @@ f <- frosting() \%>\%
 
 eng <- linear_reg(engine = "flatline")
 
-wf <- epi_workflow(r, eng, f) \%>\% fit(case_death_rate_subset)
+wf <- epi_workflow(r, eng, f) \%>\% fit(covid_case_death_rates)
 preds <- forecast(wf) \%>\%
   select(-time_value) \%>\%
   mutate(forecast_date = forecast_date)
@@ -120,7 +120,7 @@ if (require("ggplot2")) {
     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
     geom_line(aes(y = .pred), color = "orange") +
     geom_line(
-      data = case_death_rate_subset \%>\% filter(geo_value \%in\% four_states),
+      data = covid_case_death_rates \%>\% filter(geo_value \%in\% four_states),
       aes(x = time_value, y = death_rate)
     ) +
     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
diff --git a/man/layer_naomit.Rd b/man/layer_naomit.Rd
index d77112f95..06e09d4b0 100644
--- a/man/layer_naomit.Rd
+++ b/man/layer_naomit.Rd
@@ -25,7 +25,7 @@ Omit \code{NA}s from predictions or other columns
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_point_from_distn.Rd b/man/layer_point_from_distn.Rd
index 276f7cb17..bde2323b1 100644
--- a/man/layer_point_from_distn.Rd
+++ b/man/layer_point_from_distn.Rd
@@ -35,7 +35,7 @@ or set the \code{name} argument to something specific.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_predict.Rd b/man/layer_predict.Rd
index 8ae92f4c8..db771b882 100644
--- a/man/layer_predict.Rd
+++ b/man/layer_predict.Rd
@@ -59,7 +59,7 @@ postprocessor.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_predictive_distn.Rd b/man/layer_predictive_distn.Rd
index 240db5f5b..3bd95425b 100644
--- a/man/layer_predictive_distn.Rd
+++ b/man/layer_predictive_distn.Rd
@@ -40,7 +40,7 @@ should be reasonably accurate for models fit using \code{lm} when the new point
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index 68192deee..3a5cb60e2 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -46,7 +46,7 @@ If these engines were used, then this layer will grab out estimated
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index 39e1ecfbe..a7deded71 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -40,7 +40,7 @@ Creates predictions based on residual quantiles
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_threshold.Rd b/man/layer_threshold.Rd
index 0f4b1dfb7..702c5d713 100644
--- a/man/layer_threshold.Rd
+++ b/man/layer_threshold.Rd
@@ -41,7 +41,7 @@ to the threshold values.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value < "2021-03-08", geo_value \%in\% c("ak", "ca", "ar"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
index b34b718ca..0fa0fe8cc 100644
--- a/man/nested_quantiles.Rd
+++ b/man/nested_quantiles.Rd
@@ -18,7 +18,7 @@ Turn a vector of quantile distributions into a list-col
 \examples{
 library(dplyr)
 library(tidyr)
-edf <- case_death_rate_subset[1:3, ]
+edf <- covid_case_death_rates[1:3, ]
 edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
 
 edf_nested <- edf \%>\% mutate(q = nested_quantiles(q))
diff --git a/man/predict-epi_workflow.Rd b/man/predict-epi_workflow.Rd
index 130279249..0b605d556 100644
--- a/man/predict-epi_workflow.Rd
+++ b/man/predict-epi_workflow.Rd
@@ -66,7 +66,7 @@ possible. Specifically, the output will have \code{time_value} and
 }
 }
 \examples{
-jhu <- case_death_rate_subset
+jhu <- covid_case_death_rates
 
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/step_epi_naomit.Rd b/man/step_epi_naomit.Rd
index b579dd6d6..faf7484da 100644
--- a/man/step_epi_naomit.Rd
+++ b/man/step_epi_naomit.Rd
@@ -19,7 +19,7 @@ of data loss.
 Unified NA omission wrapper function for recipes
 }
 \examples{
-case_death_rate_subset \%>\%
+covid_case_death_rates \%>\%
   epi_recipe() \%>\%
   step_epi_naomit()
 }
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 2bf22c15d..2817708e2 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -78,7 +78,7 @@ are always set to \code{"ahead_"} and \code{"epi_ahead"} respectively, while for
 \code{step_epi_lag}, they are set to \code{"lag_"} and \verb{"epi_lag}, respectively.
 }
 \examples{
-r <- epi_recipe(case_death_rate_subset) \%>\%
+r <- epi_recipe(covid_case_death_rates) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
 r
diff --git a/man/step_epi_slide.Rd b/man/step_epi_slide.Rd
index 46bb386ad..52fb2ad36 100644
--- a/man/step_epi_slide.Rd
+++ b/man/step_epi_slide.Rd
@@ -75,7 +75,7 @@ a computation along existing data.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-01-01"), geo_value \%in\% c("ca", "ny"))
 rec <- epi_recipe(jhu) \%>\%
   step_epi_slide(case_rate, death_rate,
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index bc6da0bef..fcf3d3bd7 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -73,13 +73,13 @@ sequence of any existing operations.
 that will generate one or more new columns of derived data.
 }
 \examples{
-r <- epi_recipe(case_death_rate_subset) \%>\%
+r <- epi_recipe(covid_case_death_rates) \%>\%
   step_growth_rate(case_rate, death_rate)
 r
 
 r \%>\%
-  prep(case_death_rate_subset) \%>\%
-  bake(case_death_rate_subset)
+  prep(covid_case_death_rates) \%>\%
+  bake(covid_case_death_rates)
 }
 \seealso{
 Other row operation steps: 
diff --git a/man/step_lag_difference.Rd b/man/step_lag_difference.Rd
index 7969ea3a7..7e7873916 100644
--- a/man/step_lag_difference.Rd
+++ b/man/step_lag_difference.Rd
@@ -47,14 +47,14 @@ sequence of any existing operations.
 that will generate one or more new columns of derived data.
 }
 \examples{
-r <- epi_recipe(case_death_rate_subset) \%>\%
+r <- epi_recipe(covid_case_death_rates) \%>\%
   step_lag_difference(case_rate, death_rate, horizon = c(7, 14)) \%>\%
   step_epi_naomit()
 r
 
 r \%>\%
-  prep(case_death_rate_subset) \%>\%
-  bake(case_death_rate_subset)
+  prep(covid_case_death_rates) \%>\%
+  bake(covid_case_death_rates)
 }
 \seealso{
 Other row operation steps: 
diff --git a/man/tidy.frosting.Rd b/man/tidy.frosting.Rd
index ba3c0f3d5..8152b1440 100644
--- a/man/tidy.frosting.Rd
+++ b/man/tidy.frosting.Rd
@@ -38,7 +38,7 @@ version of the \code{tidy} method for a recipe.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/update.layer.Rd b/man/update.layer.Rd
index 9604992e1..f151beea9 100644
--- a/man/update.layer.Rd
+++ b/man/update.layer.Rd
@@ -19,7 +19,7 @@ Analogous to \code{update.step()} from the \code{recipes} package.
 }
 \examples{
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 4907e2724..4aac20e7d 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -80,13 +80,13 @@ weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
 
 # Using some actual forecasts --------
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 preds <- flatline_forecaster(
   jhu, "death_rate",
   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 )$predictions
-actuals <- case_death_rate_subset \%>\%
+actuals <- covid_case_death_rates \%>\%
   filter(time_value == as.Date("2021-12-01") + 7) \%>\%
   select(geo_value, time_value, actual = death_rate)
 preds <- left_join(preds, actuals,
diff --git a/tests/testthat/test-bake-method.R b/tests/testthat/test-bake-method.R
index 0e2746cf2..70fa12718 100644
--- a/tests/testthat/test-bake-method.R
+++ b/tests/testthat/test-bake-method.R
@@ -1,5 +1,5 @@
 test_that("bake method works in all cases", {
-  edf <- case_death_rate_subset %>%
+  edf <- covid_case_death_rates %>%
     filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
   r <- epi_recipe(edf) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-blueprint.R b/tests/testthat/test-blueprint.R
index 2d22aff6e..b37bd5e4a 100644
--- a/tests/testthat/test-blueprint.R
+++ b/tests/testthat/test-blueprint.R
@@ -4,7 +4,7 @@ test_that("epi_recipe blueprint keeps the class, mold works", {
   expect_s3_class(bp, "default_epi_recipe_blueprint")
   expect_s3_class(refresh_blueprint(bp), "default_epi_recipe_blueprint")
 
-  jhu <- case_death_rate_subset
+  jhu <- covid_case_death_rates
   # expect_s3_class(er_check_is_data_like(jhu), "epi_df")
 
   r <- epi_recipe(jhu) %>%
diff --git a/tests/testthat/test-epi_recipe.R b/tests/testthat/test-epi_recipe.R
index ed27d88c0..ea84893bd 100644
--- a/tests/testthat/test-epi_recipe.R
+++ b/tests/testthat/test-epi_recipe.R
@@ -118,7 +118,7 @@ test_that("epi_recipe epi_df works", {
 
 
 test_that("add/update/adjust/remove epi_recipe works as intended", {
-  jhu <- case_death_rate_subset
+  jhu <- covid_case_death_rates
 
   r <- epi_recipe(jhu) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-epi_workflow.R b/tests/testthat/test-epi_workflow.R
index 09dd6fe82..75c8dc3ff 100644
--- a/tests/testthat/test-epi_workflow.R
+++ b/tests/testthat/test-epi_workflow.R
@@ -1,5 +1,5 @@
 test_that("postprocesser was evaluated", {
-  r <- epi_recipe(case_death_rate_subset)
+  r <- epi_recipe(covid_case_death_rates)
   s <- parsnip::linear_reg()
   f <- frosting()
 
@@ -12,7 +12,7 @@ test_that("postprocesser was evaluated", {
 
 
 test_that("outcome of the two methods are the same", {
-  jhu <- case_death_rate_subset
+  jhu <- covid_case_death_rates
 
   r <- epi_recipe(jhu) %>%
     step_epi_lag(death_rate, lag = c(0, 7)) %>%
@@ -33,7 +33,7 @@ test_that("outcome of the two methods are the same", {
 })
 
 test_that("model can be added/updated/removed from epi_workflow", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 
   r <- epi_recipe(jhu) %>%
@@ -64,7 +64,7 @@ test_that("model can be added/updated/removed from epi_workflow", {
 })
 
 test_that("forecast method works", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
   r <- epi_recipe(jhu) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -95,7 +95,7 @@ test_that("forecast method works", {
 })
 
 test_that("forecast method errors when workflow not fit", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
   r <- epi_recipe(jhu) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-extract_argument.R b/tests/testthat/test-extract_argument.R
index 3250b2991..aa61a829e 100644
--- a/tests/testthat/test-extract_argument.R
+++ b/tests/testthat/test-extract_argument.R
@@ -28,7 +28,7 @@ test_that("layer argument extractor works", {
 })
 
 test_that("recipe argument extractor works", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-08-01") %>%
     dplyr::arrange(geo_value, time_value)
 
diff --git a/tests/testthat/test-frosting.R b/tests/testthat/test-frosting.R
index 5cab9c494..28619ce13 100644
--- a/tests/testthat/test-frosting.R
+++ b/tests/testthat/test-frosting.R
@@ -40,7 +40,7 @@ test_that("frosting can be created/added/updated/adjusted/removed", {
 
 
 test_that("prediction works without any postprocessor", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
   r <- epi_recipe(jhu) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -62,7 +62,7 @@ test_that("prediction works without any postprocessor", {
 
 
 test_that("layer_predict is added by default if missing", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 
   r <- epi_recipe(jhu) %>%
@@ -89,7 +89,7 @@ test_that("layer_predict is added by default if missing", {
 
 
 test_that("parsnip settings can be passed through predict.epi_workflow", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 
   r <- epi_recipe(jhu) %>%
diff --git a/tests/testthat/test-get_test_data.R b/tests/testthat/test-get_test_data.R
index 035fc6463..6bf0f7d1f 100644
--- a/tests/testthat/test-get_test_data.R
+++ b/tests/testthat/test-get_test_data.R
@@ -1,17 +1,17 @@
 library(dplyr)
 test_that("return expected number of rows and returned dataset is ungrouped", {
-  r <- epi_recipe(case_death_rate_subset) %>%
+  r <- epi_recipe(covid_case_death_rates) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14, 21, 28)) %>%
     step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
 
-  test <- get_test_data(recipe = r, x = case_death_rate_subset)
+  test <- get_test_data(recipe = r, x = covid_case_death_rates)
 
   expect_equal(
     nrow(test),
-    dplyr::n_distinct(case_death_rate_subset$geo_value) * 29
+    dplyr::n_distinct(covid_case_death_rates$geo_value) * 29
   )
 
   expect_false(dplyr::is.grouped_df(test))
@@ -19,25 +19,25 @@ test_that("return expected number of rows and returned dataset is ungrouped", {
 
 
 test_that("expect insufficient training data error", {
-  r <- epi_recipe(case_death_rate_subset) %>%
+  r <- epi_recipe(covid_case_death_rates) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
     step_epi_lag(death_rate, lag = c(0, 367)) %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
 
-  expect_error(get_test_data(recipe = r, x = case_death_rate_subset))
+  expect_error(get_test_data(recipe = r, x = covid_case_death_rates))
 })
 
 
 test_that("expect error that geo_value or time_value does not exist", {
-  r <- epi_recipe(case_death_rate_subset) %>%
+  r <- epi_recipe(covid_case_death_rates) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
 
-  wrong_epi_df <- case_death_rate_subset %>% dplyr::select(-geo_value)
+  wrong_epi_df <- covid_case_death_rates %>% dplyr::select(-geo_value)
 
   expect_error(get_test_data(recipe = r, x = wrong_epi_df))
 })
@@ -137,7 +137,7 @@ test_that("Omit end rows according to minimum lag when that’s not lag 0", {
 
   # Ex. using real built-in data
 
-  ca <- case_death_rate_subset %>%
+  ca <- covid_case_death_rates %>%
     filter(geo_value == "ca")
 
   rec <- epi_recipe(ca) %>%
diff --git a/tests/testthat/test-key_colnames.R b/tests/testthat/test-key_colnames.R
index d55a515ca..e378c0b92 100644
--- a/tests/testthat/test-key_colnames.R
+++ b/tests/testthat/test-key_colnames.R
@@ -1,9 +1,9 @@
 test_that("Extracts keys from a recipe; roles are NA, giving an empty vector", {
-  expect_equal(key_colnames(recipe(case_death_rate_subset)), character(0L))
+  expect_equal(key_colnames(recipe(covid_case_death_rates)), character(0L))
 })
 
 test_that("key_colnames extracts time_value and geo_value, but not raw", {
-  my_recipe <- epi_recipe(case_death_rate_subset) %>%
+  my_recipe <- epi_recipe(covid_case_death_rates) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
@@ -14,7 +14,7 @@ test_that("key_colnames extracts time_value and geo_value, but not raw", {
   my_workflow <- epi_workflow() %>%
     add_epi_recipe(my_recipe) %>%
     add_model(linear_reg()) %>%
-    fit(data = case_death_rate_subset)
+    fit(data = covid_case_death_rates)
 
   expect_identical(key_colnames(my_workflow), c("geo_value", "time_value"))
 })
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 9595b47b6..7e52c11a2 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) %>%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index e5349839b..132812ca6 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) %>%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-layer_naomit.R b/tests/testthat/test-layer_naomit.R
index 1d5b4ee25..8eb597f41 100644
--- a/tests/testthat/test-layer_naomit.R
+++ b/tests/testthat/test-layer_naomit.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) %>%
diff --git a/tests/testthat/test-layer_predict.R b/tests/testthat/test-layer_predict.R
index 041516b29..ae51a5ec6 100644
--- a/tests/testthat/test-layer_predict.R
+++ b/tests/testthat/test-layer_predict.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) %>%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index e3668b249..438e9bd60 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 
 r <- epi_recipe(jhu) %>%
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 9df7e64ab..324f60a1b 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -1,4 +1,4 @@
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value < "2021-03-08", geo_value %in% c("ak", "ca", "ar"))
 r <- epi_recipe(jhu) %>%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index a94b40b82..d9753875a 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -7,7 +7,7 @@ test_that("Column names can be passed with and without the tidy way", {
 
   pop_data2 <- pop_data %>% dplyr::rename(geo_value = states)
 
-  newdata <- case_death_rate_subset %>%
+  newdata <- covid_case_death_rates %>%
     filter(geo_value %in% c("ak", "al", "ar", "as", "az", "ca"))
 
   r1 <- epi_recipe(newdata) %>%
@@ -150,7 +150,7 @@ test_that("Postprocessing workflow works and values correct", {
 })
 
 test_that("Postprocessing to get cases from case rate", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
@@ -193,7 +193,7 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
@@ -248,7 +248,7 @@ test_that("test joining by default columns", {
 
 
 test_that("expect error if `by` selector does not match", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index d624a4c21..934e1d0f9 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -72,7 +72,7 @@ test_that("arx_forecaster snapshots", {
 
 test_that("arx_classifier snapshots", {
   arc1 <- arx_classifier(
-    case_death_rate_subset %>%
+    covid_case_death_rates %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate")
diff --git a/vignettes/articles/smooth-qr.Rmd b/vignettes/articles/smooth-qr.Rmd
index 07e237181..f96d840ef 100644
--- a/vignettes/articles/smooth-qr.Rmd
+++ b/vignettes/articles/smooth-qr.Rmd
@@ -97,7 +97,7 @@ state cases and deaths. This sample data ranges from Dec. 31, 2020 to
 Dec. 31, 2021.
 
 ```{r}
-edf <- case_death_rate_subset
+edf <- covid_case_death_rates
 ```
 
 We will set the forecast date to be November 30, 2021 so that we can produce
diff --git a/vignettes/arx-classifier.Rmd b/vignettes/arx-classifier.Rmd
index ae1641cce..446edc121 100644
--- a/vignettes/arx-classifier.Rmd
+++ b/vignettes/arx-classifier.Rmd
@@ -36,14 +36,14 @@ or ahead value.
 
 To get a sense of how the `arx_classifier()` works, let's consider a simple
 example with minimal inputs. For this, we will use the built-in
-`case_death_rate_subset` that contains confirmed COVID-19 cases and deaths from
+`covid_case_death_rates` that contains confirmed COVID-19 cases and deaths from
 JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this, we'll take
 a subset of data for five states over June 4, 2021 to December 31, 2021. Our
 objective is to predict whether the case rates are increasing when considering
 the 0, 7 and 14 day case rates:
 
 ```{r}
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   filter(
     time_value >= "2021-06-04",
     time_value <= "2021-12-31",
@@ -172,7 +172,7 @@ $$
 g_{\text{up}}(x) = \log\left ( \frac{\Pr(Z_{l, t} = \text{up} \vert x)}{\Pr(Z_{l, t} = \text{not up} \vert x)} \right ) = \beta_{10} + \beta_{11}Y_{l,t}^\Delta + \beta_{12}Y_{l,t-7}^\Delta + \beta_{13}Y_{l,t-14}^\Delta.
 $$
 
-Now then, we will operate on the same subset of the `case_death_rate_subset`
+Now then, we will operate on the same subset of the `covid_case_death_rates`
 that we used in our above example. This time, we will use it to investigate
 whether the number of newly reported cases over the past 7 days has increased by
 at least 25% compared to the preceding week for our sample of states.
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 7e24b04c6..6402724f5 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -90,7 +90,7 @@ package. There is much more to see there, but for the moment, it's enough to
 look at a simple one:
 
 ```{r epidf}
-jhu <- case_death_rate_subset
+jhu <- covid_case_death_rates
 jhu
 ```
 
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 0dea322f2..461469f53 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -31,13 +31,13 @@ theme_set(theme_bw())
 [Panel data](https://en.wikipedia.org/wiki/Panel_data), or longitudinal data, 
 contain cross-sectional measurements of subjects over time. The `epipredict` 
 package is most suitable for running forecasters on epidemiological panel data. 
-A built-in example of this is the [`case_death_rate_subset`](
-  https://cmu-delphi.github.io/epipredict/reference/case_death_rate_subset.html) 
+A built-in example of this is the [`covid_case_death_rates`](
+  https://cmu-delphi.github.io/epipredict/reference/covid_case_death_rates.html)
 dataset, which contains daily state-wise measures of `case_rate` and 
 `death_rate` for COVID-19 in 2021:
 
 ```{r epi-panel-ex, include=T}
-head(case_death_rate_subset, 3)
+head(covid_case_death_rates, 3)
 ```
 
 `epipredict` functions work with data in 
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 63a27bd55..6b9dc8433 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -295,11 +295,11 @@ behav_ind %>%
 ```
 
 We will take a subset of death rate and case rate data from the built-in dataset
-`case_death_rate_subset`.
+`covid_case_death_rates`.
 
 ```{r}
 jhu <- filter(
-  case_death_rate_subset,
+  covid_case_death_rates,
   time_value >= "2021-06-04",
   time_value <= "2021-12-31",
   geo_value %in% c("ca", "fl", "tx", "ny", "nj")
@@ -445,10 +445,10 @@ g_{\text{up}}(x) &= \log\left(\frac{Pr(Z_{\ell,t}=\text{up}\mid x)}{Pr(Z_{\ell,t
 Preprocessing steps are similar to the previous models with an additional step
 of categorizing the response variables. Again, we will use a subset of death
 rate and case rate data from our built-in dataset
-`case_death_rate_subset`.
+`covid_case_death_rates`.
 
 ```{r}
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(
     time_value >= "2021-06-04",
     time_value <= "2021-12-31",
@@ -517,7 +517,7 @@ Let's start with a simple dataset and preprocessing:
 
 ```{r}
 ex <- filter(
-  case_death_rate_subset,
+  covid_case_death_rates,
   time_value >= "2021-12-01",
   time_value <= "2021-12-31",
   geo_value == "ca"
diff --git a/vignettes/update.Rmd b/vignettes/update.Rmd
index fcd3653ca..9330b2576 100644
--- a/vignettes/update.Rmd
+++ b/vignettes/update.Rmd
@@ -66,13 +66,13 @@ vignette and only briefly go through some examples for a `frosting` object.
 
 ## Add/update/remove an `epi_recipe` in an `epi_workflow`
 
-We start with the built-in `case_death_rate_subset` dataset that contains JHU
+We start with the built-in `covid_case_death_rates` dataset that contains JHU
 daily COVID-19 cases and deaths by state and take a subset of it from Nov. 1,
 2021 to Dec. 31, 2021 for the four states of Alaska, California, New York, and
 South Carolina.
 
 ```{r}
-jhu <- case_death_rate_subset %>%
+jhu <- covid_case_death_rates %>%
   dplyr::filter(time_value >= as.Date("2021-11-01"), geo_value %in% c("ak", "ca", "ny", "sc"))
 
 jhu

From 80baf403a23240592292d07583ac7428c9ccf104 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 16:35:06 -0400
Subject: [PATCH 006/387] add counts_subset dataset

---
 NAMESPACE                 |  1 +
 R/reexports-epidatasets.R | 18 +++++++++++++
 man/counts_subset.Rd      | 56 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 75 insertions(+)
 create mode 100644 man/counts_subset.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 54aa672cb..689d261e7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -148,6 +148,7 @@ export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
 export(clean_f_name)
+export(counts_subset)
 export(covid_case_death_rates)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index f7f76e60c..fb1c7f5d1 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -51,3 +51,21 @@ delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
 #' data(state_census, package = "epipredict")
 #' @export
 delayedAssign("state_census", epidatasets::state_census)
+
+#' @inherit epidatasets::counts_subset description source references title
+#' @inheritSection epidatasets::counts_subset Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::counts_subset
+#'
+#' # works
+#' library(epipredict)
+#' counts_subset
+#'
+#' # fails
+#' data(counts_subset, package = "epipredict")
+#' @export
+delayedAssign("counts_subset", epidatasets::counts_subset)
diff --git a/man/counts_subset.Rd b/man/counts_subset.Rd
new file mode 100644
index 000000000..1ba03cc05
--- /dev/null
+++ b/man/counts_subset.Rd
@@ -0,0 +1,56 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/reexports-epidatasets.R
+\docType{data}
+\name{counts_subset}
+\alias{counts_subset}
+\title{Subset of COVID-19 incident cases and deaths from 5 states}
+\format{
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
+}
+\source{
+This object contains a modified part of the \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
+as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
+This data set is licensed under the terms of the
+\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
+by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
+Copyright Johns Hopkins University 2020.
+}
+\usage{
+counts_subset
+}
+\description{
+Data set for 5 states containing COVID-19 Incident Cases and Deaths as
+reported by JHU-CSSE and downloaded from the CMU Delphi COVIDcast Epidata
+API. This example data is a snapshot as of March 20, 2024, and
+ranges from June 4, 2021 to December 31, 2021. It
+is limited to California, Florida, Texas, New Jersey, and New York.
+}
+\section{Data dictionary}{
+
+
+The data has columns:
+\describe{
+\item{geo_value}{the geographic value associated with each row
+of measurements.}
+\item{time_value}{the time value associated with each row of measurements.}
+\item{cases}{Number of new confirmed COVID-19 cases, daily}
+\item{deaths}{Number of new confirmed COVID-19 deaths, daily}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::counts_subset
+
+# works
+library(epipredict)
+counts_subset
+
+# fails
+data(counts_subset, package = "epipredict")
+}
+\keyword{datasets}

From 63699acc22049287e652ca769b9a7f0de78c14b3 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 19 Sep 2024 16:49:54 -0400
Subject: [PATCH 007/387] add ctis_covid_behaviours dataset

---
 NAMESPACE                    |  1 +
 R/reexports-epidatasets.R    | 18 ++++++++++
 man/ctis_covid_behaviours.Rd | 66 ++++++++++++++++++++++++++++++++++++
 3 files changed, 85 insertions(+)
 create mode 100644 man/ctis_covid_behaviours.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 689d261e7..cddf2df56 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -150,6 +150,7 @@ export(check_enough_train_data)
 export(clean_f_name)
 export(counts_subset)
 export(covid_case_death_rates)
+export(ctis_covid_behaviours)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index fb1c7f5d1..da2e823d7 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -69,3 +69,21 @@ delayedAssign("state_census", epidatasets::state_census)
 #' data(counts_subset, package = "epipredict")
 #' @export
 delayedAssign("counts_subset", epidatasets::counts_subset)
+
+#' @inherit epidatasets::ctis_covid_behaviours description source references title
+#' @inheritSection epidatasets::ctis_covid_behaviours Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::ctis_covid_behaviours
+#'
+#' # works
+#' library(epipredict)
+#' ctis_covid_behaviours
+#'
+#' # fails
+#' data(ctis_covid_behaviours, package = "epipredict")
+#' @export
+delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
diff --git a/man/ctis_covid_behaviours.Rd b/man/ctis_covid_behaviours.Rd
new file mode 100644
index 000000000..bf722a726
--- /dev/null
+++ b/man/ctis_covid_behaviours.Rd
@@ -0,0 +1,66 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/reexports-epidatasets.R
+\docType{data}
+\name{ctis_covid_behaviours}
+\alias{ctis_covid_behaviours}
+\title{Subset of CTIS COVID-19-related behaviours from 5 states}
+\format{
+An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
+}
+\source{
+Data from Delphi COVIDcast. Obtained via the Delphi Epidata API.
+\url{https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html}. This
+object contains a modified part of the
+\href{https://cmu-delphi.github.io/delphi-epidata/symptom-survey/#covid-19-trends-and-impact-survey}{data
+aggregations in the API} that are prepared from the
+\href{https://www.pnas.org/doi/full/10.1073/pnas.2111454118}{COVID-19
+Trends and Impact Survey}; see the first link for more information on
+citing in publications. These aggregations are licensed under the terms of
+the \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons
+Attribution license}.
+
+Modifications:
+\itemize{
+\item Selected a small subset of the available data.
+\item Changed to wide format.
+}
+}
+\usage{
+ctis_covid_behaviours
+}
+\description{
+Data set for a handful of states on masking and distancing behaviours
+during the COVID-19 Pandemic, and downloaded from the CMU Delphi COVIDcast
+Epidata API. This example data is a snapshot as of March 20, 2024, and
+ranges from June 4, 2021 to December 31, 2021.
+It is limited to California, Florida, Texas, New Jersey, and New York.
+}
+\section{Data dictionary}{
+
+
+The data has columns:
+\describe{
+\item{geo_value}{the geographic value associated with each row
+of measurements.}
+\item{time_value}{the time value associated with each row of measurements.}
+\item{masking}{Estimated percentage of people who wore a mask for most or all of the time while in public in the past 7 days; those not in public in the past 7 days are not counted.}
+\item{distancting}{Estimated percentage of respondents who reported that all or most people they enountered in public in the past 7 days maintained a distance of at least 6 feet. Respondents who said that they have not been in public for the past 7 days are excluded.}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::ctis_covid_behaviours
+
+# works
+library(epipredict)
+ctis_covid_behaviours
+
+# fails
+data(ctis_covid_behaviours, package = "epipredict")
+}
+\keyword{datasets}

From 45aa4d5afb9f012003b75c8e0d41a4804c778bf9 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 20 Sep 2024 20:23:59 -0400
Subject: [PATCH 008/387] move datasets to epidatasets and use in vignettes

---
 R/reexports-epidatasets.R                     |  54 +++++++++++
 inst/extdata/can_prov_cases.rds               | Bin 159157 -> 0 bytes
 inst/extdata/canada-case-rates.R              |  23 -----
 inst/extdata/epi_archive.rds                  | Bin 490593 -> 0 bytes
 .../articles/case_death_rate_archive.rds      | Bin 1314556 -> 0 bytes
 vignettes/articles/sliding.Rmd                |  84 ++++++++++++------
 vignettes/articles/symptom-surveys.Rmd        |  53 ++++++++---
 vignettes/preprocessing-and-models.Rmd        |  64 +++++++++----
 8 files changed, 195 insertions(+), 83 deletions(-)
 delete mode 100644 inst/extdata/can_prov_cases.rds
 delete mode 100644 inst/extdata/canada-case-rates.R
 delete mode 100644 inst/extdata/epi_archive.rds
 delete mode 100644 vignettes/articles/case_death_rate_archive.rds

diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index da2e823d7..4fdd4c59f 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -87,3 +87,57 @@ delayedAssign("counts_subset", epidatasets::counts_subset)
 #' data(ctis_covid_behaviours, package = "epipredict")
 #' @export
 delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
+
+#' @inherit epidatasets::county_smoothed_cli_comparison description source references title
+#' @inheritSection epidatasets::county_smoothed_cli_comparison Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::county_smoothed_cli_comparison
+#'
+#' # works
+#' library(epipredict)
+#' county_smoothed_cli_comparison
+#'
+#' # fails
+#' data(county_smoothed_cli_comparison, package = "epipredict")
+#' @export
+delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli_comparison)
+
+#' @inherit epidatasets::case_death_rate_archive description source references title
+#' @inheritSection epidatasets::case_death_rate_archive Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::case_death_rate_archive
+#'
+#' # works
+#' library(epipredict)
+#' case_death_rate_archive
+#'
+#' # fails
+#' data(case_death_rate_archive, package = "epipredict")
+#' @export
+delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
+
+#' @inherit epidatasets::archive_cases_dv_subset_all_states description source references title
+#' @inheritSection epidatasets::archive_cases_dv_subset_all_states Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::archive_cases_dv_subset_all_states
+#'
+#' # works
+#' library(epipredict)
+#' archive_cases_dv_subset_all_states
+#'
+#' # fails
+#' data(archive_cases_dv_subset_all_states, package = "epipredict")
+#' @export
+delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_dv_subset_all_states)
diff --git a/inst/extdata/can_prov_cases.rds b/inst/extdata/can_prov_cases.rds
deleted file mode 100644
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GIT binary patch
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diff --git a/inst/extdata/canada-case-rates.R b/inst/extdata/canada-case-rates.R
deleted file mode 100644
index 7cf88d602..000000000
--- a/inst/extdata/canada-case-rates.R
+++ /dev/null
@@ -1,23 +0,0 @@
-path_to_csvs <- here::here("../../COVID-BC/Covid19Canada/updates.nosync/")
-files <- list.files(path_to_csvs)
-ca_as_ofs <- as.Date(substr(files, 1, 10)) %>%
-  intersect(fc_time_values) %>%
-  as.Date(origin = "1970-01-01")
-
-can <- purrr::map(ca_as_ofs, ~ {
-  readr::read_csv(here::here(path_to_csvs, paste0(.x, ".csv"))) %>%
-    left_join(ca_pop) %>%
-    mutate(time_value = lubridate::dmy(date_report)) %>%
-    filter(province %in% ca_pop$province, time_value > "2020-04-01") %>%
-    mutate(
-      geo_value = province,
-      case_rate = cases / population * 1e5
-    ) %>%
-    select(geo_value, time_value, case_rate) %>%
-    as_epi_df(geo_type = "province", as_of = .x)
-})
-names(can) <- ca_as_ofs
-can <- can %>%
-  bind_rows(.id = "version") %>%
-  mutate(version = lubridate::ymd(version))
-saveRDS(can, "inst/extdata/can_prov_cases.rds")
diff --git a/inst/extdata/epi_archive.rds b/inst/extdata/epi_archive.rds
deleted file mode 100644
index 8ca52be76ba5f5719bb6a1c24e5f0ef436a7d7ba..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/vignettes/articles/sliding.Rmd b/vignettes/articles/sliding.Rmd
index ec6f67359..5a7a9483a 100644
--- a/vignettes/articles/sliding.Rmd
+++ b/vignettes/articles/sliding.Rmd
@@ -14,7 +14,6 @@ knitr::opts_chunk$set(
 
 ```{r pkgs}
 library(epipredict)
-library(epidatr)
 library(data.table)
 library(dplyr)
 library(tidyr)
@@ -60,25 +59,54 @@ claims and the number of new confirmed COVID-19 cases per 100,000 population
 
 <summary>Load a data archive</summary>
 
-We process as before, with the
-modification that we use `sync = locf` in `epix_merge()` so that the last
-version of each observation can be carried forward to extrapolate unavailable
-versions for the less up-to-date input archive.
+This dataset is processed as before, with the modification that we use `sync =
+locf` in `epix_merge()` so that the last version of each observation can be
+carried forward to extrapolate unavailable versions for the less up-to-date
+input archive.
 
 ```{r grab-epi-data}
 theme_set(theme_bw())
 
-y <- readRDS("all_states_covidcast_signals.rds")
+x <- archive_cases_dv_subset_all_states
+```
+
+The data can also be constructed from data the Delphi API with the following code:
 
-y <- purrr::map(y, ~ select(.x, geo_value, time_value, version = issue, value))
+```{r generate-data, eval=FALSE}
+library(epidatr)
 
-x <- epix_merge(
-  y[[1]] %>% rename(percent_cli = value) %>% as_epi_archive(compactify = FALSE),
-  y[[2]] %>% rename(case_rate = value) %>% as_epi_archive(compactify = FALSE),
+dv_subset <- pub_covidcast(
+  source = "doctor-visits",
+  signals = "smoothed_adj_cli",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20211201),
+  geo_values = "*",
+  issues = epirange(20200601, 20211201)
+) %>%
+  select(geo_value, time_value, version = issue, percent_cli = value) %>%
+  # We're using compactify=FALSE here and below to avoid some testthat test
+  # failures on tests that were based on a non-compactified version.
+  as_epi_archive(compactify = FALSE)
+
+case_rate_subset <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "confirmed_7dav_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20211201),
+  geo_values = "*",
+  issues = epirange(20200601, 20211201)
+) %>%
+  select(geo_value, time_value, version = issue, case_rate_7d_av = value) %>%
+  as_epi_archive(compactify = FALSE)
+
+# Use `epiprocess::epix_merge` to avoid having to reimplement `sync`ing
+# behavior. After merging, convert DT component back to tibble.
+archive_cases_dv_subset_all_states_dt = epix_merge(
+  dv_subset, case_rate_subset,
   sync = "locf",
-  compactify = TRUE
-)
-rm(y)
+  compactify = TRUE)
 ```
 
 </details>
@@ -217,11 +245,7 @@ the American data, but here we compare the forecasts produced from using simple
 linear regression with those from using boosted regression trees.
 
 ```{r get-can-fc, warning = FALSE}
-# source("drafts/canada-case-rates.R)
-can <- readRDS(system.file(
-  "extdata", "can_prov_cases.rds",
-  package = "epipredict", mustWork = TRUE
-))
+can <- can_prov_cases
 
 can <- can %>%
   group_by(version, geo_value) %>%
@@ -325,9 +349,20 @@ combined data from all US states and territories) to train our model.
 
 <details>
 
-<summary>Download data using `{epidatr}`</summary>
-```{r load-data, eval=FALSE}
-# loading in the data
+<summary>Fetch data</summary>
+The data are included in this package (via the
+[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+and can be loaded with:
+
+```{r load-data, message = FALSE, warning = FALSE}
+x <- case_death_rate_archive
+```
+
+The data can also be constructed from data the Delphi API with the following code:
+
+```{r generate-data, eval=FALSE}
+library(epidatr)
+
 states <- "*"
 
 confirmed_incidence_prop <- pub_covidcast(
@@ -384,13 +419,6 @@ x <- x %>%
     death_rate_7d_av = slide_value_death_rate_7d_av
   ) %>%
   as_epi_archive(compactify = TRUE)
-
-saveRDS(x$DT, file = "case_death_rate_archive.rds")
-```
-
-```{r load-stored-data}
-x <- readRDS("case_death_rate_archive.rds")
-x <- as_epi_archive(x)
 ```
 </details>
 
diff --git a/vignettes/articles/symptom-surveys.Rmd b/vignettes/articles/symptom-surveys.Rmd
index e8d4a8228..006b786b1 100644
--- a/vignettes/articles/symptom-surveys.Rmd
+++ b/vignettes/articles/symptom-surveys.Rmd
@@ -145,22 +145,40 @@ own forecaster under the `epipredict` framework, we could easily add steps to
 re-scale and transform the signals to our `epi_recipe`. This would make the code
 more succinct and self-contained.
 
+We will compare two CLI-in-community indicators from
+different sources. The data are included in this package (via the
+[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+and can be loaded with:
+
 ```{r, message = FALSE, warning = FALSE}
-library(epidatr)
 library(dplyr)
 library(purrr)
 library(epipredict)
 library(recipes)
 
+z <- county_smoothed_cli_comparison
+```
+
+The data can also be constructed from data the Delphi API with the following code:
+
+```{r, message = FALSE, warning = FALSE, eval = FALSE}
+library(epidatr)
+
+d <- "2020-09-21"
+
 case_num <- 200
-as_of_date <- "2020-05-14"
-geo_values <- pub_covidcast(
+geos_date <- "2020-05-14"
+
+# Find counties that on 2020-05-14 had >= 200 cases reported.
+# For later datasets, we will only keep data for these geos.
+geo_values_initial <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_cumulative_num",
   geo_type = "county",
   time_type = "day",
   geo_values = "*",
-  time_values = epirange(20200514, 20200514)
+  time_values = epirange(geos_date, geos_date),
+  as_of = d
 ) %>%
   filter(value >= case_num) %>%
   pull(geo_value) %>%
@@ -177,9 +195,10 @@ goog_sm_cli <- pub_covidcast(
   geo_type = "county",
   time_type = "day",
   geo_values = "*",
-  time_values = epirange(start_day, end_day)
+  time_values = epirange(start_day, end_day),
+  as_of = d
 ) %>%
-  filter(geo_value %in% geo_values) %>%
+  filter(geo_value %in% geo_values_initial) %>%
   select(geo_value, time_value, value) %>%
   rename(goog = value)
 
@@ -189,9 +208,10 @@ fb_survey <- pub_covidcast(
   geo_type = "county",
   time_type = "day",
   geo_values = "*",
-  time_values = epirange(start_day, end_day)
+  time_values = epirange(start_day, end_day),
+  as_of = d
 ) %>%
-  filter(geo_value %in% geo_values) %>%
+  filter(geo_value %in% geo_values_initial) %>%
   select(geo_value, time_value, value) %>%
   rename(fb = value)
 
@@ -201,26 +221,31 @@ jhu_7dav_incid <- pub_covidcast(
   geo_type = "county",
   time_type = "day",
   geo_values = "*",
-  time_values = epirange(start_day, end_day)
+  time_values = epirange(start_day, end_day),
+  as_of = d
 ) %>%
-  filter(geo_value %in% geo_values) %>%
+  filter(geo_value %in% geo_values_initial) %>%
   select(geo_value, time_value, value) %>%
   rename(case = value)
 
-# Find "complete" counties, present in all three data signals at all times
+# Find "complete" counties, present in all three data signals, and also 
+# present in the `geo_values_initial` object.
 geo_values_complete <- intersect(
   intersect(goog_sm_cli$geo_value, fb_survey$geo_value),
   jhu_7dav_incid$geo_value
 )
 
-# Make one big matrix by joining these three data frames
-z <- full_join(full_join(goog_sm_cli, fb_survey, by = c("geo_value", "time_value")),
+# Join the three data frames together
+z <- full_join(
+  full_join(goog_sm_cli, fb_survey, by = c("geo_value", "time_value")),
   jhu_7dav_incid,
   by = c("geo_value", "time_value")
 ) %>%
   filter(geo_value %in% geo_values_complete) %>%
-  as_epi_df()
+  as_epi_df(as_of = d)
+```
 
+```{r, message = FALSE, warning = FALSE}
 Logit <- function(x, a = 0.01) log((x + a) / (1 - x + a))
 Sigmd <- function(y, a = 0.01) (exp(y) * (1 + a) - a) / (1 + exp(y))
 
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 6b9dc8433..e38fa7d8f 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -43,7 +43,6 @@ will create a classification model for hotspot predictions.
 ```{r, warning=FALSE, message=FALSE}
 library(tidyr)
 library(dplyr)
-library(epidatr)
 library(epipredict)
 library(recipes)
 library(workflows)
@@ -64,14 +63,29 @@ Although there are many state-of-the-art models, we choose to use Poisson
 regression, the textbook example for modeling count data, as an illustration
 for using the `epipredict` package with other existing tidymodels packages.
 
+The `counts_subset` dataset is included in this package (via the
+[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+and contains the number of confirmed cases and deaths from June 4, 2021 to Dec
+31, 2021 in some U.S. states. It can be loaded with:
+
 ```{r poisson-reg-data}
+x <- counts_subset
+```
+
+The data can also be fetched from the Delphi API with the following query:
+```{r, eval = FALSE}
+library(epidatr)
+
+d <- as.Date("2024-03-20")
+
 x <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_incidence_num",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj"
+  geo_values = "ca,fl,tx,ny,nj",
+  as_of = d
 ) %>%
   select(geo_value, time_value, cases = value)
 
@@ -81,18 +95,15 @@ y <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj"
+  geo_values = "ca,fl,tx,ny,nj",
+  as_of = d
 ) %>%
   select(geo_value, time_value, deaths = value)
 
-counts_subset <- full_join(x, y, by = c("geo_value", "time_value")) %>%
-  as_epi_df()
+x <- full_join(x, y, by = c("geo_value", "time_value")) %>%
+  as_epi_df(as_of = d)
 ```
 
-The `counts_subset` dataset comes from the `epidatr` package, and
-contains the number of confirmed cases and deaths from June 4, 2021 to
-Dec 31, 2021 in some U.S. states.
-
 We wish to predict the 7-day ahead death counts with lagged cases and deaths.
 Furthermore, we will let each state be a dummy variable. Using differential
 intercept coefficients, we can allow for an intercept shift between states.
@@ -247,18 +258,33 @@ most or all of the time while in public in the past 7 days and the estimated
 percentage of respondents who reported that all or most people they encountered
 in public in the past 7 days maintained a distance of at least 6 feet.
 
-State-wise population data from the 2019 U.S. Census is included in this package
-and will be used in `layer_population_scaling()`.
+State-wise population data from the 2019 U.S. Census will be used in 
+`layer_population_scaling()`.
+
+Both datasets are included in this package (via the
+[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+and can be loaded with:
 
 ```{r}
+behav_ind <- ctis_covid_behaviours
+pop_dat <- state_census %>% select(abbr, pop)
+```
+
+The data can also be fetched from the Delphi API with the following query:
+```{r, eval = FALSE}
+library(epidatr)
+
+d <- as.Date("2024-03-20")
+
 behav_ind_mask <- pub_covidcast(
   source = "fb-survey",
   signals = "smoothed_wwearing_mask_7d",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj"
-) %>%
+  geo_values = "ca,fl,tx,ny,nj",
+  as_of = d
+)  %>%
   select(geo_value, time_value, masking = value)
 
 behav_ind_distancing <- pub_covidcast(
@@ -267,14 +293,16 @@ behav_ind_distancing <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj"
-) %>%
+  geo_values = "ca,fl,tx,ny,nj",
+  as_of = d
+)  %>%
   select(geo_value, time_value, distancing = value)
 
-pop_dat <- state_census %>% select(abbr, pop)
-
 behav_ind <- behav_ind_mask %>%
-  full_join(behav_ind_distancing, by = c("geo_value", "time_value"))
+  full_join(behav_ind_distancing, by = c("geo_value", "time_value")) %>%
+  as_epi_df(as_of = d)
+
+pop_dat <- state_census %>% select(abbr, pop)
 ```
 
 Rather than using raw mask-wearing / social-distancing metrics, for the sake

From 64929e08c8f19e2f5628067a0dc8ea4566f43936 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 27 Sep 2024 16:19:11 -0400
Subject: [PATCH 009/387] reexport can_prov_covid from epidatasets

---
 R/reexports-epidatasets.R                     |  18 ++++++++++++++++++
 .../articles/all_states_covidcast_signals.rds | Bin 10838742 -> 0 bytes
 vignettes/panel-data.Rmd                      |   1 -
 3 files changed, 18 insertions(+), 1 deletion(-)
 delete mode 100644 vignettes/articles/all_states_covidcast_signals.rds

diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index 4fdd4c59f..350661615 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -141,3 +141,21 @@ delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
 #' data(archive_cases_dv_subset_all_states, package = "epipredict")
 #' @export
 delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_dv_subset_all_states)
+
+#' @inherit epidatasets::can_prov_cases description source references title
+#' @inheritSection epidatasets::can_prov_cases Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::can_prov_cases
+#'
+#' # works
+#' library(epipredict)
+#' can_prov_cases
+#'
+#' # fails
+#' data(can_prov_cases, package = "epipredict")
+#' @export
+delayedAssign("can_prov_cases", epidatasets::can_prov_cases)
diff --git a/vignettes/articles/all_states_covidcast_signals.rds b/vignettes/articles/all_states_covidcast_signals.rds
deleted file mode 100644
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GIT binary patch
literal 0
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z{DIyNPJ4$U8_=j>HAg5&k(-npV+pyip2<w2k2J(Ja$86jJILc&-QW_wPkxA+6dG~+
jgs81?bkZL|JTW*$7<5`dw%cb2kCyyza$ouP3DN%t{Jf?N

diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 461469f53..b0b2990eb 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -47,7 +47,6 @@ are also valid candidates for `epipredict` functionality, as long as they are
 in `epi_df` format.
 
 ```{r employ-stats, include=F}
-data("grad_employ_subset")
 year_start <- min(grad_employ_subset$time_value)
 year_end <- max(grad_employ_subset$time_value)
 ```

From 741b96d3bd0d722d30f0066d45dc9b3e6913a7f6 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 27 Sep 2024 17:22:22 -0400
Subject: [PATCH 010/387] remove territories from all_states archive example
 code to match original

---
 vignettes/articles/sliding.Rmd | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/vignettes/articles/sliding.Rmd b/vignettes/articles/sliding.Rmd
index 5a7a9483a..ee808f715 100644
--- a/vignettes/articles/sliding.Rmd
+++ b/vignettes/articles/sliding.Rmd
@@ -85,8 +85,8 @@ dv_subset <- pub_covidcast(
   issues = epirange(20200601, 20211201)
 ) %>%
   select(geo_value, time_value, version = issue, percent_cli = value) %>%
-  # We're using compactify=FALSE here and below to avoid some testthat test
-  # failures on tests that were based on a non-compactified version.
+  # Remove territories and DC.
+  filter(!(geo_value %in% c("as", "gu", "dc", "mp", "pr", "vi"))) %>%
   as_epi_archive(compactify = FALSE)
 
 case_rate_subset <- pub_covidcast(
@@ -99,10 +99,9 @@ case_rate_subset <- pub_covidcast(
   issues = epirange(20200601, 20211201)
 ) %>%
   select(geo_value, time_value, version = issue, case_rate_7d_av = value) %>%
+  filter(!(geo_value %in% c("as", "gu", "dc", "mp", "pr", "vi"))) %>%
   as_epi_archive(compactify = FALSE)
 
-# Use `epiprocess::epix_merge` to avoid having to reimplement `sync`ing
-# behavior. After merging, convert DT component back to tibble.
 archive_cases_dv_subset_all_states_dt = epix_merge(
   dv_subset, case_rate_subset,
   sync = "locf",

From b50d6df941cadfe62ef3ea0534fb7809a202c7d3 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 27 Sep 2024 17:31:18 -0400
Subject: [PATCH 011/387] redocument

---
 NAMESPACE                    |  4 ++++
 man/counts_subset.Rd         | 10 ++++++++++
 man/ctis_covid_behaviours.Rd | 16 +++++++++-------
 man/grad_employ_subset.Rd    |  3 +--
 man/state_census.Rd          |  8 +++++---
 5 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index cddf2df56..42dc54d5f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -136,6 +136,7 @@ export(add_model)
 export(adjust_epi_recipe)
 export(adjust_frosting)
 export(apply_frosting)
+export(archive_cases_dv_subset_all_states)
 export(arx_args_list)
 export(arx_class_args_list)
 export(arx_class_epi_workflow)
@@ -144,11 +145,14 @@ export(arx_fcast_epi_workflow)
 export(arx_forecaster)
 export(autoplot)
 export(bake)
+export(can_prov_cases)
+export(case_death_rate_archive)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
 export(clean_f_name)
 export(counts_subset)
+export(county_smoothed_cli_comparison)
 export(covid_case_death_rates)
 export(ctis_covid_behaviours)
 export(default_epi_recipe_blueprint)
diff --git a/man/counts_subset.Rd b/man/counts_subset.Rd
index 1ba03cc05..b48e2dd58 100644
--- a/man/counts_subset.Rd
+++ b/man/counts_subset.Rd
@@ -14,6 +14,16 @@ This data set is licensed under the terms of the
 \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
 by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
 Copyright Johns Hopkins University 2020.
+
+Modifications:
+\itemize{
+\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
+The signals are taken directly from the JHU CSSE
+\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
+without changes.
+\item Furthermore, the data has been limited to a very small number of rows, the
+signal names slightly altered, and formatted into an \code{epi_df}.
+}
 }
 \usage{
 counts_subset
diff --git a/man/ctis_covid_behaviours.Rd b/man/ctis_covid_behaviours.Rd
index bf722a726..5e940fea5 100644
--- a/man/ctis_covid_behaviours.Rd
+++ b/man/ctis_covid_behaviours.Rd
@@ -5,24 +5,26 @@
 \alias{ctis_covid_behaviours}
 \title{Subset of CTIS COVID-19-related behaviours from 5 states}
 \format{
-An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
 }
 \source{
-Data from Delphi COVIDcast. Obtained via the Delphi Epidata API.
-\url{https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html}. This
-object contains a modified part of the
+This object contains a modified part of the
 \href{https://cmu-delphi.github.io/delphi-epidata/symptom-survey/#covid-19-trends-and-impact-survey}{data
 aggregations in the API} that are prepared from the
 \href{https://www.pnas.org/doi/full/10.1073/pnas.2111454118}{COVID-19
 Trends and Impact Survey}; see the first link for more information on
-citing in publications. These aggregations are licensed under the terms of
+citing in publications.
+The data is made available via the
+\href{https://cmu-delphi.github.io/delphi-epidata/}{Delphi Epidata API}.
+
+These aggregations are licensed under the terms of
 the \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons
 Attribution license}.
 
 Modifications:
 \itemize{
-\item Selected a small subset of the available data.
-\item Changed to wide format.
+\item The data has been limited to a very small number of rows, the
+signal names slightly altered, and formatted into an \code{epi_df}.
 }
 }
 \usage{
diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
index fb5e8003c..67b40e8d5 100644
--- a/man/grad_employ_subset.Rd
+++ b/man/grad_employ_subset.Rd
@@ -22,7 +22,7 @@ The data is licensed under the terms of the
 Modifications:
 \itemize{
 \item Only provincial and territorial regions are kept
-\item Only age group, field of study, and educational qualification are kept as
+\item Only age group and educational qualification are kept as
 covariates. For the remaining covariates, we keep aggregated values and
 drop the level-specific rows.
 \item No modifications were made to the time range of the data
@@ -47,7 +47,6 @@ The data has columns:
 row of measurements.}
 \item{time_value}{The time value, a year integer in YYYY format}
 \item{edu_qual}{The education qualification}
-\item{fos}{The field of study}
 \item{age_group}{The age group; either 15 to 34 or 35 to 64}
 \item{num_graduates}{The number of graduates for the given row of characteristics}
 \item{med_income_2y}{The median employment income two years after graduation}
diff --git a/man/state_census.Rd b/man/state_census.Rd
index 5afa590ec..8498d5846 100644
--- a/man/state_census.Rd
+++ b/man/state_census.Rd
@@ -8,11 +8,13 @@
 An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 57 rows and 4 columns.
 }
 \source{
-United States Census Bureau, Population Division, at
+This object is derived from several datasets from the United States
+Census Bureau, Population Division, at
 \url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.pdf},
 \url{https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-puerto-rico-municipios.html},
-and \url{https://www.census.gov/data/tables/2010/dec/2010-island-areas.html}
-This data is public domain.
+and \url{https://www.census.gov/data/tables/2010/dec/2010-island-areas.html}.
+It is made available through the \code{covidcast} package. This data is
+public domain.
 }
 \usage{
 state_census

From d158b1170914ef7eec06972c7a920d07e9acf90b Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 30 Sep 2024 16:26:17 -0400
Subject: [PATCH 012/387] redocument

---
 man/autoplot-epipred.Rd | 2 --
 1 file changed, 2 deletions(-)

diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 9bfa13c2e..1025759b3 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -121,6 +121,4 @@ arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
   args_list = arx_args_list(ahead = 14L)
 )
 autoplot(arx, .max_facets = 6)
-NULL
-
 }

From d3abd332057759fbc8d159220ad19cf7bcdb0e6c Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 30 Sep 2024 17:08:04 -0400
Subject: [PATCH 013/387] state_census fips column is now chr, doesn't need
 0-padding

---
 R/flusight_hub_formatter.R | 2 --
 1 file changed, 2 deletions(-)

diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index c90f74272..b3e31822c 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -1,7 +1,6 @@
 location_to_abbr <- function(location) {
   dictionary <-
     state_census %>%
-    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
     dplyr::transmute(
       location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
       abbr
@@ -12,7 +11,6 @@ location_to_abbr <- function(location) {
 abbr_to_location <- function(abbr) {
   dictionary <-
     state_census %>%
-    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
     dplyr::transmute(
       location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
       abbr

From bf9c1014a97f8f39393aa4ef30b8ef74daa9be22 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 30 Sep 2024 18:08:50 -0400
Subject: [PATCH 014/387] wrap example dataset failures in dontrun

---
 R/reexports-epidatasets.R     | 36 ++++++++++++++++++++++++++---------
 man/counts_subset.Rd          |  4 +++-
 man/covid_case_death_rates.Rd |  4 +++-
 man/ctis_covid_behaviours.Rd  |  4 +++-
 man/grad_employ_subset.Rd     |  4 +++-
 man/state_census.Rd           |  4 +++-
 6 files changed, 42 insertions(+), 14 deletions(-)

diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index 350661615..539a216a9 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -12,7 +12,9 @@
 #' grad_employ_subset
 #'
 #' # fails
-#' data(grad_employ_subset, package = "epipredict")
+#' \dontrun{
+#'   data(grad_employ_subset, package = "epipredict")
+#' }
 #' @export
 delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
 
@@ -30,7 +32,9 @@ delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
 #' covid_case_death_rates
 #'
 #' # fails
-#' data(covid_case_death_rates, package = "epipredict")
+#' \dontrun{
+#'   data(covid_case_death_rates, package = "epipredict")
+#' }
 #' @export
 delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
 
@@ -48,7 +52,9 @@ delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
 #' state_census
 #'
 #' # fails
-#' data(state_census, package = "epipredict")
+#' \dontrun{
+#'   data(state_census, package = "epipredict")
+#' }
 #' @export
 delayedAssign("state_census", epidatasets::state_census)
 
@@ -66,7 +72,9 @@ delayedAssign("state_census", epidatasets::state_census)
 #' counts_subset
 #'
 #' # fails
-#' data(counts_subset, package = "epipredict")
+#' \dontrun{
+#'   data(counts_subset, package = "epipredict")
+#' }
 #' @export
 delayedAssign("counts_subset", epidatasets::counts_subset)
 
@@ -84,7 +92,9 @@ delayedAssign("counts_subset", epidatasets::counts_subset)
 #' ctis_covid_behaviours
 #'
 #' # fails
-#' data(ctis_covid_behaviours, package = "epipredict")
+#' \dontrun{
+#'   data(ctis_covid_behaviours, package = "epipredict")
+#' }
 #' @export
 delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
 
@@ -102,7 +112,9 @@ delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
 #' county_smoothed_cli_comparison
 #'
 #' # fails
-#' data(county_smoothed_cli_comparison, package = "epipredict")
+#' \dontrun{
+#'   data(county_smoothed_cli_comparison, package = "epipredict")
+#' }
 #' @export
 delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli_comparison)
 
@@ -120,7 +132,9 @@ delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli
 #' case_death_rate_archive
 #'
 #' # fails
-#' data(case_death_rate_archive, package = "epipredict")
+#' \dontrun{
+#'   data(case_death_rate_archive, package = "epipredict")
+#' }
 #' @export
 delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
 
@@ -138,7 +152,9 @@ delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
 #' archive_cases_dv_subset_all_states
 #'
 #' # fails
-#' data(archive_cases_dv_subset_all_states, package = "epipredict")
+#' \dontrun{
+#'   data(archive_cases_dv_subset_all_states, package = "epipredict")
+#' }
 #' @export
 delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_dv_subset_all_states)
 
@@ -156,6 +172,8 @@ delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_d
 #' can_prov_cases
 #'
 #' # fails
-#' data(can_prov_cases, package = "epipredict")
+#' \dontrun{
+#'   data(can_prov_cases, package = "epipredict")
+#' }
 #' @export
 delayedAssign("can_prov_cases", epidatasets::can_prov_cases)
diff --git a/man/counts_subset.Rd b/man/counts_subset.Rd
index b48e2dd58..5d7be6937 100644
--- a/man/counts_subset.Rd
+++ b/man/counts_subset.Rd
@@ -61,6 +61,8 @@ library(epipredict)
 counts_subset
 
 # fails
-data(counts_subset, package = "epipredict")
+\dontrun{
+  data(counts_subset, package = "epipredict")
+}
 }
 \keyword{datasets}
diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
index c553e57bd..38988e5d7 100644
--- a/man/covid_case_death_rates.Rd
+++ b/man/covid_case_death_rates.Rd
@@ -66,6 +66,8 @@ library(epipredict)
 covid_case_death_rates
 
 # fails
-data(covid_case_death_rates, package = "epipredict")
+\dontrun{
+  data(covid_case_death_rates, package = "epipredict")
+}
 }
 \keyword{datasets}
diff --git a/man/ctis_covid_behaviours.Rd b/man/ctis_covid_behaviours.Rd
index 5e940fea5..14aca1da2 100644
--- a/man/ctis_covid_behaviours.Rd
+++ b/man/ctis_covid_behaviours.Rd
@@ -63,6 +63,8 @@ library(epipredict)
 ctis_covid_behaviours
 
 # fails
-data(ctis_covid_behaviours, package = "epipredict")
+\dontrun{
+  data(ctis_covid_behaviours, package = "epipredict")
+}
 }
 \keyword{datasets}
diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
index 67b40e8d5..77cc67abb 100644
--- a/man/grad_employ_subset.Rd
+++ b/man/grad_employ_subset.Rd
@@ -67,6 +67,8 @@ library(epipredict)
 grad_employ_subset
 
 # fails
-data(grad_employ_subset, package = "epipredict")
+\dontrun{
+  data(grad_employ_subset, package = "epipredict")
+}
 }
 \keyword{datasets}
diff --git a/man/state_census.Rd b/man/state_census.Rd
index 8498d5846..91240d0e2 100644
--- a/man/state_census.Rd
+++ b/man/state_census.Rd
@@ -52,6 +52,8 @@ library(epipredict)
 state_census
 
 # fails
-data(state_census, package = "epipredict")
+\dontrun{
+  data(state_census, package = "epipredict")
+}
 }
 \keyword{datasets}

From 8ff0ea01bd5bcc05d853db700105222f37967500 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 1 Oct 2024 16:10:32 -0400
Subject: [PATCH 015/387] import checkmate::test_ fns

---
 NAMESPACE              | 7 +++++++
 R/epipredict-package.R | 3 +++
 2 files changed, 10 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index f924d7919..28490ace4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -236,6 +236,13 @@ importFrom(checkmate,assert_logical)
 importFrom(checkmate,assert_number)
 importFrom(checkmate,assert_numeric)
 importFrom(checkmate,assert_scalar)
+importFrom(checkmate,test_character)
+importFrom(checkmate,test_date)
+importFrom(checkmate,test_function)
+importFrom(checkmate,test_integerish)
+importFrom(checkmate,test_logical)
+importFrom(checkmate,test_numeric)
+importFrom(checkmate,test_scalar)
 importFrom(cli,cli_abort)
 importFrom(cli,cli_warn)
 importFrom(dplyr,across)
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index bf3a8e70e..d8306325d 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -4,6 +4,9 @@
 #' @importFrom checkmate assert_logical assert_numeric assert_number
 #' @importFrom checkmate assert_integer assert_integerish
 #' @importFrom checkmate assert_date assert_function assert_class
+#' @importFrom checkmate test_scalar test_logical test_numeric
+#' @importFrom checkmate test_integerish test_date
+#' @importFrom checkmate test_character test_function
 #' @importFrom cli cli_abort cli_warn
 #' @importFrom dplyr arrange across all_of any_of bind_cols bind_rows group_by
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup

From 249b465ee2d78c7fb453225c1660ae81c10b6959 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 15 Mar 2024 18:09:26 -0700
Subject: [PATCH 016/387] fix warnings and empty tests

This is purely before any work on latency adjusting, and is just about
geting the tests to run without warnings or skipped tests. Getting this
involved:
1. renaming some lingering examples of p to probs and `q` to
`quantile_levels` (and some `quantile_level`s to the plural)
2. Adding snapshots so the tests for printing in population_scaling work
as intended (should probably be converted to cli_informs at some point)
3. removing the nearly empty `test-propagate_samples` which seems like
something intended that was never finished. Probably want to add an
issue if we actually want it done.
4. added a bunch of `edf`'s in unhappy `prep` steps
---
 tests/testthat/_snaps/population_scaling.md | 20 +++++++++
 tests/testthat/test-dist_quantiles.R        |  1 +
 tests/testthat/test-population_scaling.R    | 47 +++++++++++++++++++++
 tests/testthat/test-replace_Inf.R           |  2 +-
 tests/testthat/test-step_lag_difference.R   |  2 +-
 5 files changed, 70 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index 9263e8e1e..149cd38ac 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -1,3 +1,23 @@
+# test joining by default columns
+
+    Code
+      prep <- prep(r, jhu)
+
+---
+
+    Code
+      b <- bake(prep, jhu)
+
+---
+
+    Code
+      wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
+
+---
+
+    Code
+      p <- predict(wf, latest)
+
 # expect error if `by` selector does not match
 
     Code
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 66f229956..9975213c6 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -41,6 +41,7 @@ test_that("quantile extrapolator works", {
   expect_s3_class(vctrs::vec_data(qq[1])[[1]], "dist_quantiles")
   expect_length(parameters(qq[1])$quantile_levels[[1]], 3L)
 
+
   dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
   qq <- extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
   expect_s3_class(qq, "distribution")
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 6337a2ea8..f5cedd9b7 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -193,6 +193,53 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
+
+  jhu <- case_death_rate_subset %>%
+    dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
+    dplyr::select(geo_value, time_value, case_rate)
+
+  reverse_pop_data = data.frame(geo_value = c("ca", "ny"),
+                                values = c(1/20000, 1/30000))
+
+  r <- epi_recipe(jhu) %>%
+    step_population_scaling(case_rate,
+                            df = reverse_pop_data,
+                            df_pop_col = "values",
+                            by = NULL,
+                            suffix = "_scaled") %>%
+    step_epi_lag(case_rate_scaled, lag = c(0, 7, 14)) %>% # cases
+    step_epi_ahead(case_rate_scaled, ahead = 7, role = "outcome") %>% # cases
+    recipes::step_naomit(recipes::all_predictors()) %>%
+    recipes::step_naomit(recipes::all_outcomes(), skip = TRUE)
+
+  expect_snapshot(prep <- prep(r, jhu))
+
+  expect_snapshot(b <- bake(prep, jhu))
+
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_threshold(.pred) %>%
+    layer_naomit(.pred) %>%
+    layer_population_scaling(.pred, df = reverse_pop_data,
+                             by =  NULL,
+                             df_pop_col = "values")
+
+  expect_snapshot(wf <- epi_workflow(r,
+                     parsnip::linear_reg()) %>%
+    fit(jhu) %>%
+    add_frosting(f))
+
+  latest <- get_test_data(recipe = r,
+                          x = case_death_rate_subset %>%
+                            dplyr::filter(time_value > "2021-11-01",
+                                          geo_value %in% c("ca", "ny")) %>%
+                            dplyr::select(geo_value, time_value, case_rate))
+
+
+  expect_snapshot(p <- predict(wf, latest))
+
+
+
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
diff --git a/tests/testthat/test-replace_Inf.R b/tests/testthat/test-replace_Inf.R
index f9993ca13..c99a045b2 100644
--- a/tests/testthat/test-replace_Inf.R
+++ b/tests/testthat/test-replace_Inf.R
@@ -7,7 +7,7 @@ test_that("replace_inf works", {
     v1 = 1:5, v2 = c(1, 2, Inf, -Inf, NA)
   )
 
-  library(dplyr)
+  suppressPackageStartupMessages(library(dplyr))
   ok <- c("geo_value", "time_value")
   df2 <- df %>% mutate(across(!all_of(ok), ~ vec_replace_inf(.x, NA)))
   expect_identical(df[, 1:3], df2[, 1:3])
diff --git a/tests/testthat/test-step_lag_difference.R b/tests/testthat/test-step_lag_difference.R
index 6ff9884a7..d23a3b4fa 100644
--- a/tests/testthat/test-step_lag_difference.R
+++ b/tests/testthat/test-step_lag_difference.R
@@ -52,7 +52,7 @@ test_that("step_lag_difference works for a single signal", {
 })
 
 
-test_that("step_lag_difference works for a two signals", {
+test_that("step_lag_difference works for a two signal epi_df", {
   df <- data.frame(
     time_value = 1:5,
     geo_value = rep("a", 5),

From c622d7d4865ed0a2a48ad1f7fde31617af8dc2f3 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 18 Mar 2024 11:15:08 -0700
Subject: [PATCH 017/387] first draft of extend_ahead

---
 R/step_epi_shift.R                   | 46 +++++++++++++++----
 R/utils-shift.R                      | 69 ++++++++++++++++++++++++++++
 man/adjust_latency.Rd                | 14 ++++++
 man/step_epi_shift.Rd                | 10 ++++
 tests/testthat/_snaps/utils-shift.md | 13 ++++++
 tests/testthat/test-utils-shift.R    | 30 ++++++++++++
 6 files changed, 174 insertions(+), 8 deletions(-)
 create mode 100644 R/utils-shift.R
 create mode 100644 man/adjust_latency.Rd
 create mode 100644 tests/testthat/_snaps/utils-shift.md
 create mode 100644 tests/testthat/test-utils-shift.R

diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 465d64e7f..62553a3f3 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -19,6 +19,10 @@
 #'  be the lag or lead for each value in the vector. Lag integers must be
 #'  nonnegative, while ahead integers must be positive.
 #' @param prefix A character string that will be prefixed to the new column.
+#' @param latency_adjustment a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
+#'   - `"extend_ahead"`: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
+#'   - `"locf"`: carries forward the last observed value up to the forecast date.
+#'   - `"extend_lags"`: per `epi_key` and `predictor`, adjusts the lag so that the shortest lag at predict time is
 #' @param default Determines what fills empty rows
 #'   left by leading/lagging (defaults to NA).
 #' @param skip A logical. Should the step be skipped when the
@@ -54,6 +58,12 @@ step_epi_lag <-
            prefix = "lag_",
            default = NA,
            skip = FALSE,
+           latency_adjustment = c(
+             "None",
+             "extend_ahead",
+             "locf",
+             "extend_lags"
+           ),
            id = rand_id("epi_lag")) {
     if (!is_epi_recipe(recipe)) {
       cli_abort("This step can only operate on an `epi_recipe`.")
@@ -65,9 +75,15 @@ step_epi_lag <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
+    latency_adjustment <- rlang::arg_match(latency_adjustment)
     arg_is_nonneg_int(lag)
-    arg_is_chr_scalar(prefix, id)
-
+    arg_is_chr_scalar(prefix, id, latency_adjustment)
+    if (!is.null(columns)) {
+      cli::cli_abort(c(
+        "The `columns` argument must be `NULL.",
+        i = "Use `tidyselect` methods to choose columns to lag."
+      ))
+    }
     recipes::add_step(
       recipe,
       step_epi_lag_new(
@@ -79,6 +95,7 @@ step_epi_lag <-
         default = default,
         keys = key_colnames(recipe),
         columns = NULL,
+        latency_adjustment = latency_adjustment,
         skip = skip,
         id = id
       )
@@ -97,6 +114,13 @@ step_epi_ahead <-
            role = "outcome",
            prefix = "ahead_",
            default = NA,
+           latency_adjustment = c(
+             "None",
+             "extend_ahead",
+             "locf",
+             "extend_lags"
+           ),
+           columns = NULL,
            skip = FALSE,
            id = rand_id("epi_ahead")) {
     if (!is_epi_recipe(recipe)) {
@@ -109,9 +133,9 @@ step_epi_ahead <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
+    latency_adjustment <- rlang::arg_match(latency_adjustment)
     arg_is_nonneg_int(ahead)
-    arg_is_chr_scalar(prefix, id)
-
+    arg_is_chr_scalar(prefix, id, latency_adjustment)
     recipes::add_step(
       recipe,
       step_epi_ahead_new(
@@ -122,7 +146,8 @@ step_epi_ahead <-
         prefix = prefix,
         default = default,
         keys = key_colnames(recipe),
-        columns = NULL,
+        latency_adjustment = latency_adjustment,
+        columns = columns,
         skip = skip,
         id = id
       )
@@ -132,7 +157,7 @@ step_epi_ahead <-
 
 step_epi_lag_new <-
   function(terms, role, trained, lag, prefix, default, keys,
-           columns, skip, id) {
+           latency_adjustment, columns, skip, id) {
     recipes::step(
       subclass = "epi_lag",
       terms = terms,
@@ -142,6 +167,7 @@ step_epi_lag_new <-
       prefix = prefix,
       default = default,
       keys = keys,
+      latency_adjustment = latency_adjustment,
       columns = columns,
       skip = skip,
       id = id
@@ -149,7 +175,7 @@ step_epi_lag_new <-
   }
 
 step_epi_ahead_new <-
-  function(terms, role, trained, ahead, prefix, default, keys,
+  function(terms, role, trained, ahead, prefix, default, keys, latency_adjustment,
            columns, skip, id) {
     recipes::step(
       subclass = "epi_ahead",
@@ -159,6 +185,7 @@ step_epi_ahead_new <-
       ahead = ahead,
       prefix = prefix,
       default = default,
+      latency_adjustment = latency_adjustment,
       keys = keys,
       columns = columns,
       skip = skip,
@@ -178,6 +205,7 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
+    latency_adjustment = x$latency_adjustment,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
     id = x$id
@@ -194,6 +222,7 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
+    latency_adjustment = x$latency_adjustment,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
     id = x$id
@@ -235,8 +264,9 @@ bake.step_epi_lag <- function(object, new_data, ...) {
 
 #' @export
 bake.step_epi_ahead <- function(object, new_data, ...) {
+  ahead <- adjust_latency(object, new_data)
   grid <- tidyr::expand_grid(col = object$columns, ahead = object$ahead) %>%
-    mutate(
+    dplyr::mutate(
       newname = glue::glue("{object$prefix}{ahead}_{col}"),
       shift_val = -ahead,
       ahead = NULL
diff --git a/R/utils-shift.R b/R/utils-shift.R
new file mode 100644
index 000000000..e1cf3a3dd
--- /dev/null
+++ b/R/utils-shift.R
@@ -0,0 +1,69 @@
+#' various ways of handling differences between the `as_of` date and the maximum
+#' time value
+#' @description
+#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
+#'   date, rather than relative to the last day of data
+#' @keywords internal
+adjust_latency <- function(object, new_data) {
+  method <- object$latency_adjustment
+  ahead <- object$ahead
+  if (is.na(method) || is.null(method) || method == "None") {
+    return(object$ahead)
+  } else if (method == "extend_ahead") {
+    as_of <- attributes(new_data)$metadata$as_of
+    if (FALSE && (typeof(as_of) != typeof(new_data$time_value))) {
+      rlang::abort(glue::glue(
+        "the data matrix `as_of` value is {as_of}, ",
+        "and not a valid `time_type` with type ",
+        "matching `time_value`'s type of ",
+        "{typeof(new_data$time_value)}."
+      ))
+    }
+    # adjust the ahead so that we're predicting relative to the as_of date,
+    # rather
+    # than the last day of data
+    time_values <- new_data$time_value
+    if (length(time_values) > 0) {
+      max_time <- max(time_values)
+      shift_amount <- as.Date(as_of) - max_time
+      if (is.null(as_of) || is.na(as_of)) {
+        cli::cli_warn(glue::glue(
+          "epi_data's `as_of` was {as_of}, setting to ",
+          "the latest time value, {max_time}."
+        ))
+        as_of <- max_time
+      } else if (as_of < max_time) {
+        cli::cli_abort(glue::glue(
+          "`as_of` ({(as_of)}) is before the most ",
+          "recent data ({max_time}). Remove before ",
+          "predicting."
+        ))
+      }
+      effective_ahead <- as.integer(shift_amount + ahead)
+      time_type <- attributes(new_data)$metadata$time_type
+
+      if ((grepl("day", time_type) && (shift_amount >= 10)) ||
+            (grepl("week", time_type) && (shift_amount >= 4))||
+            ((time_type == "yearmonth") && (shift_amount >=2)) ||
+            ((time_type == "yearquarter") && (shift_amount >= 1)) ||
+            ((time_type == "year") && (shift_amount >= 1))) {
+        cli::cli_warn(c(
+          "!" = glue::glue("The ahead has been adjusted by {shift_amount}, ",
+                           "which is questionable for it's `time_type` of ",
+                           "{time_type}"),
+          "i" = "input ahead: {ahead}",
+          "i" = "shifted ahead: {effective_ahead}",
+          "i" = "max_time = {max_time} -> as_of = {as_of}"
+        ))
+      }
+      return(effective_ahead)
+    } else {
+      rlang::abort("the `time_value` column of `new_data` is empty")
+    }
+  } else {
+    rlang::abort(glue::glue(
+      "Latency adjustment method {method} has not yet ",
+      "been implemented for `step_epi_ahead`."
+    ))
+  }
+}
diff --git a/man/adjust_latency.Rd b/man/adjust_latency.Rd
new file mode 100644
index 000000000..eaebd5c29
--- /dev/null
+++ b/man/adjust_latency.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-shift.R
+\name{adjust_latency}
+\alias{adjust_latency}
+\title{various ways of handling differences between the \code{as_of} date and the maximum
+time value}
+\usage{
+adjust_latency(object, new_data)
+}
+\description{
+adjust the ahead so that we will be predicting \code{ahead} days after the \code{as_of}
+date, rather than relative to the last day of data
+}
+\keyword{internal}
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 2bf22c15d..4a1a00104 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -13,6 +13,7 @@ step_epi_lag(
   prefix = "lag_",
   default = NA,
   skip = FALSE,
+  latency_adjustment = c("None", "extend_ahead", "locf", "extend_lags"),
   id = rand_id("epi_lag")
 )
 
@@ -23,6 +24,8 @@ step_epi_ahead(
   role = "outcome",
   prefix = "ahead_",
   default = NA,
+  latency_adjustment = c("None", "extend_ahead", "locf", "extend_lags"),
+  columns = NULL,
   skip = FALSE,
   id = rand_id("epi_ahead")
 )
@@ -53,6 +56,13 @@ conducted on new data (e.g. processing the outcome variable(s)).
 Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
+\item{latency_adjustment}{a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
+\itemize{
+\item \code{"extend_ahead"}: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
+\item \code{"locf"}: carries forward the last observed value up to the forecast date.
+\item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that the shortest lag at predict time is
+}}
+
 \item{id}{A unique identifier for the step}
 }
 \value{
diff --git a/tests/testthat/_snaps/utils-shift.md b/tests/testthat/_snaps/utils-shift.md
new file mode 100644
index 000000000..b7c5f064f
--- /dev/null
+++ b/tests/testthat/_snaps/utils-shift.md
@@ -0,0 +1,13 @@
+# extend_ahead warns in case of extreme adjustment
+
+    Code
+      adjust_latency(object, x_adjust_ahead)
+    Condition
+      Warning:
+      ! The ahead has been adjusted by 100, which is questionable for it's `time_type` of day
+      i input ahead: 7
+      i shifted ahead: 107
+      i max_time = 2021-07-19 -> as_of = 2021-10-27
+    Output
+      [1] 107
+
diff --git a/tests/testthat/test-utils-shift.R b/tests/testthat/test-utils-shift.R
new file mode 100644
index 000000000..53788f8b8
--- /dev/null
+++ b/tests/testthat/test-utils-shift.R
@@ -0,0 +1,30 @@
+time_range <- as.Date("2021-01-01") + 0:199
+x_adjust_ahead <- tibble(
+  geo_value = rep("place", 200),
+  time_value = time_range,
+  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
+  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
+) %>%
+  as_epi_df(as_of = max(time_range) + 3)
+# confirm the delay is right
+
+test_that("adjust_latency extend_ahead works", {
+  # testing that POSIXct converts correctly (as well as basic types)
+  expect_equal(
+    attributes(x_adjust_ahead)$metadata$as_of - max(x_adjust_ahead$time_value),
+    as.difftime(3, units = "days")
+  )
+  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
+  expect_no_error(adjusted_ahead <- adjust_latency(object, x_adjust_ahead))
+  expect_type(adjusted_ahead, "integer")
+  expect_equal(adjusted_ahead, 3 + 7)
+})
+
+test_that("extend_ahead warns in case of extreme adjustment", {
+  # warns if the ahead is relatively small
+  attributes(x_adjust_ahead)$metadata$as_of <-
+    max(x_adjust_ahead$time_value) + 100
+  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
+  attributes(x_adjust_ahead)$metadata$time_type
+  expect_snapshot(adjust_latency(object, x_adjust_ahead))
+})

From e6c19e92443a5df81d8154afe52e2d28e5001d45 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 15:23:16 -0500
Subject: [PATCH 018/387] extend_ahead version bump and news

---
 NEWS.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 8edddae92..3c3a0f942 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,9 @@
 # epipredict (development)
 
 Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.
+# epipredict 0.2
+
+-   add `latency_adjustment` as an option for `add_epi_ahead`, which adjusts the `ahead` so that the prediction is `ahead` relative to the `as_of` date for the `epi_data`, rather than relative to the last day of data.
 
 # epipredict 0.1
 

From 2b68062a24af33eebc42e37ac98a8c2fb9324c70 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 18 Mar 2024 11:31:16 -0700
Subject: [PATCH 019/387] styler has opinions

---
 R/utils-shift.R                          | 16 +++++----
 tests/testthat/test-population_scaling.R | 44 +++++++++++++++---------
 2 files changed, 36 insertions(+), 24 deletions(-)

diff --git a/R/utils-shift.R b/R/utils-shift.R
index e1cf3a3dd..a8eccf6fe 100644
--- a/R/utils-shift.R
+++ b/R/utils-shift.R
@@ -43,14 +43,16 @@ adjust_latency <- function(object, new_data) {
       time_type <- attributes(new_data)$metadata$time_type
 
       if ((grepl("day", time_type) && (shift_amount >= 10)) ||
-            (grepl("week", time_type) && (shift_amount >= 4))||
-            ((time_type == "yearmonth") && (shift_amount >=2)) ||
-            ((time_type == "yearquarter") && (shift_amount >= 1)) ||
-            ((time_type == "year") && (shift_amount >= 1))) {
+        (grepl("week", time_type) && (shift_amount >= 4)) ||
+        ((time_type == "yearmonth") && (shift_amount >= 2)) ||
+        ((time_type == "yearquarter") && (shift_amount >= 1)) ||
+        ((time_type == "year") && (shift_amount >= 1))) {
         cli::cli_warn(c(
-          "!" = glue::glue("The ahead has been adjusted by {shift_amount}, ",
-                           "which is questionable for it's `time_type` of ",
-                           "{time_type}"),
+          "!" = glue::glue(
+            "The ahead has been adjusted by {shift_amount}, ",
+            "which is questionable for it's `time_type` of ",
+            "{time_type}"
+          ),
           "i" = "input ahead: {ahead}",
           "i" = "shifted ahead: {effective_ahead}",
           "i" = "max_time = {max_time} -> as_of = {as_of}"
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index f5cedd9b7..d18be65f5 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -193,20 +193,22 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
-
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
-  reverse_pop_data = data.frame(geo_value = c("ca", "ny"),
-                                values = c(1/20000, 1/30000))
+  reverse_pop_data <- data.frame(
+    geo_value = c("ca", "ny"),
+    values = c(1 / 20000, 1 / 30000)
+  )
 
   r <- epi_recipe(jhu) %>%
     step_population_scaling(case_rate,
-                            df = reverse_pop_data,
-                            df_pop_col = "values",
-                            by = NULL,
-                            suffix = "_scaled") %>%
+      df = reverse_pop_data,
+      df_pop_col = "values",
+      by = NULL,
+      suffix = "_scaled"
+    ) %>%
     step_epi_lag(case_rate_scaled, lag = c(0, 7, 14)) %>% # cases
     step_epi_ahead(case_rate_scaled, ahead = 7, role = "outcome") %>% # cases
     recipes::step_naomit(recipes::all_predictors()) %>%
@@ -220,20 +222,28 @@ test_that("test joining by default columns", {
     layer_predict() %>%
     layer_threshold(.pred) %>%
     layer_naomit(.pred) %>%
-    layer_population_scaling(.pred, df = reverse_pop_data,
-                             by =  NULL,
-                             df_pop_col = "values")
+    layer_population_scaling(.pred,
+      df = reverse_pop_data,
+      by = NULL,
+      df_pop_col = "values"
+    )
 
-  expect_snapshot(wf <- epi_workflow(r,
-                     parsnip::linear_reg()) %>%
+  expect_snapshot(wf <- epi_workflow(
+    r,
+    parsnip::linear_reg()
+  ) %>%
     fit(jhu) %>%
     add_frosting(f))
 
-  latest <- get_test_data(recipe = r,
-                          x = case_death_rate_subset %>%
-                            dplyr::filter(time_value > "2021-11-01",
-                                          geo_value %in% c("ca", "ny")) %>%
-                            dplyr::select(geo_value, time_value, case_rate))
+  latest <- get_test_data(
+    recipe = r,
+    x = case_death_rate_subset %>%
+      dplyr::filter(
+        time_value > "2021-11-01",
+        geo_value %in% c("ca", "ny")
+      ) %>%
+      dplyr::select(geo_value, time_value, case_rate)
+  )
 
 
   expect_snapshot(p <- predict(wf, latest))

From 2f1ab397353f45b09bbce08e9e755ef44cfb88d1 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 29 Mar 2024 00:29:17 -0700
Subject: [PATCH 020/387] separate step version

---
 R/epi_shift.R           |  78 +++++++++------
 R/step_adjust_latency.R | 211 ++++++++++++++++++++++++++++++++++++++++
 R/step_epi_shift.R      |  59 +----------
 R/utils-latency.R       | 158 ++++++++++++++++++++++++++++++
 4 files changed, 419 insertions(+), 87 deletions(-)
 create mode 100644 R/step_adjust_latency.R
 create mode 100644 R/utils-latency.R

diff --git a/R/epi_shift.R b/R/epi_shift.R
index eb534f1ea..81dfcddc1 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -2,43 +2,61 @@
 #'
 #' This is a lower-level function. As such it performs no error checking.
 #'
-#' @param x Data frame. Variables to shift
-#' @param shifts List. Each list element is a vector of shifts.
-#'   Negative values produce leads. The list should have the same
-#'   length as the number of columns in `x`.
-#' @param time_value Vector. Same length as `x` giving time stamps.
-#' @param keys Data frame, vector, or `NULL`. Additional grouping vars.
-#' @param out_name Chr. The output list will use this as a prefix.
+#' @param x Data frame.
+#' @param shift_val a single integer. Negative values produce leads.
+#' @param newname the name for the newly shifted column
+#' @param key_cols vector, or `NULL`. Additional grouping vars.
 #'
 #' @keywords internal
 #'
 #' @return a list of tibbles
-epi_shift <- function(x, shifts, time_value, keys = NULL, out_name = "x") {
-  if (!is.data.frame(x)) x <- data.frame(x)
-  if (is.null(keys)) keys <- rep("empty", nrow(x))
-  p_in <- ncol(x)
-  out_list <- tibble(i = 1:p_in, shift = shifts) %>%
-    tidyr::unchop(shift) %>% # what is chop
-    mutate(name = paste0(out_name, 1:nrow(.))) %>%
-    # One list element for each shifted feature
-    pmap(function(i, shift, name) {
-      tibble(keys,
-        time_value = time_value + shift, # Shift back
-        !!name := x[[i]]
-      )
-    })
-  if (is.data.frame(keys)) {
-    common_names <- c(names(keys), "time_value")
-  } else {
-    common_names <- c("keys", "time_value")
-  }
-
-  reduce(out_list, dplyr::full_join, by = common_names)
-}
-
 epi_shift_single <- function(x, col, shift_val, newname, key_cols) {
   x %>%
     select(all_of(c(key_cols, col))) %>%
     mutate(time_value = time_value + shift_val) %>%
     rename(!!newname := {{ col }})
 }
+
+#' lags move columns forward to bring the past up to today, while aheads drag
+#' the future back to today
+get_sign <- function(object) {
+  if (object$prefix == "lag_") {
+    return(1)
+  } else {
+    return(-1)
+  }
+}
+
+#' backend for both `bake.step_epi_ahead` and `bake.step_epi_lag`, performs the
+#' checks missing in `epi_shift_single`
+#' @keywords internal
+#' @importFrom cli cli_abort
+add_shifted_columns <- function(new_data, object, amount) {
+  sign_shift <- get_sign(object)
+  grid <- tidyr::expand_grid(col = object$columns, amount = amount) %>%
+    dplyr::mutate(
+      newname = glue::glue("{object$prefix}{amount}_{col}"),
+      shift_val = sign_shift * amount,
+      amount = NULL
+    )
+
+  ## ensure no name clashes
+  new_data_names <- colnames(new_data)
+  intersection <- new_data_names %in% grid$newname
+  if (any(intersection)) {
+    cli_abort(c(
+      "Name collision occured in {.cls {class(object)[1]}}",
+      "The following variable name{?s} already exist{?s/}: {.val {new_data_names[intersection]}}."
+    ))
+  }
+  ok <- object$keys
+  shifted <- reduce(
+    pmap(grid, epi_shift_single, x = new_data, key_cols = ok),
+    dplyr::full_join,
+    by = ok
+  )
+  dplyr::full_join(new_data, shifted, by = ok) %>%
+    dplyr::group_by(dplyr::across(dplyr::all_of(kill_time_value(ok)))) %>%
+    dplyr::arrange(time_value) %>%
+    dplyr::ungroup()
+}
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
new file mode 100644
index 000000000..682d80b97
--- /dev/null
+++ b/R/step_adjust_latency.R
@@ -0,0 +1,211 @@
+#' adapt the pipeline to latency in the data
+#'
+#' In the standard case, the pipeline assumes that the last observation is also
+#' the day from which the forecast is being made. `step_adjust_latency` uses the
+#' `as_of` date of the `epi_df` as the `forecast_date`. This is most useful in
+#' realtime and pseudo-prospective forecasting for data where there is some
+#' delay between the day recorded and when that data is available.
+#'
+#' @param recipe A recipe object. The step will be added to the
+#'  sequence of operations for this recipe.
+#' @param ... One or more selector functions to choose variables for this step.
+#'   See [recipes::selections()] for more details. Typically you will not need
+#'   to set this manually, as the necessary adjustments will be done for the
+#'   predictors and outcome.
+#' @param method a character. Determines the method by which the
+#'   forecast handles latency. All of these assume the forecast date is the
+#'   `as_of` of the `epi_df`. The options are:
+#'   - `"extend_ahead"`: Lengthen the ahead so that forecasting from the last
+#'   observation results in a forecast `ahead` after the `as_of` date. E.g. if
+#'   there are 3 days of latency between the last observation and the `as_of`
+#'   date for a 4 day ahead forecast, the ahead used in practice is actually 7.
+#'   - `"locf"`: carries forward the last observed value(s) up to the forecast
+#'   date. See the Vignette TODO for equivalents using other steps and more
+#'   sophisticated methods of extrapolation.
+#'   - `"extend_lags"`: per `epi_key` and `predictor`, adjusts the lag so that
+#'   the shortest lag at predict time is at the last observation. E.g. if the
+#'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
+#'   become `c(3,10,17)`
+#' @param default Determines what fills empty rows
+#'   left by leading/lagging (defaults to NA).
+#' @param prefix a character. The prefix matching the one used in either
+#'   `step_epi_ahead` if `method="extend_ahead"` or `step_epi_lag`
+#'   if `method="extend_lags"` or "locf".
+#' @param skip A logical. Should the step be skipped when the
+#'  recipe is baked by [bake()]? While all operations are baked
+#'  when [prep()] is run, some operations may not be able to be
+#'  conducted on new data (e.g. processing the outcome variable(s)).
+#'  Care should be taken when using `skip = TRUE` as it may affect
+#'  the computations for subsequent operations.
+#' @param id A unique identifier for the step
+#' @template step-return
+#'
+#' @details The step assumes that the pipeline has already applied either
+#'   `step_epi_ahead` or `step_epi_lag` depending on the value of
+#'   `"method"`, and that `step_epi_naomit` has NOT been run.
+#'
+#' The `prefix` and `id` arguments are unchangeable to ensure that the code runs
+#' properly and to avoid inconsistency with naming. For `step_epi_ahead`, they
+#' are always set to `"ahead_"` and `"epi_ahead"` respectively, while for
+#' `step_epi_lag`, they are set to `"lag_"` and `"epi_lag`, respectively.
+#'
+#' @family row operation steps
+#' @rdname step_adjust_latency
+#' @export
+#' @examples
+#' r <- epi_recipe(case_death_rate_subset) %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   #   step_adjust_latency(method = "extend_ahead") %>%
+#'   step_epi_lag(death_rate, lag = c(0, 7, 14))
+#' r
+step_adjust_latency <-
+  function(recipe,
+           ...,
+           role = NA,
+           trained = FALSE,
+           method = c(
+             "extend_ahead",
+             "locf",
+             "extend_lags"
+           ),
+           default = NA,
+           skip = FALSE,
+           prefix = NULL,
+           columns = NULL,
+           id = recipes::rand_id("epi_lag")) {
+    if (!is_epi_recipe(recipe)) {
+      rlang::abort("This recipe step can only operate on an `epi_recipe`.")
+    }
+    if (!is.null(columns)) {
+      rlang::abort(c("The `columns` argument must be `NULL.",
+        i = "Use `tidyselect` methods to choose columns to lag."
+      ))
+    }
+
+    method <- rlang::arg_match(method)
+    if (method == "extend_ahead") {
+      prefix <- "ahead_"
+    } else {
+      prefix <- "lag_"
+    }
+
+    arg_is_chr_scalar(prefix, id, method)
+    recipes::add_step(
+      recipe,
+      step_adjust_latency_new(
+        terms = dplyr::enquos(...),
+        role = role,
+        method = method,
+        info = NULL,
+        trained = trained,
+        prefix = prefix,
+        default = default,
+        keys = epi_keys(recipe),
+        columns = columns,
+        skip = skip,
+        id = id
+      )
+    )
+  }
+
+step_adjust_latency_new <-
+  function(terms, role, trained, prefix, default, keys, method, info,
+           columns, skip, id) {
+    step(
+      subclass = "adjust_latency",
+      terms = terms,
+      role = role,
+      method = method,
+      info = info,
+      trained = trained,
+      prefix = prefix,
+      default = default,
+      keys = keys,
+      columns = columns,
+      skip = skip,
+      id = id
+    )
+  }
+
+#' @export
+prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
+  if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
+    cli::cli_abort(glue::glue(c('If `method` is `"extend_ahead"`, then a step ",
+          "must have already added an outcome .')))
+  } else if (!("predictor" %in% info$role)) {
+    cli::cli_abort('If `method` is `"extend_lags"` or `"locf"`, then a step ",
+"must have already added a predictor.')
+  }
+  # TODO info here is probably not the best way to handle this, hypothetically I
+  # get an info object during baking
+  step_adjust_latency_new(
+    terms = x$terms,
+    role = x$role,
+    trained = TRUE,
+    prefix = x$prefix,
+    default = x$default,
+    keys = x$keys,
+    method = x$method,
+    info = info,
+    columns = recipes::recipes_eval_select(x$terms, training, info),
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' various ways of handling differences between the `as_of` date and the maximum
+#' time value
+#' @description
+#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
+#'   date, rather than relative to the last day of data
+#' @param new_data assumes that this already has lag/ahead columns that we need
+#'   to adjust
+#' @importFrom dplyr %>%
+#' @keywords internal
+#' @importFrom dplyr %>% pull
+bake.step_adjust_latency <- function(object, new_data, ...) {
+  sign_shift <- get_sign(object)
+  # get the columns used, even if it's all of them
+  terms_used <- object$columns
+  if (length(terms_used) == 0) {
+    terms_used <- object$info %>%
+      filter(role == "raw") %>%
+      pull(variable)
+  }
+  # get and check the max_time and as_of are the right kinds of dates
+  as_of <- get_asof(object, new_data)
+
+  # infer the correct columns to be working with from the previous
+  # transformations
+  shift_cols <- get_shifted_column_tibble(object, new_data, terms_used, as_of,
+                                          sign_shift)
+
+  if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
+    # check that the shift amount isn't too extreme
+    latency <- max(shift_cols$latency)
+    i_latency <- which.max(shift_cols$latency)
+    time_type <- attributes(new_data)$metadata$time_type
+    if (
+      (grepl("day", time_type) && (latency >= 10)) ||
+        (grepl("week", time_type) && (latency >= 4)) ||
+        ((time_type == "yearmonth") && (latency >= 2)) ||
+        ((time_type == "yearquarter") && (latency >= 1)) ||
+        ((time_type == "year") && (latency >= 1))
+    ) {
+      cli::cli_warn(c(
+        "!" = glue::glue(
+          "The shift has been adjusted by {latency}, ",
+          "which is questionable for it's `time_type` of ",
+          "{time_type}"
+        ),
+        "i" = "input ahead: {shift_cols$shifts[[i_latency]]}",
+        "i" = "shifted ahead: {shift_cols$effective_shift[[i_latency]]}",
+        "i" = "max_time = {max_time} -> as_of = {as_of}"
+      ))
+    }
+    keys <- object$keys
+    return(
+      extend_either(new_data, shift_cols, keys)
+    )
+  }
+}
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 62553a3f3..3a65dd05b 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -233,68 +233,13 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_epi_lag <- function(object, new_data, ...) {
-  grid <- tidyr::expand_grid(col = object$columns, lag = object$lag) %>%
-    mutate(
-      newname = glue::glue("{object$prefix}{lag}_{col}"),
-      shift_val = lag,
-      lag = NULL
-    )
-
-  ## ensure no name clashes
-  new_data_names <- colnames(new_data)
-  intersection <- new_data_names %in% grid$newname
-  if (any(intersection)) {
-    cli_abort(c(
-      "Name collision occured in {.cls {class(object)[1]}}",
-      "The following variable name{?s} already exist{?s/}: {.val {new_data_names[intersection]}}."
-    ))
-  }
-  ok <- object$keys
-  shifted <- reduce(
-    pmap(grid, epi_shift_single, x = new_data, key_cols = ok),
-    full_join,
-    by = ok
-  )
-
-  full_join(new_data, shifted, by = ok) %>%
-    group_by(across(all_of(kill_time_value(ok)))) %>%
-    arrange(time_value) %>%
-    ungroup()
+  add_shifted_columns(new_data, object, object$lag)
 }
-
 #' @export
 bake.step_epi_ahead <- function(object, new_data, ...) {
-  ahead <- adjust_latency(object, new_data)
-  grid <- tidyr::expand_grid(col = object$columns, ahead = object$ahead) %>%
-    dplyr::mutate(
-      newname = glue::glue("{object$prefix}{ahead}_{col}"),
-      shift_val = -ahead,
-      ahead = NULL
-    )
-
-  ## ensure no name clashes
-  new_data_names <- colnames(new_data)
-  intersection <- new_data_names %in% grid$newname
-  if (any(intersection)) {
-    cli_abort(c(
-      "Name collision occured in {.cls {class(object)[1]}}",
-      "The following variable name{?s} already exist{?s/}: {.val {new_data_names[intersection]}}."
-    ))
-  }
-  ok <- object$keys
-  shifted <- reduce(
-    pmap(grid, epi_shift_single, x = new_data, key_cols = ok),
-    full_join,
-    by = ok
-  )
-
-  full_join(new_data, shifted, by = ok) %>%
-    group_by(across(all_of(kill_time_value(ok)))) %>%
-    arrange(time_value) %>%
-    ungroup()
+  add_shifted_columns(new_data, object, object$ahead)
 }
 
-
 #' @export
 print.step_epi_lag <- function(x, width = max(20, options()$width - 30), ...) {
   print_epi_step(x$columns, x$terms, x$trained, "Lagging",
diff --git a/R/utils-latency.R b/R/utils-latency.R
new file mode 100644
index 000000000..d89675bde
--- /dev/null
+++ b/R/utils-latency.R
@@ -0,0 +1,158 @@
+#' offset each relevant column by it's appropriate latency
+#' works for either adjusting aheads or lags
+#' @param shift_cols a tibble which must have the columns `column`, the name of
+#'   the column to adjust, `latency` the latency of the original column relative
+#'   to the `as_of` date, `new_name`, the names in `column` adjusted by the
+#'   latencies `latency`
+#' @param new_data just what is says
+#' @param keys the variables which are used as keys
+#' @keywords internal
+extend_either <- function(new_data, shift_cols, keys) {
+  shifted <-
+    shift_cols %>%
+    select(-shifts, -effective_shift) %>%
+    pmap(\(original_name, latency, new_name) {
+      epi_shift_single(
+        x = new_data,
+        col = original_name,
+        shift_val = latency,
+        newname = new_name,
+        key_cols = keys
+      )
+    }) %>%
+    reduce(
+      dplyr::full_join,
+      by = keys
+    )
+  return(new_data %>%
+    select(-shift_cols$original_name) %>%
+    dplyr::full_join(shifted, by = keys) %>%
+    dplyr::group_by(dplyr::across(dplyr::all_of(keys[-1]))) %>%
+    dplyr::arrange(time_value) %>%
+    dplyr::ungroup())
+}
+
+#' find the columns added with the lags or aheads, and the amounts they have
+#' been changed
+#' @param object the step and its parameters
+#' @param new_data the data transformed so far
+#' @return a tibble with columns `column` (relevant shifted names), `shift` (the
+#'   amount that one is shifted), `latency` (original columns difference between
+#'   max_time_value and as_of (on a per-initial column basis)),
+#'   `effective_shift` (shifts+latency), and `new_name` (adjusted names with the
+#'   effective_shift)
+#' @keywords internal
+get_shifted_column_tibble <- function(
+    object, new_data, terms_used, as_of, sign_shift) {
+  prefix <- object$prefix
+  relevant_columns <- names(new_data)[grepl(prefix, names(new_data))]
+  to_keep <- rep(FALSE, length(relevant_columns))
+  for (col_name in terms_used) {
+    to_keep <- to_keep | grepl(col_name, relevant_columns)
+  }
+  relevant_columns <- relevant_columns[to_keep]
+  # TODO ask about a less jank way to do this
+  shift_amounts <- as.integer(str_match(
+    relevant_columns,
+    "_\\d+_"
+  ) %>%
+    `[`(, 1) %>%
+    str_match("\\d+") %>%
+    `[`(, 1))
+  shift_cols <- dplyr::tibble(
+    original_name = relevant_columns,
+    shifts = shift_amounts
+  )
+  shift_cols %>%
+    rowwise() %>%
+    # add the latencies to shift_cols
+    mutate(latency = get_latency(
+      new_data, as_of, original_name, shifts, sign_shift
+    )) %>%
+    ungroup() %>%
+    # add the updated names to shift_cols
+    mutate(
+      effective_shift = shifts + latency,
+      new_name = adjust_name(prefix, shifts, original_name, latency)
+    )
+  return(shift_cols)
+}
+
+
+#' extract the as_of, and make sure there's nothing very off about it
+get_asof <- function(object, new_data) {
+  original_columns <- object$info %>%
+    filter(source == "original") %>%
+    pull(variable)
+  # make sure that there's enough column names
+  if (length(original_columns) < 3) {
+    cli::cli_abort(glue::glue(
+      "The original columns of `time_value`, ",
+      "`geo_value` and at least one signal. The current colums are \n",
+      paste(capture.output(object$info), collapse = "\n\n")
+    ))
+  }
+  # the source data determines the actual time_values
+  # these are the non-na time_values;
+  time_values <- new_data %>%
+    select(original_columns) %>%
+    drop_na() %>%
+    pull(time_value)
+  if (length(time_values) <= 0) {
+    rlang::abort("the `time_value` column of `new_data` is empty")
+  }
+  as_of <- attributes(new_data)$metadata$as_of
+  max_time <- max(time_values)
+  # make sure the as_of is sane
+  # TODO decide on these checks
+  if (!inherits(as_of, class(time_values))) {
+    rlang::abort(glue::glue(
+      "the data matrix `as_of` value is {as_of}, ",
+      "and not a valid `time_type` with type ",
+      "matching `time_value`'s type of ",
+      "{typeof(new_data$time_value)}."
+    ))
+  }
+  if (is.null(as_of) || is.na(as_of)) {
+    cli::cli_warn(glue::glue(
+      "epi_data's `as_of` was {as_of}, setting to ",
+      "the latest time value, {max_time}."
+    ))
+    as_of <- max_time
+  } else if (as_of < max_time) {
+    cli::cli_abort(glue::glue(
+      "`as_of` ({(as_of)}) is before the most ",
+      "recent data ({max_time}). Remove before ",
+      "predicting."
+    ))
+  }
+  # TODO cover the rest of the possible types for as_of and max_time...
+  if (class(time_values) == "Date") {
+    as_of <- as.Date(as_of)
+  }
+  return(as_of)
+}
+
+#' adjust the shifts by latency for the names in column assumes e.g.
+#' `"lag_6_case_rate"` and returns something like `"lag_10_case_rate"`
+#' @keywords internal
+adjust_name <- function(prefix, shifts, column, latency) {
+  pattern <- paste0(prefix, "\\d+", "_")
+  adjusted_shifts <- paste0(prefix, shifts + latency, "_")
+  stringi::stri_replace_all_regex(
+    column,
+    pattern, adjusted_shifts
+  )
+}
+
+#' the latency is also the amount the shift is off by
+#' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
+#'   need to shift the data to bring the 3 day lagged value to today.
+#' @keywords internal
+get_latency <- function(new_data, as_of, column, shift_amount, sign_shift) {
+  shift_max_date <- new_data %>%
+    drop_na(column) %>%
+    pull(time_value) %>%
+    max()
+  return(as.integer(as_of - (shift_max_date - sign_shift * shift_amount)))
+}

From 07a9e28c21c5d68e59332d5099fc82997e54010e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 29 Mar 2024 01:19:00 -0700
Subject: [PATCH 021/387] styler

---
 R/step_adjust_latency.R | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 682d80b97..04989e56d 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -177,8 +177,10 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
 
   # infer the correct columns to be working with from the previous
   # transformations
-  shift_cols <- get_shifted_column_tibble(object, new_data, terms_used, as_of,
-                                          sign_shift)
+  shift_cols <- get_shifted_column_tibble(
+    object, new_data, terms_used, as_of,
+    sign_shift
+  )
 
   if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
     # check that the shift amount isn't too extreme

From d4d617fb7d349912f0f6d0e6bed49d96f04acecd Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 13:03:16 -0700
Subject: [PATCH 022/387] tests for utils-latency and accompanying fixes

---
 NAMESPACE                           |   6 ++
 R/step_adjust_latency.R             |   5 +-
 R/utils-latency.R                   |  36 ++++---
 man/epi_shift.Rd                    |  28 ------
 man/step_epi_shift.Rd               |   2 +
 man/step_growth_rate.Rd             |   1 +
 man/step_lag_difference.Rd          |   1 +
 tests/testthat/test-utils_latency.R | 139 ++++++++++++++++++++++++++++
 8 files changed, 175 insertions(+), 43 deletions(-)
 delete mode 100644 man/epi_shift.Rd
 create mode 100644 tests/testthat/test-utils_latency.R

diff --git a/NAMESPACE b/NAMESPACE
index e815203eb..0cf66300f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -58,6 +58,7 @@ S3method(predict,epi_workflow)
 S3method(predict,flatline)
 S3method(prep,check_enough_train_data)
 S3method(prep,epi_recipe)
+S3method(prep,step_adjust_latency)
 S3method(prep,step_epi_ahead)
 S3method(prep,step_epi_lag)
 S3method(prep,step_epi_slide)
@@ -195,6 +196,7 @@ export(remove_frosting)
 export(remove_model)
 export(slather)
 export(smooth_quantile_reg)
+export(step_adjust_latency)
 export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
@@ -225,6 +227,7 @@ importFrom(checkmate,test_numeric)
 importFrom(checkmate,test_scalar)
 importFrom(cli,cli_abort)
 importFrom(cli,cli_warn)
+importFrom(dplyr,"%>%")
 importFrom(dplyr,across)
 importFrom(dplyr,all_of)
 importFrom(dplyr,any_of)
@@ -237,8 +240,10 @@ importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,pull)
 importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
+importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
@@ -291,6 +296,7 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
+importFrom(stringr,str_match)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 04989e56d..092f54458 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -160,9 +160,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #'   date, rather than relative to the last day of data
 #' @param new_data assumes that this already has lag/ahead columns that we need
 #'   to adjust
-#' @importFrom dplyr %>%
-#' @keywords internal
 #' @importFrom dplyr %>% pull
+#' @keywords internal
 bake.step_adjust_latency <- function(object, new_data, ...) {
   sign_shift <- get_sign(object)
   # get the columns used, even if it's all of them
@@ -178,7 +177,7 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
   # infer the correct columns to be working with from the previous
   # transformations
   shift_cols <- get_shifted_column_tibble(
-    object, new_data, terms_used, as_of,
+    object$prefix, new_data, terms_used, as_of,
     sign_shift
   )
 
diff --git a/R/utils-latency.R b/R/utils-latency.R
index d89675bde..e69c6ed34 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -20,12 +20,14 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
+    map(\(x) na.trim(x)) %>% # TODO need to talk about this
     reduce(
       dplyr::full_join,
       by = keys
     )
+
   return(new_data %>%
-    select(-shift_cols$original_name) %>%
+    select(-shift_cols$original_name) %>% # drop the original versions
     dplyr::full_join(shifted, by = keys) %>%
     dplyr::group_by(dplyr::across(dplyr::all_of(keys[-1]))) %>%
     dplyr::arrange(time_value) %>%
@@ -34,7 +36,7 @@ extend_either <- function(new_data, shift_cols, keys) {
 
 #' find the columns added with the lags or aheads, and the amounts they have
 #' been changed
-#' @param object the step and its parameters
+#' @param prefix the prefix indicating if we are adjusting lags or aheads
 #' @param new_data the data transformed so far
 #' @return a tibble with columns `column` (relevant shifted names), `shift` (the
 #'   amount that one is shifted), `latency` (original columns difference between
@@ -42,28 +44,36 @@ extend_either <- function(new_data, shift_cols, keys) {
 #'   `effective_shift` (shifts+latency), and `new_name` (adjusted names with the
 #'   effective_shift)
 #' @keywords internal
+#' @importFrom stringr str_match
+#' @importFrom dplyr rowwise %>%
 get_shifted_column_tibble <- function(
-    object, new_data, terms_used, as_of, sign_shift) {
-  prefix <- object$prefix
+    prefix, new_data, terms_used, as_of, sign_shift, call = caller_env()) {
   relevant_columns <- names(new_data)[grepl(prefix, names(new_data))]
   to_keep <- rep(FALSE, length(relevant_columns))
   for (col_name in terms_used) {
     to_keep <- to_keep | grepl(col_name, relevant_columns)
   }
   relevant_columns <- relevant_columns[to_keep]
+  if (length(relevant_columns) == 0) {
+    cli::cli_abort("There is no column(s) {terms_used}.",
+      current_column_names = names(new_data),
+      class = "epipredict_adjust_latency_nonexistent_column_used",
+      call = call
+    )
+  }
   # TODO ask about a less jank way to do this
-  shift_amounts <- as.integer(str_match(
+  shift_amounts <- as.integer(stringr::str_match(
     relevant_columns,
     "_\\d+_"
   ) %>%
     `[`(, 1) %>%
-    str_match("\\d+") %>%
+    stringr::str_match("\\d+") %>%
     `[`(, 1))
   shift_cols <- dplyr::tibble(
     original_name = relevant_columns,
     shifts = shift_amounts
   )
-  shift_cols %>%
+  shift_cols %<>%
     rowwise() %>%
     # add the latencies to shift_cols
     mutate(latency = get_latency(
@@ -72,8 +82,10 @@ get_shifted_column_tibble <- function(
     ungroup() %>%
     # add the updated names to shift_cols
     mutate(
-      effective_shift = shifts + latency,
-      new_name = adjust_name(prefix, shifts, original_name, latency)
+      effective_shift = shifts + abs(latency)
+    ) %>%
+    mutate(
+      new_name = adjust_name(prefix, original_name, effective_shift)
     )
   return(shift_cols)
 }
@@ -136,9 +148,9 @@ get_asof <- function(object, new_data) {
 #' adjust the shifts by latency for the names in column assumes e.g.
 #' `"lag_6_case_rate"` and returns something like `"lag_10_case_rate"`
 #' @keywords internal
-adjust_name <- function(prefix, shifts, column, latency) {
+adjust_name <- function(prefix, column, effective_shift) {
   pattern <- paste0(prefix, "\\d+", "_")
-  adjusted_shifts <- paste0(prefix, shifts + latency, "_")
+  adjusted_shifts <- paste0(prefix, effective_shift, "_")
   stringi::stri_replace_all_regex(
     column,
     pattern, adjusted_shifts
@@ -154,5 +166,5 @@ get_latency <- function(new_data, as_of, column, shift_amount, sign_shift) {
     drop_na(column) %>%
     pull(time_value) %>%
     max()
-  return(as.integer(as_of - (shift_max_date - sign_shift * shift_amount)))
+  return(as.integer(sign_shift * (as_of - shift_max_date) + shift_amount))
 }
diff --git a/man/epi_shift.Rd b/man/epi_shift.Rd
deleted file mode 100644
index 14316a8db..000000000
--- a/man/epi_shift.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/epi_shift.R
-\name{epi_shift}
-\alias{epi_shift}
-\title{Shift predictors while maintaining grouping and time_value ordering}
-\usage{
-epi_shift(x, shifts, time_value, keys = NULL, out_name = "x")
-}
-\arguments{
-\item{x}{Data frame. Variables to shift}
-
-\item{shifts}{List. Each list element is a vector of shifts.
-Negative values produce leads. The list should have the same
-length as the number of columns in \code{x}.}
-
-\item{time_value}{Vector. Same length as \code{x} giving time stamps.}
-
-\item{keys}{Data frame, vector, or \code{NULL}. Additional grouping vars.}
-
-\item{out_name}{Chr. The output list will use this as a prefix.}
-}
-\value{
-a list of tibbles
-}
-\description{
-This is a lower-level function. As such it performs no error checking.
-}
-\keyword{internal}
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 4a1a00104..4d7cc0a0e 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -95,10 +95,12 @@ r
 }
 \seealso{
 Other row operation steps: 
+\code{\link{step_adjust_latency}()},
 \code{\link{step_growth_rate}()},
 \code{\link{step_lag_difference}()}
 
 Other row operation steps: 
+\code{\link{step_adjust_latency}()},
 \code{\link{step_growth_rate}()},
 \code{\link{step_lag_difference}()}
 }
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index bc6da0bef..752b38dbe 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -83,6 +83,7 @@ r \%>\%
 }
 \seealso{
 Other row operation steps: 
+\code{\link{step_adjust_latency}()},
 \code{\link{step_epi_lag}()},
 \code{\link{step_lag_difference}()}
 }
diff --git a/man/step_lag_difference.Rd b/man/step_lag_difference.Rd
index 7969ea3a7..e8ec2101a 100644
--- a/man/step_lag_difference.Rd
+++ b/man/step_lag_difference.Rd
@@ -58,6 +58,7 @@ r \%>\%
 }
 \seealso{
 Other row operation steps: 
+\code{\link{step_adjust_latency}()},
 \code{\link{step_epi_lag}()},
 \code{\link{step_growth_rate}()}
 }
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
new file mode 100644
index 000000000..3873c5e6a
--- /dev/null
+++ b/tests/testthat/test-utils_latency.R
@@ -0,0 +1,139 @@
+time_values <- as.Date("2021-01-01") + 0:199
+as_of <- max(time_values) + 5
+max_time <- max(time_values)
+old_data <- tibble(
+  geo_value = rep("place", 200),
+  time_value = as.Date("2021-01-01") + 0:199,
+  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
+  tmp_death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
+) %>%
+  as_epi_df(as_of = as_of)
+old_data %>% tail()
+keys <- c("time_value", "geo_value")
+old_data %<>% full_join(epi_shift_single(
+  old_data, "tmp_death_rate", 1, "death_rate", keys
+), by = keys) %>%
+  select(-tmp_death_rate)
+# old data is created so that death rate has a latency of 4, while case_rate has
+# a latency of 5
+modified_data <-
+  old_data %>%
+  dplyr::full_join(
+    epi_shift_single(old_data, "case_rate", -4, "ahead_4_case_rate", keys),
+    by = keys
+  ) %>%
+  dplyr::full_join(
+    epi_shift_single(old_data, "case_rate", 3, "lag_3_case_rate", keys),
+    by = keys
+  ) %>%
+  dplyr::full_join(
+    epi_shift_single(old_data, "death_rate", 7, "lag_7_death_rate", keys),
+    by = keys
+  ) %>%
+  arrange(time_value)
+modified_data %>% tail()
+as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
+all_shift_cols <- tibble::tribble(
+  ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name,
+  "lag_3_case_rate", 3, 5, 8, "lag_8_case_rate",
+  "lag_7_death_rate", 7, 4, 11, "lag_11_death_rate",
+  "ahead_4_case_rate", 4, -5, 9, "ahead_9_case_rate"
+)
+
+test_that("get_latency works", {
+  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1), 4)
+  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 3, 1), 5)
+  # get_latency does't check the shift_amount
+  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 4, 1), 6)
+  # ahead works correctly
+  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, -1), -5)
+  # setting the wrong sign doubles the shift and gets the sign wrong
+  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, 1), 5 + 4 * 2)
+})
+
+test_that("adjust_name works", {
+  expect_equal(
+    adjust_name("lag_", "lag_5_case_rate_13", 10),
+    "lag_10_case_rate_13"
+  )
+  # it won't change a column with the wrong prefix
+  expect_equal(
+    adjust_name("lag_", "ahead_5_case_rate", 10),
+    "ahead_5_case_rate"
+  )
+  # it works on vectors of names
+  expect_equal(
+    adjust_name("lag_", c("lag_5_floop_35", "lag_2342352_case"), c(10, 7)),
+    c("lag_10_floop_35", "lag_7_case")
+  )
+})
+
+test_that("get_asof works", {
+  object <- list(info = tribble(
+    ~variable, ~type, ~role, ~source,
+    "time_value", "date", "time_value", "original",
+    "geo_value", "nominal", "geo_value", "original",
+    "case_rate", "numeric", "raw", "original",
+    "death_rate", "numeric", "raw", "original",
+    "not_real", "numeric", "predictor", "derived"
+  ))
+  expect_equal(get_asof(object, modified_data), as_of)
+})
+
+test_that("get_shifted_column_tibble works", {
+  case_lag <- get_shifted_column_tibble(
+    "lag_", modified_data,
+    "case_rate", as_of, 1
+  )
+  expect_equal(case_lag, all_shift_cols[1, ])
+
+  death_lag <- get_shifted_column_tibble(
+    "lag_", modified_data,
+    "death_rate", as_of, 1
+  )
+  expect_equal(death_lag, all_shift_cols[2, ])
+
+  both_lag <- get_shifted_column_tibble(
+    "lag_", modified_data,
+    c("case_rate", "death_rate"), as_of, 1
+  )
+  expect_equal(both_lag, all_shift_cols[1:2, ])
+
+  case_ahead <- get_shifted_column_tibble(
+    "ahead_", modified_data,
+    "case_rate", as_of, -1
+  )
+  expect_equal(case_ahead, all_shift_cols[3, ])
+})
+test_that("get_shifted_column_tibble objects to non-columns", {
+  expect_error(
+    get_shifted_column_tibble(
+      "lag_", modified_data, "not_present", as_of, 1
+    ),
+    class = "epipredict_adjust_latency_nonexistent_column_used"
+  )
+})
+test_that("extend_either works", {
+  keys <- c("geo_value", "time_value")
+  # extend_either doesn't differentiate between the directions, it just moves
+  # things
+  expected_post_shift <-
+    old_data %>%
+    dplyr::full_join(
+      epi_shift_single(old_data, "case_rate", 8, "lag_8_case_rate", keys),
+      by = keys
+    ) %>%
+    dplyr::full_join(
+      epi_shift_single(old_data, "death_rate", 11, "lag_11_death_rate", keys),
+      by = keys
+    ) %>%
+    dplyr::full_join(
+      epi_shift_single(old_data, "case_rate", -9, "ahead_9_case_rate", keys),
+      by = keys
+    ) %>%
+    arrange(time_value)
+  expect_equal(
+    extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value),
+    expected_post_shift
+  )
+})

From 80e64b526691ac0aa47cf8ab773e1c9458394738 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 13:59:18 -0700
Subject: [PATCH 023/387] adding stringr

---
 DESCRIPTION | 1 +
 1 file changed, 1 insertion(+)

diff --git a/DESCRIPTION b/DESCRIPTION
index 5cd468fb9..eb01cbecd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -41,6 +41,7 @@ Imports:
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
     stats,
+    stringr,
     tibble,
     tidyr,
     tidyselect,

From aa87607deca2459f83f4b683bf46869887e37037 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 14:33:57 -0700
Subject: [PATCH 024/387] old snapshots, select prefers `all_of` for vectors

---
 R/utils-latency.R                           |  6 +-
 man/epi_shift_single.Rd                     | 24 ++++++
 man/extend_either.Rd                        | 24 ++++++
 man/get_asof.Rd                             | 11 +++
 man/get_latency.Rd                          | 16 ++++
 man/get_shifted_column_tibble.Rd            | 33 +++++++
 man/get_sign.Rd                             | 13 +++
 man/step_adjust_latency.Rd                  | 96 +++++++++++++++++++++
 tests/testthat/_snaps/population_scaling.md | 30 +++----
 tests/testthat/_snaps/utils-shift.md        |  3 +-
 tests/testthat/test-epi_shift.R             | 20 -----
 11 files changed, 235 insertions(+), 41 deletions(-)
 create mode 100644 man/epi_shift_single.Rd
 create mode 100644 man/extend_either.Rd
 create mode 100644 man/get_asof.Rd
 create mode 100644 man/get_latency.Rd
 create mode 100644 man/get_shifted_column_tibble.Rd
 create mode 100644 man/get_sign.Rd
 create mode 100644 man/step_adjust_latency.Rd

diff --git a/R/utils-latency.R b/R/utils-latency.R
index e69c6ed34..4322e36cb 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -27,7 +27,7 @@ extend_either <- function(new_data, shift_cols, keys) {
     )
 
   return(new_data %>%
-    select(-shift_cols$original_name) %>% # drop the original versions
+    select(-all_of(shift_cols$original_name)) %>% # drop the original versions
     dplyr::full_join(shifted, by = keys) %>%
     dplyr::group_by(dplyr::across(dplyr::all_of(keys[-1]))) %>%
     dplyr::arrange(time_value) %>%
@@ -107,7 +107,7 @@ get_asof <- function(object, new_data) {
   # the source data determines the actual time_values
   # these are the non-na time_values;
   time_values <- new_data %>%
-    select(original_columns) %>%
+    select(all_of(original_columns)) %>%
     drop_na() %>%
     pull(time_value)
   if (length(time_values) <= 0) {
@@ -163,7 +163,7 @@ adjust_name <- function(prefix, column, effective_shift) {
 #' @keywords internal
 get_latency <- function(new_data, as_of, column, shift_amount, sign_shift) {
   shift_max_date <- new_data %>%
-    drop_na(column) %>%
+    drop_na(all_of(column)) %>%
     pull(time_value) %>%
     max()
   return(as.integer(sign_shift * (as_of - shift_max_date) + shift_amount))
diff --git a/man/epi_shift_single.Rd b/man/epi_shift_single.Rd
new file mode 100644
index 000000000..871879004
--- /dev/null
+++ b/man/epi_shift_single.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi_shift.R
+\name{epi_shift_single}
+\alias{epi_shift_single}
+\title{Shift predictors while maintaining grouping and time_value ordering}
+\usage{
+epi_shift_single(x, col, shift_val, newname, key_cols)
+}
+\arguments{
+\item{x}{Data frame.}
+
+\item{shift_val}{a single integer. Negative values produce leads.}
+
+\item{newname}{the name for the newly shifted column}
+
+\item{key_cols}{vector, or \code{NULL}. Additional grouping vars.}
+}
+\value{
+a list of tibbles
+}
+\description{
+This is a lower-level function. As such it performs no error checking.
+}
+\keyword{internal}
diff --git a/man/extend_either.Rd b/man/extend_either.Rd
new file mode 100644
index 000000000..8ec5ca38e
--- /dev/null
+++ b/man/extend_either.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{extend_either}
+\alias{extend_either}
+\title{offset each relevant column by it's appropriate latency
+works for either adjusting aheads or lags}
+\usage{
+extend_either(new_data, shift_cols, keys)
+}
+\arguments{
+\item{new_data}{just what is says}
+
+\item{shift_cols}{a tibble which must have the columns \code{column}, the name of
+the column to adjust, \code{latency} the latency of the original column relative
+to the \code{as_of} date, \code{new_name}, the names in \code{column} adjusted by the
+latencies \code{latency}}
+
+\item{keys}{the variables which are used as keys}
+}
+\description{
+offset each relevant column by it's appropriate latency
+works for either adjusting aheads or lags
+}
+\keyword{internal}
diff --git a/man/get_asof.Rd b/man/get_asof.Rd
new file mode 100644
index 000000000..44b286ffb
--- /dev/null
+++ b/man/get_asof.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_asof}
+\alias{get_asof}
+\title{extract the as_of, and make sure there's nothing very off about it}
+\usage{
+get_asof(object, new_data)
+}
+\description{
+extract the as_of, and make sure there's nothing very off about it
+}
diff --git a/man/get_latency.Rd b/man/get_latency.Rd
new file mode 100644
index 000000000..d9098b456
--- /dev/null
+++ b/man/get_latency.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_latency}
+\alias{get_latency}
+\title{the latency is also the amount the shift is off by}
+\usage{
+get_latency(new_data, as_of, column, shift_amount, sign_shift)
+}
+\arguments{
+\item{sign_shift}{integer. 1 if lag and -1 if ahead. These represent how you
+need to shift the data to bring the 3 day lagged value to today.}
+}
+\description{
+the latency is also the amount the shift is off by
+}
+\keyword{internal}
diff --git a/man/get_shifted_column_tibble.Rd b/man/get_shifted_column_tibble.Rd
new file mode 100644
index 000000000..635cab427
--- /dev/null
+++ b/man/get_shifted_column_tibble.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_shifted_column_tibble}
+\alias{get_shifted_column_tibble}
+\title{find the columns added with the lags or aheads, and the amounts they have
+been changed}
+\usage{
+get_shifted_column_tibble(
+  prefix,
+  new_data,
+  terms_used,
+  as_of,
+  sign_shift,
+  call = caller_env()
+)
+}
+\arguments{
+\item{prefix}{the prefix indicating if we are adjusting lags or aheads}
+
+\item{new_data}{the data transformed so far}
+}
+\value{
+a tibble with columns \code{column} (relevant shifted names), \code{shift} (the
+amount that one is shifted), \code{latency} (original columns difference between
+max_time_value and as_of (on a per-initial column basis)),
+\code{effective_shift} (shifts+latency), and \code{new_name} (adjusted names with the
+effective_shift)
+}
+\description{
+find the columns added with the lags or aheads, and the amounts they have
+been changed
+}
+\keyword{internal}
diff --git a/man/get_sign.Rd b/man/get_sign.Rd
new file mode 100644
index 000000000..0fbe9bb92
--- /dev/null
+++ b/man/get_sign.Rd
@@ -0,0 +1,13 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi_shift.R
+\name{get_sign}
+\alias{get_sign}
+\title{lags move columns forward to bring the past up to today, while aheads drag
+the future back to today}
+\usage{
+get_sign(object)
+}
+\description{
+lags move columns forward to bring the past up to today, while aheads drag
+the future back to today
+}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
new file mode 100644
index 000000000..c60495c84
--- /dev/null
+++ b/man/step_adjust_latency.Rd
@@ -0,0 +1,96 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_adjust_latency.R
+\name{step_adjust_latency}
+\alias{step_adjust_latency}
+\title{adapt the pipeline to latency in the data}
+\usage{
+step_adjust_latency(
+  recipe,
+  ...,
+  role = NA,
+  trained = FALSE,
+  method = c("extend_ahead", "locf", "extend_lags"),
+  default = NA,
+  skip = FALSE,
+  prefix = NULL,
+  columns = NULL,
+  id = recipes::rand_id("epi_lag")
+)
+}
+\arguments{
+\item{recipe}{A recipe object. The step will be added to the
+sequence of operations for this recipe.}
+
+\item{...}{One or more selector functions to choose variables for this step.
+See \code{\link[recipes:selections]{recipes::selections()}} for more details. Typically you will not need
+to set this manually, as the necessary adjustments will be done for the
+predictors and outcome.}
+
+\item{method}{a character. Determines the method by which the
+forecast handles latency. All of these assume the forecast date is the
+\code{as_of} of the \code{epi_df}. The options are:
+\itemize{
+\item \code{"extend_ahead"}: Lengthen the ahead so that forecasting from the last
+observation results in a forecast \code{ahead} after the \code{as_of} date. E.g. if
+there are 3 days of latency between the last observation and the \code{as_of}
+date for a 4 day ahead forecast, the ahead used in practice is actually 7.
+\item \code{"locf"}: carries forward the last observed value(s) up to the forecast
+date. See the Vignette TODO for equivalents using other steps and more
+sophisticated methods of extrapolation.
+\item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that
+the shortest lag at predict time is at the last observation. E.g. if the
+lags are \code{c(0,7,14)} for data that is 3 days latent, the actual lags used
+become \code{c(3,10,17)}
+}}
+
+\item{default}{Determines what fills empty rows
+left by leading/lagging (defaults to NA).}
+
+\item{skip}{A logical. Should the step be skipped when the
+recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
+when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
+conducted on new data (e.g. processing the outcome variable(s)).
+Care should be taken when using \code{skip = TRUE} as it may affect
+the computations for subsequent operations.}
+
+\item{prefix}{a character. The prefix matching the one used in either
+\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
+if \code{method="extend_lags"} or "locf".}
+
+\item{id}{A unique identifier for the step}
+}
+\value{
+An updated version of \code{recipe} with the new step added to the
+sequence of any existing operations.
+}
+\description{
+In the standard case, the pipeline assumes that the last observation is also
+the day from which the forecast is being made. \code{step_adjust_latency} uses the
+\code{as_of} date of the \code{epi_df} as the \code{forecast_date}. This is most useful in
+realtime and pseudo-prospective forecasting for data where there is some
+delay between the day recorded and when that data is available.
+}
+\details{
+The step assumes that the pipeline has already applied either
+\code{step_epi_ahead} or \code{step_epi_lag} depending on the value of
+\code{"method"}, and that \code{step_epi_naomit} has NOT been run.
+
+The \code{prefix} and \code{id} arguments are unchangeable to ensure that the code runs
+properly and to avoid inconsistency with naming. For \code{step_epi_ahead}, they
+are always set to \code{"ahead_"} and \code{"epi_ahead"} respectively, while for
+\code{step_epi_lag}, they are set to \code{"lag_"} and \verb{"epi_lag}, respectively.
+}
+\examples{
+r <- epi_recipe(case_death_rate_subset) \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
+  #   step_adjust_latency(method = "extend_ahead") \%>\%
+  step_epi_lag(death_rate, lag = c(0, 7, 14))
+r
+}
+\seealso{
+Other row operation steps: 
+\code{\link{step_epi_lag}()},
+\code{\link{step_growth_rate}()},
+\code{\link{step_lag_difference}()}
+}
+\concept{row operation steps}
diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index 149cd38ac..fd0f2c609 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -2,35 +2,33 @@
 
     Code
       prep <- prep(r, jhu)
+    Message <rlang_message>
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
 
 ---
 
     Code
       b <- bake(prep, jhu)
+    Message <rlang_message>
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
 
 ---
 
     Code
       wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
+    Message <rlang_message>
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
 
 ---
 
     Code
       p <- predict(wf, latest)
-
-# expect error if `by` selector does not match
-
-    Code
-      wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
-    Condition
-      Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'a'.
-
----
-
-    Code
-      forecast(wf)
-    Condition
-      Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'nothere'.
+    Message <rlang_message>
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
+      Joining with `by = join_by(geo_value)`
 
diff --git a/tests/testthat/_snaps/utils-shift.md b/tests/testthat/_snaps/utils-shift.md
index b7c5f064f..66a18fe79 100644
--- a/tests/testthat/_snaps/utils-shift.md
+++ b/tests/testthat/_snaps/utils-shift.md
@@ -2,8 +2,7 @@
 
     Code
       adjust_latency(object, x_adjust_ahead)
-    Condition
-      Warning:
+    Warning <rlang_warning>
       ! The ahead has been adjusted by 100, which is questionable for it's `time_type` of day
       i input ahead: 7
       i shifted ahead: 107
diff --git a/tests/testthat/test-epi_shift.R b/tests/testthat/test-epi_shift.R
index 78c9384f1..e8e843f9c 100644
--- a/tests/testthat/test-epi_shift.R
+++ b/tests/testthat/test-epi_shift.R
@@ -1,23 +1,3 @@
-x <- data.frame(x1 = 1:10, x2 = -10:-1)
-lags <- list(c(0, 4), 1:3)
-
-test_that("epi shift works with NULL keys", {
-  time_value <- 1:10
-  out <- epi_shift(x, lags, time_value)
-  expect_length(out, 7L)
-  expect_equal(nrow(out), 14L)
-  expect_equal(sum(complete.cases(out)), 6L)
-})
-
-test_that("epi shift works with groups", {
-  keys <- data.frame(a = rep(letters[1:2], each = 5), b = "z")
-  time_value <- 1:10
-  out <- epi_shift(x, lags, time_value, keys)
-  expect_length(out, 8L)
-  expect_equal(nrow(out), 18L)
-  expect_equal(sum(complete.cases(out)), 2L)
-})
-
 test_that("epi shift single works, renames", {
   tib <- tibble(
     x = 1:5, y = 1:5,

From 375af6dcefed6eff1275af0ed38fe54b62b7aa4d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 15:33:55 -0700
Subject: [PATCH 025/387] local renv way out of date

---
 R/utils-latency.R                           | 2 +-
 tests/testthat/_snaps/population_scaling.md | 8 ++++----
 tests/testthat/_snaps/utils-shift.md        | 3 ++-
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 4322e36cb..b97268a24 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -20,7 +20,7 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
-    map(\(x) na.trim(x)) %>% # TODO need to talk about this
+    map(\(x) zoo::na.trim(x)) %>% # TODO need to talk about this
     reduce(
       dplyr::full_join,
       by = keys
diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index fd0f2c609..2152c2e78 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -2,7 +2,7 @@
 
     Code
       prep <- prep(r, jhu)
-    Message <rlang_message>
+    Message
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
 
@@ -10,7 +10,7 @@
 
     Code
       b <- bake(prep, jhu)
-    Message <rlang_message>
+    Message
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
 
@@ -18,7 +18,7 @@
 
     Code
       wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
-    Message <rlang_message>
+    Message
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
 
@@ -26,7 +26,7 @@
 
     Code
       p <- predict(wf, latest)
-    Message <rlang_message>
+    Message
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
diff --git a/tests/testthat/_snaps/utils-shift.md b/tests/testthat/_snaps/utils-shift.md
index 66a18fe79..b7c5f064f 100644
--- a/tests/testthat/_snaps/utils-shift.md
+++ b/tests/testthat/_snaps/utils-shift.md
@@ -2,7 +2,8 @@
 
     Code
       adjust_latency(object, x_adjust_ahead)
-    Warning <rlang_warning>
+    Condition
+      Warning:
       ! The ahead has been adjusted by 100, which is questionable for it's `time_type` of day
       i input ahead: 7
       i shifted ahead: 107

From 24eca507251d7d86c279aceb86018f90350584df Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 15:38:07 -0700
Subject: [PATCH 026/387] pkgdown needs @keywords internal

---
 R/epi_shift.R     | 1 +
 R/utils-latency.R | 1 +
 2 files changed, 2 insertions(+)

diff --git a/R/epi_shift.R b/R/epi_shift.R
index 81dfcddc1..c91856237 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -19,6 +19,7 @@ epi_shift_single <- function(x, col, shift_val, newname, key_cols) {
 
 #' lags move columns forward to bring the past up to today, while aheads drag
 #' the future back to today
+#' @keywords internal
 get_sign <- function(object) {
   if (object$prefix == "lag_") {
     return(1)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index b97268a24..163643929 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -92,6 +92,7 @@ get_shifted_column_tibble <- function(
 
 
 #' extract the as_of, and make sure there's nothing very off about it
+#' @keywords internal
 get_asof <- function(object, new_data) {
   original_columns <- object$info %>%
     filter(source == "original") %>%

From 9e9b1b48301fc261e3146c7935e691eae1a1860e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 17:13:27 -0700
Subject: [PATCH 027/387] passes local tests after updating

---
 DESCRIPTION                     |  3 ++-
 man/bake.step_adjust_latency.Rd | 18 ++++++++++++++++++
 man/get_asof.Rd                 |  1 +
 man/get_sign.Rd                 |  1 +
 4 files changed, 22 insertions(+), 1 deletion(-)
 create mode 100644 man/bake.step_adjust_latency.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index eb01cbecd..16b9e07de 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -47,7 +47,8 @@ Imports:
     tidyselect,
     tsibble,
     vctrs,
-    workflows (>= 1.0.0)
+    workflows (>= 1.0.0),
+    zoo
 Suggests:
     covidcast,
     data.table,
diff --git a/man/bake.step_adjust_latency.Rd b/man/bake.step_adjust_latency.Rd
new file mode 100644
index 000000000..edb4f1f6e
--- /dev/null
+++ b/man/bake.step_adjust_latency.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_adjust_latency.R
+\name{bake.step_adjust_latency}
+\alias{bake.step_adjust_latency}
+\title{various ways of handling differences between the \code{as_of} date and the maximum
+time value}
+\usage{
+\method{bake}{step_adjust_latency}(object, new_data, ...)
+}
+\arguments{
+\item{new_data}{assumes that this already has lag/ahead columns that we need
+to adjust}
+}
+\description{
+adjust the ahead so that we will be predicting \code{ahead} days after the \code{as_of}
+date, rather than relative to the last day of data
+}
+\keyword{internal}
diff --git a/man/get_asof.Rd b/man/get_asof.Rd
index 44b286ffb..4649df7cc 100644
--- a/man/get_asof.Rd
+++ b/man/get_asof.Rd
@@ -9,3 +9,4 @@ get_asof(object, new_data)
 \description{
 extract the as_of, and make sure there's nothing very off about it
 }
+\keyword{internal}
diff --git a/man/get_sign.Rd b/man/get_sign.Rd
index 0fbe9bb92..0be3e6306 100644
--- a/man/get_sign.Rd
+++ b/man/get_sign.Rd
@@ -11,3 +11,4 @@ get_sign(object)
 lags move columns forward to bring the past up to today, while aheads drag
 the future back to today
 }
+\keyword{internal}

From 7ae26d27962bf67b8eaba8d443f123426c5bad7e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 2 Apr 2024 11:18:50 -0700
Subject: [PATCH 028/387] back to skipping some population_scaling tests

---
 tests/testthat/_snaps/population_scaling.md | 2 --
 tests/testthat/test-population_scaling.R    | 1 +
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index 2152c2e78..6d75fcc22 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -29,6 +29,4 @@
     Message
       Joining with `by = join_by(geo_value)`
       Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
 
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index d18be65f5..fbae404ad 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -193,6 +193,7 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
+  skip()
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)

From e346f7197bb18163b97aa44d9224ba15ca7c820d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 25 Apr 2024 15:33:28 -0500
Subject: [PATCH 029/387] step_adjust_latency works on tests

---
 DESCRIPTION                               |   1 +
 NAMESPACE                                 |   4 +
 R/arx_forecaster.R                        |   1 +
 R/layer_add_forecast_date.R               |   1 +
 R/layer_add_target_date.R                 |   1 +
 R/step_adjust_latency.R                   | 151 +++++++++++++++-------
 R/utils-latency.R                         |  62 ++++++---
 man/add_shifted_columns.Rd                |  14 ++
 man/adjust_name.Rd                        |  14 ++
 man/get_shifted_column_tibble.Rd          |   2 +
 man/{get_asof.Rd => set_asof.Rd}          |   6 +-
 man/step_adjust_latency.Rd                |  16 ++-
 tests/testthat/test-step_adjust_latency.R | 126 ++++++++++++++++++
 tests/testthat/test-utils_latency.R       |  65 +++++++---
 14 files changed, 379 insertions(+), 85 deletions(-)
 create mode 100644 man/add_shifted_columns.Rd
 create mode 100644 man/adjust_name.Rd
 rename man/{get_asof.Rd => set_asof.Rd} (82%)
 create mode 100644 tests/testthat/test-step_adjust_latency.R

diff --git a/DESCRIPTION b/DESCRIPTION
index 16b9e07de..e3aa871df 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -42,6 +42,7 @@ Imports:
     rlang (>= 1.1.0),
     stats,
     stringr,
+    stringi,
     tibble,
     tidyr,
     tidyselect,
diff --git a/NAMESPACE b/NAMESPACE
index 0cf66300f..53c49aac1 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -88,6 +88,7 @@ S3method(print,layer_quantile_distn)
 S3method(print,layer_residual_quantiles)
 S3method(print,layer_threshold)
 S3method(print,layer_unnest)
+S3method(print,step_adjust_latency)
 S3method(print,step_epi_ahead)
 S3method(print,step_epi_lag)
 S3method(print,step_epi_slide)
@@ -262,6 +263,8 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lifecycle,deprecated)
+importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")
 importFrom(recipes,bake)
 importFrom(recipes,prep)
@@ -296,6 +299,7 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
+importFrom(stringi,stri_replace_all_regex)
 importFrom(stringr,str_match)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 37c9aae86..7b0d84f24 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -1,3 +1,4 @@
+# TODO add latency to default forecaster
 #' Direct autoregressive forecaster with covariates
 #'
 #' This is an autoregressive forecasting model for
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 3d5ea010b..3ebc18cb1 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -1,3 +1,4 @@
+# TODO adapt this to latency
 #' Postprocessing step to add the forecast date
 #'
 #' @param frosting a `frosting` postprocessor
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 094ec8501..35bc84339 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -1,3 +1,4 @@
+# TODO adapt this to latency
 #' Postprocessing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 092f54458..d6c6bfa23 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -25,7 +25,20 @@
 #'   - `"extend_lags"`: per `epi_key` and `predictor`, adjusts the lag so that
 #'   the shortest lag at predict time is at the last observation. E.g. if the
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
-#'   become `c(3,10,17)`
+#'   become `c(3,10,17)`.
+#' @param fixed_latency either a positive integer, or a labeled positive integer
+#'   vector. Cannot be set at the same time as `fixed_asof`.   If non-`NULL`,
+#'   the amount to offset the ahead or lag by. If a single integer, this is used
+#'   for all columns; if a labeled vector, the labels must correspond to the
+#'   base column names.  If `NULL`, the latency is the distance between the
+#'   `epi_df`'s `max_time_value` and either the `fixed_asof` or the `epi_df`'s
+#'   `as_of` field.
+#' @param fixed_asof either a date of the same kind used in the `epi_df`, or
+#'   NULL. Cannot be set at the same time as `fixed_latency`. If a date, it
+#'   gives the date from which the forecast is actually occurring. If `NULL`,
+#'   the `as_of` is determined either from `fixed_latency` or automatically.
+#' @param columns A character string of column names to be adjusted; these
+#'   should be the original columns, and not the derived ones
 #' @param default Determines what fills empty rows
 #'   left by leading/lagging (defaults to NA).
 #' @param prefix a character. The prefix matching the one used in either
@@ -68,6 +81,8 @@ step_adjust_latency <-
              "locf",
              "extend_lags"
            ),
+           fixed_latency = NULL,
+           fixed_asof = NULL,
            default = NA,
            skip = FALSE,
            prefix = NULL,
@@ -85,10 +100,21 @@ step_adjust_latency <-
     method <- rlang::arg_match(method)
     if (method == "extend_ahead") {
       prefix <- "ahead_"
-    } else {
+      if (!any(map_lgl(
+        recipe$steps,
+        \(recipe_step) inherits(recipe_step, "step_epi_ahead")
+      ))) {
+        cli:cli_abort("There is no `step_epi_ahead` defined before this. For the method `extend_ahead` of `step_adjust_latency`, at least one ahead must be previously defined.")
+      }
+    } else if (method == "extend_lags") {
       prefix <- "lag_"
+      if (!any(map_lgl(
+        recipe$steps,
+        \(recipe_step) inherits(recipe_step, "step_epi_lag")
+      ))) {
+        cli:cli_abort("There is no `step_epi_lag` defined before this. For the method `extend_lags` of `step_adjust_latency`, at least one lag must be previously defined.")
+      }
     }
-
     arg_is_chr_scalar(prefix, id, method)
     recipes::add_step(
       recipe,
@@ -96,12 +122,13 @@ step_adjust_latency <-
         terms = dplyr::enquos(...),
         role = role,
         method = method,
-        info = NULL,
         trained = trained,
+        as_of = fixed_asof,
+        latency = fixed_latency,
+        shift_cols = NULL,
         prefix = prefix,
         default = default,
         keys = epi_keys(recipe),
-        columns = columns,
         skip = skip,
         id = id
       )
@@ -109,24 +136,27 @@ step_adjust_latency <-
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, prefix, default, keys, method, info,
-           columns, skip, id) {
+  function(terms, role, trained, as_of, latency, shift_cols, prefix, time_type, default,
+           keys, method, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
       role = role,
       method = method,
-      info = info,
       trained = trained,
+      as_of = as_of,
+      latency = latency,
+      shift_cols = shift_cols,
       prefix = prefix,
       default = default,
       keys = keys,
-      columns = columns,
       skip = skip,
       id = id
     )
   }
 
+# lags introduces max(lags) NA's after the max_time_value.
+# TODO all of the shifting happens before NA removal, which saves all the data I might possibly want; I should probably add a bit that makes sure this operation is happening before NA removal so data doesn't get dropped
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
@@ -136,56 +166,33 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     cli::cli_abort('If `method` is `"extend_lags"` or `"locf"`, then a step ",
 "must have already added a predictor.')
   }
-  # TODO info here is probably not the best way to handle this, hypothetically I
-  # get an info object during baking
-  step_adjust_latency_new(
-    terms = x$terms,
-    role = x$role,
-    trained = TRUE,
-    prefix = x$prefix,
-    default = x$default,
-    keys = x$keys,
-    method = x$method,
-    info = info,
-    columns = recipes::recipes_eval_select(x$terms, training, info),
-    skip = x$skip,
-    id = x$id
-  )
-}
 
-#' various ways of handling differences between the `as_of` date and the maximum
-#' time value
-#' @description
-#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
-#'   date, rather than relative to the last day of data
-#' @param new_data assumes that this already has lag/ahead columns that we need
-#'   to adjust
-#' @importFrom dplyr %>% pull
-#' @keywords internal
-bake.step_adjust_latency <- function(object, new_data, ...) {
-  sign_shift <- get_sign(object)
+  sign_shift <- get_sign(x)
   # get the columns used, even if it's all of them
-  terms_used <- object$columns
+  terms_used <- x$columns
   if (length(terms_used) == 0) {
-    terms_used <- object$info %>%
+    terms_used <- info %>%
       filter(role == "raw") %>%
       pull(variable)
   }
   # get and check the max_time and as_of are the right kinds of dates
-  as_of <- get_asof(object, new_data)
+  if (is.null(x$as_of)) {
+    as_of <- set_asof(training, info)
+  } else {
+    as_of <- x$as_of
+  }
 
   # infer the correct columns to be working with from the previous
   # transformations
   shift_cols <- get_shifted_column_tibble(
-    object$prefix, new_data, terms_used, as_of,
-    sign_shift
+    x$prefix, training, terms_used,
+    as_of, x$latency, sign_shift, info
   )
 
-  if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
+  if ((x$method == "extend_ahead") || (x$method == "extend_lags")) {
     # check that the shift amount isn't too extreme
     latency <- max(shift_cols$latency)
-    i_latency <- which.max(shift_cols$latency)
-    time_type <- attributes(new_data)$metadata$time_type
+    time_type <- attributes(training)$metadata$time_type
     if (
       (grepl("day", time_type) && (latency >= 10)) ||
         (grepl("week", time_type) && (latency >= 4)) ||
@@ -204,9 +211,63 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
         "i" = "max_time = {max_time} -> as_of = {as_of}"
       ))
     }
+  }
+
+  step_adjust_latency_new(
+    terms = shift_cols$original_name,
+    role = shift_cols$role[[1]],
+    trained = TRUE,
+    prefix = x$prefix,
+    shift_cols = shift_cols,
+    as_of = as_of,
+    latency = unique(shift_cols$latency),
+    default = x$default,
+    keys = x$keys,
+    method = x$method,
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' various ways of handling differences between the `as_of` date and the maximum
+#' time value
+#' @description
+#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
+#'   date, rather than relative to the last day of data
+#' @param new_data assumes that this already has lag/ahead columns that we need
+#'   to adjust
+#' @importFrom dplyr %>% pull
+#' @keywords internal
+bake.step_adjust_latency <- function(object, new_data, ...) {
+  if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
     keys <- object$keys
     return(
-      extend_either(new_data, shift_cols, keys)
+      extend_either(new_data, object$shift_cols, keys)
     )
   }
 }
+
+#' @export
+print.step_adjust_latency <-
+  function(x, width = max(20, options$width - 35), ...) {
+    if (length(x$terms) == 0) {
+      terms <- "all previous predictors"
+    } else {
+      terms <- x$terms
+    }
+    if (!is.null(x$as_of)) {
+      conj <- "with forecast date"
+      extra_text <- x$as_of
+    } else if (!is.null(x$shift_cols)) {
+      conj <- "with latencies"
+      extra_text <- x$shift_cols
+    } else {
+      conj <- ""
+      extra_text <- "set at train time"
+    }
+    print_epi_step(terms, NULL, x$trained, x$method,
+      conjunction = conj,
+      extra_text = extra_text
+    )
+    invisible(x)
+  }
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 163643929..0e11627dd 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -10,7 +10,7 @@
 extend_either <- function(new_data, shift_cols, keys) {
   shifted <-
     shift_cols %>%
-    select(-shifts, -effective_shift) %>%
+    select(-any_of(c("shifts", "effective_shift", "type", "role", "source"))) %>%
     pmap(\(original_name, latency, new_name) {
       epi_shift_single(
         x = new_data,
@@ -20,7 +20,7 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
-    map(\(x) zoo::na.trim(x)) %>% # TODO need to talk about this
+    map(\(x) zoo::na.trim(x)) %>%
     reduce(
       dplyr::full_join,
       by = keys
@@ -46,8 +46,10 @@ extend_either <- function(new_data, shift_cols, keys) {
 #' @keywords internal
 #' @importFrom stringr str_match
 #' @importFrom dplyr rowwise %>%
+#' @importFrom magrittr %<>%
 get_shifted_column_tibble <- function(
-    prefix, new_data, terms_used, as_of, sign_shift, call = caller_env()) {
+    prefix, new_data, terms_used, as_of, latency,
+    sign_shift, info, call = caller_env()) {
   relevant_columns <- names(new_data)[grepl(prefix, names(new_data))]
   to_keep <- rep(FALSE, length(relevant_columns))
   for (col_name in terms_used) {
@@ -61,40 +63,58 @@ get_shifted_column_tibble <- function(
       call = call
     )
   }
-  # TODO ask about a less jank way to do this
-  shift_amounts <- as.integer(stringr::str_match(
-    relevant_columns,
-    "_\\d+_"
-  ) %>%
-    `[`(, 1) %>%
-    stringr::str_match("\\d+") %>%
-    `[`(, 1))
+  # this pulls text that is any number of digits between two _, e.g. _3557_, and
+  # converts them to an integer
+  shift_amounts <- stringr::str_match(relevant_columns, "_(\\d+)_") %>%
+    `[`(, 2) %>%
+    as.integer()
+
   shift_cols <- dplyr::tibble(
     original_name = relevant_columns,
     shifts = shift_amounts
   )
+  if (is.null(latency)) {
+    shift_cols %<>%
+      rowwise() %>%
+      # add the latencies to shift_cols
+      mutate(latency = get_latency(
+        new_data, as_of, original_name, shifts, sign_shift
+      )) %>%
+      ungroup()
+  } else if (length(latency) > 1) {
+    shift_cols %<>% rowwise() %>%
+      mutate(latency = unname(latency[purrr::map_lgl(
+        names(latency),
+        \(x) grepl(x, original_name)
+      )])) %>%
+      ungroup()
+  } else {
+    shift_cols %<>% mutate(latency = latency)
+  }
+
+  # add the updated names to shift_cols
   shift_cols %<>%
-    rowwise() %>%
-    # add the latencies to shift_cols
-    mutate(latency = get_latency(
-      new_data, as_of, original_name, shifts, sign_shift
-    )) %>%
-    ungroup() %>%
-    # add the updated names to shift_cols
     mutate(
       effective_shift = shifts + abs(latency)
     ) %>%
     mutate(
       new_name = adjust_name(prefix, original_name, effective_shift)
     )
+  info %<>% select(variable, type, role)
+  shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))
+  if (length(unique(shift_cols$role)) != 1) {
+    cli::cli_error("not all roles are the same!",
+      shift_cols = shift_cols
+    )
+  }
   return(shift_cols)
 }
 
 
 #' extract the as_of, and make sure there's nothing very off about it
 #' @keywords internal
-get_asof <- function(object, new_data) {
-  original_columns <- object$info %>%
+set_asof <- function(new_data, info) {
+  original_columns <- info %>%
     filter(source == "original") %>%
     pull(variable)
   # make sure that there's enough column names
@@ -117,7 +137,6 @@ get_asof <- function(object, new_data) {
   as_of <- attributes(new_data)$metadata$as_of
   max_time <- max(time_values)
   # make sure the as_of is sane
-  # TODO decide on these checks
   if (!inherits(as_of, class(time_values))) {
     rlang::abort(glue::glue(
       "the data matrix `as_of` value is {as_of}, ",
@@ -149,6 +168,7 @@ get_asof <- function(object, new_data) {
 #' adjust the shifts by latency for the names in column assumes e.g.
 #' `"lag_6_case_rate"` and returns something like `"lag_10_case_rate"`
 #' @keywords internal
+#' @importFrom stringi stri_replace_all_regex
 adjust_name <- function(prefix, column, effective_shift) {
   pattern <- paste0(prefix, "\\d+", "_")
   adjusted_shifts <- paste0(prefix, effective_shift, "_")
diff --git a/man/add_shifted_columns.Rd b/man/add_shifted_columns.Rd
new file mode 100644
index 000000000..d7aba745b
--- /dev/null
+++ b/man/add_shifted_columns.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi_shift.R
+\name{add_shifted_columns}
+\alias{add_shifted_columns}
+\title{backend for both \code{bake.step_epi_ahead} and \code{bake.step_epi_lag}, performs the
+checks missing in \code{epi_shift_single}}
+\usage{
+add_shifted_columns(new_data, object, amount)
+}
+\description{
+backend for both \code{bake.step_epi_ahead} and \code{bake.step_epi_lag}, performs the
+checks missing in \code{epi_shift_single}
+}
+\keyword{internal}
diff --git a/man/adjust_name.Rd b/man/adjust_name.Rd
new file mode 100644
index 000000000..8cb6e5106
--- /dev/null
+++ b/man/adjust_name.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{adjust_name}
+\alias{adjust_name}
+\title{adjust the shifts by latency for the names in column assumes e.g.
+\code{"lag_6_case_rate"} and returns something like \code{"lag_10_case_rate"}}
+\usage{
+adjust_name(prefix, column, effective_shift)
+}
+\description{
+adjust the shifts by latency for the names in column assumes e.g.
+\code{"lag_6_case_rate"} and returns something like \code{"lag_10_case_rate"}
+}
+\keyword{internal}
diff --git a/man/get_shifted_column_tibble.Rd b/man/get_shifted_column_tibble.Rd
index 635cab427..c66941e0e 100644
--- a/man/get_shifted_column_tibble.Rd
+++ b/man/get_shifted_column_tibble.Rd
@@ -10,7 +10,9 @@ get_shifted_column_tibble(
   new_data,
   terms_used,
   as_of,
+  latency,
   sign_shift,
+  info,
   call = caller_env()
 )
 }
diff --git a/man/get_asof.Rd b/man/set_asof.Rd
similarity index 82%
rename from man/get_asof.Rd
rename to man/set_asof.Rd
index 4649df7cc..fabf97c8a 100644
--- a/man/get_asof.Rd
+++ b/man/set_asof.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/utils-latency.R
-\name{get_asof}
-\alias{get_asof}
+\name{set_asof}
+\alias{set_asof}
 \title{extract the as_of, and make sure there's nothing very off about it}
 \usage{
-get_asof(object, new_data)
+set_asof(new_data, info)
 }
 \description{
 extract the as_of, and make sure there's nothing very off about it
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index c60495c84..ab2975fef 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -10,6 +10,8 @@ step_adjust_latency(
   role = NA,
   trained = FALSE,
   method = c("extend_ahead", "locf", "extend_lags"),
+  fixed_latency = NULL,
+  fixed_asof = NULL,
   default = NA,
   skip = FALSE,
   prefix = NULL,
@@ -40,9 +42,18 @@ sophisticated methods of extrapolation.
 \item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that
 the shortest lag at predict time is at the last observation. E.g. if the
 lags are \code{c(0,7,14)} for data that is 3 days latent, the actual lags used
-become \code{c(3,10,17)}
 }}
 
+\item{fixed_latency}{either a positive integer, or a labeled positive integer
+vector. Cannot be set at the same time as \code{fixed_asof}.   If non-\code{NULL}, the
+amount to offset the ahead or lag by. If a single integer, this is used for
+all columns; if a labeled vector, the labels must correspond to the base
+column names.  If \code{NULL}, the latency is the distance between the \code{epi_df}'s
+\code{max_time_value} and either the \code{fixed_asof} or the \code{epi_df}'s \code{as_of}
+field.}
+
+\item{fixed_asof}{either a date of the same kind used in the \code{epi_df}, or NULL. Cannot be set at the same time as \code{fixed_latency}. If a date, it gives the date from which the forecast is actually occurring. If \code{NULL}, the \code{as_of} is determined either from \code{fixed_latency} or automatically.}
+
 \item{default}{Determines what fills empty rows
 left by leading/lagging (defaults to NA).}
 
@@ -57,6 +68,9 @@ the computations for subsequent operations.}
 \code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
 if \code{method="extend_lags"} or "locf".}
 
+\item{columns}{A character string of variable names that will
+btency'   become \code{c(3,10,17)}}
+
 \item{id}{A unique identifier for the step}
 }
 \value{
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
new file mode 100644
index 000000000..16612ee3b
--- /dev/null
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -0,0 +1,126 @@
+library(dplyr)
+
+x <- tibble(
+  geo_value = rep("place", 200),
+  time_value = as.Date("2021-01-01") + 0:199,
+  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
+  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
+) %>%
+  as_epi_df()
+max_time <- max(x$time_value)
+class(attributes(x)$metadata$as_of)
+as_of <- attributes(x)$metadata$as_of
+ahead <- 7
+latency <- 5
+
+testing_as_of <- max_time + latency
+# create x with a plausible as_of date
+real_x <- x
+attributes(real_x)$metadata$as_of <- testing_as_of
+
+slm_fit <- function(recipe, data = x) {
+  epi_workflow() %>%
+    add_epi_recipe(recipe) %>%
+    add_model(linear_reg()) %>%
+    fit(data = data)
+}
+
+test_that("epi_adjust_latency correctly extends the lags", {
+  r5 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead) %>%
+    step_adjust_latency(method = "extend_lags")
+  # the as_of on x is today's date, which is >970 days in the future
+  # also, there's no data >970 days in the past, so it gets an error trying to
+  # fit on no data
+  expect_error(expect_warning(fit5 <- slm_fit(r5)))
+
+  # now trying with the as_of a reasonable distance in the future
+  fit5 <- slm_fit(r5, data = real_x)
+  expect_equal(
+    names(fit5$pre$mold$predictors),
+    c(
+      "lag_5_death_rate", "lag_11_death_rate", "lag_16_death_rate",
+      "lag_6_case_rate", "lag_10_case_rate"
+    )
+  )
+  latest <- get_test_data(r5, x)
+  pred <- predict(fit5, latest)
+  point_pred <- pred %>% filter(!is.na(.pred))
+  expect_equal(nrow(point_pred), 1)
+  expect_equal(point_pred$time_value, as.Date(testing_as_of))
+
+  expect_equal(names(fit5$pre$mold$outcomes),
+               glue::glue("ahead_{ahead}_death_rate"))
+  latest <- get_test_data(r5, x)
+  pred <- predict(fit5, latest)
+  actual_solutions <- pred %>% filter(!is.na(.pred))
+  expect_equal(actual_solutions$time_value, testing_as_of)
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+
+  # result should be equivalent to just immediately doing the adjusted lags by
+  # hand
+  hand_adjusted <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(5, 11, 16)) %>%
+    step_epi_lag(case_rate, lag = c(6, 10)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+  fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
+  expect_equal(fit5$fit$fit$fit$coefficients,
+               fit_hand_adj$fit$fit$fit$coefficients)
+})
+
+test_that("epi_adjust_latency correctly extends the ahead", {
+  r5 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead) %>%
+    step_adjust_latency(method = "extend_ahead")
+  # the as_of on x is today's date, which is >970 days in the future
+  # also, there's no data >970 days in the past, so it gets an error trying to
+  # fit on no data
+  expect_error(expect_warning(fit5 <- slm_fit(r5)))
+  # real date example
+  fit5 <- slm_fit(r5, data = real_x)
+  expect_equal(
+    names(fit5$pre$mold$predictors),
+    c(
+      "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate",
+      "lag_1_case_rate", "lag_5_case_rate"
+    )
+  )
+  latest <- get_test_data(r5, x)
+  pred <- predict(fit5, latest)
+  point_pred <- pred %>% filter(!is.na(.pred))
+  # max time is still the forecast date
+  expect_equal(point_pred$time_value, as.Date(max_time))
+  # target column renamed
+  expect_equal(names(fit5$pre$mold$outcomes),
+               glue::glue("ahead_{ahead + latency}_death_rate"))
+  # fit an equivalent forecaster
+  equivalent <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead + latency)
+  equiv_fit <- slm_fit(equivalent, data = real_x)
+  # adjusting the ahead should do the same thing as directly adjusting the ahead
+  expect_equal(fit5$fit$fit$fit$coefficients,
+               equiv_fit$fit$fit$fit$coefficients)
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+})
+
+test_that("epi_adjust_latency fixed_* work", {})
+# todo test variants on the columns for which this is applied
+# todo need to have both on columns 1, and 2
+
+test_that("epi_adjust_latency works correctly when there's gaps in the timeseries", {})
+
+test_that("epi_adjust_latency extend_ahead uses the same adjustment when predicting on new data after being baked", {})
+
+test_that("epi_adjust_latency works for other time types", {})
+
+# todo check that epi_adjust_latency errors for nonsense `as_of`'s
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 3873c5e6a..524d98b4d 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -34,12 +34,13 @@ modified_data <-
 modified_data %>% tail()
 as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
 all_shift_cols <- tibble::tribble(
-  ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name,
-  "lag_3_case_rate", 3, 5, 8, "lag_8_case_rate",
-  "lag_7_death_rate", 7, 4, 11, "lag_11_death_rate",
-  "ahead_4_case_rate", 4, -5, 9, "ahead_9_case_rate"
+  ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+  "lag_3_case_rate", 3, 5, 8, "lag_8_case_rate", "numeric", "predictor",
+  "lag_7_death_rate", 7, 4, 11, "lag_11_death_rate", "numeric", "predictor",
+  "ahead_4_case_rate", 4, -5, 9, "ahead_9_case_rate", "numeric", "outcome"
 )
 
+
 test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1), 4)
   expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 3, 1), 5)
@@ -51,6 +52,8 @@ test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, 1), 5 + 4 * 2)
 })
 
+test_that("get_latency infers max_time to be the minimum `max time` across the columns", {})
+
 test_that("adjust_name works", {
   expect_equal(
     adjust_name("lag_", "lag_5_case_rate_13", 10),
@@ -69,50 +72,80 @@ test_that("adjust_name works", {
 })
 
 test_that("get_asof works", {
-  object <- list(info = tribble(
+  info <- tribble(
     ~variable, ~type, ~role, ~source,
     "time_value", "date", "time_value", "original",
     "geo_value", "nominal", "geo_value", "original",
     "case_rate", "numeric", "raw", "original",
     "death_rate", "numeric", "raw", "original",
     "not_real", "numeric", "predictor", "derived"
-  ))
-  expect_equal(get_asof(object, modified_data), as_of)
+  )
+  expect_equal(set_asof(modified_data, info), as_of)
 })
 
-test_that("get_shifted_column_tibble works", {
+test_that("get_shifted_column_tibble infers latency and works correctly", {
+  info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
   case_lag <- get_shifted_column_tibble(
-    "lag_", modified_data,
-    "case_rate", as_of, 1
+    "lag_", modified_data, "case_rate",
+    as_of, NULL, 1, info
   )
   expect_equal(case_lag, all_shift_cols[1, ])
 
   death_lag <- get_shifted_column_tibble(
     "lag_", modified_data,
-    "death_rate", as_of, 1
+    "death_rate", as_of, NULL, 1, info
   )
   expect_equal(death_lag, all_shift_cols[2, ])
 
   both_lag <- get_shifted_column_tibble(
-    "lag_", modified_data,
-    c("case_rate", "death_rate"), as_of, 1
+    "lag_", modified_data, c("case_rate", "death_rate"),
+    as_of, NULL, 1, info
   )
   expect_equal(both_lag, all_shift_cols[1:2, ])
+})
+
+test_that("get_shifted_column_tibble assigns given latencies", {
+  # non-null latency
+  info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
+  both_lag <- get_shifted_column_tibble(
+    "lag_", modified_data,
+    c("case_rate", "death_rate"), as_of, 50, 1, info
+  )
+  weird_latencies <- tibble::tribble(
+    ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "lag_3_case_rate", 3, 50, 53, "lag_53_case_rate", "numeric", "predictor",
+    "lag_7_death_rate", 7, 50, 57, "lag_57_death_rate", "numeric", "predictor",
+  )
+  expect_equal(both_lag, weird_latencies)
+
+  # supposing we add the latencies by hand, and they're different, and in a different order
+  weird_latencies <- tibble::tribble(
+    ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "lag_3_case_rate", 3, 70, 73, "lag_73_case_rate", "numeric", "predictor",
+    "lag_7_death_rate", 7, 30, 37, "lag_37_death_rate", "numeric", "predictor",
+  )
+  both_lag <- get_shifted_column_tibble(
+    "lag_", modified_data,
+    c("case_rate", "death_rate"), as_of, c(death_rate = 30, case_rate = 70), 1, info
+  )
+  expect_equal(both_lag, weird_latencies[1:2, ])
 
   case_ahead <- get_shifted_column_tibble(
     "ahead_", modified_data,
-    "case_rate", as_of, -1
+    "case_rate", as_of, NULL, -1, info
   )
   expect_equal(case_ahead, all_shift_cols[3, ])
 })
+
 test_that("get_shifted_column_tibble objects to non-columns", {
   expect_error(
     get_shifted_column_tibble(
-      "lag_", modified_data, "not_present", as_of, 1
+      "lag_", modified_data, "not_present", as_of, NULL, 1, info
     ),
     class = "epipredict_adjust_latency_nonexistent_column_used"
   )
 })
+
 test_that("extend_either works", {
   keys <- c("geo_value", "time_value")
   # extend_either doesn't differentiate between the directions, it just moves
@@ -137,3 +170,5 @@ test_that("extend_either works", {
     expected_post_shift
   )
 })
+
+# todo case where somehow columns of different roles are selected

From 05b5cbf2e4ebe26d6f1a0aec03abf63ad236615e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 15:59:14 -0500
Subject: [PATCH 030/387] spurious lifecycle addition removed

---
 NAMESPACE                  |  1 -
 man/step_adjust_latency.Rd | 22 +++++++++++++---------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 53c49aac1..96dd0e67f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -263,7 +263,6 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
-importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")
 importFrom(recipes,bake)
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index ab2975fef..76f1a2d51 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -42,17 +42,21 @@ sophisticated methods of extrapolation.
 \item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that
 the shortest lag at predict time is at the last observation. E.g. if the
 lags are \code{c(0,7,14)} for data that is 3 days latent, the actual lags used
+become \code{c(3,10,17)}.
 }}
 
 \item{fixed_latency}{either a positive integer, or a labeled positive integer
-vector. Cannot be set at the same time as \code{fixed_asof}.   If non-\code{NULL}, the
-amount to offset the ahead or lag by. If a single integer, this is used for
-all columns; if a labeled vector, the labels must correspond to the base
-column names.  If \code{NULL}, the latency is the distance between the \code{epi_df}'s
-\code{max_time_value} and either the \code{fixed_asof} or the \code{epi_df}'s \code{as_of}
-field.}
+vector. Cannot be set at the same time as \code{fixed_asof}.   If non-\code{NULL},
+the amount to offset the ahead or lag by. If a single integer, this is used
+for all columns; if a labeled vector, the labels must correspond to the
+base column names.  If \code{NULL}, the latency is the distance between the
+\code{epi_df}'s \code{max_time_value} and either the \code{fixed_asof} or the \code{epi_df}'s
+\code{as_of} field.}
 
-\item{fixed_asof}{either a date of the same kind used in the \code{epi_df}, or NULL. Cannot be set at the same time as \code{fixed_latency}. If a date, it gives the date from which the forecast is actually occurring. If \code{NULL}, the \code{as_of} is determined either from \code{fixed_latency} or automatically.}
+\item{fixed_asof}{either a date of the same kind used in the \code{epi_df}, or
+NULL. Cannot be set at the same time as \code{fixed_latency}. If a date, it
+gives the date from which the forecast is actually occurring. If \code{NULL},
+the \code{as_of} is determined either from \code{fixed_latency} or automatically.}
 
 \item{default}{Determines what fills empty rows
 left by leading/lagging (defaults to NA).}
@@ -68,8 +72,8 @@ the computations for subsequent operations.}
 \code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
 if \code{method="extend_lags"} or "locf".}
 
-\item{columns}{A character string of variable names that will
-btency'   become \code{c(3,10,17)}}
+\item{columns}{A character string of column names to be adjusted; these
+should be the original columns, and not the derived ones}
 
 \item{id}{A unique identifier for the step}
 }

From 47cb5b7c0c4caca248ffe1b6b105db474437bde9 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 16:32:52 -0500
Subject: [PATCH 031/387] fixing RMDcheck remote

---
 man/step_adjust_latency.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 76f1a2d51..e4088ba7b 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/step_adjust_latency.R
 \name{step_adjust_latency}
 \alias{step_adjust_latency}
-\title{adapt the pipeline to latency in the data}
+\title{Adapt the pipeline to latency in the data}
 \usage{
 step_adjust_latency(
   recipe,

From 4b0b668295d1e816b2ee6fb79f46d334b9413cbf Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 17:00:00 -0500
Subject: [PATCH 032/387] nothing but `rlang::abort` -> `cli::cli_abort`s

---
 R/compat-recipes.R      |  2 +-
 R/epi_recipe.R          | 13 ++++++-------
 R/frosting.R            |  3 +--
 R/step_adjust_latency.R |  4 ++--
 R/utils-latency.R       |  4 ++--
 R/utils-shift.R         |  6 +++---
 6 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/R/compat-recipes.R b/R/compat-recipes.R
index 12d11049a..0e4a557cb 100644
--- a/R/compat-recipes.R
+++ b/R/compat-recipes.R
@@ -18,7 +18,7 @@ inline_check <- function(x) {
   funs <- fun_calls(x)
   funs <- funs[!(funs %in% c("~", "+", "-"))]
   if (length(funs) > 0) {
-    rlang::abort(paste0(
+    cli::cli_abort(paste0(
       "No in-line functions should be used here; ",
       "use steps to define baking actions."
     ))
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index f8216c2af..5c4de0c78 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -61,7 +61,7 @@ epi_recipe.epi_df <-
     attr(x, "decay_to_tibble") <- FALSE
     if (!is.null(formula)) {
       if (!is.null(vars)) {
-        rlang::abort(
+        cli::cli_abort(
           paste0(
             "This `vars` specification will be ignored ",
             "when a formula is used"
@@ -69,7 +69,7 @@ epi_recipe.epi_df <-
         )
       }
       if (!is.null(roles)) {
-        rlang::abort(
+        cli::cli_abort(
           paste0(
             "This `roles` specification will be ignored ",
             "when a formula is used"
@@ -82,10 +82,10 @@ epi_recipe.epi_df <-
     }
     if (is.null(vars)) vars <- colnames(x)
     if (any(table(vars) > 1)) {
-      rlang::abort("`vars` should have unique members")
+      cli::cli_abort("`vars` should have unique members")
     }
     if (any(!(vars %in% colnames(x)))) {
-      rlang::abort("1 or more elements of `vars` are not in the data")
+      cli::cli_abort("1 or more elements of `vars` are not in the data")
     }
 
     keys <- key_colnames(x) # we know x is an epi_df
@@ -96,7 +96,7 @@ epi_recipe.epi_df <-
     ## Check and add roles when available
     if (!is.null(roles)) {
       if (length(roles) != length(vars)) {
-        rlang::abort(c(
+        cli::cli_abort(c(
           "The number of roles should be the same as the number of ",
           "variables."
         ))
@@ -142,7 +142,6 @@ epi_recipe.epi_df <-
 
 
 #' @rdname epi_recipe
-#' @importFrom rlang abort
 #' @export
 epi_recipe.formula <- function(formula, data, ...) {
   # we ensure that there's only 1 row in the template
@@ -475,7 +474,7 @@ prep.epi_recipe <- function(
         "You cannot `prep()` a tuneable recipe. Argument(s) with `tune()`: ",
         arg, ". Do you want to use a tuning function such as `tune_grid()`?"
       )
-      rlang::abort(msg)
+      cli::cli_abort(msg)
     }
     note <- paste("oper", i, gsub("_", " ", class(x$steps[[i]])[1]))
     if (!x$steps[[i]]$trained | fresh) {
diff --git a/R/frosting.R b/R/frosting.R
index 8474edbdf..d7ba22902 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -89,7 +89,7 @@ validate_has_postprocessor <- function(x, ..., call = caller_env()) {
       "The workflow must have a frosting postprocessor.",
       i = "Provide one with `add_frosting()`."
     )
-    rlang::abort(message, call = call)
+    cli::cli_abort(message, call = call)
   }
   invisible(x)
 }
@@ -356,7 +356,6 @@ apply_frosting.default <- function(workflow, components, ...) {
 
 #' @rdname apply_frosting
 #' @importFrom rlang is_null
-#' @importFrom rlang abort
 #' @param type,opts forwarded (along with `...`) to [`predict.model_fit()`] and
 #'   [`slather()`] for supported layers
 #' @export
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index d6c6bfa23..a53dadffc 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -89,10 +89,10 @@ step_adjust_latency <-
            columns = NULL,
            id = recipes::rand_id("epi_lag")) {
     if (!is_epi_recipe(recipe)) {
-      rlang::abort("This recipe step can only operate on an `epi_recipe`.")
+      cli::cli_abort("This recipe step can only operate on an `epi_recipe`.")
     }
     if (!is.null(columns)) {
-      rlang::abort(c("The `columns` argument must be `NULL.",
+      cli::cli_abort(c("The `columns` argument must be `NULL.",
         i = "Use `tidyselect` methods to choose columns to lag."
       ))
     }
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 0e11627dd..decaade1e 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -132,13 +132,13 @@ set_asof <- function(new_data, info) {
     drop_na() %>%
     pull(time_value)
   if (length(time_values) <= 0) {
-    rlang::abort("the `time_value` column of `new_data` is empty")
+    cli::cli_abort("the `time_value` column of `new_data` is empty")
   }
   as_of <- attributes(new_data)$metadata$as_of
   max_time <- max(time_values)
   # make sure the as_of is sane
   if (!inherits(as_of, class(time_values))) {
-    rlang::abort(glue::glue(
+    cli::cli_abort(glue::glue(
       "the data matrix `as_of` value is {as_of}, ",
       "and not a valid `time_type` with type ",
       "matching `time_value`'s type of ",
diff --git a/R/utils-shift.R b/R/utils-shift.R
index a8eccf6fe..702a52308 100644
--- a/R/utils-shift.R
+++ b/R/utils-shift.R
@@ -12,7 +12,7 @@ adjust_latency <- function(object, new_data) {
   } else if (method == "extend_ahead") {
     as_of <- attributes(new_data)$metadata$as_of
     if (FALSE && (typeof(as_of) != typeof(new_data$time_value))) {
-      rlang::abort(glue::glue(
+      cli::cli_abort(glue::glue(
         "the data matrix `as_of` value is {as_of}, ",
         "and not a valid `time_type` with type ",
         "matching `time_value`'s type of ",
@@ -60,10 +60,10 @@ adjust_latency <- function(object, new_data) {
       }
       return(effective_ahead)
     } else {
-      rlang::abort("the `time_value` column of `new_data` is empty")
+      cli::cli_abort("the `time_value` column of `new_data` is empty")
     }
   } else {
-    rlang::abort(glue::glue(
+    cli::cli_abort(glue::glue(
       "Latency adjustment method {method} has not yet ",
       "been implemented for `step_epi_ahead`."
     ))

From 2731160bec866bfa0abfdd9d16ac9bf2e00ee857 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 17:58:40 -0500
Subject: [PATCH 033/387] smaller suggestions and styling

---
 R/step_adjust_latency.R                   | 23 +++++++++++-----------
 R/step_epi_shift.R                        |  8 +-------
 R/utils-latency.R                         | 13 ++++++------
 man/step_adjust_latency.Rd                |  7 +++++++
 tests/testthat/test-step_adjust_latency.R | 24 +++++++++++++++--------
 tests/testthat/test-utils_latency.R       |  7 ++++---
 6 files changed, 47 insertions(+), 35 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index a53dadffc..d43950b44 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -1,4 +1,4 @@
-#' adapt the pipeline to latency in the data
+#' Adapt the pipeline to latency in the data
 #'
 #' In the standard case, the pipeline assumes that the last observation is also
 #' the day from which the forecast is being made. `step_adjust_latency` uses the
@@ -37,6 +37,11 @@
 #'   NULL. Cannot be set at the same time as `fixed_latency`. If a date, it
 #'   gives the date from which the forecast is actually occurring. If `NULL`,
 #'   the `as_of` is determined either from `fixed_latency` or automatically.
+#' @param role For model terms created by this step, what analysis role should
+#'   they be assigned? `lag` is default a predictor while `ahead` is an outcome.
+#'   It should be correctly inferred and not need setting
+#' @param trained A logical to indicate if the quantities for preprocessing have
+#'   been estimated.
 #' @param columns A character string of column names to be adjusted; these
 #'   should be the original columns, and not the derived ones
 #' @param default Determines what fills empty rows
@@ -92,7 +97,7 @@ step_adjust_latency <-
       cli::cli_abort("This recipe step can only operate on an `epi_recipe`.")
     }
     if (!is.null(columns)) {
-      cli::cli_abort(c("The `columns` argument must be `NULL.",
+      cli::cli_abort(c("The `columns` argument must be `NULL`.",
         i = "Use `tidyselect` methods to choose columns to lag."
       ))
     }
@@ -102,7 +107,7 @@ step_adjust_latency <-
       prefix <- "ahead_"
       if (!any(map_lgl(
         recipe$steps,
-        \(recipe_step) inherits(recipe_step, "step_epi_ahead")
+        function(recipe_step) inherits(recipe_step, "step_epi_ahead")
       ))) {
         cli:cli_abort("There is no `step_epi_ahead` defined before this. For the method `extend_ahead` of `step_adjust_latency`, at least one ahead must be previously defined.")
       }
@@ -110,7 +115,7 @@ step_adjust_latency <-
       prefix <- "lag_"
       if (!any(map_lgl(
         recipe$steps,
-        \(recipe_step) inherits(recipe_step, "step_epi_lag")
+        function(recipe_step) inherits(recipe_step, "step_epi_lag")
       ))) {
         cli:cli_abort("There is no `step_epi_lag` defined before this. For the method `extend_lags` of `step_adjust_latency`, at least one lag must be previously defined.")
       }
@@ -160,8 +165,8 @@ step_adjust_latency_new <-
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
-    cli::cli_abort(glue::glue(c('If `method` is `"extend_ahead"`, then a step ",
-          "must have already added an outcome .')))
+    cli::cli_abort('If `method` is `"extend_ahead"`, then a step ",
+          "must have already added an outcome .')
   } else if (!("predictor" %in% info$role)) {
     cli::cli_abort('If `method` is `"extend_lags"` or `"locf"`, then a step ",
 "must have already added a predictor.')
@@ -176,11 +181,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       pull(variable)
   }
   # get and check the max_time and as_of are the right kinds of dates
-  if (is.null(x$as_of)) {
-    as_of <- set_asof(training, info)
-  } else {
-    as_of <- x$as_of
-  }
+  as_of <- x$as_of %||% set_asof(training, info)
 
   # infer the correct columns to be working with from the previous
   # transformations
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 3a65dd05b..7cdf0680d 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -58,12 +58,6 @@ step_epi_lag <-
            prefix = "lag_",
            default = NA,
            skip = FALSE,
-           latency_adjustment = c(
-             "None",
-             "extend_ahead",
-             "locf",
-             "extend_lags"
-           ),
            id = rand_id("epi_lag")) {
     if (!is_epi_recipe(recipe)) {
       cli_abort("This step can only operate on an `epi_recipe`.")
@@ -80,7 +74,7 @@ step_epi_lag <-
     arg_is_chr_scalar(prefix, id, latency_adjustment)
     if (!is.null(columns)) {
       cli::cli_abort(c(
-        "The `columns` argument must be `NULL.",
+        "The `columns` argument must be `NULL`.",
         i = "Use `tidyselect` methods to choose columns to lag."
       ))
     }
diff --git a/R/utils-latency.R b/R/utils-latency.R
index decaade1e..54a2ddb08 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -74,7 +74,7 @@ get_shifted_column_tibble <- function(
     shifts = shift_amounts
   )
   if (is.null(latency)) {
-    shift_cols %<>%
+    shift_cols <- shift_cols %>%
       rowwise() %>%
       # add the latencies to shift_cols
       mutate(latency = get_latency(
@@ -82,28 +82,29 @@ get_shifted_column_tibble <- function(
       )) %>%
       ungroup()
   } else if (length(latency) > 1) {
-    shift_cols %<>% rowwise() %>%
+    shift_cols <- shift_cols %>%
+      rowwise() %>%
       mutate(latency = unname(latency[purrr::map_lgl(
         names(latency),
         \(x) grepl(x, original_name)
       )])) %>%
       ungroup()
   } else {
-    shift_cols %<>% mutate(latency = latency)
+    shift_cols <- shift_cols %>% mutate(latency = latency)
   }
 
   # add the updated names to shift_cols
-  shift_cols %<>%
+  shift_cols <- shift_cols %>%
     mutate(
       effective_shift = shifts + abs(latency)
     ) %>%
     mutate(
       new_name = adjust_name(prefix, original_name, effective_shift)
     )
-  info %<>% select(variable, type, role)
+  info <- shift_cols %>% select(variable, type, role)
   shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))
   if (length(unique(shift_cols$role)) != 1) {
-    cli::cli_error("not all roles are the same!",
+    cli::cli_abort("not all roles are the same!",
       shift_cols = shift_cols
     )
   }
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index e4088ba7b..9e8efc377 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -28,6 +28,13 @@ See \code{\link[recipes:selections]{recipes::selections()}} for more details. Ty
 to set this manually, as the necessary adjustments will be done for the
 predictors and outcome.}
 
+\item{role}{For model terms created by this step, what analysis role should
+they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.
+It should be correctly inferred and not need setting}
+
+\item{trained}{A logical to indicate if the quantities for preprocessing have
+been estimated.}
+
 \item{method}{a character. Determines the method by which the
 forecast handles latency. All of these assume the forecast date is the
 \code{as_of} of the \code{epi_df}. The options are:
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 16612ee3b..28608de6d 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -51,8 +51,10 @@ test_that("epi_adjust_latency correctly extends the lags", {
   expect_equal(nrow(point_pred), 1)
   expect_equal(point_pred$time_value, as.Date(testing_as_of))
 
-  expect_equal(names(fit5$pre$mold$outcomes),
-               glue::glue("ahead_{ahead}_death_rate"))
+  expect_equal(
+    names(fit5$pre$mold$outcomes),
+    glue::glue("ahead_{ahead}_death_rate")
+  )
   latest <- get_test_data(r5, x)
   pred <- predict(fit5, latest)
   actual_solutions <- pred %>% filter(!is.na(.pred))
@@ -68,8 +70,10 @@ test_that("epi_adjust_latency correctly extends the lags", {
     step_epi_lag(case_rate, lag = c(6, 10)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
   fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
-  expect_equal(fit5$fit$fit$fit$coefficients,
-               fit_hand_adj$fit$fit$fit$coefficients)
+  expect_equal(
+    fit5$fit$fit$fit$coefficients,
+    fit_hand_adj$fit$fit$fit$coefficients
+  )
 })
 
 test_that("epi_adjust_latency correctly extends the ahead", {
@@ -97,8 +101,10 @@ test_that("epi_adjust_latency correctly extends the ahead", {
   # max time is still the forecast date
   expect_equal(point_pred$time_value, as.Date(max_time))
   # target column renamed
-  expect_equal(names(fit5$pre$mold$outcomes),
-               glue::glue("ahead_{ahead + latency}_death_rate"))
+  expect_equal(
+    names(fit5$pre$mold$outcomes),
+    glue::glue("ahead_{ahead + latency}_death_rate")
+  )
   # fit an equivalent forecaster
   equivalent <- epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
@@ -106,8 +112,10 @@ test_that("epi_adjust_latency correctly extends the ahead", {
     step_epi_ahead(death_rate, ahead = ahead + latency)
   equiv_fit <- slm_fit(equivalent, data = real_x)
   # adjusting the ahead should do the same thing as directly adjusting the ahead
-  expect_equal(fit5$fit$fit$fit$coefficients,
-               equiv_fit$fit$fit$fit$coefficients)
+  expect_equal(
+    fit5$fit$fit$fit$coefficients,
+    equiv_fit$fit$fit$fit$coefficients
+  )
 
   # should have four predictors, including the intercept
   expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 524d98b4d..a9692b1e9 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -10,9 +10,10 @@ old_data <- tibble(
   as_epi_df(as_of = as_of)
 old_data %>% tail()
 keys <- c("time_value", "geo_value")
-old_data %<>% full_join(epi_shift_single(
-  old_data, "tmp_death_rate", 1, "death_rate", keys
-), by = keys) %>%
+old_data <- shift_cols %>%
+  full_join(epi_shift_single(
+    old_data, "tmp_death_rate", 1, "death_rate", keys
+  ), by = keys) %>%
   select(-tmp_death_rate)
 # old data is created so that death rate has a latency of 4, while case_rate has
 # a latency of 5

From 5c1e15ef8d6d68c2feb52c8747703194c4d1822d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 6 May 2024 16:34:01 -0500
Subject: [PATCH 034/387] smaller suggestions: local tests passing again

---
 R/step_adjust_latency.R                   |  5 +++--
 R/step_epi_shift.R                        | 16 +++-------------
 R/utils-latency.R                         |  4 ++--
 man/step_epi_shift.Rd                     |  9 ---------
 tests/testthat/test-step_adjust_latency.R |  3 ++-
 tests/testthat/test-utils_latency.R       |  3 +--
 6 files changed, 11 insertions(+), 29 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index d43950b44..d104b5aeb 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -194,6 +194,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     # check that the shift amount isn't too extreme
     latency <- max(shift_cols$latency)
     time_type <- attributes(training)$metadata$time_type
+    i_latency <- which.max(shift_cols$latency)
     if (
       (grepl("day", time_type) && (latency >= 10)) ||
         (grepl("week", time_type) && (latency >= 4)) ||
@@ -207,8 +208,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
           "which is questionable for it's `time_type` of ",
           "{time_type}"
         ),
-        "i" = "input ahead: {shift_cols$shifts[[i_latency]]}",
-        "i" = "shifted ahead: {shift_cols$effective_shift[[i_latency]]}",
+        "i" = "input shift: {shift_cols$shifts[[i_latency]]}",
+        "i" = "latency adjusted shift: {shift_cols$effective_shift[[i_latency]]}",
         "i" = "max_time = {max_time} -> as_of = {as_of}"
       ))
     }
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 7cdf0680d..bbb0fc93d 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -69,9 +69,8 @@ step_epi_lag <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
-    latency_adjustment <- rlang::arg_match(latency_adjustment)
     arg_is_nonneg_int(lag)
-    arg_is_chr_scalar(prefix, id, latency_adjustment)
+    arg_is_chr_scalar(prefix, id)
     if (!is.null(columns)) {
       cli::cli_abort(c(
         "The `columns` argument must be `NULL`.",
@@ -88,7 +87,7 @@ step_epi_lag <-
         prefix = prefix,
         default = default,
         keys = key_colnames(recipe),
-        columns = NULL,
+        columns = columns,
         latency_adjustment = latency_adjustment,
         skip = skip,
         id = id
@@ -108,12 +107,6 @@ step_epi_ahead <-
            role = "outcome",
            prefix = "ahead_",
            default = NA,
-           latency_adjustment = c(
-             "None",
-             "extend_ahead",
-             "locf",
-             "extend_lags"
-           ),
            columns = NULL,
            skip = FALSE,
            id = rand_id("epi_ahead")) {
@@ -127,7 +120,6 @@ step_epi_ahead <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
-    latency_adjustment <- rlang::arg_match(latency_adjustment)
     arg_is_nonneg_int(ahead)
     arg_is_chr_scalar(prefix, id, latency_adjustment)
     recipes::add_step(
@@ -161,7 +153,6 @@ step_epi_lag_new <-
       prefix = prefix,
       default = default,
       keys = keys,
-      latency_adjustment = latency_adjustment,
       columns = columns,
       skip = skip,
       id = id
@@ -169,7 +160,7 @@ step_epi_lag_new <-
   }
 
 step_epi_ahead_new <-
-  function(terms, role, trained, ahead, prefix, default, keys, latency_adjustment,
+  function(terms, role, trained, ahead, prefix, default, keys,
            columns, skip, id) {
     recipes::step(
       subclass = "epi_ahead",
@@ -179,7 +170,6 @@ step_epi_ahead_new <-
       ahead = ahead,
       prefix = prefix,
       default = default,
-      latency_adjustment = latency_adjustment,
       keys = keys,
       columns = columns,
       skip = skip,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 54a2ddb08..837397ddb 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -101,7 +101,7 @@ get_shifted_column_tibble <- function(
     mutate(
       new_name = adjust_name(prefix, original_name, effective_shift)
     )
-  info <- shift_cols %>% select(variable, type, role)
+  info <- info %>% select(variable, type, role)
   shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))
   if (length(unique(shift_cols$role)) != 1) {
     cli::cli_abort("not all roles are the same!",
@@ -138,7 +138,7 @@ set_asof <- function(new_data, info) {
   as_of <- attributes(new_data)$metadata$as_of
   max_time <- max(time_values)
   # make sure the as_of is sane
-  if (!inherits(as_of, class(time_values))) {
+  if (!inherits(as_of, class(time_values)) & !inherits(as_of, "POSIXt")) {
     cli::cli_abort(glue::glue(
       "the data matrix `as_of` value is {as_of}, ",
       "and not a valid `time_type` with type ",
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 4d7cc0a0e..f0f7f2a2f 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -13,7 +13,6 @@ step_epi_lag(
   prefix = "lag_",
   default = NA,
   skip = FALSE,
-  latency_adjustment = c("None", "extend_ahead", "locf", "extend_lags"),
   id = rand_id("epi_lag")
 )
 
@@ -24,7 +23,6 @@ step_epi_ahead(
   role = "outcome",
   prefix = "ahead_",
   default = NA,
-  latency_adjustment = c("None", "extend_ahead", "locf", "extend_lags"),
   columns = NULL,
   skip = FALSE,
   id = rand_id("epi_ahead")
@@ -56,13 +54,6 @@ conducted on new data (e.g. processing the outcome variable(s)).
 Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
-\item{latency_adjustment}{a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
-\itemize{
-\item \code{"extend_ahead"}: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
-\item \code{"locf"}: carries forward the last observed value up to the forecast date.
-\item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that the shortest lag at predict time is
-}}
-
 \item{id}{A unique identifier for the step}
 }
 \value{
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 28608de6d..08673a34a 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -34,10 +34,11 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5)))
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1022"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = real_x)
+
   expect_equal(
     names(fit5$pre$mold$predictors),
     c(
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index a9692b1e9..f364f4c68 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -8,9 +8,8 @@ old_data <- tibble(
   tmp_death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
 ) %>%
   as_epi_df(as_of = as_of)
-old_data %>% tail()
 keys <- c("time_value", "geo_value")
-old_data <- shift_cols %>%
+old_data <- old_data %>%
   full_join(epi_shift_single(
     old_data, "tmp_death_rate", 1, "death_rate", keys
   ), by = keys) %>%

From c48e81a851777cfe2b4bbc16aa5f6c040406af36 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 16:27:48 -0500
Subject: [PATCH 035/387] moving shift detection earlier,dropping string*dep

---
 DESCRIPTION                                   |  2 -
 NAMESPACE                                     |  8 +-
 R/step_adjust_latency.R                       | 71 +++++++-------
 R/utils-latency.R                             | 95 +++++++++----------
 man/adjust_name.Rd                            | 14 ---
 man/construct_shift_tibble.Rd                 | 12 +++
 ..._tibble.Rd => get_latent_column_tibble.Rd} | 15 ++-
 man/step_adjust_latency.Rd                    |  9 +-
 tests/testthat/test-step_adjust_latency.R     |  2 +-
 tests/testthat/test-utils_latency.R           | 94 +++++++++---------
 10 files changed, 152 insertions(+), 170 deletions(-)
 delete mode 100644 man/adjust_name.Rd
 create mode 100644 man/construct_shift_tibble.Rd
 rename man/{get_shifted_column_tibble.Rd => get_latent_column_tibble.Rd} (79%)

diff --git a/DESCRIPTION b/DESCRIPTION
index e3aa871df..f7886f314 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -41,8 +41,6 @@ Imports:
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
     stats,
-    stringr,
-    stringi,
     tibble,
     tidyr,
     tidyselect,
diff --git a/NAMESPACE b/NAMESPACE
index 96dd0e67f..037b64ed3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -248,6 +248,7 @@ importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
+importFrom(dplyr,tibble)
 importFrom(dplyr,ungroup)
 importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
@@ -261,10 +262,11 @@ importFrom(ggplot2,geom_line)
 importFrom(ggplot2,geom_linerange)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
+importFrom(glue,glue)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
-importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")
+importFrom(purrr,map_lgl)
 importFrom(recipes,bake)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
@@ -273,7 +275,6 @@ importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
 importFrom(rlang,"%||%")
 importFrom(rlang,":=")
-importFrom(rlang,abort)
 importFrom(rlang,arg_match)
 importFrom(rlang,as_function)
 importFrom(rlang,caller_arg)
@@ -298,11 +299,10 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
-importFrom(stringi,stri_replace_all_regex)
-importFrom(stringr,str_match)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
+importFrom(tidyr,unnest)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index d104b5aeb..844db4871 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -90,9 +90,9 @@ step_adjust_latency <-
            fixed_asof = NULL,
            default = NA,
            skip = FALSE,
-           prefix = NULL,
            columns = NULL,
            id = recipes::rand_id("epi_lag")) {
+    arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
       cli::cli_abort("This recipe step can only operate on an `epi_recipe`.")
     }
@@ -103,24 +103,28 @@ step_adjust_latency <-
     }
 
     method <- rlang::arg_match(method)
+    terms_used <- recipes_eval_select(enquos(...), recipe$template, recipe$term_info)
+    if (length(terms_used) == 0) {
+      terms_used <- recipe$term_info %>%
+        filter(role == "raw") %>%
+        pull(variable)
+    }
     if (method == "extend_ahead") {
-      prefix <- "ahead_"
-      if (!any(map_lgl(
-        recipe$steps,
-        function(recipe_step) inherits(recipe_step, "step_epi_ahead")
-      ))) {
-        cli:cli_abort("There is no `step_epi_ahead` defined before this. For the method `extend_ahead` of `step_adjust_latency`, at least one ahead must be previously defined.")
-      }
+      rel_step_type <- "step_epi_ahead"
+      shift_name <- "ahead"
     } else if (method == "extend_lags") {
-      prefix <- "lag_"
-      if (!any(map_lgl(
-        recipe$steps,
-        function(recipe_step) inherits(recipe_step, "step_epi_lag")
-      ))) {
-        cli:cli_abort("There is no `step_epi_lag` defined before this. For the method `extend_lags` of `step_adjust_latency`, at least one lag must be previously defined.")
-      }
+      rel_step_type <- "step_epi_lag"
+      shift_name <- "lag"
+    }
+    relevant_shifts <- construct_shift_tibble(terms_used, recipe, rel_step_type, shift_name)
+
+    if (!any(map_lgl(
+      recipe$steps,
+      function(recipe_step) inherits(recipe_step, rel_step_type)
+    ))) {
+      cli:cli_abort("there is no `{rel_step_type}` defined before this. for the method `extend_{shift_name}` of `step_adjust_latency`, at least one {shift_name} must be previously defined.")
     }
-    arg_is_chr_scalar(prefix, id, method)
+
     recipes::add_step(
       recipe,
       step_adjust_latency_new(
@@ -130,8 +134,7 @@ step_adjust_latency <-
         trained = trained,
         as_of = fixed_asof,
         latency = fixed_latency,
-        shift_cols = NULL,
-        prefix = prefix,
+        shift_cols = relevant_shifts,
         default = default,
         keys = epi_keys(recipe),
         skip = skip,
@@ -141,7 +144,7 @@ step_adjust_latency <-
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, as_of, latency, shift_cols, prefix, time_type, default,
+  function(terms, role, trained, as_of, latency, shift_cols, time_type, default,
            keys, method, skip, id) {
     step(
       subclass = "adjust_latency",
@@ -152,7 +155,6 @@ step_adjust_latency_new <-
       as_of = as_of,
       latency = latency,
       shift_cols = shift_cols,
-      prefix = prefix,
       default = default,
       keys = keys,
       skip = skip,
@@ -163,6 +165,7 @@ step_adjust_latency_new <-
 # lags introduces max(lags) NA's after the max_time_value.
 # TODO all of the shifting happens before NA removal, which saves all the data I might possibly want; I should probably add a bit that makes sure this operation is happening before NA removal so data doesn't get dropped
 #' @export
+#' @importFrom glue glue
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
     cli::cli_abort('If `method` is `"extend_ahead"`, then a step ",
@@ -172,7 +175,6 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 "must have already added a predictor.')
   }
 
-  sign_shift <- get_sign(x)
   # get the columns used, even if it's all of them
   terms_used <- x$columns
   if (length(terms_used) == 0) {
@@ -185,16 +187,18 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 
   # infer the correct columns to be working with from the previous
   # transformations
-  shift_cols <- get_shifted_column_tibble(
-    x$prefix, training, terms_used,
-    as_of, x$latency, sign_shift, info
+  x$prefix <- x$shift_cols$prefix[[1]]
+  sign_shift <- get_sign(x)
+  latency_cols <- get_latent_column_tibble(
+    x$shift_cols, training, as_of,
+    x$latency, sign_shift, info
   )
 
   if ((x$method == "extend_ahead") || (x$method == "extend_lags")) {
     # check that the shift amount isn't too extreme
-    latency <- max(shift_cols$latency)
+    latency <- max(latency_cols$latency)
     time_type <- attributes(training)$metadata$time_type
-    i_latency <- which.max(shift_cols$latency)
+    i_latency <- which.max(latency_cols$latency)
     if (
       (grepl("day", time_type) && (latency >= 10)) ||
         (grepl("week", time_type) && (latency >= 4)) ||
@@ -203,26 +207,25 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         ((time_type == "year") && (latency >= 1))
     ) {
       cli::cli_warn(c(
-        "!" = glue::glue(
+        "!" = glue(
           "The shift has been adjusted by {latency}, ",
           "which is questionable for it's `time_type` of ",
           "{time_type}"
         ),
-        "i" = "input shift: {shift_cols$shifts[[i_latency]]}",
-        "i" = "latency adjusted shift: {shift_cols$effective_shift[[i_latency]]}",
+        "i" = "input shift: {latency_cols$shift[[i_latency]]}",
+        "i" = "latency adjusted shift: {latency_cols$effective_shift[[i_latency]]}",
         "i" = "max_time = {max_time} -> as_of = {as_of}"
       ))
     }
   }
 
   step_adjust_latency_new(
-    terms = shift_cols$original_name,
-    role = shift_cols$role[[1]],
+    terms = latency_cols$original_name,
+    role = latency_cols$role[[1]],
     trained = TRUE,
-    prefix = x$prefix,
-    shift_cols = shift_cols,
+    shift_cols = latency_cols,
     as_of = as_of,
-    latency = unique(shift_cols$latency),
+    latency = unique(latency_cols$latency),
     default = x$default,
     keys = x$keys,
     method = x$method,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 837397ddb..9a49a1b57 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -10,8 +10,8 @@
 extend_either <- function(new_data, shift_cols, keys) {
   shifted <-
     shift_cols %>%
-    select(-any_of(c("shifts", "effective_shift", "type", "role", "source"))) %>%
-    pmap(\(original_name, latency, new_name) {
+    select(original_name, latency, new_name) %>%
+    pmap(function(original_name, latency, new_name) {
       epi_shift_single(
         x = new_data,
         col = original_name,
@@ -20,7 +20,7 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
-    map(\(x) zoo::na.trim(x)) %>%
+    map(function(x) zoo::na.trim(x)) %>%
     reduce(
       dplyr::full_join,
       by = keys
@@ -34,6 +34,36 @@ extend_either <- function(new_data, shift_cols, keys) {
     dplyr::ungroup())
 }
 
+#' create a table of the columns to modify, their shifts, and their prefixes
+#' @keywords internal
+#' @importFrom dplyr tibble
+#' @importFrom tidyr unnest
+construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name) {
+  # for the right step types (either "step_epi_lag" or "step_epi_shift"), grab
+  # the useful parameters, including the evaluated column names
+  extract_named_rates <- function(recipe_step) {
+    if (inherits(recipe_step, rel_step_type)) {
+      recipe_columns <- recipes_eval_select(recipe_step$terms, recipe$template, recipe$term_info)
+      if (any(recipe_columns %in% terms_used)) {
+        return(list(term = recipe_columns, shift = recipe_step[shift_name], prefix = recipe_step$prefix))
+      }
+    }
+    return(NULL)
+  }
+  rel_list <- recipe$steps %>%
+    purrr::map(extract_named_rates) %>%
+    unlist(recursive = FALSE) %>%
+    split(c("term", "shift", "prefix"))
+  relevant_shifts <- tibble(
+    terms = lapply(rel_list$term, unname),
+    shift = lapply(rel_list$shift, unname),
+    prefix = unname(unlist(rel_list$prefix))
+  ) %>%
+    unnest(c(terms, shift)) %>%
+    unnest(shift)
+  return(relevant_shifts)
+}
+
 #' find the columns added with the lags or aheads, and the amounts they have
 #' been changed
 #' @param prefix the prefix indicating if we are adjusting lags or aheads
@@ -41,53 +71,29 @@ extend_either <- function(new_data, shift_cols, keys) {
 #' @return a tibble with columns `column` (relevant shifted names), `shift` (the
 #'   amount that one is shifted), `latency` (original columns difference between
 #'   max_time_value and as_of (on a per-initial column basis)),
-#'   `effective_shift` (shifts+latency), and `new_name` (adjusted names with the
+#'   `effective_shift` (shift+latency), and `new_name` (adjusted names with the
 #'   effective_shift)
 #' @keywords internal
-#' @importFrom stringr str_match
 #' @importFrom dplyr rowwise %>%
-#' @importFrom magrittr %<>%
-get_shifted_column_tibble <- function(
-    prefix, new_data, terms_used, as_of, latency,
+#' @importFrom purrr map_lgl
+#' @importFrom glue glue
+get_latent_column_tibble <- function(
+    shift_cols, new_data, as_of, latency,
     sign_shift, info, call = caller_env()) {
-  relevant_columns <- names(new_data)[grepl(prefix, names(new_data))]
-  to_keep <- rep(FALSE, length(relevant_columns))
-  for (col_name in terms_used) {
-    to_keep <- to_keep | grepl(col_name, relevant_columns)
-  }
-  relevant_columns <- relevant_columns[to_keep]
-  if (length(relevant_columns) == 0) {
-    cli::cli_abort("There is no column(s) {terms_used}.",
-      current_column_names = names(new_data),
-      class = "epipredict_adjust_latency_nonexistent_column_used",
-      call = call
-    )
-  }
-  # this pulls text that is any number of digits between two _, e.g. _3557_, and
-  # converts them to an integer
-  shift_amounts <- stringr::str_match(relevant_columns, "_(\\d+)_") %>%
-    `[`(, 2) %>%
-    as.integer()
-
-  shift_cols <- dplyr::tibble(
-    original_name = relevant_columns,
-    shifts = shift_amounts
-  )
+  shift_cols <- shift_cols %>% mutate(original_name = glue("{prefix}{shift}_{terms}"))
   if (is.null(latency)) {
     shift_cols <- shift_cols %>%
       rowwise() %>%
       # add the latencies to shift_cols
       mutate(latency = get_latency(
-        new_data, as_of, original_name, shifts, sign_shift
+        new_data, as_of, original_name, shift, sign_shift
       )) %>%
       ungroup()
   } else if (length(latency) > 1) {
+    # if latency has a length, we assign based on comparing the name in the list with the `terms` column
     shift_cols <- shift_cols %>%
       rowwise() %>%
-      mutate(latency = unname(latency[purrr::map_lgl(
-        names(latency),
-        \(x) grepl(x, original_name)
-      )])) %>%
+      mutate(latency = unname(latency[names(latency) == terms])) %>%
       ungroup()
   } else {
     shift_cols <- shift_cols %>% mutate(latency = latency)
@@ -96,10 +102,10 @@ get_shifted_column_tibble <- function(
   # add the updated names to shift_cols
   shift_cols <- shift_cols %>%
     mutate(
-      effective_shift = shifts + abs(latency)
+      effective_shift = shift + abs(latency)
     ) %>%
     mutate(
-      new_name = adjust_name(prefix, original_name, effective_shift)
+      new_name = glue("{prefix}{effective_shift}_{terms}")
     )
   info <- info %>% select(variable, type, role)
   shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))
@@ -166,19 +172,6 @@ set_asof <- function(new_data, info) {
   return(as_of)
 }
 
-#' adjust the shifts by latency for the names in column assumes e.g.
-#' `"lag_6_case_rate"` and returns something like `"lag_10_case_rate"`
-#' @keywords internal
-#' @importFrom stringi stri_replace_all_regex
-adjust_name <- function(prefix, column, effective_shift) {
-  pattern <- paste0(prefix, "\\d+", "_")
-  adjusted_shifts <- paste0(prefix, effective_shift, "_")
-  stringi::stri_replace_all_regex(
-    column,
-    pattern, adjusted_shifts
-  )
-}
-
 #' the latency is also the amount the shift is off by
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
diff --git a/man/adjust_name.Rd b/man/adjust_name.Rd
deleted file mode 100644
index 8cb6e5106..000000000
--- a/man/adjust_name.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-latency.R
-\name{adjust_name}
-\alias{adjust_name}
-\title{adjust the shifts by latency for the names in column assumes e.g.
-\code{"lag_6_case_rate"} and returns something like \code{"lag_10_case_rate"}}
-\usage{
-adjust_name(prefix, column, effective_shift)
-}
-\description{
-adjust the shifts by latency for the names in column assumes e.g.
-\code{"lag_6_case_rate"} and returns something like \code{"lag_10_case_rate"}
-}
-\keyword{internal}
diff --git a/man/construct_shift_tibble.Rd b/man/construct_shift_tibble.Rd
new file mode 100644
index 000000000..619583f1d
--- /dev/null
+++ b/man/construct_shift_tibble.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{construct_shift_tibble}
+\alias{construct_shift_tibble}
+\title{create a table of the columns to modify, their shifts, and their prefixes}
+\usage{
+construct_shift_tibble(terms_used, recipe, rel_step_type, shift_name)
+}
+\description{
+create a table of the columns to modify, their shifts, and their prefixes
+}
+\keyword{internal}
diff --git a/man/get_shifted_column_tibble.Rd b/man/get_latent_column_tibble.Rd
similarity index 79%
rename from man/get_shifted_column_tibble.Rd
rename to man/get_latent_column_tibble.Rd
index c66941e0e..cbf2c7fe1 100644
--- a/man/get_shifted_column_tibble.Rd
+++ b/man/get_latent_column_tibble.Rd
@@ -1,14 +1,13 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/utils-latency.R
-\name{get_shifted_column_tibble}
-\alias{get_shifted_column_tibble}
+\name{get_latent_column_tibble}
+\alias{get_latent_column_tibble}
 \title{find the columns added with the lags or aheads, and the amounts they have
 been changed}
 \usage{
-get_shifted_column_tibble(
-  prefix,
+get_latent_column_tibble(
+  shift_cols,
   new_data,
-  terms_used,
   as_of,
   latency,
   sign_shift,
@@ -17,15 +16,15 @@ get_shifted_column_tibble(
 )
 }
 \arguments{
-\item{prefix}{the prefix indicating if we are adjusting lags or aheads}
-
 \item{new_data}{the data transformed so far}
+
+\item{prefix}{the prefix indicating if we are adjusting lags or aheads}
 }
 \value{
 a tibble with columns \code{column} (relevant shifted names), \code{shift} (the
 amount that one is shifted), \code{latency} (original columns difference between
 max_time_value and as_of (on a per-initial column basis)),
-\code{effective_shift} (shifts+latency), and \code{new_name} (adjusted names with the
+\code{effective_shift} (shift+latency), and \code{new_name} (adjusted names with the
 effective_shift)
 }
 \description{
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 9e8efc377..4df945204 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -14,7 +14,6 @@ step_adjust_latency(
   fixed_asof = NULL,
   default = NA,
   skip = FALSE,
-  prefix = NULL,
   columns = NULL,
   id = recipes::rand_id("epi_lag")
 )
@@ -75,14 +74,14 @@ conducted on new data (e.g. processing the outcome variable(s)).
 Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
-\item{prefix}{a character. The prefix matching the one used in either
-\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
-if \code{method="extend_lags"} or "locf".}
-
 \item{columns}{A character string of column names to be adjusted; these
 should be the original columns, and not the derived ones}
 
 \item{id}{A unique identifier for the step}
+
+\item{prefix}{a character. The prefix matching the one used in either
+\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
+if \code{method="extend_lags"} or "locf".}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 08673a34a..ecd4fa888 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -34,7 +34,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1022"), class = "simpleError")
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1024"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = real_x)
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index f364f4c68..b96768b50 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -34,12 +34,24 @@ modified_data <-
 modified_data %>% tail()
 as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
 all_shift_cols <- tibble::tribble(
-  ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-  "lag_3_case_rate", 3, 5, 8, "lag_8_case_rate", "numeric", "predictor",
-  "lag_7_death_rate", 7, 4, 11, "lag_11_death_rate", "numeric", "predictor",
-  "ahead_4_case_rate", 4, -5, 9, "ahead_9_case_rate", "numeric", "outcome"
+  ~terms       , ~shift, ~prefix, ~original_name,      ~latency, ~effective_shift,            ~new_name,     ~type,       ~role,
+  "case_rate"  ,      3,  "lag_", "lag_3_case_rate",          5,                8,    "lag_8_case_rate", "numeric", "predictor",
+  "death_rate" ,      7,  "lag_", "lag_7_death_rate",         4,                11, "lag_11_death_rate", "numeric", "predictor",
+  "case_rate"  ,      4,  "ahead_", "ahead_4_case_rate",       -5,                9,  "ahead_9_case_rate", "numeric", "outcome"
 )
-
+test_recipe <- epi_recipe(modified_data) %>%
+  step_epi_lag(case_rate, lag = c(3)) %>%
+  step_epi_lag(death_rate, lag = 7) %>%
+  step_epi_ahead(case_rate, ahead = 4)
+shift_cols <- construct_shift_tibble(c("case_rate", "death_rate"), test_recipe, "step_epi_lag", "lag")
+test_that("construct_shift_tibble constructs the right tibble", {
+  expected_shift_cols <- tibble::tribble(
+    ~terms, ~shift, ~prefix,
+    "case_rate", 3, "lag_",
+    "death_rate", 7, "lag_"
+  )
+  expect_equal(shift_cols, expected_shift_cols)
+})
 
 test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1), 4)
@@ -54,23 +66,6 @@ test_that("get_latency works", {
 
 test_that("get_latency infers max_time to be the minimum `max time` across the columns", {})
 
-test_that("adjust_name works", {
-  expect_equal(
-    adjust_name("lag_", "lag_5_case_rate_13", 10),
-    "lag_10_case_rate_13"
-  )
-  # it won't change a column with the wrong prefix
-  expect_equal(
-    adjust_name("lag_", "ahead_5_case_rate", 10),
-    "ahead_5_case_rate"
-  )
-  # it works on vectors of names
-  expect_equal(
-    adjust_name("lag_", c("lag_5_floop_35", "lag_2342352_case"), c(10, 7)),
-    c("lag_10_floop_35", "lag_7_case")
-  )
-})
-
 test_that("get_asof works", {
   info <- tribble(
     ~variable, ~type, ~role, ~source,
@@ -83,66 +78,63 @@ test_that("get_asof works", {
   expect_equal(set_asof(modified_data, info), as_of)
 })
 
-test_that("get_shifted_column_tibble infers latency and works correctly", {
+test_that("get_latent_column_tibble infers latency and works correctly", {
   info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
-  case_lag <- get_shifted_column_tibble(
-    "lag_", modified_data, "case_rate",
-    as_of, NULL, 1, info
+
+  case_lag <- get_latent_column_tibble(
+    shift_cols[1,], modified_data, as_of, NULL, 1, info
   )
   expect_equal(case_lag, all_shift_cols[1, ])
 
-  death_lag <- get_shifted_column_tibble(
-    "lag_", modified_data,
-    "death_rate", as_of, NULL, 1, info
+  death_lag <- get_latent_column_tibble(
+    shift_cols[2,], modified_data, as_of, NULL, 1, info
   )
   expect_equal(death_lag, all_shift_cols[2, ])
 
-  both_lag <- get_shifted_column_tibble(
-    "lag_", modified_data, c("case_rate", "death_rate"),
-    as_of, NULL, 1, info
+  both_lag <- get_latent_column_tibble(
+    shift_cols, modified_data, as_of, NULL, 1, info
   )
   expect_equal(both_lag, all_shift_cols[1:2, ])
 })
 
-test_that("get_shifted_column_tibble assigns given latencies", {
+test_that("get_latent_column_tibble assigns given latencies", {
   # non-null latency
   info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
-  both_lag <- get_shifted_column_tibble(
-    "lag_", modified_data,
-    c("case_rate", "death_rate"), as_of, 50, 1, info
+  both_lag <- get_latent_column_tibble(
+    shift_cols, modified_data, as_of, 50, 1, info
   )
   weird_latencies <- tibble::tribble(
-    ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "lag_3_case_rate", 3, 50, 53, "lag_53_case_rate", "numeric", "predictor",
-    "lag_7_death_rate", 7, 50, 57, "lag_57_death_rate", "numeric", "predictor",
+          ~terms,    ~shift, ~prefix,     ~original_name, ~latency, ~effective_shift,           ~new_name,     ~type,       ~role,
+     "case_rate",         3, "lag_",  "lag_3_case_rate",       50,               53,  "lag_53_case_rate", "numeric", "predictor",
+    "death_rate",         7, "lag_", "lag_7_death_rate",       50,               57, "lag_57_death_rate", "numeric", "predictor",
   )
   expect_equal(both_lag, weird_latencies)
 
   # supposing we add the latencies by hand, and they're different, and in a different order
   weird_latencies <- tibble::tribble(
-    ~original_name, ~shifts, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "lag_3_case_rate", 3, 70, 73, "lag_73_case_rate", "numeric", "predictor",
-    "lag_7_death_rate", 7, 30, 37, "lag_37_death_rate", "numeric", "predictor",
+          ~terms,    ~shift, ~prefix,     ~original_name, ~latency, ~effective_shift,           ~new_name,     ~type,       ~role,
+     "case_rate",         3, "lag_",  "lag_3_case_rate",       70,               73,  "lag_73_case_rate", "numeric", "predictor",
+    "death_rate",         7, "lag_", "lag_7_death_rate",       30,               37, "lag_37_death_rate", "numeric", "predictor",
   )
-  both_lag <- get_shifted_column_tibble(
-    "lag_", modified_data,
-    c("case_rate", "death_rate"), as_of, c(death_rate = 30, case_rate = 70), 1, info
+  both_lag <- get_latent_column_tibble(
+    shift_cols, modified_data, as_of, c(death_rate = 30, case_rate = 70), 1, info
   )
   expect_equal(both_lag, weird_latencies[1:2, ])
 
-  case_ahead <- get_shifted_column_tibble(
-    "ahead_", modified_data,
-    "case_rate", as_of, NULL, -1, info
+  ahead_shift_cols <- construct_shift_tibble(c("case_rate"), test_recipe, "step_epi_ahead", "ahead")
+  case_ahead <- get_latent_column_tibble(
+    ahead_shift_cols, modified_data, as_of, NULL, -1, info
   )
   expect_equal(case_ahead, all_shift_cols[3, ])
 })
 
 test_that("get_shifted_column_tibble objects to non-columns", {
+  non_shift_cols <- tibble(terms = "not_present", shift = 99, prefix = "lag_")
   expect_error(
-    get_shifted_column_tibble(
-      "lag_", modified_data, "not_present", as_of, NULL, 1, info
+    get_latent_column_tibble(
+      non_shift_cols, modified_data, as_of, NULL, 1, info
     ),
-    class = "epipredict_adjust_latency_nonexistent_column_used"
+    regexp = "Can't subset elements that don't exist"
   )
 })
 

From 802837447d8a29349d58d734c048f7e2697add55 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 16:33:08 -0500
Subject: [PATCH 036/387] +purrr, styling

---
 DESCRIPTION                         |  2 ++
 tests/testthat/test-utils_latency.R | 24 ++++++++++++------------
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index f7886f314..4f3b5597a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -40,6 +40,8 @@ Imports:
     magrittr,
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
+    purrr,
+    smoothqr,
     stats,
     tibble,
     tidyr,
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index b96768b50..1b8f93b9a 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -34,10 +34,10 @@ modified_data <-
 modified_data %>% tail()
 as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
 all_shift_cols <- tibble::tribble(
-  ~terms       , ~shift, ~prefix, ~original_name,      ~latency, ~effective_shift,            ~new_name,     ~type,       ~role,
-  "case_rate"  ,      3,  "lag_", "lag_3_case_rate",          5,                8,    "lag_8_case_rate", "numeric", "predictor",
-  "death_rate" ,      7,  "lag_", "lag_7_death_rate",         4,                11, "lag_11_death_rate", "numeric", "predictor",
-  "case_rate"  ,      4,  "ahead_", "ahead_4_case_rate",       -5,                9,  "ahead_9_case_rate", "numeric", "outcome"
+  ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+  "case_rate", 3, "lag_", "lag_3_case_rate", 5, 8, "lag_8_case_rate", "numeric", "predictor",
+  "death_rate", 7, "lag_", "lag_7_death_rate", 4, 11, "lag_11_death_rate", "numeric", "predictor",
+  "case_rate", 4, "ahead_", "ahead_4_case_rate", -5, 9, "ahead_9_case_rate", "numeric", "outcome"
 )
 test_recipe <- epi_recipe(modified_data) %>%
   step_epi_lag(case_rate, lag = c(3)) %>%
@@ -82,12 +82,12 @@ test_that("get_latent_column_tibble infers latency and works correctly", {
   info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
 
   case_lag <- get_latent_column_tibble(
-    shift_cols[1,], modified_data, as_of, NULL, 1, info
+    shift_cols[1, ], modified_data, as_of, NULL, 1, info
   )
   expect_equal(case_lag, all_shift_cols[1, ])
 
   death_lag <- get_latent_column_tibble(
-    shift_cols[2,], modified_data, as_of, NULL, 1, info
+    shift_cols[2, ], modified_data, as_of, NULL, 1, info
   )
   expect_equal(death_lag, all_shift_cols[2, ])
 
@@ -104,17 +104,17 @@ test_that("get_latent_column_tibble assigns given latencies", {
     shift_cols, modified_data, as_of, 50, 1, info
   )
   weird_latencies <- tibble::tribble(
-          ~terms,    ~shift, ~prefix,     ~original_name, ~latency, ~effective_shift,           ~new_name,     ~type,       ~role,
-     "case_rate",         3, "lag_",  "lag_3_case_rate",       50,               53,  "lag_53_case_rate", "numeric", "predictor",
-    "death_rate",         7, "lag_", "lag_7_death_rate",       50,               57, "lag_57_death_rate", "numeric", "predictor",
+    ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "case_rate", 3, "lag_", "lag_3_case_rate", 50, 53, "lag_53_case_rate", "numeric", "predictor",
+    "death_rate", 7, "lag_", "lag_7_death_rate", 50, 57, "lag_57_death_rate", "numeric", "predictor",
   )
   expect_equal(both_lag, weird_latencies)
 
   # supposing we add the latencies by hand, and they're different, and in a different order
   weird_latencies <- tibble::tribble(
-          ~terms,    ~shift, ~prefix,     ~original_name, ~latency, ~effective_shift,           ~new_name,     ~type,       ~role,
-     "case_rate",         3, "lag_",  "lag_3_case_rate",       70,               73,  "lag_73_case_rate", "numeric", "predictor",
-    "death_rate",         7, "lag_", "lag_7_death_rate",       30,               37, "lag_37_death_rate", "numeric", "predictor",
+    ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "case_rate", 3, "lag_", "lag_3_case_rate", 70, 73, "lag_73_case_rate", "numeric", "predictor",
+    "death_rate", 7, "lag_", "lag_7_death_rate", 30, 37, "lag_37_death_rate", "numeric", "predictor",
   )
   both_lag <- get_latent_column_tibble(
     shift_cols, modified_data, as_of, c(death_rate = 30, case_rate = 70), 1, info

From 4a0ed4815bd3ef17fcc9bc31249104a940431584 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 16:41:07 -0500
Subject: [PATCH 037/387] glue -> glue::glue

---
 R/step_adjust_latency.R | 3 +--
 R/utils-latency.R       | 5 ++---
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 844db4871..6467eb067 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -165,7 +165,6 @@ step_adjust_latency_new <-
 # lags introduces max(lags) NA's after the max_time_value.
 # TODO all of the shifting happens before NA removal, which saves all the data I might possibly want; I should probably add a bit that makes sure this operation is happening before NA removal so data doesn't get dropped
 #' @export
-#' @importFrom glue glue
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
     cli::cli_abort('If `method` is `"extend_ahead"`, then a step ",
@@ -207,7 +206,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         ((time_type == "year") && (latency >= 1))
     ) {
       cli::cli_warn(c(
-        "!" = glue(
+        "!" = glue::glue(
           "The shift has been adjusted by {latency}, ",
           "which is questionable for it's `time_type` of ",
           "{time_type}"
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9a49a1b57..5acf4644f 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -76,11 +76,10 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @keywords internal
 #' @importFrom dplyr rowwise %>%
 #' @importFrom purrr map_lgl
-#' @importFrom glue glue
 get_latent_column_tibble <- function(
     shift_cols, new_data, as_of, latency,
     sign_shift, info, call = caller_env()) {
-  shift_cols <- shift_cols %>% mutate(original_name = glue("{prefix}{shift}_{terms}"))
+  shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"))
   if (is.null(latency)) {
     shift_cols <- shift_cols %>%
       rowwise() %>%
@@ -105,7 +104,7 @@ get_latent_column_tibble <- function(
       effective_shift = shift + abs(latency)
     ) %>%
     mutate(
-      new_name = glue("{prefix}{effective_shift}_{terms}")
+      new_name = glue::glue("{prefix}{effective_shift}_{terms}")
     )
   info <- info %>% select(variable, type, role)
   shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))

From 909e47ce2c25d126a68581936f175390493d386a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 16:51:12 -0500
Subject: [PATCH 038/387] fix get_latent_column_tibble docs

---
 R/utils-latency.R               | 5 ++++-
 man/get_latent_column_tibble.Rd | 6 +++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 5acf4644f..21d81a217 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -66,8 +66,11 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 
 #' find the columns added with the lags or aheads, and the amounts they have
 #' been changed
-#' @param prefix the prefix indicating if we are adjusting lags or aheads
+#' @param shift_cols a list of columns to operate on, as created by `construct_shift_tibble`
 #' @param new_data the data transformed so far
+#' @param as_of
+#' @param latency `NULL`, int, or vector, as described in `step_eip_latency`
+#' @param sign_shift -1 if ahead, 1 if lag
 #' @return a tibble with columns `column` (relevant shifted names), `shift` (the
 #'   amount that one is shifted), `latency` (original columns difference between
 #'   max_time_value and as_of (on a per-initial column basis)),
diff --git a/man/get_latent_column_tibble.Rd b/man/get_latent_column_tibble.Rd
index cbf2c7fe1..9c91182a1 100644
--- a/man/get_latent_column_tibble.Rd
+++ b/man/get_latent_column_tibble.Rd
@@ -16,9 +16,13 @@ get_latent_column_tibble(
 )
 }
 \arguments{
+\item{shift_cols}{a list of columns to operate on, as created by \code{construct_shift_tibble}}
+
 \item{new_data}{the data transformed so far}
 
-\item{prefix}{the prefix indicating if we are adjusting lags or aheads}
+\item{latency}{\code{NULL}, int, or vector, as described in \code{step_eip_latency}}
+
+\item{sign_shift}{-1 if ahead, 1 if lag}
 }
 \value{
 a tibble with columns \code{column} (relevant shifted names), \code{shift} (the

From 8639ebd06edee383cf284305ac7ae343cb3295ba Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 17:13:27 -0500
Subject: [PATCH 039/387] step_adjust_latency arg docs

---
 R/step_adjust_latency.R    | 3 ---
 man/step_adjust_latency.Rd | 4 ----
 2 files changed, 7 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 6467eb067..c1af1f5cb 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -46,9 +46,6 @@
 #'   should be the original columns, and not the derived ones
 #' @param default Determines what fills empty rows
 #'   left by leading/lagging (defaults to NA).
-#' @param prefix a character. The prefix matching the one used in either
-#'   `step_epi_ahead` if `method="extend_ahead"` or `step_epi_lag`
-#'   if `method="extend_lags"` or "locf".
 #' @param skip A logical. Should the step be skipped when the
 #'  recipe is baked by [bake()]? While all operations are baked
 #'  when [prep()] is run, some operations may not be able to be
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 4df945204..e123769f0 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -78,10 +78,6 @@ the computations for subsequent operations.}
 should be the original columns, and not the derived ones}
 
 \item{id}{A unique identifier for the step}
-
-\item{prefix}{a character. The prefix matching the one used in either
-\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
-if \code{method="extend_lags"} or "locf".}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the

From 55314a8cca2fd2276c2f2fac62aeab57a99b4867 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 13 May 2024 12:00:29 -0500
Subject: [PATCH 040/387] rec formatting things, dropping `purrr`

---
 NAMESPACE                       |  1 +
 R/epi_workflow.R                | 10 ++++++----
 R/step_adjust_latency.R         | 20 ++++++++++++++------
 R/step_growth_rate.R            |  2 +-
 R/step_lag_difference.R         |  2 +-
 R/utils-latency.R               |  3 +--
 man/get_latent_column_tibble.Rd |  2 ++
 7 files changed, 26 insertions(+), 14 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 037b64ed3..a93df107a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -267,6 +267,7 @@ importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
 importFrom(purrr,map_lgl)
+importFrom(quantreg,rq)
 importFrom(recipes,bake)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index af4555303..fe9b656ba 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -187,10 +187,12 @@ augment.epi_workflow <- function(x, new_data, ...) {
   if (is_epi_df(predictions)) {
     join_by <- key_colnames(predictions)
   } else {
-    cli_abort(c(
-      "Cannot determine how to join new_data with the predictions.",
-      "Try converting new_data to an epi_df with `as_epi_df(new_data)`."
-    ))
+    cli::cli_abort(
+      c(
+        "Cannot determine how to join `new_data` with the `predictions`.",
+        "Try converting `new_data` to an {.cls epi_df} with `as_epi_df(new_data)`."
+      )
+    )
   }
   complete_overlap <- intersect(names(new_data), join_by)
   if (length(complete_overlap) < length(join_by)) {
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index c1af1f5cb..0ed7d77bd 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -91,7 +91,7 @@ step_adjust_latency <-
            id = recipes::rand_id("epi_lag")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an `epi_recipe`.")
+      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
     if (!is.null(columns)) {
       cli::cli_abort(c("The `columns` argument must be `NULL`.",
@@ -119,7 +119,11 @@ step_adjust_latency <-
       recipe$steps,
       function(recipe_step) inherits(recipe_step, rel_step_type)
     ))) {
-      cli:cli_abort("there is no `{rel_step_type}` defined before this. for the method `extend_{shift_name}` of `step_adjust_latency`, at least one {shift_name} must be previously defined.")
+      cli::cli_abort(glue::glue(
+        "There is no `{rel_step_type}` defined before this.",
+        " For the method `extend_{shift_name}` of `step_adjust_latency`,",
+        " at least one {shift_name} must be previously defined."
+      ))
     }
 
     recipes::add_step(
@@ -164,11 +168,15 @@ step_adjust_latency_new <-
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
-    cli::cli_abort('If `method` is `"extend_ahead"`, then a step ",
-          "must have already added an outcome .')
+    cli::cli_abort(glue::glue(
+      "If `method` is `\"extend_ahead\"`, then a step ",
+      "must have already added an outcome."
+    ))
   } else if (!("predictor" %in% info$role)) {
-    cli::cli_abort('If `method` is `"extend_lags"` or `"locf"`, then a step ",
-"must have already added a predictor.')
+    cli::cli_abort(glue::glue(
+      "If `method` is `\"extend_lags\"` or `\"locf\"`, then a step ",
+      "must have already added a predictor."
+    ))
   }
 
   # get the columns used, even if it's all of them
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 06f8da4cf..70d0ac2ab 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -58,7 +58,7 @@ step_growth_rate <-
     arg_is_pos_int(horizon)
     arg_is_scalar(horizon)
     if (!is.null(replace_Inf)) {
-      if (length(replace_Inf) != 1L) cli_abort("replace_Inf must be a scalar.")
+      if (length(replace_Inf) != 1L) cli::cli_abort("`replace_Inf` must be a scalar.")
       if (!is.na(replace_Inf)) arg_is_numeric(replace_Inf)
     }
     arg_is_chr(role)
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index 39ae1ba59..e8c7c36f7 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -32,7 +32,7 @@ step_lag_difference <-
            skip = FALSE,
            id = rand_id("lag_diff")) {
     if (!is_epi_recipe(recipe)) {
-      cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
+      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
     arg_is_pos_int(horizon)
     arg_is_chr(role)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 21d81a217..03d8c575f 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -68,7 +68,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' been changed
 #' @param shift_cols a list of columns to operate on, as created by `construct_shift_tibble`
 #' @param new_data the data transformed so far
-#' @param as_of
+#' @param as_of the forecast date
 #' @param latency `NULL`, int, or vector, as described in `step_eip_latency`
 #' @param sign_shift -1 if ahead, 1 if lag
 #' @return a tibble with columns `column` (relevant shifted names), `shift` (the
@@ -78,7 +78,6 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #'   effective_shift)
 #' @keywords internal
 #' @importFrom dplyr rowwise %>%
-#' @importFrom purrr map_lgl
 get_latent_column_tibble <- function(
     shift_cols, new_data, as_of, latency,
     sign_shift, info, call = caller_env()) {
diff --git a/man/get_latent_column_tibble.Rd b/man/get_latent_column_tibble.Rd
index 9c91182a1..168145ed8 100644
--- a/man/get_latent_column_tibble.Rd
+++ b/man/get_latent_column_tibble.Rd
@@ -20,6 +20,8 @@ get_latent_column_tibble(
 
 \item{new_data}{the data transformed so far}
 
+\item{as_of}{the forecast date}
+
 \item{latency}{\code{NULL}, int, or vector, as described in \code{step_eip_latency}}
 
 \item{sign_shift}{-1 if ahead, 1 if lag}

From ce230ac021d3589efeffce16d5648846e4c039af Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 14 May 2024 16:59:47 -0500
Subject: [PATCH 041/387] glue->paste, dropping zoo

---
 DESCRIPTION                      |  3 +--
 R/epi_recipe.R                   |  4 ++--
 R/frosting.R                     |  2 +-
 R/layer_cdc_flatline_quantiles.R |  6 +++---
 R/layer_residual_quantiles.R     |  6 +++---
 R/pivot_quantiles.R              |  2 +-
 R/step_adjust_latency.R          | 14 +++++++-------
 R/utils-latency.R                |  8 ++++----
 R/utils-misc.R                   |  2 +-
 R/utils-shift.R                  | 10 +++++-----
 man/extend_either.Rd             |  4 +++-
 11 files changed, 31 insertions(+), 30 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 4f3b5597a..552857a36 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -48,8 +48,7 @@ Imports:
     tidyselect,
     tsibble,
     vctrs,
-    workflows (>= 1.0.0),
-    zoo
+    workflows (>= 1.0.0)
 Suggests:
     covidcast,
     data.table,
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 5c4de0c78..684642075 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -96,7 +96,7 @@ epi_recipe.epi_df <-
     ## Check and add roles when available
     if (!is.null(roles)) {
       if (length(roles) != length(vars)) {
-        cli::cli_abort(c(
+        cli::cli_abort(paste(
           "The number of roles should be the same as the number of ",
           "variables."
         ))
@@ -456,7 +456,7 @@ prep.epi_recipe <- function(
   }
   skippers <- map_lgl(x$steps, recipes:::is_skipable)
   if (any(skippers) & !retain) {
-    cli::cli_warn(c(
+    cli::cli_warn(paste(
       "Since some operations have `skip = TRUE`, using ",
       "`retain = TRUE` will allow those steps results to ",
       "be accessible."
diff --git a/R/frosting.R b/R/frosting.R
index d7ba22902..13d2293a6 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -374,7 +374,7 @@ apply_frosting.epi_workflow <-
     }
 
     if (!has_postprocessor_frosting(workflow)) {
-      cli_warn(c(
+      cli::cli_warn(paste(
         "Only postprocessors of class {.cls frosting} are allowed.",
         "Returning unpostprocessed predictions."
       ))
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 8d16ba32f..daeaa1a3e 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -176,7 +176,7 @@ slather.layer_cdc_flatline_quantiles <-
     if (length(object$by_key) > 0L) {
       cols_in_preds <- hardhat::check_column_names(p, object$by_key)
       if (!cols_in_preds$ok) {
-        cli::cli_warn(c(
+        cli::cli_warn(paste(
           "Predicted values are missing key columns: {.val {cols_in_preds$missing_names}}.",
           "Ignoring these."
         ))
@@ -184,7 +184,7 @@ slather.layer_cdc_flatline_quantiles <-
       if (inherits(the_fit, "_flatline")) {
         cols_in_resids <- hardhat::check_column_names(r, object$by_key)
         if (!cols_in_resids$ok) {
-          cli::cli_warn(c(
+          cli::cli_warn(paste(
             "Existing residuals are missing key columns: {.val {cols_in_resids$missing_names}}.",
             "Ignoring these."
           ))
@@ -201,7 +201,7 @@ slather.layer_cdc_flatline_quantiles <-
         )
         cols_in_resids <- hardhat::check_column_names(key_cols, object$by_key)
         if (!cols_in_resids$ok) {
-          cli::cli_warn(c(
+          cli::cli_warn(paste(
             "Requested residuals are missing key columns: {.val {cols_in_resids$missing_names}}.",
             "Ignoring these."
           ))
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index eae151905..257a951a9 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -102,7 +102,7 @@ slather.layer_residual_quantiles <-
       common <- intersect(object$by_key, names(key_cols))
       excess <- setdiff(object$by_key, names(key_cols))
       if (length(excess) > 0L) {
-        cli::cli_warn(c(
+        cli::cli_warn(paste(
           "Requested residual grouping key(s) {.val {excess}} are unavailable ",
           "in the original data. Grouping by the remainder: {.val {common}}."
         ))
@@ -113,7 +113,7 @@ slather.layer_residual_quantiles <-
         if (length(common_in_r) == length(common)) {
           r <- left_join(key_cols, r, by = common_in_r)
         } else {
-          cli::cli_warn(c(
+          cli::cli_warn(paste(
             "Some grouping keys are not in data.frame returned by the",
             "`residuals()` method. Groupings may not be correct."
           ))
@@ -168,7 +168,7 @@ grab_residuals <- function(the_fit, components) {
     } else if (is.vector(drop(r))) { # also success
       return(tibble(.resid = drop(r)))
     } else { # failure
-      cli::cli_warn(c(
+      cli::cli_warn(paste(
         "The `residuals()` method for objects of class {.cls {cl}} results in an",
         "object that is neither a data frame with a column named `.resid`,",
         "nor something coercible to a vector.",
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index f014961e6..4abf9d257 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -71,7 +71,7 @@ pivot_quantiles_longer <- function(.data, ..., .ignore_length_check = FALSE) {
           .data <- .data %>% tidyr::unnest(all_of(col), names_sep = "_")
         }
       } else {
-        cli::cli_abort(c(
+        cli::cli_abort(paste(
           "Some selected columns contain different numbers of quantiles.",
           "The result would be a {.emph very} long {.cls tibble}.",
           "To do this anyway, rerun with `.ignore_length_check = TRUE`."
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 0ed7d77bd..14b646431 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -119,7 +119,7 @@ step_adjust_latency <-
       recipe$steps,
       function(recipe_step) inherits(recipe_step, rel_step_type)
     ))) {
-      cli::cli_abort(glue::glue(
+      cli::cli_abort(paste(
         "There is no `{rel_step_type}` defined before this.",
         " For the method `extend_{shift_name}` of `step_adjust_latency`,",
         " at least one {shift_name} must be previously defined."
@@ -168,13 +168,13 @@ step_adjust_latency_new <-
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
-    cli::cli_abort(glue::glue(
-      "If `method` is `\"extend_ahead\"`, then a step ",
+    cli::cli_abort(paste(
+      "If `method` is {.val extend_ahead}, then a step ",
       "must have already added an outcome."
     ))
   } else if (!("predictor" %in% info$role)) {
-    cli::cli_abort(glue::glue(
-      "If `method` is `\"extend_lags\"` or `\"locf\"`, then a step ",
+    cli::cli_abort(paste(
+      "If `method` is {.val extend_lags} or {.val locf}, then a step ",
       "must have already added a predictor."
     ))
   }
@@ -210,8 +210,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         ((time_type == "yearquarter") && (latency >= 1)) ||
         ((time_type == "year") && (latency >= 1))
     ) {
-      cli::cli_warn(c(
-        "!" = glue::glue(
+      cli::cli_warn(paste(
+        "!" = paste(
           "The shift has been adjusted by {latency}, ",
           "which is questionable for it's `time_type` of ",
           "{time_type}"
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 03d8c575f..5fa4ce3fc 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -1,5 +1,6 @@
 #' offset each relevant column by it's appropriate latency
 #' works for either adjusting aheads or lags
+#' note that this may introduce new NA values when one column is shifted farther than another
 #' @param shift_cols a tibble which must have the columns `column`, the name of
 #'   the column to adjust, `latency` the latency of the original column relative
 #'   to the `as_of` date, `new_name`, the names in `column` adjusted by the
@@ -20,7 +21,6 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
-    map(function(x) zoo::na.trim(x)) %>%
     reduce(
       dplyr::full_join,
       by = keys
@@ -146,7 +146,7 @@ set_asof <- function(new_data, info) {
   max_time <- max(time_values)
   # make sure the as_of is sane
   if (!inherits(as_of, class(time_values)) & !inherits(as_of, "POSIXt")) {
-    cli::cli_abort(glue::glue(
+    cli::cli_abort(paste(
       "the data matrix `as_of` value is {as_of}, ",
       "and not a valid `time_type` with type ",
       "matching `time_value`'s type of ",
@@ -154,13 +154,13 @@ set_asof <- function(new_data, info) {
     ))
   }
   if (is.null(as_of) || is.na(as_of)) {
-    cli::cli_warn(glue::glue(
+    cli::cli_warn(paste(
       "epi_data's `as_of` was {as_of}, setting to ",
       "the latest time value, {max_time}."
     ))
     as_of <- max_time
   } else if (as_of < max_time) {
-    cli::cli_abort(glue::glue(
+    cli::cli_abort(paste(
       "`as_of` ({(as_of)}) is before the most ",
       "recent data ({max_time}). Remove before ",
       "predicting."
diff --git a/R/utils-misc.R b/R/utils-misc.R
index b4d1c28b7..8fc16a968 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -42,7 +42,7 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   # 3. these are the keys in the test data as input
   new_df_keys <- key_colnames(new_data, extra_keys = setdiff(new_keys, c("geo_value", "time_value")))
   if (!(setequal(old_keys, new_df_keys) && setequal(new_keys, new_df_keys))) {
-    cli::cli_warn(c(
+    cli::cli_warn(paste(
       "Not all epi keys that were present in the training data are available",
       "in `new_data`. Predictions will have only the available keys."
     ))
diff --git a/R/utils-shift.R b/R/utils-shift.R
index 702a52308..75a9a6bd1 100644
--- a/R/utils-shift.R
+++ b/R/utils-shift.R
@@ -12,7 +12,7 @@ adjust_latency <- function(object, new_data) {
   } else if (method == "extend_ahead") {
     as_of <- attributes(new_data)$metadata$as_of
     if (FALSE && (typeof(as_of) != typeof(new_data$time_value))) {
-      cli::cli_abort(glue::glue(
+      cli::cli_abort(paste(
         "the data matrix `as_of` value is {as_of}, ",
         "and not a valid `time_type` with type ",
         "matching `time_value`'s type of ",
@@ -27,13 +27,13 @@ adjust_latency <- function(object, new_data) {
       max_time <- max(time_values)
       shift_amount <- as.Date(as_of) - max_time
       if (is.null(as_of) || is.na(as_of)) {
-        cli::cli_warn(glue::glue(
+        cli::cli_warn(paste(
           "epi_data's `as_of` was {as_of}, setting to ",
           "the latest time value, {max_time}."
         ))
         as_of <- max_time
       } else if (as_of < max_time) {
-        cli::cli_abort(glue::glue(
+        cli::cli_abort(paste(
           "`as_of` ({(as_of)}) is before the most ",
           "recent data ({max_time}). Remove before ",
           "predicting."
@@ -47,7 +47,7 @@ adjust_latency <- function(object, new_data) {
         ((time_type == "yearmonth") && (shift_amount >= 2)) ||
         ((time_type == "yearquarter") && (shift_amount >= 1)) ||
         ((time_type == "year") && (shift_amount >= 1))) {
-        cli::cli_warn(c(
+        cli::cli_warn(paste(
           "!" = glue::glue(
             "The ahead has been adjusted by {shift_amount}, ",
             "which is questionable for it's `time_type` of ",
@@ -63,7 +63,7 @@ adjust_latency <- function(object, new_data) {
       cli::cli_abort("the `time_value` column of `new_data` is empty")
     }
   } else {
-    cli::cli_abort(glue::glue(
+    cli::cli_abort(paste(
       "Latency adjustment method {method} has not yet ",
       "been implemented for `step_epi_ahead`."
     ))
diff --git a/man/extend_either.Rd b/man/extend_either.Rd
index 8ec5ca38e..b7e306944 100644
--- a/man/extend_either.Rd
+++ b/man/extend_either.Rd
@@ -3,7 +3,8 @@
 \name{extend_either}
 \alias{extend_either}
 \title{offset each relevant column by it's appropriate latency
-works for either adjusting aheads or lags}
+works for either adjusting aheads or lags
+note that this may introduce new NA values when one column is shifted farther than another}
 \usage{
 extend_either(new_data, shift_cols, keys)
 }
@@ -20,5 +21,6 @@ latencies \code{latency}}
 \description{
 offset each relevant column by it's appropriate latency
 works for either adjusting aheads or lags
+note that this may introduce new NA values when one column is shifted farther than another
 }
 \keyword{internal}

From c8f6b85170bdf783e9dc603d0c2c80e9135b61e2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 16 May 2024 15:43:13 -0500
Subject: [PATCH 042/387] Detecting required/forbidden steps beforehand

---
 R/step_adjust_latency.R                   | 28 +++++++++++++----------
 tests/testthat/test-step_adjust_latency.R | 23 +++++++++++++++++++
 2 files changed, 39 insertions(+), 12 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 14b646431..4d4c0ad0c 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -73,6 +73,7 @@
 #'   #   step_adjust_latency(method = "extend_ahead") %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
+#' @importFrom recipes detect_step
 step_adjust_latency <-
   function(recipe,
            ...,
@@ -98,6 +99,21 @@ step_adjust_latency <-
         i = "Use `tidyselect` methods to choose columns to lag."
       ))
     }
+    if ((method == "extend_ahead") && (!detect_step(recipe, "epi_ahead"))) {
+      cli::cli_abort(
+        "If `method` is {.val extend_ahead}, then a step
+        must have already added an outcome."
+      )
+    } else if ((method == "extend_lags") && (!detect_step(recipe, "epi_lag"))) {
+      cli::cli_abort(
+        "If `method` is {.val extend_lags} or {.val locf}, then a step
+        must have already added a predictor."
+      )
+    }
+    if (detect_step(recipe, "naomit")) {
+      cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
+                      as columns may be moved around")
+    }
 
     method <- rlang::arg_match(method)
     terms_used <- recipes_eval_select(enquos(...), recipe$template, recipe$term_info)
@@ -164,20 +180,8 @@ step_adjust_latency_new <-
   }
 
 # lags introduces max(lags) NA's after the max_time_value.
-# TODO all of the shifting happens before NA removal, which saves all the data I might possibly want; I should probably add a bit that makes sure this operation is happening before NA removal so data doesn't get dropped
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
-  if ((x$method == "extend_ahead") && (!("outcome" %in% info$role))) {
-    cli::cli_abort(paste(
-      "If `method` is {.val extend_ahead}, then a step ",
-      "must have already added an outcome."
-    ))
-  } else if (!("predictor" %in% info$role)) {
-    cli::cli_abort(paste(
-      "If `method` is {.val extend_lags} or {.val locf}, then a step ",
-      "must have already added a predictor."
-    ))
-  }
 
   # get the columns used, even if it's all of them
   terms_used <- x$columns
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index ecd4fa888..afb6cedd9 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -132,4 +132,27 @@ test_that("epi_adjust_latency extend_ahead uses the same adjustment when predict
 
 test_that("epi_adjust_latency works for other time types", {})
 
+test_that("epi_adjust_latency insist there's steps before it", {
+  expect_error(
+    r5 <- epi_recipe(x) %>%
+      step_epi_ahead(death_rate, ahead = ahead) %>%
+      step_adjust_latency(method = "extend_lags"),
+    regexp = "extend_lags"
+  )
+  expect_error(
+    r5 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+      step_adjust_latency(method = "extend_ahead"),
+    regexp = "extend_ahead"
+  )
+})
+
+test_that("epi_adjust_latency warns against removing NA's beforehand", {
+  expect_error(r5 <- epi_recipe(x) %>%
+                 step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+                 step_epi_lag(case_rate, lag = c(1, 5)) %>%
+                 step_epi_naomit() %>%
+                 step_adjust_latency(method = "extend_lags"),
+    regexp = "adjust_latency needs to occur before any `NA` removal")
+})
 # todo check that epi_adjust_latency errors for nonsense `as_of`'s

From 4927f0e9635f2574322ea73daf5c7594e228843f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 11:41:45 -0500
Subject: [PATCH 043/387] minor rebase woes

---
 NAMESPACE                                 |  1 +
 R/step_adjust_latency.R                   |  1 -
 tests/testthat/test-step_adjust_latency.R | 16 +++++++++-------
 3 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index a93df107a..0f8da385b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -269,6 +269,7 @@ importFrom(magrittr,"%>%")
 importFrom(purrr,map_lgl)
 importFrom(quantreg,rq)
 importFrom(recipes,bake)
+importFrom(recipes,detect_step)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
 importFrom(rlang,"!!!")
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 4d4c0ad0c..227b0ee74 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -182,7 +182,6 @@ step_adjust_latency_new <-
 # lags introduces max(lags) NA's after the max_time_value.
 #' @export
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
-
   # get the columns used, even if it's all of them
   terms_used <- x$columns
   if (length(terms_used) == 0) {
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index afb6cedd9..f52888a68 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -141,18 +141,20 @@ test_that("epi_adjust_latency insist there's steps before it", {
   )
   expect_error(
     r5 <- epi_recipe(x) %>%
-    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
       step_adjust_latency(method = "extend_ahead"),
     regexp = "extend_ahead"
   )
 })
 
 test_that("epi_adjust_latency warns against removing NA's beforehand", {
-  expect_error(r5 <- epi_recipe(x) %>%
-                 step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-                 step_epi_lag(case_rate, lag = c(1, 5)) %>%
-                 step_epi_naomit() %>%
-                 step_adjust_latency(method = "extend_lags"),
-    regexp = "adjust_latency needs to occur before any `NA` removal")
+  expect_error(
+    r5 <- epi_recipe(x) %>%
+      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+      step_epi_lag(case_rate, lag = c(1, 5)) %>%
+      step_epi_naomit() %>%
+      step_adjust_latency(method = "extend_lags"),
+    regexp = "adjust_latency needs to occur before any `NA` removal"
+  )
 })
 # todo check that epi_adjust_latency errors for nonsense `as_of`'s

From 7752b17fd5605aa9421a4082c0bcc2aaba74e375 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 13:03:16 -0700
Subject: [PATCH 044/387] tests for utils-latency and accompanying fixes

---
 NAMESPACE                           |  1 +
 R/utils-latency.R                   |  1 +
 tests/testthat/test-utils_latency.R | 42 ++++++++++++++++++++++++++++-
 3 files changed, 43 insertions(+), 1 deletion(-)

diff --git a/NAMESPACE b/NAMESPACE
index 0f8da385b..1c8585356 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -302,6 +302,7 @@ importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
+importFrom(tibble,is_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
 importFrom(tidyr,unnest)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 5fa4ce3fc..6ab093fda 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -21,6 +21,7 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
+    map(\(x) na.trim(x)) %>% # TODO need to talk about this
     reduce(
       dplyr::full_join,
       by = keys
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 1b8f93b9a..6fd3672aa 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -75,7 +75,7 @@ test_that("get_asof works", {
     "death_rate", "numeric", "raw", "original",
     "not_real", "numeric", "predictor", "derived"
   )
-  expect_equal(set_asof(modified_data, info), as_of)
+  expect_equal(set_forecast_date(modified_data, info), as_of)
 })
 
 test_that("get_latent_column_tibble infers latency and works correctly", {
@@ -156,6 +156,11 @@ test_that("extend_either works", {
       epi_shift_single(old_data, "case_rate", -9, "ahead_9_case_rate", keys),
       by = keys
     ) %>%
+    dplyr::add_row(tibble(
+      geo_value = "place",
+      time_value = as.Date("2021-08-01"), case_rate = NA, death_rate = NA,
+      lag_8_case_rate = NA, lag_11_death_rate = NA, ahead_9_case_rate = NA
+    )) %>%
     arrange(time_value)
   expect_equal(
     extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value),
@@ -163,4 +168,39 @@ test_that("extend_either works", {
   )
 })
 
+
+
+
+
+time_range <- as.Date("2021-01-01") + 0:199
+x_adjust_ahead <- tibble(
+  geo_value = rep("place", 200),
+  time_value = time_range,
+  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
+  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
+) %>%
+  as_epi_df(as_of = max(time_range) + 3)
+# confirm the delay is right
+
+test_that("adjust_latency extend_ahead works", {
+  # testing that POSIXct converts correctly (as well as basic types)
+  expect_equal(
+    attributes(x_adjust_ahead)$metadata$as_of - max(x_adjust_ahead$time_value),
+    as.difftime(3, units = "days")
+  )
+  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
+  expect_no_error(adjusted_ahead <- adjust_latency(object, x_adjust_ahead))
+  expect_type(adjusted_ahead, "integer")
+  expect_equal(adjusted_ahead, 3 + 7)
+})
+
+test_that("extend_ahead warns in case of extreme adjustment", {
+  # warns if the ahead is relatively small
+  attributes(x_adjust_ahead)$metadata$as_of <-
+    max(x_adjust_ahead$time_value) + 100
+  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
+  attributes(x_adjust_ahead)$metadata$time_type
+  testthat::expect_warning(adjust_latency(object, x_adjust_ahead), regexp = "The ahead has been adjusted by 100")
+})
+
 # todo case where somehow columns of different roles are selected

From 8f3641b132f6971dabc62f0a529e1efc2d838a59 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 1 Apr 2024 13:59:18 -0700
Subject: [PATCH 045/387] adding stringr

---
 DESCRIPTION | 1 +
 1 file changed, 1 insertion(+)

diff --git a/DESCRIPTION b/DESCRIPTION
index 552857a36..1c690792a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -43,6 +43,7 @@ Imports:
     purrr,
     smoothqr,
     stats,
+    stringr,
     tibble,
     tidyr,
     tidyselect,

From ba0c4b8f83f63fb14ce6edd3cda3fd3d67989fed Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 3 May 2024 17:00:00 -0500
Subject: [PATCH 046/387] nothing but `rlang::abort` -> `cli::cli_abort`s

---
 R/epi_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 684642075..e4bcaaf91 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -431,7 +431,7 @@ prep.epi_recipe <- function(
     x, training = NULL, fresh = FALSE, verbose = FALSE,
     retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ...) {
   if (is.null(training)) {
-    cli::cli_warn(c(
+    cli::cli_warn(paste(
       "!" = "No training data was supplied to {.fn prep}.",
       "!" = "Unlike a {.cls recipe}, an {.cls epi_recipe} does not ",
       "!" = "store the full template data in the object.",

From 27694ef1a5da681734e6df47ea34410efc8e35f4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 8 May 2024 16:27:48 -0500
Subject: [PATCH 047/387] moving shift detection earlier,dropping string*dep

---
 DESCRIPTION                | 1 -
 R/step_adjust_latency.R    | 7 ++-----
 R/utils-latency.R          | 1 -
 man/step_adjust_latency.Rd | 4 ++++
 4 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1c690792a..552857a36 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -43,7 +43,6 @@ Imports:
     purrr,
     smoothqr,
     stats,
-    stringr,
     tibble,
     tidyr,
     tidyselect,
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 227b0ee74..6d8c3d3d3 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -135,11 +135,7 @@ step_adjust_latency <-
       recipe$steps,
       function(recipe_step) inherits(recipe_step, rel_step_type)
     ))) {
-      cli::cli_abort(paste(
-        "There is no `{rel_step_type}` defined before this.",
-        " For the method `extend_{shift_name}` of `step_adjust_latency`,",
-        " at least one {shift_name} must be previously defined."
-      ))
+      cli:cli_abort("there is no `{rel_step_type}` defined before this. for the method `extend_{shift_name}` of `step_adjust_latency`, at least one {shift_name} must be previously defined.")
     }
 
     recipes::add_step(
@@ -181,6 +177,7 @@ step_adjust_latency_new <-
 
 # lags introduces max(lags) NA's after the max_time_value.
 #' @export
+#' @importFrom glue glue
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   # get the columns used, even if it's all of them
   terms_used <- x$columns
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 6ab093fda..5fa4ce3fc 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -21,7 +21,6 @@ extend_either <- function(new_data, shift_cols, keys) {
         key_cols = keys
       )
     }) %>%
-    map(\(x) na.trim(x)) %>% # TODO need to talk about this
     reduce(
       dplyr::full_join,
       by = keys
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index e123769f0..4df945204 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -78,6 +78,10 @@ the computations for subsequent operations.}
 should be the original columns, and not the derived ones}
 
 \item{id}{A unique identifier for the step}
+
+\item{prefix}{a character. The prefix matching the one used in either
+\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
+if \code{method="extend_lags"} or "locf".}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the

From 3eab9c21bc44b5c2cd1af5f20b65008e5a915a06 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 13 May 2024 12:00:29 -0500
Subject: [PATCH 048/387] rec formatting things, dropping `purrr`

---
 NAMESPACE               | 1 -
 R/step_adjust_latency.R | 6 +++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 1c8585356..2914e52a4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -266,7 +266,6 @@ importFrom(glue,glue)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
-importFrom(purrr,map_lgl)
 importFrom(quantreg,rq)
 importFrom(recipes,bake)
 importFrom(recipes,detect_step)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 6d8c3d3d3..7480ac96d 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -135,7 +135,11 @@ step_adjust_latency <-
       recipe$steps,
       function(recipe_step) inherits(recipe_step, rel_step_type)
     ))) {
-      cli:cli_abort("there is no `{rel_step_type}` defined before this. for the method `extend_{shift_name}` of `step_adjust_latency`, at least one {shift_name} must be previously defined.")
+      cli::cli_abort(glue::glue(
+        "There is no `{rel_step_type}` defined before this.",
+        " For the method `extend_{shift_name}` of `step_adjust_latency`,",
+        " at least one {shift_name} must be previously defined."
+      ))
     }
 
     recipes::add_step(

From 7aa06e713f10d1f5a6cc12fb337712d74efd5174 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 15 May 2024 18:16:00 -0500
Subject: [PATCH 049/387] initial layer adjustments

---
 NAMESPACE                                     |  1 +
 R/layer_add_forecast_date.R                   | 19 ++---
 R/layer_add_target_date.R                     | 63 +++++++++++------
 R/step_adjust_latency.R                       | 68 +++++++++++-------
 R/utils-latency.R                             | 70 +++++++++++++------
 man/extend_either.Rd                          |  2 +-
 man/get_forecast_date_in_layer.Rd             | 11 +++
 man/get_latency.Rd                            |  2 +-
 man/get_latent_column_tibble.Rd               |  8 +--
 man/layer_add_forecast_date.Rd                |  9 +--
 man/layer_add_target_date.Rd                  | 55 ++++++++++-----
 man/set_asof.Rd                               | 12 ----
 man/set_forecast_date.Rd                      | 12 ++++
 man/step_adjust_latency.Rd                    | 48 ++++++++-----
 tests/testthat/_snaps/utils-shift.md          | 13 ----
 tests/testthat/test-layer_add_forecast_date.R | 28 ++++++++
 tests/testthat/test-layer_add_target_date.R   | 22 ++++++
 tests/testthat/test-step_adjust_latency.R     |  2 +-
 tests/testthat/test-utils-shift.R             | 30 --------
 19 files changed, 299 insertions(+), 176 deletions(-)
 create mode 100644 man/get_forecast_date_in_layer.Rd
 delete mode 100644 man/set_asof.Rd
 create mode 100644 man/set_forecast_date.Rd
 delete mode 100644 tests/testthat/_snaps/utils-shift.md

diff --git a/NAMESPACE b/NAMESPACE
index 2914e52a4..6b316bca4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -314,3 +314,4 @@ importFrom(vctrs,vec_data)
 importFrom(vctrs,vec_ptype_abbr)
 importFrom(vctrs,vec_ptype_full)
 importFrom(vctrs,vec_recycle_common)
+importFrom(workflows,extract_preprocessor)
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 3ebc18cb1..41cda35a9 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -3,10 +3,11 @@
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param forecast_date The forecast date to add as a column to the `epi_df`.
-#' For most cases, this should be specified in the form "yyyy-mm-dd". Note that
-#' when the forecast date is left unspecified, it is set to the maximum time
-#' value from the data used in pre-processing, fitting the model, and
-#' postprocessing.
+#'   For most cases, this should be specified in the form "yyyy-mm-dd". Note
+#'   that when the forecast date is left unspecified, it is set to one of two
+#'   values.  If there is a `step_adjust_latency` step present, it uses the
+#'   `as_of`  the maximum time value from the data used in pre-processing,
+#'   fitting the model, and postprocessing.
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
@@ -87,16 +88,16 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 }
 
 #' @export
+#' @importFrom workflows extract_preprocessor
 slather.layer_add_forecast_date <- function(object, components, workflow,
                                             new_data, ...) {
   rlang::check_dots_empty()
   if (is.null(object$forecast_date)) {
-    max_time_value <- as.Date(max(
-      workflows::extract_preprocessor(workflow)$max_time_value,
+    forecast_date <- get_forecast_date_in_layer(
+      extract_preprocessor(workflow),
       workflow$fit$meta$max_time_value,
-      max(new_data$time_value)
-    ))
-    forecast_date <- max_time_value
+      new_data
+    )
   } else {
     forecast_date <- object$forecast_date
   }
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 35bc84339..a1bdc3e4a 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -2,22 +2,26 @@
 #' Postprocessing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
-#' @param target_date The target date to add as a column to the
-#' `epi_df`. If there's a forecast date specified in a layer, then
-#' it is the forecast date plus `ahead` (from `step_epi_ahead` in
-#' the `epi_recipe`). Otherwise, it is the maximum `time_value`
-#' (from the data used in pre-processing, fitting the model, and
-#' postprocessing) plus `ahead`, where `ahead` has been specified in
-#'  preprocessing. The user may override these by specifying a
-#' target date of their own (of the form "yyyy-mm-dd").
+#' @param target_date The target date to add as a column to the `epi_df`. If
+#'   there's a forecast date specified upstream (either in a
+#'   `step_adjust_latency` or in a `layer_forecast_date`), then it is the
+#'   forecast date plus `ahead` (from `step_epi_ahead` in the `epi_recipe`).
+#'   Otherwise, it is the maximum `time_value` (from the data used in
+#'   pre-processing, fitting the model, and postprocessing) plus `ahead`, where
+#'   `ahead` has been specified in preprocessing. The user may override these by
+#'   specifying a target date of their own (of the form "yyyy-mm-dd").
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
 #'
 #' @details By default, this function assumes that a value for `ahead`
 #' has been specified in a preprocessing step (most likely in
-#' `step_epi_ahead`). Then, `ahead` is added to the maximum `time_value`
-#' in the test data to get the target date.
+#' `step_epi_ahead`). Then, `ahead` is added to the `forecast_date`
+#' in the test data to get the target date. `forecast_date` can be set in 3 ways:
+#' 1. `step_adjust_latency`, which typically uses the training `epi_df`'s `as_of`
+#' 2. `layer_add_forecast_date`, which inherits from 1 if not manually specifed
+#' 3. if none of those are the case, it is simply the maximum `time_value` over
+#'   every dataset used (prep, training, and prediction).
 #'
 #' @export
 #' @examples
@@ -42,8 +46,14 @@
 #' p <- forecast(wf1)
 #' p
 #'
-#' # Use ahead + max time value from pre, fit, post
-#' # which is the same if include `layer_add_forecast_date()`
+#' # Use ahead + forecast_date from adjust_latency
+#' # setting the `as_of` to something realistic
+#' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+#' r <- epi_recipe(jhu) %>%
+#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   step_adjust_latency(method = "extend_ahead") %>%
+#'   step_epi_naomit()
 #' f2 <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_add_target_date() %>%
@@ -53,15 +63,26 @@
 #' p2 <- forecast(wf2)
 #' p2
 #'
-#' # Specify own target date
+#' # Use ahead + max time value from pre, fit, post
+#' # which is the same if include `layer_add_forecast_date()`
 #' f3 <- frosting() %>%
 #'   layer_predict() %>%
-#'   layer_add_target_date(target_date = "2022-01-08") %>%
+#'   layer_add_target_date() %>%
 #'   layer_naomit(.pred)
 #' wf3 <- wf %>% add_frosting(f3)
 #'
-#' p3 <- forecast(wf3)
-#' p3
+#' p3 <- forecast(wf2)
+#' p2
+#'
+#' # Specify own target date
+#' f4 <- frosting() %>%
+#'   layer_predict() %>%
+#'   layer_add_target_date(target_date = "2022-01-08") %>%
+#'   layer_naomit(.pred)
+#' wf4 <- wf %>% add_frosting(f4)
+#'
+#' p4 <- forecast(wf4)
+#' p4
 layer_add_target_date <-
   function(frosting, target_date = NULL, id = rand_id("add_target_date")) {
     arg_is_chr_scalar(id)
@@ -113,13 +134,13 @@ slather.layer_add_target_date <- function(object, components, workflow,
     ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
     target_date <- forecast_date + ahead
   } else {
-    max_time_value <- as.Date(max(
-      workflows::extract_preprocessor(workflow)$max_time_value,
+    forecast_date <- get_forecast_date_in_layer(
+      extract_preprocessor(workflow),
       workflow$fit$meta$max_time_value,
-      max(new_data$time_value)
-    ))
+      new_data
+    )
     ahead <- extract_argument(the_recipe, "step_epi_ahead", "ahead")
-    target_date <- max_time_value + ahead
+    target_date <- forecast_date + ahead
   }
 
   object$target_date <- target_date
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 7480ac96d..7de07a995 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -13,12 +13,12 @@
 #'   to set this manually, as the necessary adjustments will be done for the
 #'   predictors and outcome.
 #' @param method a character. Determines the method by which the
-#'   forecast handles latency. All of these assume the forecast date is the
-#'   `as_of` of the `epi_df`. The options are:
+#'   forecast handles latency. The options are:
 #'   - `"extend_ahead"`: Lengthen the ahead so that forecasting from the last
-#'   observation results in a forecast `ahead` after the `as_of` date. E.g. if
-#'   there are 3 days of latency between the last observation and the `as_of`
-#'   date for a 4 day ahead forecast, the ahead used in practice is actually 7.
+#'   observation results in a forecast `ahead` after the `forecast_date` date.
+#'   E.g. if there are 3 days of latency between the last observation and the
+#'   `forecast_date` date for a 4 day ahead forecast, the ahead used in practice
+#'   is actually 7.
 #'   - `"locf"`: carries forward the last observed value(s) up to the forecast
 #'   date. See the Vignette TODO for equivalents using other steps and more
 #'   sophisticated methods of extrapolation.
@@ -27,16 +27,18 @@
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
 #'   become `c(3,10,17)`.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
-#'   vector. Cannot be set at the same time as `fixed_asof`.   If non-`NULL`,
-#'   the amount to offset the ahead or lag by. If a single integer, this is used
-#'   for all columns; if a labeled vector, the labels must correspond to the
-#'   base column names.  If `NULL`, the latency is the distance between the
-#'   `epi_df`'s `max_time_value` and either the `fixed_asof` or the `epi_df`'s
-#'   `as_of` field.
-#' @param fixed_asof either a date of the same kind used in the `epi_df`, or
-#'   NULL. Cannot be set at the same time as `fixed_latency`. If a date, it
-#'   gives the date from which the forecast is actually occurring. If `NULL`,
-#'   the `as_of` is determined either from `fixed_latency` or automatically.
+#'   vector. Cannot be set at the same time as `fixed_asof`. If non-`NULL`, the
+#'   amount to offset the ahead or lag by. If a single integer, this is used for
+#'   all columns; if a labeled vector, the labels must correspond to the base
+#'   column names (before lags/aheads).  If `NULL`, the latency is the distance
+#'   between the `epi_df`'s `max_time_value` and either the
+#'   `fixed_forecast_date` or the `epi_df`'s `as_of` field (the default for
+#'   `forecast_date`).
+#' @param fixed_forecast_date either a date of the same kind used in the
+#'   `epi_df`, or `NULL`. Exclusive with `fixed_latency`. If a date, it gives
+#'   the date from which the forecast is actually occurring. If `NULL`, the
+#'   `forecast_date` is determined either via the `fixed_latency`, or is set to
+#'   the `epi_df`'s `as_of` value if `fixed_latency` is also `NULL`.
 #' @param role For model terms created by this step, what analysis role should
 #'   they be assigned? `lag` is default a predictor while `ahead` is an outcome.
 #'   It should be correctly inferred and not need setting
@@ -68,11 +70,23 @@
 #' @rdname step_adjust_latency
 #' @export
 #' @examples
+#' jhu <- case_death_rate_subset %>%
+#'   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
+#' # setting the `as_of` to something realistic
+#' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+#'
 #' r <- epi_recipe(case_death_rate_subset) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
-#'   #   step_adjust_latency(method = "extend_ahead") %>%
+#'   step_adjust_latency(method = "extend_ahead") %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
+#'
+#' jhu_fit <- epi_workflow() %>%
+#'   add_epi_recipe(r) %>%
+#'   add_model(linear_reg()) %>%
+#'   fit(data = jhu)
+#' jhu_fit
+#'
 #' @importFrom recipes detect_step
 step_adjust_latency <-
   function(recipe,
@@ -85,7 +99,7 @@ step_adjust_latency <-
              "extend_lags"
            ),
            fixed_latency = NULL,
-           fixed_asof = NULL,
+           fixed_forecast_date = NULL,
            default = NA,
            skip = FALSE,
            columns = NULL,
@@ -149,7 +163,7 @@ step_adjust_latency <-
         role = role,
         method = method,
         trained = trained,
-        as_of = fixed_asof,
+        forecast_date = fixed_forecast_date,
         latency = fixed_latency,
         shift_cols = relevant_shifts,
         default = default,
@@ -161,7 +175,7 @@ step_adjust_latency <-
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, as_of, latency, shift_cols, time_type, default,
+  function(terms, role, trained, forecast_date, latency, shift_cols, time_type, default,
            keys, method, skip, id) {
     step(
       subclass = "adjust_latency",
@@ -169,7 +183,7 @@ step_adjust_latency_new <-
       role = role,
       method = method,
       trained = trained,
-      as_of = as_of,
+      forecast_date = forecast_date,
       latency = latency,
       shift_cols = shift_cols,
       default = default,
@@ -190,15 +204,15 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       filter(role == "raw") %>%
       pull(variable)
   }
-  # get and check the max_time and as_of are the right kinds of dates
-  as_of <- x$as_of %||% set_asof(training, info)
+  # get and check the max_time and forecast_date are the right kinds of dates
+  forecast_date <- x$forecast_date %||% set_forecast_date(training, info)
 
   # infer the correct columns to be working with from the previous
   # transformations
   x$prefix <- x$shift_cols$prefix[[1]]
   sign_shift <- get_sign(x)
   latency_cols <- get_latent_column_tibble(
-    x$shift_cols, training, as_of,
+    x$shift_cols, training, forecast_date,
     x$latency, sign_shift, info
   )
 
@@ -222,7 +236,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         ),
         "i" = "input shift: {latency_cols$shift[[i_latency]]}",
         "i" = "latency adjusted shift: {latency_cols$effective_shift[[i_latency]]}",
-        "i" = "max_time = {max_time} -> as_of = {as_of}"
+        "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
       ))
     }
   }
@@ -232,7 +246,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     role = latency_cols$role[[1]],
     trained = TRUE,
     shift_cols = latency_cols,
-    as_of = as_of,
+    forecast_date = forecast_date,
     latency = unique(latency_cols$latency),
     default = x$default,
     keys = x$keys,
@@ -268,9 +282,9 @@ print.step_adjust_latency <-
     } else {
       terms <- x$terms
     }
-    if (!is.null(x$as_of)) {
+    if (!is.null(x$forecast_date)) {
       conj <- "with forecast date"
-      extra_text <- x$as_of
+      extra_text <- x$forecast_date
     } else if (!is.null(x$shift_cols)) {
       conj <- "with latencies"
       extra_text <- x$shift_cols
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 5fa4ce3fc..9e0ef8db1 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -3,7 +3,7 @@
 #' note that this may introduce new NA values when one column is shifted farther than another
 #' @param shift_cols a tibble which must have the columns `column`, the name of
 #'   the column to adjust, `latency` the latency of the original column relative
-#'   to the `as_of` date, `new_name`, the names in `column` adjusted by the
+#'   to the `forecast_date`, `new_name`, the names in `column` adjusted by the
 #'   latencies `latency`
 #' @param new_data just what is says
 #' @param keys the variables which are used as keys
@@ -68,18 +68,18 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' been changed
 #' @param shift_cols a list of columns to operate on, as created by `construct_shift_tibble`
 #' @param new_data the data transformed so far
-#' @param as_of the forecast date
-#' @param latency `NULL`, int, or vector, as described in `step_eip_latency`
+#' @param forecast_date the forecast date
+#' @param latency `NULL`, int, or vector, as described in `step_epi_latency`
 #' @param sign_shift -1 if ahead, 1 if lag
 #' @return a tibble with columns `column` (relevant shifted names), `shift` (the
 #'   amount that one is shifted), `latency` (original columns difference between
-#'   max_time_value and as_of (on a per-initial column basis)),
+#'   the max `time_value` and `forecast_date` (on a per-initial column basis)),
 #'   `effective_shift` (shift+latency), and `new_name` (adjusted names with the
 #'   effective_shift)
 #' @keywords internal
 #' @importFrom dplyr rowwise %>%
 get_latent_column_tibble <- function(
-    shift_cols, new_data, as_of, latency,
+    shift_cols, new_data, forecast_date, latency,
     sign_shift, info, call = caller_env()) {
   shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"))
   if (is.null(latency)) {
@@ -87,7 +87,7 @@ get_latent_column_tibble <- function(
       rowwise() %>%
       # add the latencies to shift_cols
       mutate(latency = get_latency(
-        new_data, as_of, original_name, shift, sign_shift
+        new_data, forecast_date, original_name, shift, sign_shift
       )) %>%
       ungroup()
   } else if (length(latency) > 1) {
@@ -119,9 +119,9 @@ get_latent_column_tibble <- function(
 }
 
 
-#' extract the as_of, and make sure there's nothing very off about it
+#' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
-set_asof <- function(new_data, info) {
+set_forecast_date <- function(new_data, info) {
   original_columns <- info %>%
     filter(source == "original") %>%
     pull(variable)
@@ -142,45 +142,75 @@ set_asof <- function(new_data, info) {
   if (length(time_values) <= 0) {
     cli::cli_abort("the `time_value` column of `new_data` is empty")
   }
-  as_of <- attributes(new_data)$metadata$as_of
+  forecast_date <- attributes(new_data)$metadata$as_of
   max_time <- max(time_values)
   # make sure the as_of is sane
-  if (!inherits(as_of, class(time_values)) & !inherits(as_of, "POSIXt")) {
+  if (!inherits(forecast_date, class(time_values)) & !inherits(forecast_date, "POSIXt")) {
     cli::cli_abort(paste(
-      "the data matrix `as_of` value is {as_of}, ",
+      "the data matrix `forecast_date` value is {forecast_date}, ",
       "and not a valid `time_type` with type ",
       "matching `time_value`'s type of ",
       "{typeof(new_data$time_value)}."
     ))
   }
-  if (is.null(as_of) || is.na(as_of)) {
+  if (is.null(forecast_date) || is.na(forecast_date)) {
     cli::cli_warn(paste(
-      "epi_data's `as_of` was {as_of}, setting to ",
+      "epi_data's `forecast_date` was {forecast_date}, setting to ",
       "the latest time value, {max_time}."
     ))
-    as_of <- max_time
-  } else if (as_of < max_time) {
+    forecast_date <- max_time
+  } else if (forecast_date < max_time) {
     cli::cli_abort(paste(
-      "`as_of` ({(as_of)}) is before the most ",
+      "`forecast_date` ({(forecast_date)}) is before the most ",
       "recent data ({max_time}). Remove before ",
       "predicting."
     ))
   }
   # TODO cover the rest of the possible types for as_of and max_time...
   if (class(time_values) == "Date") {
-    as_of <- as.Date(as_of)
+    forecast_date <- as.Date(forecast_date)
   }
-  return(as_of)
+  return(forecast_date)
 }
 
 #' the latency is also the amount the shift is off by
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
 #' @keywords internal
-get_latency <- function(new_data, as_of, column, shift_amount, sign_shift) {
+get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shift) {
   shift_max_date <- new_data %>%
     drop_na(all_of(column)) %>%
     pull(time_value) %>%
     max()
-  return(as.integer(sign_shift * (as_of - shift_max_date) + shift_amount))
+  return(as.integer(sign_shift * (forecast_date - shift_max_date) + shift_amount))
+}
+
+
+
+#' get the target date while in a layer
+get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new_data) {
+  max_time_value <- max(
+    workflow_max_time_value,
+    this_recipe$max_time_value,
+    max(new_data$time_value)
+  )
+  if (this_recipe %>% recipes::detect_step("adjust_latency")) {
+    # get the as_of in an `adjust_latency` step, regardless of where
+    handpicked_as_of <- map(
+      this_recipe$steps,
+      function(x) {
+        if (inherits(this_recipe$steps[[3]], "step_adjust_latency")) x$as_of
+      }
+    ) %>% Filter(Negate(is.null), .)
+    if (length(handpicked_as_of) > 0) {
+      max_time_value <- handpicked_as_of[[1]]
+    } else {
+      # if we haven't chosen one, use either the max_time_value or the as_of
+      max_time_value <- max(
+        max_time_value,
+        attributes(new_data)$metadata$as_of
+      )
+    }
+  }
+  max_time_value
 }
diff --git a/man/extend_either.Rd b/man/extend_either.Rd
index b7e306944..ae55fa46a 100644
--- a/man/extend_either.Rd
+++ b/man/extend_either.Rd
@@ -13,7 +13,7 @@ extend_either(new_data, shift_cols, keys)
 
 \item{shift_cols}{a tibble which must have the columns \code{column}, the name of
 the column to adjust, \code{latency} the latency of the original column relative
-to the \code{as_of} date, \code{new_name}, the names in \code{column} adjusted by the
+to the \code{forecast_date}, \code{new_name}, the names in \code{column} adjusted by the
 latencies \code{latency}}
 
 \item{keys}{the variables which are used as keys}
diff --git a/man/get_forecast_date_in_layer.Rd b/man/get_forecast_date_in_layer.Rd
new file mode 100644
index 000000000..2f9e03548
--- /dev/null
+++ b/man/get_forecast_date_in_layer.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_forecast_date_in_layer}
+\alias{get_forecast_date_in_layer}
+\title{get the target date while in a layer}
+\usage{
+get_forecast_date_in_layer(this_recipe, workflow_max_time_value, new_data)
+}
+\description{
+get the target date while in a layer
+}
diff --git a/man/get_latency.Rd b/man/get_latency.Rd
index d9098b456..4cde42ab6 100644
--- a/man/get_latency.Rd
+++ b/man/get_latency.Rd
@@ -4,7 +4,7 @@
 \alias{get_latency}
 \title{the latency is also the amount the shift is off by}
 \usage{
-get_latency(new_data, as_of, column, shift_amount, sign_shift)
+get_latency(new_data, forecast_date, column, shift_amount, sign_shift)
 }
 \arguments{
 \item{sign_shift}{integer. 1 if lag and -1 if ahead. These represent how you
diff --git a/man/get_latent_column_tibble.Rd b/man/get_latent_column_tibble.Rd
index 168145ed8..3134a42ae 100644
--- a/man/get_latent_column_tibble.Rd
+++ b/man/get_latent_column_tibble.Rd
@@ -8,7 +8,7 @@ been changed}
 get_latent_column_tibble(
   shift_cols,
   new_data,
-  as_of,
+  forecast_date,
   latency,
   sign_shift,
   info,
@@ -20,16 +20,16 @@ get_latent_column_tibble(
 
 \item{new_data}{the data transformed so far}
 
-\item{as_of}{the forecast date}
+\item{forecast_date}{the forecast date}
 
-\item{latency}{\code{NULL}, int, or vector, as described in \code{step_eip_latency}}
+\item{latency}{\code{NULL}, int, or vector, as described in \code{step_epi_latency}}
 
 \item{sign_shift}{-1 if ahead, 1 if lag}
 }
 \value{
 a tibble with columns \code{column} (relevant shifted names), \code{shift} (the
 amount that one is shifted), \code{latency} (original columns difference between
-max_time_value and as_of (on a per-initial column basis)),
+the max \code{time_value} and \code{forecast_date} (on a per-initial column basis)),
 \code{effective_shift} (shift+latency), and \code{new_name} (adjusted names with the
 effective_shift)
 }
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index e27f2bacd..4c3336536 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -14,10 +14,11 @@ layer_add_forecast_date(
 \item{frosting}{a \code{frosting} postprocessor}
 
 \item{forecast_date}{The forecast date to add as a column to the \code{epi_df}.
-For most cases, this should be specified in the form "yyyy-mm-dd". Note that
-when the forecast date is left unspecified, it is set to the maximum time
-value from the data used in pre-processing, fitting the model, and
-postprocessing.}
+For most cases, this should be specified in the form "yyyy-mm-dd". Note
+that when the forecast date is left unspecified, it is set to one of two
+values.  If there is a \code{step_adjust_latency} step present, it uses the
+\code{as_of}  the maximum time value from the data used in pre-processing,
+fitting the model, and postprocessing.}
 
 \item{id}{a random id string}
 }
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index dc0d2f190..e522cd6da 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -13,14 +13,14 @@ layer_add_target_date(
 \arguments{
 \item{frosting}{a \code{frosting} postprocessor}
 
-\item{target_date}{The target date to add as a column to the
-\code{epi_df}. If there's a forecast date specified in a layer, then
-it is the forecast date plus \code{ahead} (from \code{step_epi_ahead} in
-the \code{epi_recipe}). Otherwise, it is the maximum \code{time_value}
-(from the data used in pre-processing, fitting the model, and
-postprocessing) plus \code{ahead}, where \code{ahead} has been specified in
-preprocessing. The user may override these by specifying a
-target date of their own (of the form "yyyy-mm-dd").}
+\item{target_date}{The target date to add as a column to the \code{epi_df}. If
+there's a forecast date specified upstream (either in a
+\code{step_adjust_latency} or in a \code{layer_forecast_date}), then it is the
+forecast date plus \code{ahead} (from \code{step_epi_ahead} in the \code{epi_recipe}).
+Otherwise, it is the maximum \code{time_value} (from the data used in
+pre-processing, fitting the model, and postprocessing) plus \code{ahead}, where
+\code{ahead} has been specified in preprocessing. The user may override these by
+specifying a target date of their own (of the form "yyyy-mm-dd").}
 
 \item{id}{a random id string}
 }
@@ -33,8 +33,14 @@ Postprocessing step to add the target date
 \details{
 By default, this function assumes that a value for \code{ahead}
 has been specified in a preprocessing step (most likely in
-\code{step_epi_ahead}). Then, \code{ahead} is added to the maximum \code{time_value}
-in the test data to get the target date.
+\code{step_epi_ahead}). Then, \code{ahead} is added to the \code{forecast_date}
+in the test data to get the target date. \code{forecast_date} can be set in 3 ways:
+\enumerate{
+\item \code{step_adjust_latency}, which typically uses the training \code{epi_df}'s \code{as_of}
+\item \code{layer_add_forecast_date}, which inherits from 1 if not manually specifed
+\item if none of those are the case, it is simply the maximum \code{time_value} over
+every dataset used (prep, training, and prediction).
+}
 }
 \examples{
 library(dplyr)
@@ -58,8 +64,14 @@ wf1 <- wf \%>\% add_frosting(f)
 p <- forecast(wf1)
 p
 
-# Use ahead + max time value from pre, fit, post
-# which is the same if include `layer_add_forecast_date()`
+# Use ahead + forecast_date from adjust_latency
+# setting the `as_of` to something realistic
+attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+r <- epi_recipe(jhu) \%>\%
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
+  step_adjust_latency(method = "extend_ahead") \%>\%
+  step_epi_naomit()
 f2 <- frosting() \%>\%
   layer_predict() \%>\%
   layer_add_target_date() \%>\%
@@ -69,13 +81,24 @@ wf2 <- wf \%>\% add_frosting(f2)
 p2 <- forecast(wf2)
 p2
 
-# Specify own target date
+# Use ahead + max time value from pre, fit, post
+# which is the same if include `layer_add_forecast_date()`
 f3 <- frosting() \%>\%
   layer_predict() \%>\%
-  layer_add_target_date(target_date = "2022-01-08") \%>\%
+  layer_add_target_date() \%>\%
   layer_naomit(.pred)
 wf3 <- wf \%>\% add_frosting(f3)
 
-p3 <- forecast(wf3)
-p3
+p3 <- forecast(wf2)
+p2
+
+# Specify own target date
+f4 <- frosting() \%>\%
+  layer_predict() \%>\%
+  layer_add_target_date(target_date = "2022-01-08") \%>\%
+  layer_naomit(.pred)
+wf4 <- wf \%>\% add_frosting(f4)
+
+p4 <- forecast(wf4)
+p4
 }
diff --git a/man/set_asof.Rd b/man/set_asof.Rd
deleted file mode 100644
index fabf97c8a..000000000
--- a/man/set_asof.Rd
+++ /dev/null
@@ -1,12 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-latency.R
-\name{set_asof}
-\alias{set_asof}
-\title{extract the as_of, and make sure there's nothing very off about it}
-\usage{
-set_asof(new_data, info)
-}
-\description{
-extract the as_of, and make sure there's nothing very off about it
-}
-\keyword{internal}
diff --git a/man/set_forecast_date.Rd b/man/set_forecast_date.Rd
new file mode 100644
index 000000000..c238758c9
--- /dev/null
+++ b/man/set_forecast_date.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{set_forecast_date}
+\alias{set_forecast_date}
+\title{Extract the as_of for the forecast date, and make sure there's nothing very off about it.}
+\usage{
+set_forecast_date(new_data, info)
+}
+\description{
+Extract the as_of for the forecast date, and make sure there's nothing very off about it.
+}
+\keyword{internal}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 4df945204..30832a17e 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -11,7 +11,7 @@ step_adjust_latency(
   trained = FALSE,
   method = c("extend_ahead", "locf", "extend_lags"),
   fixed_latency = NULL,
-  fixed_asof = NULL,
+  fixed_forecast_date = NULL,
   default = NA,
   skip = FALSE,
   columns = NULL,
@@ -35,13 +35,13 @@ It should be correctly inferred and not need setting}
 been estimated.}
 
 \item{method}{a character. Determines the method by which the
-forecast handles latency. All of these assume the forecast date is the
-\code{as_of} of the \code{epi_df}. The options are:
+forecast handles latency. The options are:
 \itemize{
 \item \code{"extend_ahead"}: Lengthen the ahead so that forecasting from the last
-observation results in a forecast \code{ahead} after the \code{as_of} date. E.g. if
-there are 3 days of latency between the last observation and the \code{as_of}
-date for a 4 day ahead forecast, the ahead used in practice is actually 7.
+observation results in a forecast \code{ahead} after the \code{forecast_date} date.
+E.g. if there are 3 days of latency between the last observation and the
+\code{forecast_date} date for a 4 day ahead forecast, the ahead used in practice
+is actually 7.
 \item \code{"locf"}: carries forward the last observed value(s) up to the forecast
 date. See the Vignette TODO for equivalents using other steps and more
 sophisticated methods of extrapolation.
@@ -52,17 +52,19 @@ become \code{c(3,10,17)}.
 }}
 
 \item{fixed_latency}{either a positive integer, or a labeled positive integer
-vector. Cannot be set at the same time as \code{fixed_asof}.   If non-\code{NULL},
-the amount to offset the ahead or lag by. If a single integer, this is used
-for all columns; if a labeled vector, the labels must correspond to the
-base column names.  If \code{NULL}, the latency is the distance between the
-\code{epi_df}'s \code{max_time_value} and either the \code{fixed_asof} or the \code{epi_df}'s
-\code{as_of} field.}
+vector. Cannot be set at the same time as \code{fixed_asof}. If non-\code{NULL}, the
+amount to offset the ahead or lag by. If a single integer, this is used for
+all columns; if a labeled vector, the labels must correspond to the base
+column names (before lags/aheads).  If \code{NULL}, the latency is the distance
+between the \code{epi_df}'s \code{max_time_value} and either the
+\code{fixed_forecast_date} or the \code{epi_df}'s \code{as_of} field (the default for
+\code{forecast_date}).}
 
-\item{fixed_asof}{either a date of the same kind used in the \code{epi_df}, or
-NULL. Cannot be set at the same time as \code{fixed_latency}. If a date, it
-gives the date from which the forecast is actually occurring. If \code{NULL},
-the \code{as_of} is determined either from \code{fixed_latency} or automatically.}
+\item{fixed_forecast_date}{either a date of the same kind used in the
+\code{epi_df}, or \code{NULL}. Exclusive with \code{fixed_latency}. If a date, it gives
+the date from which the forecast is actually occurring. If \code{NULL}, the
+\code{forecast_date} is determined either via the \code{fixed_latency}, or is set to
+the \code{epi_df}'s \code{as_of} value if \code{fixed_latency} is also \code{NULL}.}
 
 \item{default}{Determines what fills empty rows
 left by leading/lagging (defaults to NA).}
@@ -105,11 +107,23 @@ are always set to \code{"ahead_"} and \code{"epi_ahead"} respectively, while for
 \code{step_epi_lag}, they are set to \code{"lag_"} and \verb{"epi_lag}, respectively.
 }
 \examples{
+jhu <- case_death_rate_subset \%>\%
+  dplyr::filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
+# setting the `as_of` to something realistic
+attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+
 r <- epi_recipe(case_death_rate_subset) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
-  #   step_adjust_latency(method = "extend_ahead") \%>\%
+  step_adjust_latency(method = "extend_ahead") \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
 r
+
+jhu_fit <- epi_workflow() \%>\%
+  add_epi_recipe(r) \%>\%
+  add_model(linear_reg()) \%>\%
+  fit(data = jhu)
+jhu_fit
+
 }
 \seealso{
 Other row operation steps: 
diff --git a/tests/testthat/_snaps/utils-shift.md b/tests/testthat/_snaps/utils-shift.md
deleted file mode 100644
index b7c5f064f..000000000
--- a/tests/testthat/_snaps/utils-shift.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# extend_ahead warns in case of extreme adjustment
-
-    Code
-      adjust_latency(object, x_adjust_ahead)
-    Condition
-      Warning:
-      ! The ahead has been adjusted by 100, which is questionable for it's `time_type` of day
-      i input ahead: 7
-      i shifted ahead: 107
-      i max_time = 2021-07-19 -> as_of = 2021-10-27
-    Output
-      [1] 107
-
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 428922f46..cad6e79bf 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -1,5 +1,7 @@
 jhu <- case_death_rate_subset %>%
   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
+attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+
 r <- epi_recipe(jhu) %>%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
   step_epi_ahead(death_rate, ahead = 7) %>%
@@ -67,6 +69,8 @@ test_that("Do not specify a forecast_date in `layer_add_forecast_date()`", {
   #   p3 <- predict(wf3, latest),
   #   "forecast_date is less than the most recent update date of the data."
   # )
+  p3 <- predict(wf3, latest)
+  p3
   expect_silent(p3 <- predict(wf3, latest))
   expect_equal(ncol(p3), 4L)
   expect_s3_class(p3, "epi_df")
@@ -75,6 +79,30 @@ test_that("Do not specify a forecast_date in `layer_add_forecast_date()`", {
   expect_named(p3, c("geo_value", "time_value", ".pred", "forecast_date"))
 })
 
+test_that("`layer_add_forecast_date()` infers correct date when using `adjust_latency`", {
+  r_latent <- epi_recipe(jhu) %>%
+    step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+    step_epi_ahead(death_rate, ahead = 7) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
+    step_naomit(all_predictors()) %>%
+    step_naomit(all_outcomes(), skip = TRUE)
+  frost_latent <- frosting() %>%
+    layer_predict() %>%
+    layer_add_forecast_date() %>%
+    layer_naomit(.pred)
+  wf_latent <- epi_workflow(r_latent, parsnip::linear_reg()) %>%
+    fit(jhu) %>%
+    add_frosting(frost_latent)
+  p_latent <- predict(wf_latent, latest)
+  expect_equal(
+    p_latent$forecast_date,
+    rep(as.Date("2022-01-03"), times = 3)
+  )
+  expect_equal(
+    p_latent$forecast_date - p_latent$time_value,
+    as.difftime(rep(3, times = 3), units = "days")
+  )
+})
 
 test_that("forecast date works for daily", {
   f <- frosting() %>%
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index 53506ad07..72f3c1d7f 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -6,6 +6,7 @@ r <- epi_recipe(jhu) %>%
   step_naomit(all_predictors()) %>%
   step_naomit(all_outcomes(), skip = TRUE)
 wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
+attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 latest <- jhu %>%
   dplyr::filter(time_value >= max(time_value) - 14)
 
@@ -39,6 +40,27 @@ test_that("Use ahead + max time value from pre, fit, post", {
   expect_equal(p2$target_date, rep(as.Date("2022-01-07"), times = 3))
   expect_named(p2, c("geo_value", "time_value", ".pred", "forecast_date", "target_date"))
 })
+test_that("latency adjust doesn't interfere with correct target date", {
+  r_latent <- epi_recipe(jhu) %>%
+    step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+    step_epi_ahead(death_rate, ahead = 7) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
+    step_naomit(all_predictors()) %>%
+    step_naomit(all_outcomes(), skip = TRUE)
+  wf_latent <- epi_workflow(r_latent, parsnip::linear_reg()) %>% fit(jhu)
+  f_latent <- frosting() %>%
+    layer_predict() %>%
+    layer_add_target_date() %>%
+    layer_naomit(.pred)
+  wf_latent <- wf_latent %>% add_frosting(f_latent)
+
+  expect_silent(p_latent <- predict(wf_latent, latest))
+  expect_equal(ncol(p_latent), 4L)
+  expect_s3_class(p_latent, "epi_df")
+  expect_equal(nrow(p_latent), 3L)
+  expect_equal(p_latent$target_date, rep(as.Date("2022-01-10"), times = 3))
+  expect_named(p_latent, c("geo_value", "time_value", ".pred", "target_date"))
+})
 
 test_that("Use ahead + specified forecast date", {
   f <- frosting() %>%
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index f52888a68..61fe249c1 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -34,7 +34,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1024"), class = "simpleError")
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1031"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = real_x)
diff --git a/tests/testthat/test-utils-shift.R b/tests/testthat/test-utils-shift.R
index 53788f8b8..e69de29bb 100644
--- a/tests/testthat/test-utils-shift.R
+++ b/tests/testthat/test-utils-shift.R
@@ -1,30 +0,0 @@
-time_range <- as.Date("2021-01-01") + 0:199
-x_adjust_ahead <- tibble(
-  geo_value = rep("place", 200),
-  time_value = time_range,
-  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
-  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
-) %>%
-  as_epi_df(as_of = max(time_range) + 3)
-# confirm the delay is right
-
-test_that("adjust_latency extend_ahead works", {
-  # testing that POSIXct converts correctly (as well as basic types)
-  expect_equal(
-    attributes(x_adjust_ahead)$metadata$as_of - max(x_adjust_ahead$time_value),
-    as.difftime(3, units = "days")
-  )
-  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
-  expect_no_error(adjusted_ahead <- adjust_latency(object, x_adjust_ahead))
-  expect_type(adjusted_ahead, "integer")
-  expect_equal(adjusted_ahead, 3 + 7)
-})
-
-test_that("extend_ahead warns in case of extreme adjustment", {
-  # warns if the ahead is relatively small
-  attributes(x_adjust_ahead)$metadata$as_of <-
-    max(x_adjust_ahead$time_value) + 100
-  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
-  attributes(x_adjust_ahead)$metadata$time_type
-  expect_snapshot(adjust_latency(object, x_adjust_ahead))
-})

From be3474c56eefd07007f2328aae10214451048f18 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 12:32:26 -0500
Subject: [PATCH 050/387] namespace and doc fixes

---
 NAMESPACE                  | 2 --
 man/step_adjust_latency.Rd | 4 ----
 2 files changed, 6 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 6b316bca4..bcf310888 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -266,7 +266,6 @@ importFrom(glue,glue)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
-importFrom(quantreg,rq)
 importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
@@ -301,7 +300,6 @@ importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
-importFrom(tibble,is_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
 importFrom(tidyr,unnest)
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 30832a17e..b503f3eeb 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -80,10 +80,6 @@ the computations for subsequent operations.}
 should be the original columns, and not the derived ones}
 
 \item{id}{A unique identifier for the step}
-
-\item{prefix}{a character. The prefix matching the one used in either
-\code{step_epi_ahead} if \code{method="extend_ahead"} or \code{step_epi_lag}
-if \code{method="extend_lags"} or "locf".}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the

From 6c158ce6c60fbe89f3443da62eb9db48e7ebef8c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 13:20:22 -0500
Subject: [PATCH 051/387] full rebase fixes

---
 NAMESPACE                                 | 1 +
 R/epi_recipe.R                            | 1 -
 R/step_adjust_latency.R                   | 2 +-
 man/bake.step_adjust_latency.Rd           | 1 -
 tests/testthat/test-step_adjust_latency.R | 4 ++--
 5 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index bcf310888..29b1d9591 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -19,6 +19,7 @@ S3method(autoplot,canned_epipred)
 S3method(autoplot,epi_workflow)
 S3method(bake,check_enough_train_data)
 S3method(bake,epi_recipe)
+S3method(bake,step_adjust_latency)
 S3method(bake,step_epi_ahead)
 S3method(bake,step_epi_lag)
 S3method(bake,step_epi_slide)
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index e4bcaaf91..edb7f352b 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -583,7 +583,6 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
   new_data
 }
 
-
 kill_levels <- function(x, keys) {
   for (i in which(names(x) %in% keys)) x[[i]] <- list(values = NA, ordered = NA)
   x
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 7de07a995..763c893a9 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -264,7 +264,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @param new_data assumes that this already has lag/ahead columns that we need
 #'   to adjust
 #' @importFrom dplyr %>% pull
-#' @keywords internal
+#' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
     keys <- object$keys
diff --git a/man/bake.step_adjust_latency.Rd b/man/bake.step_adjust_latency.Rd
index edb4f1f6e..dac3f5509 100644
--- a/man/bake.step_adjust_latency.Rd
+++ b/man/bake.step_adjust_latency.Rd
@@ -15,4 +15,3 @@ to adjust}
 adjust the ahead so that we will be predicting \code{ahead} days after the \code{as_of}
 date, rather than relative to the last day of data
 }
-\keyword{internal}
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 61fe249c1..f08652ca5 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -6,7 +6,7 @@ x <- tibble(
   case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
   death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
 ) %>%
-  as_epi_df()
+  as_epi_df(as_of = as.POSIXct("2024-05-17"))
 max_time <- max(x$time_value)
 class(attributes(x)$metadata$as_of)
 as_of <- attributes(x)$metadata$as_of
@@ -34,7 +34,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1031"), class = "simpleError")
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = real_x)

From 4f717155c69dd0896ef56e9efb0751d1b9c48068 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 17:26:35 -0500
Subject: [PATCH 052/387] adding latency adjusting to arx_forecaster

---
 R/arx_forecaster.R              | 48 +++++++++++++++++++++++++++------
 R/layer_add_forecast_date.R     |  7 ++---
 R/utils-latency.R               |  2 +-
 tests/testthat/test-snapshots.R | 20 ++++++++++++++
 4 files changed, 63 insertions(+), 14 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 7b0d84f24..3bcf02586 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -54,7 +54,7 @@ arx_forecaster <- function(
 
   preds <- forecast(
     wf,
-    fill_locf = TRUE,
+    fill_locf = is.null(args_list$adjust_latency),
     n_recent = args_list$nafill_buffer,
     forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
   ) %>%
@@ -120,6 +120,13 @@ arx_fcast_epi_workflow <- function(
   if (!(is.null(trainer) || is_regression(trainer))) {
     cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'regression'.")
   }
+  # forecast_date is first what they set;
+  # if they don't and they're not adjusting latency, it defaults to the max time_value
+  # if they're adjusting as_of, it defaults to the as_of
+  forecast_date <- args_list$forecast_date %||%
+    if (is.null(args_list$adjust_latency)) max(epi_data$time_value) else attributes(epi_data)$metadata$as_of
+  target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
+
   lags <- arx_lags_validator(predictors, args_list$lags)
 
   # --- preprocessor
@@ -130,6 +137,26 @@ arx_fcast_epi_workflow <- function(
   }
   r <- r %>%
     step_epi_ahead(!!outcome, ahead = args_list$ahead) %>%
+    {
+      method <- args_list$adjust_latency
+      if (!is.null(method)) {
+        if (method == "extend_ahead") {
+          step_adjust_latency(.,
+            all_outcomes(),
+            fixed_forecast_date = forecast_date,
+            method = method
+          )
+        } else if (method == "extend_lags") {
+          step_adjust_latency(.,
+            all_predictors(),
+            fixed_forecast_date = forecast_date,
+            method = method
+          )
+        }
+      } else {
+        .
+      }
+    } %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training)
 
@@ -145,8 +172,6 @@ arx_fcast_epi_workflow <- function(
   }
 
 
-  forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
-  target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
 
   # --- postprocessor
   f <- frosting() %>% layer_predict() # %>% layer_naomit()
@@ -188,10 +213,15 @@ arx_fcast_epi_workflow <- function(
 #' @param n_training Integer. An upper limit for the number of rows per
 #'   key that are used for training
 #'   (in the time unit of the `epi_df`).
-#' @param forecast_date Date. The date on which the forecast is created.
-#'   The default `NULL` will attempt to determine this automatically.
-#' @param target_date Date. The date for which the forecast is intended.
-#'   The default `NULL` will attempt to determine this automatically.
+#' @param forecast_date Date. The date on which the forecast is created.  The
+#'   default `NULL` will attempt to determine this automatically either as the
+#'   max time value if there is no latency adjustment, or as the `as_of` of
+#'   `epi_data` if `adjust_latency` is non-`NULL`.
+#' @param target_date Date. The date for which the forecast is intended. The
+#'   default `NULL` will attempt to determine this automatically as
+#'   `forecast_date + ahead`.
+#' @param adjust_latency Character or `NULL`. one of the `method`s of
+#'   `step_adjust_latency`, or `NULL` (in which case there is no adjustment).
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
 #'   prediction intervals. These are created by computing the quantiles of
 #'   training residuals. A `NULL` value will result in point forecasts only.
@@ -237,6 +267,7 @@ arx_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
+    adjust_latency = NULL,
     quantile_levels = c(0.05, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
@@ -252,7 +283,7 @@ arx_args_list <- function(
 
   arg_is_scalar(ahead, n_training, symmetrize, nonneg)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
-  arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
+  arg_is_scalar(forecast_date, target_date, adjust_latency, allow_null = TRUE)
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags)
   arg_is_lgl(symmetrize, nonneg)
@@ -281,6 +312,7 @@ arx_args_list <- function(
       quantile_levels,
       forecast_date,
       target_date,
+      adjust_latency,
       symmetrize,
       nonneg,
       max_lags,
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 41cda35a9..43b7d7bd4 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -92,15 +92,12 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 slather.layer_add_forecast_date <- function(object, components, workflow,
                                             new_data, ...) {
   rlang::check_dots_empty()
-  if (is.null(object$forecast_date)) {
-    forecast_date <- get_forecast_date_in_layer(
+  forecast_date <- object$forecast_date %||%
+    get_forecast_date_in_layer(
       extract_preprocessor(workflow),
       workflow$fit$meta$max_time_value,
       new_data
     )
-  } else {
-    forecast_date <- object$forecast_date
-  }
 
   expected_time_type <- attr(
     workflows::extract_preprocessor(workflow)$template, "metadata"
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9e0ef8db1..7f87bece6 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -182,7 +182,7 @@ get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shif
     drop_na(all_of(column)) %>%
     pull(time_value) %>%
     max()
-  return(as.integer(sign_shift * (forecast_date - shift_max_date) + shift_amount))
+  return(as.integer(sign_shift * (as.Date(forecast_date) - shift_max_date) + shift_amount))
 }
 
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index d624a4c21..ebfb38ca2 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -68,6 +68,26 @@ test_that("arx_forecaster snapshots", {
     )
   )
   expect_snapshot_tibble(arx2$predictions)
+  attributes(train_data)$metadata$as_of <- max(train_data$time_value) + 5
+  arx3 <- arx_forecaster(
+    train_data,
+    "death_rate_7d_av",
+    c("death_rate_7d_av", "case_rate_7d_av"),
+    args_list = arx_args_list(
+      ahead = 1L,
+      adjust_latency = "extend_ahead"
+    )
+  )
+  # consistency check
+  expect_snapshot_tibble(arx3$predictions)
+  expect_equal(arx3$predictions$target_date,
+               rep(attributes(train_data)$metadata$as_of + 1, times = 6))
+  expect_equal(arx3$predictions$target_date,
+               arx2$predictions$target_date + 5)
+  expect_equal(arx3$predictions$forecast_date,
+               arx2$predictions$forecast_date + 5)
+  # not the same predictions
+  expect_false(all(arx2$predictions == arx3$predictions))
 })
 
 test_that("arx_classifier snapshots", {

From e102d413938ab607a633797fabf13f42b43b1920 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 17:34:47 -0500
Subject: [PATCH 053/387] arx_classifier more or less free

---
 R/arx_classifier.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index ca6a3537b..97c4deed1 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -57,7 +57,7 @@ arx_classifier <- function(
 
   preds <- forecast(
     wf,
-    fill_locf = TRUE,
+    fill_locf = is.null(args_list$adjust_latency),
     n_recent = args_list$nafill_buffer,
     forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
   ) %>%

From 65535c53cf182448916e28500123b8bdc5cae3a4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 17 May 2024 17:43:11 -0500
Subject: [PATCH 054/387] formatting and snapshots

---
 tests/testthat/_snaps/snapshots.md | 28 ++++++++++++++++++++++++++++
 tests/testthat/test-snapshots.R    | 18 ++++++++++++------
 2 files changed, 40 insertions(+), 6 deletions(-)

diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 84abf57d2..c103ffe0e 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1031,6 +1031,34 @@
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
+---
+
+    structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
+    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
+    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c("5%" = 0.136509784083987, "95%" = 0.469979623951498
+        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c("5%" = 0.364597933377326, "95%" = 0.698067773244837
+            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c("5%" = 0.422093024752224, "95%" = 0.755562864619735
+            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c("5%" = 0.821955329282474, "95%" = 1.15542516914998
+            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c("5%" = 0.628067077067883, "95%" = 0.961536916935394
+            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c("5%" = 0.140160537291566, "95%" = 0.473630377159077
+            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
+    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
+    18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
+    -6L), class = c("tbl_df", "tbl", "data.frame"))
+
 # arx_classifier snapshots
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index ebfb38ca2..25bc13bde 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -80,12 +80,18 @@ test_that("arx_forecaster snapshots", {
   )
   # consistency check
   expect_snapshot_tibble(arx3$predictions)
-  expect_equal(arx3$predictions$target_date,
-               rep(attributes(train_data)$metadata$as_of + 1, times = 6))
-  expect_equal(arx3$predictions$target_date,
-               arx2$predictions$target_date + 5)
-  expect_equal(arx3$predictions$forecast_date,
-               arx2$predictions$forecast_date + 5)
+  expect_equal(
+    arx3$predictions$target_date,
+    rep(attributes(train_data)$metadata$as_of + 1, times = 6)
+  )
+  expect_equal(
+    arx3$predictions$target_date,
+    arx2$predictions$target_date + 5
+  )
+  expect_equal(
+    arx3$predictions$forecast_date,
+    arx2$predictions$forecast_date + 5
+  )
   # not the same predictions
   expect_false(all(arx2$predictions == arx3$predictions))
 })

From 5d5cfbb1e5963c6d7ed2e5a3710182de4a3fe6aa Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 22 May 2024 18:05:36 -0500
Subject: [PATCH 055/387] updated man pages

---
 man/arx_args_list.Rd          | 15 +++++++++++----
 man/arx_class_args_list.Rd    | 11 +++++++----
 man/cdc_baseline_args_list.Rd |  6 ++++--
 man/flatline_args_list.Rd     | 11 +++++++----
 4 files changed, 29 insertions(+), 14 deletions(-)

diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index c9ae4e733..68d445872 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -10,6 +10,7 @@ arx_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
+  adjust_latency = NULL,
   quantile_levels = c(0.05, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
@@ -32,11 +33,17 @@ date for which forecasts should be produced.}
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{forecast_date}{Date. The date on which the forecast is created.  The
+default \code{NULL} will attempt to determine this automatically either as the
+max time value if there is no latency adjustment, or as the \code{as_of} of
+\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
 
-\item{target_date}{Date. The date for which the forecast is intended.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{target_date}{Date. The date for which the forecast is intended. The
+default \code{NULL} will attempt to determine this automatically as
+\code{forecast_date + ahead}.}
+
+\item{adjust_latency}{Character or \code{NULL}. one of the \code{method}s of
+\code{step_adjust_latency}, or \code{NULL} (in which case there is no adjustment).}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 311950d62..2c780e822 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -34,11 +34,14 @@ date for which forecasts should be produced.}
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{forecast_date}{Date. The date on which the forecast is created.  The
+default \code{NULL} will attempt to determine this automatically either as the
+max time value if there is no latency adjustment, or as the \code{as_of} of
+\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
 
-\item{target_date}{Date. The date for which the forecast is intended.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{target_date}{Date. The date for which the forecast is intended. The
+default \code{NULL} will attempt to determine this automatically as
+\code{forecast_date + ahead}.}
 
 \item{outcome_transform}{Scalar character. Whether the outcome should
 be created using growth rates (as the predictors are) or lagged
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 2f9300572..981c3c7e5 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -34,8 +34,10 @@ set of prediction horizons for \code{\link[=layer_cdc_flatline_quantiles]{layer_
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{forecast_date}{Date. The date on which the forecast is created.  The
+default \code{NULL} will attempt to determine this automatically either as the
+max time value if there is no latency adjustment, or as the \code{as_of} of
+\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 059dfa038..633d45020 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -29,11 +29,14 @@ So for example, \code{ahead = 7} will create residuals by comparing values
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{forecast_date}{Date. The date on which the forecast is created.  The
+default \code{NULL} will attempt to determine this automatically either as the
+max time value if there is no latency adjustment, or as the \code{as_of} of
+\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
 
-\item{target_date}{Date. The date for which the forecast is intended.
-The default \code{NULL} will attempt to determine this automatically.}
+\item{target_date}{Date. The date for which the forecast is intended. The
+default \code{NULL} will attempt to determine this automatically as
+\code{forecast_date + ahead}.}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of

From 084acb685c68a492c9e83425a42d2c3bc1c034a5 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 23 May 2024 20:57:35 -0500
Subject: [PATCH 056/387] group_by options to get the max_time_value

---
 R/step_adjust_latency.R                   | 20 +++++--
 R/utils-latency.R                         | 47 +++++++++++------
 tests/testthat/test-step_adjust_latency.R | 64 +++++++++++++++++++++++
 tests/testthat/test-utils_latency.R       | 51 +++++++++++-------
 4 files changed, 144 insertions(+), 38 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 763c893a9..38af9477b 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -26,6 +26,16 @@
 #'   the shortest lag at predict time is at the last observation. E.g. if the
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
 #'   become `c(3,10,17)`.
+#' @param epi_keys_checked a character vector. A list of keys to group by before
+#'   finding the `max_time_value`; only used if both `fixed_latency` and
+#'   `fixed_forecast_date` are `NULL`.  The default value of this is
+#'   `c("geo_value")`, but it can be any collection of `epi_keys`.  Different
+#'   locations may have different latencies; to produce a forecast at every
+#'   location, we need to use the largest latency across every location; this
+#'   means taking `max_time_value` to be the minimum of the `max_time_value`s
+#'   for each `geo_value` (or whichever collection of keys are specified).  If
+#'   `NULL` or an empty character vector, it will take the maximum across all
+#'   values, irrespective of any keys.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
 #'   vector. Cannot be set at the same time as `fixed_asof`. If non-`NULL`, the
 #'   amount to offset the ahead or lag by. If a single integer, this is used for
@@ -98,6 +108,7 @@ step_adjust_latency <-
              "locf",
              "extend_lags"
            ),
+           epi_keys_checked = c("geo_value"),
            fixed_latency = NULL,
            fixed_forecast_date = NULL,
            default = NA,
@@ -162,6 +173,7 @@ step_adjust_latency <-
         terms = dplyr::enquos(...),
         role = role,
         method = method,
+        epi_keys_checked = epi_keys_checked,
         trained = trained,
         forecast_date = fixed_forecast_date,
         latency = fixed_latency,
@@ -176,12 +188,13 @@ step_adjust_latency <-
 
 step_adjust_latency_new <-
   function(terms, role, trained, forecast_date, latency, shift_cols, time_type, default,
-           keys, method, skip, id) {
+           keys, method, epi_keys_checked, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
       role = role,
       method = method,
+      epi_keys_checked = epi_keys_checked,
       trained = trained,
       forecast_date = forecast_date,
       latency = latency,
@@ -205,7 +218,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       pull(variable)
   }
   # get and check the max_time and forecast_date are the right kinds of dates
-  forecast_date <- x$forecast_date %||% set_forecast_date(training, info)
+  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked)
 
   # infer the correct columns to be working with from the previous
   # transformations
@@ -213,7 +226,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   sign_shift <- get_sign(x)
   latency_cols <- get_latent_column_tibble(
     x$shift_cols, training, forecast_date,
-    x$latency, sign_shift, info
+    x$latency, sign_shift, info, x$epi_keys_checked
   )
 
   if ((x$method == "extend_ahead") || (x$method == "extend_lags")) {
@@ -251,6 +264,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     default = x$default,
     keys = x$keys,
     method = x$method,
+    epi_keys_checked = x$epi_keys_checked,
     skip = x$skip,
     id = x$id
   )
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 7f87bece6..00407898c 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -7,8 +7,9 @@
 #'   latencies `latency`
 #' @param new_data just what is says
 #' @param keys the variables which are used as keys
+#' @param epi_keys_checked the keys used to group_by to find max_time_values
 #' @keywords internal
-extend_either <- function(new_data, shift_cols, keys) {
+extend_either <- function(new_data, shift_cols, keys, epi_keys_checked) {
   shifted <-
     shift_cols %>%
     select(original_name, latency, new_name) %>%
@@ -80,14 +81,14 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @importFrom dplyr rowwise %>%
 get_latent_column_tibble <- function(
     shift_cols, new_data, forecast_date, latency,
-    sign_shift, info, call = caller_env()) {
+    sign_shift, info, epi_keys_checked, call = caller_env()) {
   shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"))
   if (is.null(latency)) {
     shift_cols <- shift_cols %>%
       rowwise() %>%
       # add the latencies to shift_cols
       mutate(latency = get_latency(
-        new_data, forecast_date, original_name, shift, sign_shift
+        new_data, forecast_date, original_name, shift, sign_shift, epi_keys_checked
       )) %>%
       ungroup()
   } else if (length(latency) > 1) {
@@ -121,7 +122,7 @@ get_latent_column_tibble <- function(
 
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
-set_forecast_date <- function(new_data, info) {
+set_forecast_date <- function(new_data, info, epi_keys_checked) {
   original_columns <- info %>%
     filter(source == "original") %>%
     pull(variable)
@@ -135,22 +136,29 @@ set_forecast_date <- function(new_data, info) {
   }
   # the source data determines the actual time_values
   # these are the non-na time_values;
-  time_values <- new_data %>%
+  # get the minimum value across the checked epi_keys' maximum time values
+  max_time <- new_data %>%
     select(all_of(original_columns)) %>%
     drop_na() %>%
-    pull(time_value)
-  if (length(time_values) <= 0) {
-    cli::cli_abort("the `time_value` column of `new_data` is empty")
-  }
+    {
+      # null and "" don't work in `group_by`
+      if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
+        group_by(., get(epi_keys_checked))
+      } else {
+        .
+      }
+    } %>%
+    summarize(time_value = max(time_value)) %>%
+    pull(time_value) %>%
+    min()
   forecast_date <- attributes(new_data)$metadata$as_of
-  max_time <- max(time_values)
   # make sure the as_of is sane
-  if (!inherits(forecast_date, class(time_values)) & !inherits(forecast_date, "POSIXt")) {
+  if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
     cli::cli_abort(paste(
       "the data matrix `forecast_date` value is {forecast_date}, ",
       "and not a valid `time_type` with type ",
       "matching `time_value`'s type of ",
-      "{typeof(new_data$time_value)}."
+      "{class(max_time)}."
     ))
   }
   if (is.null(forecast_date) || is.na(forecast_date)) {
@@ -167,7 +175,7 @@ set_forecast_date <- function(new_data, info) {
     ))
   }
   # TODO cover the rest of the possible types for as_of and max_time...
-  if (class(time_values) == "Date") {
+  if (class(max_time) == "Date") {
     forecast_date <- as.Date(forecast_date)
   }
   return(forecast_date)
@@ -177,11 +185,20 @@ set_forecast_date <- function(new_data, info) {
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
 #' @keywords internal
-get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shift) {
+get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shift, epi_keys_checked) {
   shift_max_date <- new_data %>%
     drop_na(all_of(column)) %>%
+    {
+      # null and "" don't work in `group_by`
+      if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
+        group_by(., get(epi_keys_checked))
+      } else {
+        .
+      }
+    } %>%
+    summarize(time_value = max(time_value)) %>%
     pull(time_value) %>%
-    max()
+    min()
   return(as.integer(sign_shift * (as.Date(forecast_date) - shift_max_date) + shift_amount))
 }
 
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index f08652ca5..70d127327 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -25,6 +25,15 @@ slm_fit <- function(recipe, data = x) {
     fit(data = data)
 }
 
+
+# making a toy dataset with lag between geo_values
+x_lagged <- x
+x_lagged$time_value <- x$time_value - 1
+x_lagged$geo_value <- "other"
+x_lagged <- add_row(x, x_lagged)
+x_lagged
+attributes(x_lagged)$metadata$as_of <- testing_as_of
+
 test_that("epi_adjust_latency correctly extends the lags", {
   r5 <- epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
@@ -158,3 +167,58 @@ test_that("epi_adjust_latency warns against removing NA's beforehand", {
   )
 })
 # todo check that epi_adjust_latency errors for nonsense `as_of`'s
+
+
+
+# todo make sure that `epi_keys_checked` works correctly for extra epi_keys
+test_that("epi_adjust_latency correctly extends the lags", {
+  r5 <- epi_recipe(x_lagged) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead) %>%
+    step_adjust_latency(method = "extend_lags", epi_keys_checked = NULL)
+  # the as_of on x is today's date, which is >970 days in the future
+  # also, there's no data >970 days in the past, so it gets an error trying to
+  # fit on no data
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
+
+  # now trying with the as_of a reasonable distance in the future
+  fit5 <- slm_fit(r5, data = x_lagged)
+  expect_equal(
+    names(fit5$pre$mold$predictors),
+    c(
+      "lag_5_death_rate", "lag_11_death_rate", "lag_16_death_rate",
+      "lag_6_case_rate", "lag_10_case_rate"
+    )
+  )
+  latest <- get_test_data(r5, x_lagged)
+  latest$time_value %>% unique()
+  pred <- predict(fit5, latest)
+  point_pred <- pred %>% filter(!is.na(.pred))
+  expect_equal(nrow(point_pred), 1)
+  expect_equal(point_pred$time_value, as.Date(testing_as_of))
+
+  expect_equal(
+    names(fit5$pre$mold$outcomes),
+    glue::glue("ahead_{ahead}_death_rate")
+  )
+  latest <- get_test_data(r5, x)
+  pred <- predict(fit5, latest)
+  actual_solutions <- pred %>% filter(!is.na(.pred))
+  expect_equal(actual_solutions$time_value, testing_as_of)
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+
+  # result should be equivalent to just immediately doing the adjusted lags by
+  # hand
+  hand_adjusted <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(5, 11, 16)) %>%
+    step_epi_lag(case_rate, lag = c(6, 10)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+  fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
+  expect_equal(
+    fit5$fit$fit$fit$coefficients,
+    fit_hand_adj$fit$fit$fit$coefficients
+  )
+})
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 6fd3672aa..b8ec678cd 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -1,13 +1,16 @@
-time_values <- as.Date("2021-01-01") + 0:199
+time_values <- as.Date("2021-01-01") + +floor(seq(0, 100, by = .5))[1:200]
 as_of <- max(time_values) + 5
 max_time <- max(time_values)
 old_data <- tibble(
-  geo_value = rep("place", 200),
-  time_value = as.Date("2021-01-01") + 0:199,
+  geo_value = rep(c("place1", "place2"), 100),
+  time_value = as.Date("2021-01-01") + +floor(seq(0, 100, by = .5))[1:200],
   case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
   tmp_death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
 ) %>%
+  # place2 is slightly more recent than place1
+  mutate(time_value = as.Date(ifelse(geo_value == "place2", time_value + 1, time_value))) %>%
   as_epi_df(as_of = as_of)
+old_data
 keys <- c("time_value", "geo_value")
 old_data <- old_data %>%
   full_join(epi_shift_single(
@@ -54,17 +57,19 @@ test_that("construct_shift_tibble constructs the right tibble", {
 })
 
 test_that("get_latency works", {
-  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1), 4)
-  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 3, 1), 5)
+  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1, "geo_value"), 4)
+  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 3, 1, "geo_value"), 5)
   # get_latency does't check the shift_amount
-  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 4, 1), 6)
+  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 4, 1, "geo_value"), 6)
   # ahead works correctly
-  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, -1), -5)
+  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, -1, "geo_value"), -5)
   # setting the wrong sign doubles the shift and gets the sign wrong
-  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, 1), 5 + 4 * 2)
+  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, 1, "geo_value"), 5 + 4 * 2)
+  # minimizing over everything decreases the latency
+  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1, NULL), 3)
 })
 
-test_that("get_latency infers max_time to be the minimum `max time` across the columns", {})
+test_that("get_latency infers max_time to be the minimum `max time` across the epi_keys", {})
 
 test_that("get_asof works", {
   info <- tribble(
@@ -75,24 +80,29 @@ test_that("get_asof works", {
     "death_rate", "numeric", "raw", "original",
     "not_real", "numeric", "predictor", "derived"
   )
-  expect_equal(set_forecast_date(modified_data, info), as_of)
+  expect_equal(set_forecast_date(modified_data, info, "geo_value"), as_of)
+  expect_equal(set_forecast_date(modified_data, info, ""), as_of)
+  expect_equal(set_forecast_date(modified_data, info, NULL), as_of)
 })
 
 test_that("get_latent_column_tibble infers latency and works correctly", {
   info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
 
   case_lag <- get_latent_column_tibble(
-    shift_cols[1, ], modified_data, as_of, NULL, 1, info
+    shift_cols[1, ], modified_data, as_of, NULL, 1, info,
+    epi_keys_checked = "geo_value"
   )
   expect_equal(case_lag, all_shift_cols[1, ])
 
   death_lag <- get_latent_column_tibble(
-    shift_cols[2, ], modified_data, as_of, NULL, 1, info
+    shift_cols[2, ], modified_data, as_of, NULL, 1, info,
+    epi_keys_checked = "geo_value"
   )
   expect_equal(death_lag, all_shift_cols[2, ])
 
   both_lag <- get_latent_column_tibble(
-    shift_cols, modified_data, as_of, NULL, 1, info
+    shift_cols, modified_data, as_of, NULL, 1, info,
+    epi_keys_checked = "geo_value"
   )
   expect_equal(both_lag, all_shift_cols[1:2, ])
 })
@@ -123,7 +133,7 @@ test_that("get_latent_column_tibble assigns given latencies", {
 
   ahead_shift_cols <- construct_shift_tibble(c("case_rate"), test_recipe, "step_epi_ahead", "ahead")
   case_ahead <- get_latent_column_tibble(
-    ahead_shift_cols, modified_data, as_of, NULL, -1, info
+    ahead_shift_cols, modified_data, as_of, NULL, -1, info, "geo_value"
   )
   expect_equal(case_ahead, all_shift_cols[3, ])
 })
@@ -156,16 +166,17 @@ test_that("extend_either works", {
       epi_shift_single(old_data, "case_rate", -9, "ahead_9_case_rate", keys),
       by = keys
     ) %>%
-    dplyr::add_row(tibble(
-      geo_value = "place",
-      time_value = as.Date("2021-08-01"), case_rate = NA, death_rate = NA,
-      lag_8_case_rate = NA, lag_11_death_rate = NA, ahead_9_case_rate = NA
+    dplyr::bind_rows(tibble(
+      geo_value = c("place1", "place2"),
+      time_value = as.Date(c("2021-04-23", "2021-04-24")), case_rate = c(NA, NA), death_rate = c(NA, NA),
+      lag_8_case_rate = c(NA, NA), lag_11_death_rate = c(NA, NA), ahead_9_case_rate = c(NA, NA)
     )) %>%
-    arrange(time_value)
+    arrange(time_value, geo_value)
   expect_equal(
-    extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value),
+    extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value, geo_value),
     expected_post_shift
   )
+  extended <- extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value, geo_value)
 })
 
 

From d2e2f95051a848b8d92b88012d5ff6787d4860bd Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 29 May 2024 16:10:10 -0500
Subject: [PATCH 057/387] PR review recs

- drop multiline pipes
- better docs
- check exclusive parameters aren't used simultaneously
- inherit typo
- additional placeholders for future tests
---
 R/arx_forecaster.R                        | 58 ++++++++++-------------
 R/layer_add_forecast_date.R               |  6 +--
 R/layer_add_target_date.R                 |  1 -
 R/step_adjust_latency.R                   | 19 ++++----
 R/utils-latency.R                         | 20 ++++----
 man/get_latency.Rd                        |  9 +++-
 man/get_latent_column_tibble.Rd           |  1 +
 man/layer_add_forecast_date.Rd            |  5 +-
 man/set_forecast_date.Rd                  |  2 +-
 man/step_adjust_latency.Rd                | 23 ++++++---
 tests/testthat/test-step_adjust_latency.R |  4 ++
 11 files changed, 82 insertions(+), 66 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 3bcf02586..3a2df792f 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -1,4 +1,3 @@
-# TODO add latency to default forecaster
 #' Direct autoregressive forecaster with covariates
 #'
 #' This is an autoregressive forecasting model for
@@ -136,41 +135,36 @@ arx_fcast_epi_workflow <- function(
     r <- step_epi_lag(r, !!p, lag = lags[[l]])
   }
   r <- r %>%
-    step_epi_ahead(!!outcome, ahead = args_list$ahead) %>%
+    step_epi_ahead(!!outcome, ahead = args_list$ahead)
+  method <- args_list$adjust_latency
+  if (!is.null(method)) {
+    if (method == "extend_ahead") {
+      r <- r %>% step_adjust_latency(all_outcomes(),
+                          fixed_forecast_date = forecast_date,
+                          method = method
+                          )
+    } else if (method == "extend_lags") {
+      r <- r %>% step_adjust_latency(all_predictors(),
+                          fixed_forecast_date = forecast_date,
+                          method = method
+                          )
+    }
+    r <- r %>% step_epi_naomit() %>%
+    step_training_window(n_recent = args_list$n_training) %>%
     {
-      method <- args_list$adjust_latency
-      if (!is.null(method)) {
-        if (method == "extend_ahead") {
-          step_adjust_latency(.,
-            all_outcomes(),
-            fixed_forecast_date = forecast_date,
-            method = method
-          )
-        } else if (method == "extend_lags") {
-          step_adjust_latency(.,
-            all_predictors(),
-            fixed_forecast_date = forecast_date,
-            method = method
-          )
-        }
+      if (!is.null(args_list$check_enough_data_n)) {
+        check_enough_train_data(
+          .,
+          all_predictors(),
+          !!outcome,
+          n = args_list$check_enough_data_n,
+          epi_keys = args_list$check_enough_data_epi_keys,
+          drop_na = FALSE
+        )
       } else {
         .
       }
-    } %>%
-    step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training)
-
-  if (!is.null(args_list$check_enough_data_n)) {
-    r <- check_enough_train_data(
-      r,
-      all_predictors(),
-      !!outcome,
-      n = args_list$check_enough_data_n,
-      epi_keys = args_list$check_enough_data_epi_keys,
-      drop_na = FALSE
-    )
-  }
-
+    }
 
 
   # --- postprocessor
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 43b7d7bd4..3c81ded57 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -1,4 +1,3 @@
-# TODO adapt this to latency
 #' Postprocessing step to add the forecast date
 #'
 #' @param frosting a `frosting` postprocessor
@@ -6,8 +5,9 @@
 #'   For most cases, this should be specified in the form "yyyy-mm-dd". Note
 #'   that when the forecast date is left unspecified, it is set to one of two
 #'   values.  If there is a `step_adjust_latency` step present, it uses the
-#'   `as_of`  the maximum time value from the data used in pre-processing,
-#'   fitting the model, and postprocessing.
+#'   `forecast_date` as set in that function. Otherwise, it uses the maximum
+#'   `time_value` across the data used for pre-processing, fitting the model,
+#'   and postprocessing.
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index a1bdc3e4a..991ec2140 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -1,4 +1,3 @@
-# TODO adapt this to latency
 #' Postprocessing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 38af9477b..3b8405944 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -27,8 +27,7 @@
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
 #'   become `c(3,10,17)`.
 #' @param epi_keys_checked a character vector. A list of keys to group by before
-#'   finding the `max_time_value`; only used if both `fixed_latency` and
-#'   `fixed_forecast_date` are `NULL`.  The default value of this is
+#'   finding the `max_time_value`.  The default value of this is
 #'   `c("geo_value")`, but it can be any collection of `epi_keys`.  Different
 #'   locations may have different latencies; to produce a forecast at every
 #'   location, we need to use the largest latency across every location; this
@@ -37,12 +36,12 @@
 #'   `NULL` or an empty character vector, it will take the maximum across all
 #'   values, irrespective of any keys.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
-#'   vector. Cannot be set at the same time as `fixed_asof`. If non-`NULL`, the
-#'   amount to offset the ahead or lag by. If a single integer, this is used for
-#'   all columns; if a labeled vector, the labels must correspond to the base
-#'   column names (before lags/aheads).  If `NULL`, the latency is the distance
-#'   between the `epi_df`'s `max_time_value` and either the
-#'   `fixed_forecast_date` or the `epi_df`'s `as_of` field (the default for
+#'   vector. Cannot be set at the same time as `fixed_forecast_date`. If
+#'   non-`NULL`, the amount to offset the ahead or lag by. If a single integer,
+#'   this is used for all columns; if a labeled vector, the labels must
+#'   correspond to the base column names (before lags/aheads).  If `NULL`, the
+#'   latency is the distance between the `epi_df`'s `max_time_value` and either
+#'   the `fixed_forecast_date` or the `epi_df`'s `as_of` field (the default for
 #'   `forecast_date`).
 #' @param fixed_forecast_date either a date of the same kind used in the
 #'   `epi_df`, or `NULL`. Exclusive with `fixed_latency`. If a date, it gives
@@ -139,6 +138,10 @@ step_adjust_latency <-
       cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
                       as columns may be moved around")
     }
+    if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
+      cli::cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
+ can be non-`NULL` at a time!")
+    }
 
     method <- rlang::arg_match(method)
     terms_used <- recipes_eval_select(enquos(...), recipe$template, recipe$term_info)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 00407898c..4ddd0dc08 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -7,9 +7,8 @@
 #'   latencies `latency`
 #' @param new_data just what is says
 #' @param keys the variables which are used as keys
-#' @param epi_keys_checked the keys used to group_by to find max_time_values
 #' @keywords internal
-extend_either <- function(new_data, shift_cols, keys, epi_keys_checked) {
+extend_either <- function(new_data, shift_cols, keys) {
   shifted <-
     shift_cols %>%
     select(original_name, latency, new_name) %>%
@@ -187,15 +186,12 @@ set_forecast_date <- function(new_data, info, epi_keys_checked) {
 #' @keywords internal
 get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shift, epi_keys_checked) {
   shift_max_date <- new_data %>%
-    drop_na(all_of(column)) %>%
-    {
-      # null and "" don't work in `group_by`
-      if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
-        group_by(., get(epi_keys_checked))
-      } else {
-        .
-      }
-    } %>%
+    drop_na(all_of(column))
+  # null and "" don't work in `group_by`
+  if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
+    shift_max_date <- shift_max_date %>% group_by(get(epi_keys_checked))
+  }
+  shift_max_date <- shift_max_date %>%
     summarize(time_value = max(time_value)) %>%
     pull(time_value) %>%
     min()
@@ -216,7 +212,7 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
     handpicked_as_of <- map(
       this_recipe$steps,
       function(x) {
-        if (inherits(this_recipe$steps[[3]], "step_adjust_latency")) x$as_of
+        if (inherits(x, "step_adjust_latency")) x$as_of
       }
     ) %>% Filter(Negate(is.null), .)
     if (length(handpicked_as_of) > 0) {
diff --git a/man/get_latency.Rd b/man/get_latency.Rd
index 4cde42ab6..f5bf7f0c2 100644
--- a/man/get_latency.Rd
+++ b/man/get_latency.Rd
@@ -4,7 +4,14 @@
 \alias{get_latency}
 \title{the latency is also the amount the shift is off by}
 \usage{
-get_latency(new_data, forecast_date, column, shift_amount, sign_shift)
+get_latency(
+  new_data,
+  forecast_date,
+  column,
+  shift_amount,
+  sign_shift,
+  epi_keys_checked
+)
 }
 \arguments{
 \item{sign_shift}{integer. 1 if lag and -1 if ahead. These represent how you
diff --git a/man/get_latent_column_tibble.Rd b/man/get_latent_column_tibble.Rd
index 3134a42ae..0a0298625 100644
--- a/man/get_latent_column_tibble.Rd
+++ b/man/get_latent_column_tibble.Rd
@@ -12,6 +12,7 @@ get_latent_column_tibble(
   latency,
   sign_shift,
   info,
+  epi_keys_checked,
   call = caller_env()
 )
 }
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index 4c3336536..aa224013f 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -17,8 +17,9 @@ layer_add_forecast_date(
 For most cases, this should be specified in the form "yyyy-mm-dd". Note
 that when the forecast date is left unspecified, it is set to one of two
 values.  If there is a \code{step_adjust_latency} step present, it uses the
-\code{as_of}  the maximum time value from the data used in pre-processing,
-fitting the model, and postprocessing.}
+\code{forecast_date} as set in that function. Otherwise, it uses the maximum
+\code{time_value} across the data used for pre-processing, fitting the model,
+and postprocessing.}
 
 \item{id}{a random id string}
 }
diff --git a/man/set_forecast_date.Rd b/man/set_forecast_date.Rd
index c238758c9..58682bd2b 100644
--- a/man/set_forecast_date.Rd
+++ b/man/set_forecast_date.Rd
@@ -4,7 +4,7 @@
 \alias{set_forecast_date}
 \title{Extract the as_of for the forecast date, and make sure there's nothing very off about it.}
 \usage{
-set_forecast_date(new_data, info)
+set_forecast_date(new_data, info, epi_keys_checked)
 }
 \description{
 Extract the as_of for the forecast date, and make sure there's nothing very off about it.
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index b503f3eeb..970b9075c 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -10,6 +10,7 @@ step_adjust_latency(
   role = NA,
   trained = FALSE,
   method = c("extend_ahead", "locf", "extend_lags"),
+  epi_keys_checked = c("geo_value"),
   fixed_latency = NULL,
   fixed_forecast_date = NULL,
   default = NA,
@@ -51,13 +52,23 @@ lags are \code{c(0,7,14)} for data that is 3 days latent, the actual lags used
 become \code{c(3,10,17)}.
 }}
 
+\item{epi_keys_checked}{a character vector. A list of keys to group by before
+finding the \code{max_time_value}.  The default value of this is
+\code{c("geo_value")}, but it can be any collection of \code{epi_keys}.  Different
+locations may have different latencies; to produce a forecast at every
+location, we need to use the largest latency across every location; this
+means taking \code{max_time_value} to be the minimum of the \code{max_time_value}s
+for each \code{geo_value} (or whichever collection of keys are specified).  If
+\code{NULL} or an empty character vector, it will take the maximum across all
+values, irrespective of any keys.}
+
 \item{fixed_latency}{either a positive integer, or a labeled positive integer
-vector. Cannot be set at the same time as \code{fixed_asof}. If non-\code{NULL}, the
-amount to offset the ahead or lag by. If a single integer, this is used for
-all columns; if a labeled vector, the labels must correspond to the base
-column names (before lags/aheads).  If \code{NULL}, the latency is the distance
-between the \code{epi_df}'s \code{max_time_value} and either the
-\code{fixed_forecast_date} or the \code{epi_df}'s \code{as_of} field (the default for
+vector. Cannot be set at the same time as \code{fixed_forecast_date}. If
+non-\code{NULL}, the amount to offset the ahead or lag by. If a single integer,
+this is used for all columns; if a labeled vector, the labels must
+correspond to the base column names (before lags/aheads).  If \code{NULL}, the
+latency is the distance between the \code{epi_df}'s \code{max_time_value} and either
+the \code{fixed_forecast_date} or the \code{epi_df}'s \code{as_of} field (the default for
 \code{forecast_date}).}
 
 \item{fixed_forecast_date}{either a date of the same kind used in the
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 70d127327..ab7817799 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -222,3 +222,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
     fit_hand_adj$fit$fit$fit$coefficients
   )
 })
+
+test_that("`step_adjust_latency` only allows one instance of itself", {})
+
+test_that("setting fixed_* works for `step_adjust_latency`", {})

From 99d8099117b37dc0bbeb4edf4bd379585b8a0c21 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 29 May 2024 16:56:58 -0500
Subject: [PATCH 058/387] typo in multiline pipe replacement

---
 R/arx_forecaster.R | 48 +++++++++++++++++++++-------------------------
 1 file changed, 22 insertions(+), 26 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 3a2df792f..df59a8177 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -140,31 +140,28 @@ arx_fcast_epi_workflow <- function(
   if (!is.null(method)) {
     if (method == "extend_ahead") {
       r <- r %>% step_adjust_latency(all_outcomes(),
-                          fixed_forecast_date = forecast_date,
-                          method = method
-                          )
+        fixed_forecast_date = forecast_date,
+        method = method
+      )
     } else if (method == "extend_lags") {
       r <- r %>% step_adjust_latency(all_predictors(),
-                          fixed_forecast_date = forecast_date,
-                          method = method
-                          )
-    }
-    r <- r %>% step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training) %>%
-    {
-      if (!is.null(args_list$check_enough_data_n)) {
-        check_enough_train_data(
-          .,
-          all_predictors(),
-          !!outcome,
-          n = args_list$check_enough_data_n,
-          epi_keys = args_list$check_enough_data_epi_keys,
-          drop_na = FALSE
-        )
-      } else {
-        .
-      }
+        fixed_forecast_date = forecast_date,
+        method = method
+      )
     }
+  }
+  r <- r %>%
+    step_epi_naomit() %>%
+    step_training_window(n_recent = args_list$n_training)
+  if (!is.null(args_list$check_enough_data_n)) {
+    r <- r %>% check_enough_train_data(
+      all_predictors(),
+      !!outcome,
+      n = args_list$check_enough_data_n,
+      epi_keys = args_list$check_enough_data_epi_keys,
+      drop_na = FALSE
+    )
+  }
 
 
   # --- postprocessor
@@ -176,12 +173,11 @@ arx_fcast_epi_workflow <- function(
       rlang::eval_tidy(trainer$args$quantile_levels)
     ))
     args_list$quantile_levels <- quantile_levels
-    trainer$args$quantile_levels <- enquo(quantile_levels)
-    f <- layer_quantile_distn(f, quantile_levels = quantile_levels) %>%
+    trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
+    f <- f %>% layer_quantile_distn(quantile_levels = quantile_levels) %>%
       layer_point_from_distn()
   } else {
-    f <- layer_residual_quantiles(
-      f,
+    f <- f %>% layer_residual_quantiles(
       quantile_levels = args_list$quantile_levels,
       symmetrize = args_list$symmetrize,
       by_key = args_list$quantile_by_key

From c4fce2e906814649b3a2827c5416053853d9d95e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 3 Jun 2024 15:46:11 -0500
Subject: [PATCH 059/387] happy styler

---
 R/arx_forecaster.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index df59a8177..98ff4f037 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -174,7 +174,8 @@ arx_fcast_epi_workflow <- function(
     ))
     args_list$quantile_levels <- quantile_levels
     trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
-    f <- f %>% layer_quantile_distn(quantile_levels = quantile_levels) %>%
+    f <- f %>%
+      layer_quantile_distn(quantile_levels = quantile_levels) %>%
       layer_point_from_distn()
   } else {
     f <- f %>% layer_residual_quantiles(

From f5ae9d1182fa773dcd58266650c239043be5091a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 14 Jun 2024 15:37:13 -0500
Subject: [PATCH 060/387] various requested changes, check passes

---
 NAMESPACE                              |  1 +
 R/arx_classifier.R                     |  6 +++-
 R/arx_forecaster.R                     |  8 ++++--
 R/get_test_data.R                      | 10 +++----
 R/layer_residual_quantiles.R           |  2 +-
 R/step_adjust_latency.R                | 33 +++++++++++-----------
 R/utils-latency.R                      | 39 +++++++++++++++-----------
 man/bake.step_adjust_latency.Rd        | 17 -----------
 man/get_forecast_date_in_layer.Rd      | 11 ++++++++
 man/step_adjust_latency.Rd             | 23 +++++++++------
 vignettes/articles/symptom-surveys.Rmd | 16 +++++------
 11 files changed, 89 insertions(+), 77 deletions(-)
 delete mode 100644 man/bake.step_adjust_latency.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 29b1d9591..9b8035714 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -240,6 +240,7 @@ importFrom(dplyr,everything)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
+importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 97c4deed1..e97d6675f 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -55,11 +55,15 @@ arx_classifier <- function(
   wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
+  latency_adjust_fd <- if (is.null(args_list$adjust_latency))
+                         max(epi_data$time_value) else attributes(epi_data)$metadata$as_of
+  forecast_date <- args_list$forecast_date %||% latency_adjust_fd
+  target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   preds <- forecast(
     wf,
     fill_locf = is.null(args_list$adjust_latency),
     n_recent = args_list$nafill_buffer,
-    forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
+    forecast_date = forecast_date
   ) %>%
     as_tibble() %>%
     select(-time_value)
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 98ff4f037..f97d2682d 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -122,8 +122,12 @@ arx_fcast_epi_workflow <- function(
   # forecast_date is first what they set;
   # if they don't and they're not adjusting latency, it defaults to the max time_value
   # if they're adjusting as_of, it defaults to the as_of
-  forecast_date <- args_list$forecast_date %||%
-    if (is.null(args_list$adjust_latency)) max(epi_data$time_value) else attributes(epi_data)$metadata$as_of
+  latency_adjust_fd <- if (is.null(args_list$adjust_latency)) {
+    max(epi_data$time_value)
+  } else {
+    attributes(epi_data)$metadata$as_of
+  }
+  forecast_date <- args_list$forecast_date %||% latency_adjust_fd
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
 
   lags <- arx_lags_validator(predictors, args_list$lags)
diff --git a/R/get_test_data.R b/R/get_test_data.R
index 694e73b06..a491ed40d 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -111,11 +111,11 @@ get_test_data <- function(
   # Now, fill forward missing data if requested
   if (fill_locf) {
     cannot_be_used <- x %>%
-      filter(forecast_date - time_value <= n_recent) %>%
-      mutate(fillers = forecast_date - time_value > min_required) %>%
-      summarize(
-        across(
-          -any_of(key_colnames(recipe)),
+      dplyr::filter(forecast_date - time_value <= n_recent) %>%
+      dplyr::mutate(fillers = forecast_date - time_value > min_required) %>%
+      dplyr::summarise(
+        dplyr::across(
+          -tidyselect::any_of(epi_keys(recipe)),
           ~ all(is.na(.x[fillers])) & is.na(head(.x[!fillers], 1))
         ),
         .groups = "drop"
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 257a951a9..b21bdcfcc 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -124,7 +124,7 @@ slather.layer_residual_quantiles <-
     }
 
     r <- r %>%
-      summarize(
+      dplyr::summarise(
         dstn = list(quantile(
           c(.resid, s * .resid),
           probs = object$quantile_levels, na.rm = TRUE
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 3b8405944..28480097d 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -1,10 +1,13 @@
-#' Adapt the pipeline to latency in the data
+#' Adapt the model to latent data
 #'
-#' In the standard case, the pipeline assumes that the last observation is also
-#' the day from which the forecast is being made. `step_adjust_latency` uses the
-#' `as_of` date of the `epi_df` as the `forecast_date`. This is most useful in
-#' realtime and pseudo-prospective forecasting for data where there is some
-#' delay between the day recorded and when that data is available.
+#' In the standard case, the arx models assume that the last observation is also
+#' the day from which the forecast is being made. But if the data has latency,
+#' then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
+#' to compensate. This allows the model to create bleeding-edge forecasts using
+#' the lags actually observed rather than anticipated. `step_adjust_latency`
+#' uses the `as_of` date of the `epi_df` as the `forecast_date`. This is most
+#' useful in realtime and pseudo-prospective forecasting for data where there is
+#' some delay between the day recorded and when that data is available.
 #'
 #' @param recipe A recipe object. The step will be added to the
 #'  sequence of operations for this recipe.
@@ -67,8 +70,10 @@
 #' @template step-return
 #'
 #' @details The step assumes that the pipeline has already applied either
-#'   `step_epi_ahead` or `step_epi_lag` depending on the value of
-#'   `"method"`, and that `step_epi_naomit` has NOT been run.
+#'   `step_epi_ahead` or `step_epi_lag` depending on the value of `"method"`,
+#'   and that `step_epi_naomit` has NOT been run.  By default, the latency will
+#'   be determined using the arguments below, but can be set explicitly using
+#'   either `fixed_latency` or `fixed_forecast_date`.
 #'
 #' The `prefix` and `id` arguments are unchangeable to ensure that the code runs
 #' properly and to avoid inconsistency with naming. For `step_epi_ahead`, they
@@ -97,6 +102,7 @@
 #' jhu_fit
 #'
 #' @importFrom recipes detect_step
+#' @importFrom rlang enquos
 step_adjust_latency <-
   function(recipe,
            ...,
@@ -113,7 +119,7 @@ step_adjust_latency <-
            default = NA,
            skip = FALSE,
            columns = NULL,
-           id = recipes::rand_id("epi_lag")) {
+           id = recipes::rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
       cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
@@ -173,7 +179,7 @@ step_adjust_latency <-
     recipes::add_step(
       recipe,
       step_adjust_latency_new(
-        terms = dplyr::enquos(...),
+        terms = enquos(...),
         role = role,
         method = method,
         epi_keys_checked = epi_keys_checked,
@@ -273,13 +279,6 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   )
 }
 
-#' various ways of handling differences between the `as_of` date and the maximum
-#' time value
-#' @description
-#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
-#'   date, rather than relative to the last day of data
-#' @param new_data assumes that this already has lag/ahead columns that we need
-#'   to adjust
 #' @importFrom dplyr %>% pull
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 4ddd0dc08..9c5f61423 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -77,7 +77,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #'   `effective_shift` (shift+latency), and `new_name` (adjusted names with the
 #'   effective_shift)
 #' @keywords internal
-#' @importFrom dplyr rowwise %>%
+#' @importFrom dplyr rowwise left_join join_by
 get_latent_column_tibble <- function(
     shift_cols, new_data, forecast_date, latency,
     sign_shift, info, epi_keys_checked, call = caller_env()) {
@@ -121,6 +121,7 @@ get_latent_column_tibble <- function(
 
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
+#' @importFrom dplyr select
 set_forecast_date <- function(new_data, info, epi_keys_checked) {
   original_columns <- info %>%
     filter(source == "original") %>%
@@ -138,16 +139,13 @@ set_forecast_date <- function(new_data, info, epi_keys_checked) {
   # get the minimum value across the checked epi_keys' maximum time values
   max_time <- new_data %>%
     select(all_of(original_columns)) %>%
-    drop_na() %>%
-    {
-      # null and "" don't work in `group_by`
-      if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
-        group_by(., get(epi_keys_checked))
-      } else {
-        .
-      }
-    } %>%
-    summarize(time_value = max(time_value)) %>%
+    drop_na()
+  # null and "" don't work in `group_by`
+  if (!is.null(epi_keys_checked) && (epi_keys_checked != "")) {
+    max_time <- max_time %>% group_by(get(epi_keys_checked))
+  }
+  max_time <- max_time %>%
+    summarise(time_value = max(time_value)) %>%
     pull(time_value) %>%
     min()
   forecast_date <- attributes(new_data)$metadata$as_of
@@ -174,7 +172,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked) {
     ))
   }
   # TODO cover the rest of the possible types for as_of and max_time...
-  if (class(max_time) == "Date") {
+  if (inherits(max_time, "Date")) {
     forecast_date <- as.Date(forecast_date)
   }
   return(forecast_date)
@@ -192,7 +190,7 @@ get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shif
     shift_max_date <- shift_max_date %>% group_by(get(epi_keys_checked))
   }
   shift_max_date <- shift_max_date %>%
-    summarize(time_value = max(time_value)) %>%
+    summarise(time_value = max(time_value)) %>%
     pull(time_value) %>%
     min()
   return(as.integer(sign_shift * (as.Date(forecast_date) - shift_max_date) + shift_amount))
@@ -201,6 +199,13 @@ get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shif
 
 
 #' get the target date while in a layer
+#' @param this_recipe the recipe to check for `step_adjust_latency`
+#' @param workflow_max_time_value the `max_time` value coming out of the fit
+#'   workflow (this will be the maximal time value in a potentially different
+#'   dataset)
+#' @param new_data the data we're currently working with, from which we'll take
+#'   a potentially different max_time_value
+#' @keywords internal
 get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new_data) {
   max_time_value <- max(
     workflow_max_time_value,
@@ -209,14 +214,14 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
   )
   if (this_recipe %>% recipes::detect_step("adjust_latency")) {
     # get the as_of in an `adjust_latency` step, regardless of where
-    handpicked_as_of <- map(
+    handpicked_forecast_date <- map(
       this_recipe$steps,
       function(x) {
-        if (inherits(x, "step_adjust_latency")) x$as_of
+        if (inherits(x, "step_adjust_latency")) x$forecast_date
       }
     ) %>% Filter(Negate(is.null), .)
-    if (length(handpicked_as_of) > 0) {
-      max_time_value <- handpicked_as_of[[1]]
+    if (length(handpicked_forecast_date) > 0) {
+      max_time_value <- handpicked_forecast_date[[1]]
     } else {
       # if we haven't chosen one, use either the max_time_value or the as_of
       max_time_value <- max(
diff --git a/man/bake.step_adjust_latency.Rd b/man/bake.step_adjust_latency.Rd
deleted file mode 100644
index dac3f5509..000000000
--- a/man/bake.step_adjust_latency.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/step_adjust_latency.R
-\name{bake.step_adjust_latency}
-\alias{bake.step_adjust_latency}
-\title{various ways of handling differences between the \code{as_of} date and the maximum
-time value}
-\usage{
-\method{bake}{step_adjust_latency}(object, new_data, ...)
-}
-\arguments{
-\item{new_data}{assumes that this already has lag/ahead columns that we need
-to adjust}
-}
-\description{
-adjust the ahead so that we will be predicting \code{ahead} days after the \code{as_of}
-date, rather than relative to the last day of data
-}
diff --git a/man/get_forecast_date_in_layer.Rd b/man/get_forecast_date_in_layer.Rd
index 2f9e03548..c866a88e7 100644
--- a/man/get_forecast_date_in_layer.Rd
+++ b/man/get_forecast_date_in_layer.Rd
@@ -6,6 +6,17 @@
 \usage{
 get_forecast_date_in_layer(this_recipe, workflow_max_time_value, new_data)
 }
+\arguments{
+\item{this_recipe}{the recipe to check for \code{step_adjust_latency}}
+
+\item{workflow_max_time_value}{the \code{max_time} value coming out of the fit
+workflow (this will be the maximal time value in a potentially different
+dataset)}
+
+\item{new_data}{the data we're currently working with, from which we'll take
+a potentially different max_time_value}
+}
 \description{
 get the target date while in a layer
 }
+\keyword{internal}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 970b9075c..c4f407b3c 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/step_adjust_latency.R
 \name{step_adjust_latency}
 \alias{step_adjust_latency}
-\title{Adapt the pipeline to latency in the data}
+\title{Adapt the model to latent data}
 \usage{
 step_adjust_latency(
   recipe,
@@ -16,7 +16,7 @@ step_adjust_latency(
   default = NA,
   skip = FALSE,
   columns = NULL,
-  id = recipes::rand_id("epi_lag")
+  id = recipes::rand_id("adjust_latency")
 )
 }
 \arguments{
@@ -97,16 +97,21 @@ An updated version of \code{recipe} with the new step added to the
 sequence of any existing operations.
 }
 \description{
-In the standard case, the pipeline assumes that the last observation is also
-the day from which the forecast is being made. \code{step_adjust_latency} uses the
-\code{as_of} date of the \code{epi_df} as the \code{forecast_date}. This is most useful in
-realtime and pseudo-prospective forecasting for data where there is some
-delay between the day recorded and when that data is available.
+In the standard case, the arx models assume that the last observation is also
+the day from which the forecast is being made. But if the data has latency,
+then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
+to compensate. This allows the model to create bleeding-edge forecasts using
+the lags actually observed rather than anticipated. \code{step_adjust_latency}
+uses the \code{as_of} date of the \code{epi_df} as the \code{forecast_date}. This is most
+useful in realtime and pseudo-prospective forecasting for data where there is
+some delay between the day recorded and when that data is available.
 }
 \details{
 The step assumes that the pipeline has already applied either
-\code{step_epi_ahead} or \code{step_epi_lag} depending on the value of
-\code{"method"}, and that \code{step_epi_naomit} has NOT been run.
+\code{step_epi_ahead} or \code{step_epi_lag} depending on the value of \code{"method"},
+and that \code{step_epi_naomit} has NOT been run.  By default, the latency will
+be determined using the arguments below, but can be set explicitly using
+either \code{fixed_latency} or \code{fixed_forecast_date}.
 
 The \code{prefix} and \code{id} arguments are unchangeable to ensure that the code runs
 properly and to avoid inconsistency with naming. For \code{step_epi_ahead}, they
diff --git a/vignettes/articles/symptom-surveys.Rmd b/vignettes/articles/symptom-surveys.Rmd
index f480db575..1e51a9963 100644
--- a/vignettes/articles/symptom-surveys.Rmd
+++ b/vignettes/articles/symptom-surveys.Rmd
@@ -423,7 +423,7 @@ res_err4 <- res_all4 %>%
 knitr::kable(
   res_err4 %>%
     group_by(model, lead) %>%
-    summarize(err = median(err), n = length(unique(forecast_date))) %>%
+    summarise(err = median(err), n = length(unique(forecast_date))) %>%
     arrange(lead) %>% ungroup() %>%
     rename(
       "Model" = model, "Median scaled error" = err,
@@ -472,7 +472,7 @@ res_dif4 <- res_all4 %>%
 knitr::kable(
   res_dif4 %>%
     group_by(model, lead) %>%
-    summarize(p = binom.test(
+    summarise(p = binom.test(
       x = sum(diff > 0, na.rm = TRUE),
       n = n(), alt = "greater"
     )$p.val) %>%
@@ -501,7 +501,7 @@ ggplot_colors <- c("#FC4E07", "#00AFBB", "#E7B800")
 
 ggplot(res_dif4 %>%
   group_by(model, lead, forecast_date) %>%
-  summarize(p = binom.test(
+  summarise(p = binom.test(
     x = sum(diff > 0, na.rm = TRUE),
     n = n(), alt = "greater"
   )$p.val) %>%
@@ -649,7 +649,7 @@ knitr::kable(
   res_err2 %>%
     select(-ends_with("diff")) %>%
     group_by(model, lead) %>%
-    summarize(err = median(err), n = length(unique(forecast_date))) %>%
+    summarise(err = median(err), n = length(unique(forecast_date))) %>%
     arrange(lead) %>% ungroup() %>%
     rename(
       "Model" = model, "Median scaled error" = err,
@@ -684,7 +684,7 @@ to incorporate during periods of time where forecasting is easier.
 ggplot(
   res_err2 %>%
     group_by(model, lead, forecast_date) %>%
-    summarize(err = median(err)) %>% ungroup(),
+    summarise(err = median(err)) %>% ungroup(),
   aes(x = forecast_date, y = err)
 ) +
   geom_line(aes(color = model)) +
@@ -722,7 +722,7 @@ res_dif2 <- res_all2 %>%
 knitr::kable(
   res_dif2 %>%
     group_by(model, lead) %>%
-    summarize(p = binom.test(
+    summarise(p = binom.test(
       x = sum(diff > 0, na.rm = TRUE),
       n = n(), alt = "greater"
     )$p.val) %>%
@@ -739,7 +739,7 @@ quite small.
 ```{r}
 ggplot(res_dif2 %>%
   group_by(model, lead, forecast_date) %>%
-  summarize(p = binom.test(
+  summarise(p = binom.test(
     x = sum(diff > 0, na.rm = TRUE),
     n = n(), alt = "greater"
   )$p.val) %>%
@@ -802,7 +802,7 @@ err_by_lead <- res %>%
   ) %>%
   mutate(model = factor(model, labels = model_names[1:2])) %>%
   group_by(model, lead) %>%
-  summarize(err = median(err)) %>%
+  summarise(err = median(err)) %>%
   ungroup()
 
 ggplot(err_by_lead, aes(x = lead, y = err)) +

From be9607bafceb2ccab985467c8d7e3aada7b73314 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 14 Jun 2024 15:52:31 -0500
Subject: [PATCH 061/387] style fix

---
 R/arx_classifier.R | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index e97d6675f..851da1e2d 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -55,8 +55,11 @@ arx_classifier <- function(
   wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
-  latency_adjust_fd <- if (is.null(args_list$adjust_latency))
-                         max(epi_data$time_value) else attributes(epi_data)$metadata$as_of
+  latency_adjust_fd <- if (is.null(args_list$adjust_latency)) {
+    max(epi_data$time_value)
+  } else {
+    attributes(epi_data)$metadata$as_of
+  }
   forecast_date <- args_list$forecast_date %||% latency_adjust_fd
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   preds <- forecast(

From a86b3c7fe5ab9dd577ebad145f003da5ade786f1 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 14 Jun 2024 17:47:23 -0500
Subject: [PATCH 062/387] inheritParams, correct print, test adjust subset

---
 R/step_adjust_latency.R                   | 43 +++++++++--------------
 man/step_adjust_latency.Rd                | 24 ++++++-------
 tests/testthat/test-step_adjust_latency.R | 20 +++++++++++
 3 files changed, 48 insertions(+), 39 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 28480097d..eaa5d4236 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -9,12 +9,6 @@
 #' useful in realtime and pseudo-prospective forecasting for data where there is
 #' some delay between the day recorded and when that data is available.
 #'
-#' @param recipe A recipe object. The step will be added to the
-#'  sequence of operations for this recipe.
-#' @param ... One or more selector functions to choose variables for this step.
-#'   See [recipes::selections()] for more details. Typically you will not need
-#'   to set this manually, as the necessary adjustments will be done for the
-#'   predictors and outcome.
 #' @param method a character. Determines the method by which the
 #'   forecast handles latency. The options are:
 #'   - `"extend_ahead"`: Lengthen the ahead so that forecasting from the last
@@ -52,22 +46,12 @@
 #'   `forecast_date` is determined either via the `fixed_latency`, or is set to
 #'   the `epi_df`'s `as_of` value if `fixed_latency` is also `NULL`.
 #' @param role For model terms created by this step, what analysis role should
-#'   they be assigned? `lag` is default a predictor while `ahead` is an outcome.
-#'   It should be correctly inferred and not need setting
-#' @param trained A logical to indicate if the quantities for preprocessing have
-#'   been estimated.
-#' @param columns A character string of column names to be adjusted; these
-#'   should be the original columns, and not the derived ones
+#'   they be assigned? `lag` is a predictor while `ahead` is an outcome.  It
+#'   should be correctly inferred and not need setting
 #' @param default Determines what fills empty rows
 #'   left by leading/lagging (defaults to NA).
-#' @param skip A logical. Should the step be skipped when the
-#'  recipe is baked by [bake()]? While all operations are baked
-#'  when [prep()] is run, some operations may not be able to be
-#'  conducted on new data (e.g. processing the outcome variable(s)).
-#'  Care should be taken when using `skip = TRUE` as it may affect
-#'  the computations for subsequent operations.
-#' @param id A unique identifier for the step
 #' @template step-return
+#' @inheritParams recipes::step_lag
 #'
 #' @details The step assumes that the pipeline has already applied either
 #'   `step_epi_ahead` or `step_epi_lag` depending on the value of `"method"`,
@@ -189,6 +173,7 @@ step_adjust_latency <-
         shift_cols = relevant_shifts,
         default = default,
         keys = epi_keys(recipe),
+        columns = columns,
         skip = skip,
         id = id
       )
@@ -197,7 +182,7 @@ step_adjust_latency <-
 
 step_adjust_latency_new <-
   function(terms, role, trained, forecast_date, latency, shift_cols, time_type, default,
-           keys, method, epi_keys_checked, skip, id) {
+           keys, method, epi_keys_checked, columns, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
@@ -210,6 +195,7 @@ step_adjust_latency_new <-
       shift_cols = shift_cols,
       default = default,
       keys = keys,
+      columns = columns,
       skip = skip,
       id = id
     )
@@ -220,7 +206,7 @@ step_adjust_latency_new <-
 #' @importFrom glue glue
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   # get the columns used, even if it's all of them
-  terms_used <- x$columns
+  terms_used <- x$terms
   if (length(terms_used) == 0) {
     terms_used <- info %>%
       filter(role == "raw") %>%
@@ -274,6 +260,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
+    columns = recipes_eval_select(latency_cols$original_name, training, info),
     skip = x$skip,
     id = x$id
   )
@@ -301,14 +288,18 @@ print.step_adjust_latency <-
     if (!is.null(x$forecast_date)) {
       conj <- "with forecast date"
       extra_text <- x$forecast_date
-    } else if (!is.null(x$shift_cols)) {
-      conj <- "with latencies"
-      extra_text <- x$shift_cols
+    } else if (!is.null(x$latency)) {
+      conj <- if (length(x$latency == 1)) {
+        "with latency"
+      } else {
+      "with latencies"
+      }
+      extra_text <- x$latency
     } else {
-      conj <- ""
+      conj <- "with latency"
       extra_text <- "set at train time"
     }
-    print_epi_step(terms, NULL, x$trained, x$method,
+    print_epi_step(terms, terms, x$trained, x$method,
       conjunction = conj,
       extra_text = extra_text
     )
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index c4f407b3c..fc4c55878 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -23,17 +23,15 @@ step_adjust_latency(
 \item{recipe}{A recipe object. The step will be added to the
 sequence of operations for this recipe.}
 
-\item{...}{One or more selector functions to choose variables for this step.
-See \code{\link[recipes:selections]{recipes::selections()}} for more details. Typically you will not need
-to set this manually, as the necessary adjustments will be done for the
-predictors and outcome.}
+\item{...}{One or more selector functions to choose variables
+for this step. See \code{\link[recipes:selections]{selections()}} for more details.}
 
 \item{role}{For model terms created by this step, what analysis role should
-they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.
-It should be correctly inferred and not need setting}
+they be assigned? \code{lag} is a predictor while \code{ahead} is an outcome.  It
+should be correctly inferred and not need setting}
 
-\item{trained}{A logical to indicate if the quantities for preprocessing have
-been estimated.}
+\item{trained}{A logical to indicate if the quantities for
+preprocessing have been estimated.}
 
 \item{method}{a character. Determines the method by which the
 forecast handles latency. The options are:
@@ -81,16 +79,16 @@ the \code{epi_df}'s \code{as_of} value if \code{fixed_latency} is also \code{NUL
 left by leading/lagging (defaults to NA).}
 
 \item{skip}{A logical. Should the step be skipped when the
-recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
-when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
+recipe is baked by \code{\link[recipes:bake]{bake()}}? While all operations are baked
+when \code{\link[recipes:prep]{prep()}} is run, some operations may not be able to be
 conducted on new data (e.g. processing the outcome variable(s)).
 Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
-\item{columns}{A character string of column names to be adjusted; these
-should be the original columns, and not the derived ones}
+\item{columns}{A character string of the selected variable names. This field
+is a placeholder and will be populated once \code{\link[recipes:prep]{prep()}} is used.}
 
-\item{id}{A unique identifier for the step}
+\item{id}{A character string that is unique to this step to identify it.}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index ab7817799..fb02e740d 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -225,4 +225,24 @@ test_that("epi_adjust_latency correctly extends the lags", {
 
 test_that("`step_adjust_latency` only allows one instance of itself", {})
 
+test_that("`step_adjust_latency` only uses the columns specified in the `...`", {
+  r5 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead) %>%
+    step_adjust_latency(case_rate, method = "extend_lags")
+
+  fit5 <- slm_fit(r5, data = real_x)
+  expect_equal(names(fit5$fit$fit$fit$coefficients), c("(Intercept)", "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate", "lag_6_case_rate", "lag_10_case_rate"))
+})
+
 test_that("setting fixed_* works for `step_adjust_latency`", {})
+
+test_that("printing step_adjust_latency results in expected output", {
+  r5 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead) %>%
+    step_adjust_latency(case_rate, method = "extend_lags")
+  expect_snapshot(r5)
+})

From 5b7eff1f79f44e180148fc9b1b238912abb58355 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 14 Jun 2024 18:03:23 -0500
Subject: [PATCH 063/387] space

---
 R/step_adjust_latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index eaa5d4236..5522a83b6 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -292,7 +292,7 @@ print.step_adjust_latency <-
       conj <- if (length(x$latency == 1)) {
         "with latency"
       } else {
-      "with latencies"
+        "with latencies"
       }
       extra_text <- x$latency
     } else {

From 63b02c9046b1d4d77bd67b63ddb351060ba416fa Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Sat, 22 Jun 2024 17:41:07 -0500
Subject: [PATCH 064/387] print fix and tests

---
 R/step_adjust_latency.R                      | 53 +++++++++++++++++---
 man/epi_shift_single.Rd                      | 24 ---------
 tests/testthat/_snaps/step_adjust_latency.md | 39 ++++++++++++++
 tests/testthat/test-step_adjust_latency.R    |  5 ++
 4 files changed, 89 insertions(+), 32 deletions(-)
 delete mode 100644 man/epi_shift_single.Rd
 create mode 100644 tests/testthat/_snaps/step_adjust_latency.md

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 5522a83b6..7036b2fe1 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -280,11 +280,6 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
 #' @export
 print.step_adjust_latency <-
   function(x, width = max(20, options$width - 35), ...) {
-    if (length(x$terms) == 0) {
-      terms <- "all previous predictors"
-    } else {
-      terms <- x$terms
-    }
     if (!is.null(x$forecast_date)) {
       conj <- "with forecast date"
       extra_text <- x$forecast_date
@@ -299,9 +294,51 @@ print.step_adjust_latency <-
       conj <- "with latency"
       extra_text <- "set at train time"
     }
-    print_epi_step(terms, terms, x$trained, x$method,
-      conjunction = conj,
-      extra_text = extra_text
+    # what follows is a somewhat modified version of print_epi_step, since the case of no arguments for adjust_latency means apply to all relevant columns, and not none of them
+    theme_div_id <- cli::cli_div(
+      theme = list(.pkg = list(`vec-trunc` = Inf, `vec-last` = ", "))
+    )
+    # this is a slightly modified copy of
+    title <- trimws(x$method)
+    trained_text <- dplyr::if_else(x$trained, "Trained", "")
+    vline_seperator <- dplyr::if_else(trained_text == "", "", "|")
+    comma_seperator <- dplyr::if_else(
+      trained_text != "", true = ",", false = ""
     )
+    extra_text <- recipes::format_ch_vec(extra_text)
+    width_title <- nchar(paste0(
+      "* ", title, ":", " ", conj, " ", extra_text, " ", vline_seperator,
+      " ", trained_text, " "
+    ))
+    width_diff <- cli::console_width() * 1 - width_title
+    if (x$trained) {
+      elements <- x$columns
+    } else {
+      if (length(x$terms) == 0) {
+        elements <- "all previous predictors"
+      } else {
+        elements <- lapply(x$terms, function(x) {
+          rlang::expr_deparse(rlang::quo_get_expr(x), width = Inf)
+        })
+        elements <- vctrs::list_unchop(elements, ptype = character())
+      }
+    }
+
+    element_print_lengths <- cumsum(nchar(elements)) +
+      c(0L, cumsum(rep(2L, length(elements) - 1))) +
+      c(rep(5L, length(elements) - 1), 0L)
+    first_line <- which(width_diff >= element_print_lengths)
+    first_line <- unname(first_line)
+    first_line <- ifelse(
+      test = identical(first_line, integer(0)),
+      yes = length(element_print_lengths),
+      no = max(first_line)
+    )
+    more_dots <- ifelse(first_line == length(elements), "", ", ...")
+    cli::cli_bullets(
+      c("\n    {title}: \\\n    {.pkg {cli::cli_vec(elements[seq_len(first_line)])}}\\\n    {more_dots} \\\n    {conj} \\\n    {.pkg {extra_text}} \\\n    {vline_seperator} \\\n    {.emph {trained_text}}")
+    )
+
+    cli::cli_end(theme_div_id)
     invisible(x)
   }
diff --git a/man/epi_shift_single.Rd b/man/epi_shift_single.Rd
deleted file mode 100644
index 871879004..000000000
--- a/man/epi_shift_single.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/epi_shift.R
-\name{epi_shift_single}
-\alias{epi_shift_single}
-\title{Shift predictors while maintaining grouping and time_value ordering}
-\usage{
-epi_shift_single(x, col, shift_val, newname, key_cols)
-}
-\arguments{
-\item{x}{Data frame.}
-
-\item{shift_val}{a single integer. Negative values produce leads.}
-
-\item{newname}{the name for the newly shifted column}
-
-\item{key_cols}{vector, or \code{NULL}. Additional grouping vars.}
-}
-\value{
-a list of tibbles
-}
-\description{
-This is a lower-level function. As such it performs no error checking.
-}
-\keyword{internal}
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
new file mode 100644
index 000000000..354d02d84
--- /dev/null
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -0,0 +1,39 @@
+# printing step_adjust_latency results in expected output
+
+    Code
+      r5
+    Message
+      
+      -- Epi Recipe ------------------------------------------------------------------
+      
+      -- Inputs 
+      Number of variables by role
+      raw:        2
+      geo_value:  1
+      time_value: 1
+      
+      -- Operations 
+      1. Lagging: death_rate by 0, 6, 11
+      2. Lagging: case_rate by 1, 5
+      3. Leading: death_rate by 7
+      4. extend_lags: case_rate with latency set at train time
+
+---
+
+    Code
+      r
+    Message
+      
+      -- Epi Recipe ------------------------------------------------------------------
+      
+      -- Inputs 
+      Number of variables by role
+      raw:        2
+      geo_value:  1
+      time_value: 1
+      
+      -- Operations 
+      1. Leading: death_rate by 7
+      2. extend_ahead: all previous predictors with latency set at train time
+      3. Lagging: death_rate by 0, 7, 14
+
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index fb02e740d..af36e185c 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -245,4 +245,9 @@ test_that("printing step_adjust_latency results in expected output", {
     step_epi_ahead(death_rate, ahead = ahead) %>%
     step_adjust_latency(case_rate, method = "extend_lags")
   expect_snapshot(r5)
+  r <- epi_recipe(case_death_rate_subset) %>%
+    step_epi_ahead(death_rate, ahead = 7) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
+    step_epi_lag(death_rate, lag = c(0, 7, 14))
+  expect_snapshot(r)
 })

From fc8b0c0fdd71e247878f2e183bf6d2cd0f86798b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 24 Jun 2024 16:45:31 -0500
Subject: [PATCH 065/387] multi-aheads do work

---
 tests/testthat/test-step_adjust_latency.R | 105 +++++++++++++++++-----
 1 file changed, 82 insertions(+), 23 deletions(-)

diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index af36e185c..7a0aac51c 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -35,7 +35,7 @@ x_lagged
 attributes(x_lagged)$metadata$as_of <- testing_as_of
 
 test_that("epi_adjust_latency correctly extends the lags", {
-  r5 <- epi_recipe(x) %>%
+  r1 <- epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead) %>%
@@ -43,35 +43,35 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
+  expect_error(expect_warning(fit1 <- slm_fit(r1), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
-  fit5 <- slm_fit(r5, data = real_x)
+  fit1 <- slm_fit(r1, data = real_x)
 
   expect_equal(
-    names(fit5$pre$mold$predictors),
+    names(fit1$pre$mold$predictors),
     c(
       "lag_5_death_rate", "lag_11_death_rate", "lag_16_death_rate",
       "lag_6_case_rate", "lag_10_case_rate"
     )
   )
-  latest <- get_test_data(r5, x)
-  pred <- predict(fit5, latest)
+  latest <- get_test_data(r1, x)
+  pred <- predict(fit1, latest)
   point_pred <- pred %>% filter(!is.na(.pred))
   expect_equal(nrow(point_pred), 1)
   expect_equal(point_pred$time_value, as.Date(testing_as_of))
 
   expect_equal(
-    names(fit5$pre$mold$outcomes),
+    names(fit1$pre$mold$outcomes),
     glue::glue("ahead_{ahead}_death_rate")
   )
-  latest <- get_test_data(r5, x)
-  pred <- predict(fit5, latest)
-  actual_solutions <- pred %>% filter(!is.na(.pred))
+  latest <- get_test_data(r1, x)
+  pred1 <- predict(fit1, latest)
+  actual_solutions <- pred1 %>% filter(!is.na(.pred))
   expect_equal(actual_solutions$time_value, testing_as_of)
 
   # should have four predictors, including the intercept
-  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+  expect_equal(length(fit1$fit$fit$fit$coefficients), 6)
 
   # result should be equivalent to just immediately doing the adjusted lags by
   # hand
@@ -81,13 +81,13 @@ test_that("epi_adjust_latency correctly extends the lags", {
     step_epi_ahead(death_rate, ahead = ahead)
   fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
   expect_equal(
-    fit5$fit$fit$fit$coefficients,
+    fit1$fit$fit$fit$coefficients,
     fit_hand_adj$fit$fit$fit$coefficients
   )
 })
 
 test_that("epi_adjust_latency correctly extends the ahead", {
-  r5 <- epi_recipe(x) %>%
+  r2 <- epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead) %>%
@@ -95,24 +95,24 @@ test_that("epi_adjust_latency correctly extends the ahead", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5)))
+  expect_error(expect_warning(fit5 <- slm_fit(r2)))
   # real date example
-  fit5 <- slm_fit(r5, data = real_x)
+  fit2 <- slm_fit(r2, data = real_x)
   expect_equal(
-    names(fit5$pre$mold$predictors),
+    names(fit2$pre$mold$predictors),
     c(
       "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate",
       "lag_1_case_rate", "lag_5_case_rate"
     )
   )
-  latest <- get_test_data(r5, x)
-  pred <- predict(fit5, latest)
-  point_pred <- pred %>% filter(!is.na(.pred))
+  latest <- get_test_data(r2, x)
+  pred2 <- predict(fit2, latest)
+  point_pred2 <- pred2 %>% filter(!is.na(.pred))
   # max time is still the forecast date
-  expect_equal(point_pred$time_value, as.Date(max_time))
+  expect_equal(point_pred2$time_value, as.Date(max_time))
   # target column renamed
   expect_equal(
-    names(fit5$pre$mold$outcomes),
+    names(fit2$pre$mold$outcomes),
     glue::glue("ahead_{ahead + latency}_death_rate")
   )
   # fit an equivalent forecaster
@@ -123,12 +123,71 @@ test_that("epi_adjust_latency correctly extends the ahead", {
   equiv_fit <- slm_fit(equivalent, data = real_x)
   # adjusting the ahead should do the same thing as directly adjusting the ahead
   expect_equal(
-    fit5$fit$fit$fit$coefficients,
+    fit2$fit$fit$fit$coefficients,
     equiv_fit$fit$fit$fit$coefficients
   )
 
   # should have four predictors, including the intercept
-  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+  expect_equal(length(fit2$fit$fit$fit$coefficients), 6)
+})
+
+test_that("epi_adjust_latency extends multiple aheads", {
+  aheads <- 1:3
+  r3 <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = aheads) %>%
+    step_adjust_latency(method = "extend_ahead")
+  fitter <- smooth_quantile_reg(
+    quantile_levels = 0.5,
+    outcome_locations = aheads,
+    degree = 1L
+  )
+  epi_wf <- epi_workflow(r3, fitter)
+  # the as_of on x is today's date, which is >970 days in the future
+  # also, there's no data >970 days in the past, so it gets an error trying to
+  # fit on no data
+  expect_error(expect_warning(fit3 <- fit(epi_wf, data = x)))
+  # real date example
+  fit3 <- fit(epi_wf, data = real_x)
+  expect_equal(
+    names(fit3$pre$mold$outcomes),
+    c(
+      "ahead_6_death_rate", "ahead_7_death_rate", "ahead_8_death_rate"
+    )
+  )
+  expect_equal(
+    names(fit3$pre$mold$predictors),
+    c(
+      "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate",
+      "lag_1_case_rate", "lag_5_case_rate"
+    )
+  )
+  latest <- get_test_data(r3, real_x)
+  pred3 <- predict(fit3, latest)
+  point_pred <- pred3 %>%
+    unnest(.pred) %>%
+    filter(!is.na(distn))
+  # max time is still the forecast date
+  expect_equal(
+    point_pred$time_value,
+    rep(as.Date(max_time), 3)
+  )
+  # fit an equivalent forecaster
+  equivalent <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead + latency)
+  equiv_fit <- fit(epi_wf, data = real_x)
+  # adjusting the ahead should do the same thing as directly adjusting the ahead
+  equiv_fit
+  expect_equal(
+    fit3$fit$fit$fit$rqfit,
+    equiv_fit$fit$fit$fit$rqfit
+  )
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit3$fit$fit$fit$rqfit$coefficients), 6)
 })
 
 test_that("epi_adjust_latency fixed_* work", {})

From a570a0ee9256eb425e1a9bc32fb6e71550d7470b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 28 Jun 2024 12:42:01 -0500
Subject: [PATCH 066/387] arx_fc better fc_date info, docs

---
 R/arx_forecaster.R                  | 47 +++++++++++++------
 R/canned-epipred.R                  | 21 ++++++++-
 R/import-standalone-purrr.R         |  2 +-
 R/utils-latency.R                   |  2 +-
 tests/testthat/_snaps/snapshots.md  | 71 +++++++++++++++++++++++++++++
 tests/testthat/test-snapshots.R     | 35 ++++++++++++++
 tests/testthat/test-utils_latency.R | 22 +++++----
 7 files changed, 173 insertions(+), 27 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index f97d2682d..78bcd785a 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -119,16 +119,25 @@ arx_fcast_epi_workflow <- function(
   if (!(is.null(trainer) || is_regression(trainer))) {
     cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'regression'.")
   }
-  # forecast_date is first what they set;
+  # forecast_date is above all what they set;
   # if they don't and they're not adjusting latency, it defaults to the max time_value
-  # if they're adjusting as_of, it defaults to the as_of
-  latency_adjust_fd <- if (is.null(args_list$adjust_latency)) {
-    max(epi_data$time_value)
+  # if they're adjusting, it defaults to the as_of
+  if (is.null(args_list$adjust_latency)) {
+    forecast_date_default <- max(epi_data$time_value)
+    if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
+      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+    }
   } else {
-    attributes(epi_data)$metadata$as_of
+    forecast_date_default <- attributes(epi_data)$metadata$as_of
   }
-  forecast_date <- args_list$forecast_date %||% latency_adjust_fd
+  forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
+  if (forecast_date + args_list$ahead != target_date) {
+    cli::cli_warn(c(
+      "`forecast_date` + `ahead` must equal `target_date`.",
+      i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+    ))
+  }
 
   lags <- arx_lags_validator(predictors, args_list$lags)
 
@@ -140,17 +149,17 @@ arx_fcast_epi_workflow <- function(
   }
   r <- r %>%
     step_epi_ahead(!!outcome, ahead = args_list$ahead)
-  method <- args_list$adjust_latency
-  if (!is.null(method)) {
-    if (method == "extend_ahead") {
+  method_adjust_latency <- args_list$adjust_latency
+  if (!is.null(method_adjust_latency)) {
+    if (method_adjust_latency == "extend_ahead") {
       r <- r %>% step_adjust_latency(all_outcomes(),
         fixed_forecast_date = forecast_date,
-        method = method
+        method = method_adjust_latency
       )
-    } else if (method == "extend_lags") {
+    } else if (method_adjust_latency == "extend_lags") {
       r <- r %>% step_adjust_latency(all_predictors(),
         fixed_forecast_date = forecast_date,
-        method = method
+        method = method_adjust_latency
       )
     }
   }
@@ -215,8 +224,18 @@ arx_fcast_epi_workflow <- function(
 #' @param target_date Date. The date for which the forecast is intended. The
 #'   default `NULL` will attempt to determine this automatically as
 #'   `forecast_date + ahead`.
-#' @param adjust_latency Character or `NULL`. one of the `method`s of
-#'   `step_adjust_latency`, or `NULL` (in which case there is no adjustment).
+#' @param adjust_latency Character or `NULL`. One of the `method`s of
+#'   [step_adjust_latency()], or `NULL` (in which case there is no adjustment).
+#'   If there is a difference between the `forecast_date` and the last day of
+#'   data, this determines how to shift the model to account for this
+#'   difference. The options are:
+#'   - `NULL` the default, assumes the `forecast_date` is the last day of data
+#'   - `"extend_ahead"`: increase the `ahead` by the latency so it's relative to
+#'   the last day of data. If the last day of data was 3 days ago, the ahead
+#'   becomes `ahead+3`.
+#'   - `"extend_lags"`: increase the lags so they're relative to the actual forecast date. If the lags are
+#'   `c(0,7,14)` and the last day of data was 3 days ago, the lags become
+#'   `c(3,10,17)`.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
 #'   prediction intervals. These are created by computing the quantiles of
 #'   training residuals. A `NULL` value will result in point forecasts only.
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 0adc0536a..227aaec09 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -81,6 +81,7 @@ print.canned_epipred <- function(x, name, ...) {
     }
     cli::cli_li("Time type: {.field {x$metadata$training$time_type}},")
     cli::cli_li("Using data up-to-date as of: {.field {format(x$metadata$training$as_of)}}.")
+    cli::cli_li("With the last data available on {.field {format(max(x$epi_workflow$original_data$time_value))}}")
     cli::cli_end()
   }
   fn_meta()
@@ -103,10 +104,28 @@ print.canned_epipred <- function(x, name, ...) {
     "A total of {.val {nrow(x$predictions)}} prediction{?s}",
     " {?is/are} available for"
   ))
+
   cli::cli_ul(c(
     "{.val {n_geos}} unique geographic region{?s},",
     "At forecast date{?s}: {.val {fds}},",
-    "For target date{?s}: {.val {tds}}."
+    "For target date{?s}: {.val {tds}},"
   ))
+  if (detect_step(x$epi_workflow$pre$actions$recipe$recipe, "adjust_latency")) {
+    latency_step <- keep(x$epi_workflow$pre$mold$blueprint$recipe$steps,
+                         \(x) inherits(x, "step_adjust_latency"))[[1]]
+    latency_per_base_col <- latency_step$shift_cols %>%
+      group_by(latency) %>%
+      reframe(variable = parent_name) %>%
+      distinct() %>%
+      mutate(latency = abs(latency)) %>%
+      relocate(variable, latency)
+    if (nrow(latency_per_base_col)>1) {
+      intro_text <- "Latency adjusted per column: "
+    } else {
+      intro_text <- "Latency adjusted for "
+    }
+    latency_info <- paste0(intro_text, paste(apply(latency_per_base_col, 1, paste0, collapse = "="), collapse = ", "))
+    cli::cli_ul(latency_info)
+  }
   cli::cli_text("")
 }
diff --git a/R/import-standalone-purrr.R b/R/import-standalone-purrr.R
index 623142a0e..e4e83f428 100644
--- a/R/import-standalone-purrr.R
+++ b/R/import-standalone-purrr.R
@@ -123,7 +123,7 @@ map_if <- function(.x, .p, .f, ...) {
   .x
 }
 .rlang_purrr_probe <- function(.x, .p, ...) {
-  if (is_logical(.p)) {
+  if (rlang::is_logical(.p)) {
     stopifnot(length(.p) == length(.x))
     .p
   } else {
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9c5f61423..11d89ee92 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -81,7 +81,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 get_latent_column_tibble <- function(
     shift_cols, new_data, forecast_date, latency,
     sign_shift, info, epi_keys_checked, call = caller_env()) {
-  shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"))
+  shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"), parent_name = terms)
   if (is.null(latency)) {
     shift_cols <- shift_cols %>%
       rowwise() %>%
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index c103ffe0e..90b647d86 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1059,6 +1059,77 @@
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
+# arx_forecasrte output format snapshots
+
+    Code
+      out1
+    Message
+      == A basic forecaster of type ARX Forecaster ===================================
+      
+      This forecaster was fit on 999-01-01.
+      
+      Training data was an <epi_df> with:
+      * Geography: state,
+      * Time type: day,
+      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * With the last data available on 2021-12-31
+      
+      -- Predictions -----------------------------------------------------------------
+      
+      A total of 56 predictions are available for
+      * 56 unique geographic regions,
+      * At forecast date: 2021-12-31,
+      * For target date: 2022-01-07,
+      
+
+---
+
+    Code
+      out2
+    Message
+      == A basic forecaster of type ARX Forecaster ===================================
+      
+      This forecaster was fit on 999-01-01.
+      
+      Training data was an <epi_df> with:
+      * Geography: state,
+      * Time type: day,
+      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * With the last data available on 2021-12-31
+      
+      -- Predictions -----------------------------------------------------------------
+      
+      A total of 56 predictions are available for
+      * 56 unique geographic regions,
+      * At forecast date: 2022-01-03,
+      * For target date: 2022-01-10,
+      * Latency adjusted per column: case_rate=3, death_rate=3
+      
+
+---
+
+    Code
+      out3
+    Message
+      == A basic forecaster of type ARX Forecaster ===================================
+      
+      This forecaster was fit on 999-01-01.
+      
+      Training data was an <epi_df> with:
+      * Geography: state,
+      * Time type: day,
+      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * With the last data available on 2021-12-31
+      
+      -- Predictions -----------------------------------------------------------------
+      
+      A total of 56 predictions are available for
+      * 56 unique geographic regions,
+      * At forecast date: 2022-01-03,
+      * For target date: 2022-01-10,
+      * Latency adjusted for death_rate=3
+      
+
 # arx_classifier snapshots
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 25bc13bde..8627fe5ed 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -96,6 +96,41 @@ test_that("arx_forecaster snapshots", {
   expect_false(all(arx2$predictions == arx3$predictions))
 })
 
+test_that("arx_forecasrte output format snapshots", {
+  jhu <- case_death_rate_subset %>%
+    dplyr::filter(time_value >= as.Date("2021-12-01"))
+  out1 <- arx_forecaster(
+    jhu, "death_rate",
+    c("case_rate", "death_rate")
+  )
+  expect_equal(as.Date(out1$metadata$forecast_created), Sys.Date())
+  out1$metadata$forecast_created <- as.Date("0999-01-01")
+  expect_snapshot(out1)
+  out2 <- arx_forecaster(jhu, "case_rate",
+    c("case_rate", "death_rate"),
+    trainer = quantile_reg(),
+    args_list = arx_args_list(
+      quantile_levels = 1:9 / 10,
+      adjust_latency = "extend_lags",
+      forecast_date = as.Date("2022-01-03")
+    )
+  )
+  expect_equal(as.Date(out2$metadata$forecast_created), Sys.Date())
+  out2$metadata$forecast_created <- as.Date("0999-01-01")
+  expect_snapshot(out2)
+  out3 <- arx_forecaster(jhu, "death_rate",
+    c("case_rate", "death_rate"),
+    trainer = quantile_reg(),
+    args_list = arx_args_list(
+      adjust_latency = "extend_ahead",
+      forecast_date = as.Date("2022-01-03")
+    )
+  )
+  expect_equal(as.Date(out3$metadata$forecast_created), Sys.Date())
+  out3$metadata$forecast_created <- as.Date("0999-01-01")
+  expect_snapshot(out3)
+})
+
 test_that("arx_classifier snapshots", {
   arc1 <- arx_classifier(
     case_death_rate_subset %>%
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index b8ec678cd..f52b1a950 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -37,16 +37,18 @@ modified_data <-
 modified_data %>% tail()
 as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
 all_shift_cols <- tibble::tribble(
-  ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-  "case_rate", 3, "lag_", "lag_3_case_rate", 5, 8, "lag_8_case_rate", "numeric", "predictor",
-  "death_rate", 7, "lag_", "lag_7_death_rate", 4, 11, "lag_11_death_rate", "numeric", "predictor",
-  "case_rate", 4, "ahead_", "ahead_4_case_rate", -5, 9, "ahead_9_case_rate", "numeric", "outcome"
+  ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+  "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 5, 8, "lag_8_case_rate", "numeric", "predictor",
+  "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 4, 11, "lag_11_death_rate", "numeric", "predictor",
+  "case_rate", 4, "ahead_", "ahead_4_case_rate", "case_rate", -5, 9, "ahead_9_case_rate", "numeric", "outcome"
 )
 test_recipe <- epi_recipe(modified_data) %>%
   step_epi_lag(case_rate, lag = c(3)) %>%
   step_epi_lag(death_rate, lag = 7) %>%
   step_epi_ahead(case_rate, ahead = 4)
 shift_cols <- construct_shift_tibble(c("case_rate", "death_rate"), test_recipe, "step_epi_lag", "lag")
+
+
 test_that("construct_shift_tibble constructs the right tibble", {
   expected_shift_cols <- tibble::tribble(
     ~terms, ~shift, ~prefix,
@@ -114,17 +116,17 @@ test_that("get_latent_column_tibble assigns given latencies", {
     shift_cols, modified_data, as_of, 50, 1, info
   )
   weird_latencies <- tibble::tribble(
-    ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "case_rate", 3, "lag_", "lag_3_case_rate", 50, 53, "lag_53_case_rate", "numeric", "predictor",
-    "death_rate", 7, "lag_", "lag_7_death_rate", 50, 57, "lag_57_death_rate", "numeric", "predictor",
+    ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 50, 53, "lag_53_case_rate", "numeric", "predictor",
+    "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 50, 57, "lag_57_death_rate", "numeric", "predictor",
   )
   expect_equal(both_lag, weird_latencies)
 
   # supposing we add the latencies by hand, and they're different, and in a different order
   weird_latencies <- tibble::tribble(
-    ~terms, ~shift, ~prefix, ~original_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "case_rate", 3, "lag_", "lag_3_case_rate", 70, 73, "lag_73_case_rate", "numeric", "predictor",
-    "death_rate", 7, "lag_", "lag_7_death_rate", 30, 37, "lag_37_death_rate", "numeric", "predictor",
+    ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
+    "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 70, 73, "lag_73_case_rate", "numeric", "predictor",
+    "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 30, 37, "lag_37_death_rate", "numeric", "predictor",
   )
   both_lag <- get_latent_column_tibble(
     shift_cols, modified_data, as_of, c(death_rate = 30, case_rate = 70), 1, info

From a65cad08261147a407cc3bebcbbbb9ed1d1199e2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 28 Jun 2024 13:33:19 -0500
Subject: [PATCH 067/387] classifier latency ahead adjustment

---
 R/arx_classifier.R | 41 +++++++++++++++++++++++++++++++++++------
 1 file changed, 35 insertions(+), 6 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 851da1e2d..ba3fe1271 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -55,12 +55,15 @@ arx_classifier <- function(
   wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
-  latency_adjust_fd <- if (is.null(args_list$adjust_latency)) {
-    max(epi_data$time_value)
+  if (is.null(args_list$adjust_latency)) {
+    forecast_date_default <- max(epi_data$time_value)
+    if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
+      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+    }
   } else {
-    attributes(epi_data)$metadata$as_of
+    forecast_date_default <- attributes(epi_data)$metadata$as_of
   }
-  forecast_date <- args_list$forecast_date %||% latency_adjust_fd
+  forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   preds <- forecast(
     wf,
@@ -132,6 +135,18 @@ arx_class_epi_workflow <- function(
   if (!(is.null(trainer) || is_classification(trainer))) {
     cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
   }
+
+  if (is.null(args_list$adjust_latency)) {
+    forecast_date_default <- max(epi_data$time_value)
+    if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
+      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+    }
+  } else {
+    forecast_date_default <- attributes(epi_data)$metadata$as_of
+  }
+  forecast_date <- args_list$forecast_date %||% forecast_date_default
+  target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
+
   lags <- arx_lags_validator(predictors, args_list$lags)
 
   # --- preprocessor
@@ -180,7 +195,16 @@ arx_class_epi_workflow <- function(
   }
   o2 <- rlang::sym(paste0("ahead_", args_list$ahead, "_", o))
   r <- r %>%
-    step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome") %>%
+    step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome")
+  method_adjust_latency <- args_list$adjust_latency
+  if (!is.null(method_adjust_latency)) {
+    # only extend_ahead is supported atm
+    r <- r %>% step_adjust_latency(all_outcomes(),
+      fixed_forecast_date = forecast_date,
+      method = method_adjust_latency
+    )
+  }
+  r <- r %>%
     recipes::step_mutate(
       outcome_class = cut(!!o2, breaks = args_list$breaks),
       role = "outcome"
@@ -267,6 +291,7 @@ arx_class_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
+    adjust_latency = NULL,
     outcome_transform = c("growth_rate", "lag_difference"),
     breaks = 0.25,
     horizon = 7L,
@@ -284,7 +309,10 @@ arx_class_args_list <- function(
   outcome_transform <- rlang::arg_match(outcome_transform)
 
   arg_is_scalar(ahead, n_training, horizon, log_scale)
-  arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
+  arg_is_scalar(forecast_date, target_date, adjust_latency, allow_null = TRUE)
+  if (adjust_latency == "adjust_lags") {
+    cli::cli_abort("step_adjust_latency is not yet implemented for lagged differences and growth rates")
+  }
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags, horizon)
   arg_is_numeric(breaks)
@@ -325,6 +353,7 @@ arx_class_args_list <- function(
       breaks,
       forecast_date,
       target_date,
+      adjust_latency,
       outcome_transform,
       max_lags,
       horizon,

From e3a368edba126b38df5353973877f37598a0fda7 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 3 Jul 2024 15:53:53 -0500
Subject: [PATCH 068/387] refactor step_adjust_ahead to be early step

---
 NAMESPACE                                     |   4 +-
 R/arx_classifier.R                            |  13 +-
 R/arx_forecaster.R                            |  13 +-
 R/canned-epipred.R                            |  39 +++--
 R/epi_shift.R                                 |  45 ++++--
 R/layer_add_forecast_date.R                   |   2 +-
 R/step_adjust_latency.R                       | 124 +++++++++-------
 R/utils-latency.R                             |  94 +-----------
 man/arx_args_list.Rd                          |  16 +-
 man/arx_class_args_list.Rd                    |  16 ++
 man/extend_either.Rd                          |  26 ----
 man/get_latency.Rd                            |   9 +-
 man/get_latent_column_tibble.Rd               |  41 -----
 tests/testthat.R                              |   4 +
 tests/testthat/_snaps/snapshots.md            |   6 +-
 tests/testthat/_snaps/step_adjust_latency.md  |  14 +-
 tests/testthat/test-layer_add_forecast_date.R |   8 +-
 tests/testthat/test-layer_add_target_date.R   |   2 +-
 tests/testthat/test-snapshots.R               |   4 +-
 tests/testthat/test-step_adjust_latency.R     |  87 +++++++----
 tests/testthat/test-utils_latency.R           | 140 ++----------------
 21 files changed, 268 insertions(+), 439 deletions(-)
 delete mode 100644 man/extend_either.Rd
 delete mode 100644 man/get_latent_column_tibble.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 9b8035714..19e42fef5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -246,7 +246,6 @@ importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
 importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
-importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
@@ -265,6 +264,7 @@ importFrom(ggplot2,geom_linerange)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(glue,glue)
+importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
@@ -272,6 +272,7 @@ importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
+importFrom(recipes,recipes_eval_select)
 importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
@@ -304,6 +305,7 @@ importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
+importFrom(tidyr,expand_grid)
 importFrom(tidyr,unnest)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index ba3fe1271..f07ae3e71 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -194,8 +194,6 @@ arx_class_epi_workflow <- function(
     }
   }
   o2 <- rlang::sym(paste0("ahead_", args_list$ahead, "_", o))
-  r <- r %>%
-    step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome")
   method_adjust_latency <- args_list$adjust_latency
   if (!is.null(method_adjust_latency)) {
     # only extend_ahead is supported atm
@@ -205,7 +203,9 @@ arx_class_epi_workflow <- function(
     )
   }
   r <- r %>%
-    recipes::step_mutate(
+    step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome")
+  r <- r %>%
+    step_mutate(
       outcome_class = cut(!!o2, breaks = args_list$breaks),
       role = "outcome"
     ) %>%
@@ -223,10 +223,6 @@ arx_class_epi_workflow <- function(
     )
   }
 
-
-  forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
-  target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
-
   # --- postprocessor
   f <- frosting() %>% layer_predict() # %>% layer_naomit()
   f <- layer_add_forecast_date(f, forecast_date = forecast_date) %>%
@@ -310,9 +306,6 @@ arx_class_args_list <- function(
 
   arg_is_scalar(ahead, n_training, horizon, log_scale)
   arg_is_scalar(forecast_date, target_date, adjust_latency, allow_null = TRUE)
-  if (adjust_latency == "adjust_lags") {
-    cli::cli_abort("step_adjust_latency is not yet implemented for lagged differences and growth rates")
-  }
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags, horizon)
   arg_is_numeric(breaks)
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 78bcd785a..3abbeb2eb 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -143,12 +143,7 @@ arx_fcast_epi_workflow <- function(
 
   # --- preprocessor
   r <- epi_recipe(epi_data)
-  for (l in seq_along(lags)) {
-    p <- predictors[l]
-    r <- step_epi_lag(r, !!p, lag = lags[[l]])
-  }
-  r <- r %>%
-    step_epi_ahead(!!outcome, ahead = args_list$ahead)
+  # adjust latency if the user asks
   method_adjust_latency <- args_list$adjust_latency
   if (!is.null(method_adjust_latency)) {
     if (method_adjust_latency == "extend_ahead") {
@@ -163,6 +158,12 @@ arx_fcast_epi_workflow <- function(
       )
     }
   }
+  for (l in seq_along(lags)) {
+    p <- predictors[l]
+    r <- step_epi_lag(r, !!p, lag = lags[[l]])
+  }
+  r <- r %>%
+    step_epi_ahead(!!outcome, ahead = args_list$ahead)
   r <- r %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training)
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 227aaec09..4f95f3d22 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -63,6 +63,7 @@ print.alist <- function(x, ...) {
 }
 
 #' @export
+#' @importFrom hardhat extract_recipe
 print.canned_epipred <- function(x, name, ...) {
   d <- cli::cli_div(theme = list(rule = list("line-type" = "double")))
   cli::cli_rule("A basic forecaster of type {name}")
@@ -110,19 +111,33 @@ print.canned_epipred <- function(x, name, ...) {
     "At forecast date{?s}: {.val {fds}},",
     "For target date{?s}: {.val {tds}},"
   ))
-  if (detect_step(x$epi_workflow$pre$actions$recipe$recipe, "adjust_latency")) {
-    latency_step <- keep(x$epi_workflow$pre$mold$blueprint$recipe$steps,
-                         \(x) inherits(x, "step_adjust_latency"))[[1]]
-    latency_per_base_col <- latency_step$shift_cols %>%
-      group_by(latency) %>%
-      reframe(variable = parent_name) %>%
-      distinct() %>%
-      mutate(latency = abs(latency)) %>%
-      relocate(variable, latency)
-    if (nrow(latency_per_base_col)>1) {
-      intro_text <- "Latency adjusted per column: "
+  fit_recipe <- extract_recipe(x$epi_workflow)
+  if (detect_step(fit_recipe, "adjust_latency")) {
+    is_adj_latency <- map_lgl(fit_recipe$steps, \(x) inherits(x, "step_adjust_latency"))
+    latency_step <- fit_recipe$steps[is_adj_latency][[1]]
+    # all steps after adjust_latency
+    later_steps <- fit_recipe$steps[-(1:which(is_adj_latency))]
+    if (latency_step$method == "extend_ahead") {
+      step_names <- "step_epi_ahead"
+      type_str <- "Aheads"
+    } else if (latency_step$method == "extend_lags") {
+      step_names <- "step_epi_lag"
+      type_str <- "Lags"
     } else {
-      intro_text <- "Latency adjusted for "
+      step_names <- ""
+      type_str <- "columns locf"
+    }
+    later_steps[[1]]$columns
+    valid_columns <- later_steps %>%
+      keep(\(x) inherits(x, step_names)) %>%
+      purrr::map("columns") %>%
+      reduce(c)
+    latency_per_base_col <- latency_step$latency_table %>%
+      filter(col_name %in% valid_columns) %>% mutate(latency = abs(latency))
+    if (latency_step$method != "locf" && nrow(latency_per_base_col) > 1) {
+      intro_text <- glue::glue("{type_str} adjusted per column: ")
+    } else if (latency_step$method != "locf") {
+      intro_text <- glue::glue("{type_str} adjusted for ")
     }
     latency_info <- paste0(intro_text, paste(apply(latency_per_base_col, 1, paste0, collapse = "="), collapse = ", "))
     cli::cli_ul(latency_info)
diff --git a/R/epi_shift.R b/R/epi_shift.R
index c91856237..43876a53a 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -21,7 +21,13 @@ epi_shift_single <- function(x, col, shift_val, newname, key_cols) {
 #' the future back to today
 #' @keywords internal
 get_sign <- function(object) {
-  if (object$prefix == "lag_") {
+  if (!is.null(object$prefix)) {
+    if (object$prefix == "lag_") {
+      return(1)
+    } else {
+      return(-1)
+    }
+  } else if (object$method == "extend_lags") {
     return(1)
   } else {
     return(-1)
@@ -32,13 +38,28 @@ get_sign <- function(object) {
 #' checks missing in `epi_shift_single`
 #' @keywords internal
 #' @importFrom cli cli_abort
+#' @importFrom tidyr expand_grid
+#' @importFrom dplyr mutate left_join join_by
 add_shifted_columns <- function(new_data, object, amount) {
   sign_shift <- get_sign(object)
-  grid <- tidyr::expand_grid(col = object$columns, amount = amount) %>%
-    dplyr::mutate(
-      newname = glue::glue("{object$prefix}{amount}_{col}"),
-      shift_val = sign_shift * amount,
-      amount = NULL
+  latency_table <- attributes(new_data)$metadata$latency_table
+  shift_sign_lat <- attributes(new_data)$metadata$shift_sign
+  if (!is.null(latency_table) &&
+    shift_sign_lat == sign_shift) {
+    #TODO this doesn't work on lags of transforms
+    rel_latency <- latency_table %>% filter(col_name %in% object$columns)
+  } else {
+    rel_latency <- tibble(col_name = object$columns, latency = 0L)
+  }
+  grid <- expand_grid(col = object$columns, amount = sign_shift *amount) %>%
+    left_join(rel_latency, by = join_by(col == col_name), ) %>%
+    tidyr::replace_na(list(latency = 0)) %>%
+    mutate(
+      shift_val = amount + latency) %>%
+    mutate(
+      newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"), # name is always positive
+      amount = NULL,
+      latency = NULL
     )
 
   ## ensure no name clashes
@@ -56,8 +77,12 @@ add_shifted_columns <- function(new_data, object, amount) {
     dplyr::full_join,
     by = ok
   )
-  dplyr::full_join(new_data, shifted, by = ok) %>%
-    dplyr::group_by(dplyr::across(dplyr::all_of(kill_time_value(ok)))) %>%
-    dplyr::arrange(time_value) %>%
-    dplyr::ungroup()
+  processed <- new_data %>%
+    full_join(shifted, by = ok) %>%
+    group_by(dplyr::across(dplyr::all_of(kill_time_value(ok)))) %>%
+    arrange(time_value) %>%
+    ungroup() %>%
+    as_epi_df()
+  attributes(processed)$metadata <- attributes(new_data)$metadata
+  return(processed)
 }
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 3c81ded57..c8f857c89 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -94,7 +94,7 @@ slather.layer_add_forecast_date <- function(object, components, workflow,
   rlang::check_dots_empty()
   forecast_date <- object$forecast_date %||%
     get_forecast_date_in_layer(
-      extract_preprocessor(workflow),
+      extract_recipe(workflow),
       workflow$fit$meta$max_time_value,
       new_data
     )
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 7036b2fe1..40fd7c37d 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -74,8 +74,8 @@
 #' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 #'
 #' r <- epi_recipe(case_death_rate_subset) %>%
-#'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_adjust_latency(method = "extend_ahead") %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
 #'
@@ -113,15 +113,14 @@ step_adjust_latency <-
         i = "Use `tidyselect` methods to choose columns to lag."
       ))
     }
-    if ((method == "extend_ahead") && (!detect_step(recipe, "epi_ahead"))) {
-      cli::cli_abort(
-        "If `method` is {.val extend_ahead}, then a step
-        must have already added an outcome."
+    if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
+      cli::cli_warn(
+        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified."
       )
-    } else if ((method == "extend_lags") && (!detect_step(recipe, "epi_lag"))) {
-      cli::cli_abort(
-        "If `method` is {.val extend_lags} or {.val locf}, then a step
-        must have already added a predictor."
+    } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
+      cli::cli_warn(
+        "If `method` is {.val extend_lags} or {.val locf},
+then the previous `step_epi_lag`s won't work with modified data."
       )
     }
     if (detect_step(recipe, "naomit")) {
@@ -132,10 +131,18 @@ step_adjust_latency <-
       cli::cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
  can be non-`NULL` at a time!")
     }
+    if (length(fixed_latency > 1)) {
+      template <- recipe$template
+      data_names <- names(template)[!names(template) %in% epi_keys(template)]
+      wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
+      if (length(wrong_names) > 0) {
+        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}")
+      }
+    }
 
     method <- rlang::arg_match(method)
     terms_used <- recipes_eval_select(enquos(...), recipe$template, recipe$term_info)
-    if (length(terms_used) == 0) {
+    if (is_empty(terms_used)) {
       terms_used <- recipe$term_info %>%
         filter(role == "raw") %>%
         pull(variable)
@@ -147,18 +154,6 @@ step_adjust_latency <-
       rel_step_type <- "step_epi_lag"
       shift_name <- "lag"
     }
-    relevant_shifts <- construct_shift_tibble(terms_used, recipe, rel_step_type, shift_name)
-
-    if (!any(map_lgl(
-      recipe$steps,
-      function(recipe_step) inherits(recipe_step, rel_step_type)
-    ))) {
-      cli::cli_abort(glue::glue(
-        "There is no `{rel_step_type}` defined before this.",
-        " For the method `extend_{shift_name}` of `step_adjust_latency`,",
-        " at least one {shift_name} must be previously defined."
-      ))
-    }
 
     recipes::add_step(
       recipe,
@@ -170,7 +165,7 @@ step_adjust_latency <-
         trained = trained,
         forecast_date = fixed_forecast_date,
         latency = fixed_latency,
-        shift_cols = relevant_shifts,
+        latency_table = NULL,
         default = default,
         keys = epi_keys(recipe),
         columns = columns,
@@ -181,7 +176,7 @@ step_adjust_latency <-
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, forecast_date, latency, shift_cols, time_type, default,
+  function(terms, role, trained, forecast_date, latency, latency_table, time_type, default,
            keys, method, epi_keys_checked, columns, skip, id) {
     step(
       subclass = "adjust_latency",
@@ -192,7 +187,7 @@ step_adjust_latency_new <-
       trained = trained,
       forecast_date = forecast_date,
       latency = latency,
-      shift_cols = shift_cols,
+      latency_table = latency_table,
       default = default,
       keys = keys,
       columns = columns,
@@ -200,14 +195,44 @@ step_adjust_latency_new <-
       id = id
     )
   }
-
+#' @importFrom recipes recipes_eval_select
+construct_latency_table <- function(x, latency, training, info) {
+  return(latency_table)
+}
 # lags introduces max(lags) NA's after the max_time_value.
 #' @export
 #' @importFrom glue glue
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
+  sign_shift <- get_sign(x)
+  latency <- x$latency
+  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked)
+  # construct the latency table
+  latency_table <- names(training)[!names(training) %in% epi_keys(training)] %>%
+    tibble(col_name = .)
+    if (length(recipes_eval_select(x$terms, training, info)) > 0) {
+    latency_table <- latency_table %>% filter(col_name %in%
+      recipes_eval_select(x$terms, training, info))
+    }
+
+  if (is.null(latency)) {
+    latency_table <- latency_table %>%
+      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, x$epi_keys_checked))
+  } else if (length(latency) > 1) {
+    # if latency has a length, it must also have named elements. We assign based on comparing the name in the list
+    # with the column names, and drop any which don't have a latency assigned
+    latency_table <- latency_table %>%
+      filter(col_name %in% names(latency)) %>%
+      rowwise() %>%
+      mutate(latency = unname(latency[names(latency) == col_name])) %>%
+      ungroup()
+  } else {
+    latency_table <- latency_table %>% mutate(latency = latency)
+  }
+  attributes(training)$metadata$latency_table <- latency_table
   # get the columns used, even if it's all of them
   terms_used <- x$terms
-  if (length(terms_used) == 0) {
+  # TODO replace with is_empty as in bake.recipe
+  if (is_empty(terms_used)) {
     terms_used <- info %>%
       filter(role == "raw") %>%
       pull(variable)
@@ -215,20 +240,10 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   # get and check the max_time and forecast_date are the right kinds of dates
   forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked)
 
-  # infer the correct columns to be working with from the previous
-  # transformations
-  x$prefix <- x$shift_cols$prefix[[1]]
-  sign_shift <- get_sign(x)
-  latency_cols <- get_latent_column_tibble(
-    x$shift_cols, training, forecast_date,
-    x$latency, sign_shift, info, x$epi_keys_checked
-  )
-
-  if ((x$method == "extend_ahead") || (x$method == "extend_lags")) {
     # check that the shift amount isn't too extreme
-    latency <- max(latency_cols$latency)
+    latency <- max(latency_table$latency)
     time_type <- attributes(training)$metadata$time_type
-    i_latency <- which.max(latency_cols$latency)
+    i_latency <- which.max(latency_table$latency)
     if (
       (grepl("day", time_type) && (latency >= 10)) ||
         (grepl("week", time_type) && (latency >= 4)) ||
@@ -238,29 +253,27 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     ) {
       cli::cli_warn(paste(
         "!" = paste(
-          "The shift has been adjusted by {latency}, ",
+          "The latency is {latency}, ",
           "which is questionable for it's `time_type` of ",
-          "{time_type}"
+          "{time_type}."
         ),
-        "i" = "input shift: {latency_cols$shift[[i_latency]]}",
-        "i" = "latency adjusted shift: {latency_cols$effective_shift[[i_latency]]}",
+        "i" = "latency: {latency_table$latency[[i_latency]]}",
         "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
       ))
     }
-  }
 
   step_adjust_latency_new(
-    terms = latency_cols$original_name,
-    role = latency_cols$role[[1]],
+    terms = x$terms,
+    role = x$role,
     trained = TRUE,
-    shift_cols = latency_cols,
     forecast_date = forecast_date,
-    latency = unique(latency_cols$latency),
+    latency = unique(latency_table$latency),
+    latency_table = latency_table,
     default = x$default,
     keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
-    columns = recipes_eval_select(latency_cols$original_name, training, info),
+    columns = recipes_eval_select(latency_table$col_name, training, info),
     skip = x$skip,
     id = x$id
   )
@@ -269,14 +282,19 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @importFrom dplyr %>% pull
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
+  if (!isa(new_data, "epi_df")) {
+    new_data <- new_data %>% as_epi_df(as_of = object$forecast_date)
+  }
+  attributes(new_data)$metadata$latency_method <- object$method
+  attributes(new_data)$metadata$shift_sign <- get_sign(object)
+  attributes(new_data)$metadata$latency_table <- object$latency_table
   if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
     keys <- object$keys
     return(
-      extend_either(new_data, object$shift_cols, keys)
+      new_data
     )
   }
 }
-
 #' @export
 print.step_adjust_latency <-
   function(x, width = max(20, options$width - 35), ...) {
@@ -314,8 +332,8 @@ print.step_adjust_latency <-
     if (x$trained) {
       elements <- x$columns
     } else {
-      if (length(x$terms) == 0) {
-        elements <- "all previous predictors"
+      if (is_empty(x$terms)) {
+        elements <- "all future predictors"
       } else {
         elements <- lapply(x$terms, function(x) {
           rlang::expr_deparse(rlang::quo_get_expr(x), width = Inf)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 11d89ee92..a567dae06 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -1,39 +1,3 @@
-#' offset each relevant column by it's appropriate latency
-#' works for either adjusting aheads or lags
-#' note that this may introduce new NA values when one column is shifted farther than another
-#' @param shift_cols a tibble which must have the columns `column`, the name of
-#'   the column to adjust, `latency` the latency of the original column relative
-#'   to the `forecast_date`, `new_name`, the names in `column` adjusted by the
-#'   latencies `latency`
-#' @param new_data just what is says
-#' @param keys the variables which are used as keys
-#' @keywords internal
-extend_either <- function(new_data, shift_cols, keys) {
-  shifted <-
-    shift_cols %>%
-    select(original_name, latency, new_name) %>%
-    pmap(function(original_name, latency, new_name) {
-      epi_shift_single(
-        x = new_data,
-        col = original_name,
-        shift_val = latency,
-        newname = new_name,
-        key_cols = keys
-      )
-    }) %>%
-    reduce(
-      dplyr::full_join,
-      by = keys
-    )
-
-  return(new_data %>%
-    select(-all_of(shift_cols$original_name)) %>% # drop the original versions
-    dplyr::full_join(shifted, by = keys) %>%
-    dplyr::group_by(dplyr::across(dplyr::all_of(keys[-1]))) %>%
-    dplyr::arrange(time_value) %>%
-    dplyr::ungroup())
-}
-
 #' create a table of the columns to modify, their shifts, and their prefixes
 #' @keywords internal
 #' @importFrom dplyr tibble
@@ -64,60 +28,6 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
   return(relevant_shifts)
 }
 
-#' find the columns added with the lags or aheads, and the amounts they have
-#' been changed
-#' @param shift_cols a list of columns to operate on, as created by `construct_shift_tibble`
-#' @param new_data the data transformed so far
-#' @param forecast_date the forecast date
-#' @param latency `NULL`, int, or vector, as described in `step_epi_latency`
-#' @param sign_shift -1 if ahead, 1 if lag
-#' @return a tibble with columns `column` (relevant shifted names), `shift` (the
-#'   amount that one is shifted), `latency` (original columns difference between
-#'   the max `time_value` and `forecast_date` (on a per-initial column basis)),
-#'   `effective_shift` (shift+latency), and `new_name` (adjusted names with the
-#'   effective_shift)
-#' @keywords internal
-#' @importFrom dplyr rowwise left_join join_by
-get_latent_column_tibble <- function(
-    shift_cols, new_data, forecast_date, latency,
-    sign_shift, info, epi_keys_checked, call = caller_env()) {
-  shift_cols <- shift_cols %>% mutate(original_name = glue::glue("{prefix}{shift}_{terms}"), parent_name = terms)
-  if (is.null(latency)) {
-    shift_cols <- shift_cols %>%
-      rowwise() %>%
-      # add the latencies to shift_cols
-      mutate(latency = get_latency(
-        new_data, forecast_date, original_name, shift, sign_shift, epi_keys_checked
-      )) %>%
-      ungroup()
-  } else if (length(latency) > 1) {
-    # if latency has a length, we assign based on comparing the name in the list with the `terms` column
-    shift_cols <- shift_cols %>%
-      rowwise() %>%
-      mutate(latency = unname(latency[names(latency) == terms])) %>%
-      ungroup()
-  } else {
-    shift_cols <- shift_cols %>% mutate(latency = latency)
-  }
-
-  # add the updated names to shift_cols
-  shift_cols <- shift_cols %>%
-    mutate(
-      effective_shift = shift + abs(latency)
-    ) %>%
-    mutate(
-      new_name = glue::glue("{prefix}{effective_shift}_{terms}")
-    )
-  info <- info %>% select(variable, type, role)
-  shift_cols <- left_join(shift_cols, info, by = join_by(original_name == variable))
-  if (length(unique(shift_cols$role)) != 1) {
-    cli::cli_abort("not all roles are the same!",
-      shift_cols = shift_cols
-    )
-  }
-  return(shift_cols)
-}
-
 
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
@@ -182,7 +92,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked) {
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
 #' @keywords internal
-get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shift, epi_keys_checked) {
+get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_checked) {
   shift_max_date <- new_data %>%
     drop_na(all_of(column))
   # null and "" don't work in `group_by`
@@ -193,7 +103,7 @@ get_latency <- function(new_data, forecast_date, column, shift_amount, sign_shif
     summarise(time_value = max(time_value)) %>%
     pull(time_value) %>%
     min()
-  return(as.integer(sign_shift * (as.Date(forecast_date) - shift_max_date) + shift_amount))
+  return(as.integer(sign_shift * (as.Date(forecast_date) - shift_max_date)))
 }
 
 
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 68d445872..2612d4a3e 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -42,8 +42,20 @@ max time value if there is no latency adjustment, or as the \code{as_of} of
 default \code{NULL} will attempt to determine this automatically as
 \code{forecast_date + ahead}.}
 
-\item{adjust_latency}{Character or \code{NULL}. one of the \code{method}s of
-\code{step_adjust_latency}, or \code{NULL} (in which case there is no adjustment).}
+\item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
+\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
+If there is a difference between the \code{forecast_date} and the last day of
+data, this determines how to shift the model to account for this
+difference. The options are:
+\itemize{
+\item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
+\item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
+the last day of data. If the last day of data was 3 days ago, the ahead
+becomes \code{ahead+3}.
+\item \code{"extend_lags"}: increase the lags so they're relative to the actual forecast date. If the lags are
+\code{c(0,7,14)} and the last day of data was 3 days ago, the lags become
+\code{c(3,10,17)}.
+}}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 2c780e822..e801dcc4e 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -10,6 +10,7 @@ arx_class_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
+  adjust_latency = NULL,
   outcome_transform = c("growth_rate", "lag_difference"),
   breaks = 0.25,
   horizon = 7L,
@@ -43,6 +44,21 @@ max time value if there is no latency adjustment, or as the \code{as_of} of
 default \code{NULL} will attempt to determine this automatically as
 \code{forecast_date + ahead}.}
 
+\item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
+\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
+If there is a difference between the \code{forecast_date} and the last day of
+data, this determines how to shift the model to account for this
+difference. The options are:
+\itemize{
+\item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
+\item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
+the last day of data. If the last day of data was 3 days ago, the ahead
+becomes \code{ahead+3}.
+\item \code{"extend_lags"}: increase the lags so they're relative to the actual forecast date. If the lags are
+\code{c(0,7,14)} and the last day of data was 3 days ago, the lags become
+\code{c(3,10,17)}.
+}}
+
 \item{outcome_transform}{Scalar character. Whether the outcome should
 be created using growth rates (as the predictors are) or lagged
 differences. The second case is closer to the requirements for the
diff --git a/man/extend_either.Rd b/man/extend_either.Rd
deleted file mode 100644
index ae55fa46a..000000000
--- a/man/extend_either.Rd
+++ /dev/null
@@ -1,26 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-latency.R
-\name{extend_either}
-\alias{extend_either}
-\title{offset each relevant column by it's appropriate latency
-works for either adjusting aheads or lags
-note that this may introduce new NA values when one column is shifted farther than another}
-\usage{
-extend_either(new_data, shift_cols, keys)
-}
-\arguments{
-\item{new_data}{just what is says}
-
-\item{shift_cols}{a tibble which must have the columns \code{column}, the name of
-the column to adjust, \code{latency} the latency of the original column relative
-to the \code{forecast_date}, \code{new_name}, the names in \code{column} adjusted by the
-latencies \code{latency}}
-
-\item{keys}{the variables which are used as keys}
-}
-\description{
-offset each relevant column by it's appropriate latency
-works for either adjusting aheads or lags
-note that this may introduce new NA values when one column is shifted farther than another
-}
-\keyword{internal}
diff --git a/man/get_latency.Rd b/man/get_latency.Rd
index f5bf7f0c2..5d6d7190f 100644
--- a/man/get_latency.Rd
+++ b/man/get_latency.Rd
@@ -4,14 +4,7 @@
 \alias{get_latency}
 \title{the latency is also the amount the shift is off by}
 \usage{
-get_latency(
-  new_data,
-  forecast_date,
-  column,
-  shift_amount,
-  sign_shift,
-  epi_keys_checked
-)
+get_latency(new_data, forecast_date, column, sign_shift, epi_keys_checked)
 }
 \arguments{
 \item{sign_shift}{integer. 1 if lag and -1 if ahead. These represent how you
diff --git a/man/get_latent_column_tibble.Rd b/man/get_latent_column_tibble.Rd
deleted file mode 100644
index 0a0298625..000000000
--- a/man/get_latent_column_tibble.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-latency.R
-\name{get_latent_column_tibble}
-\alias{get_latent_column_tibble}
-\title{find the columns added with the lags or aheads, and the amounts they have
-been changed}
-\usage{
-get_latent_column_tibble(
-  shift_cols,
-  new_data,
-  forecast_date,
-  latency,
-  sign_shift,
-  info,
-  epi_keys_checked,
-  call = caller_env()
-)
-}
-\arguments{
-\item{shift_cols}{a list of columns to operate on, as created by \code{construct_shift_tibble}}
-
-\item{new_data}{the data transformed so far}
-
-\item{forecast_date}{the forecast date}
-
-\item{latency}{\code{NULL}, int, or vector, as described in \code{step_epi_latency}}
-
-\item{sign_shift}{-1 if ahead, 1 if lag}
-}
-\value{
-a tibble with columns \code{column} (relevant shifted names), \code{shift} (the
-amount that one is shifted), \code{latency} (original columns difference between
-the max \code{time_value} and \code{forecast_date} (on a per-initial column basis)),
-\code{effective_shift} (shift+latency), and \code{new_name} (adjusted names with the
-effective_shift)
-}
-\description{
-find the columns added with the lags or aheads, and the amounts they have
-been changed
-}
-\keyword{internal}
diff --git a/tests/testthat.R b/tests/testthat.R
index 296d916e8..27254bec7 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -1,4 +1,8 @@
 library(testthat)
 library(epipredict)
+library(parsnip)
+library(workflows)
+library(dplyr)
+
 
 test_check("epipredict")
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 90b647d86..fb11026dd 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1059,7 +1059,7 @@
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
-# arx_forecasrte output format snapshots
+# arx_forecaster output format snapshots
 
     Code
       out1
@@ -1103,7 +1103,7 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
-      * Latency adjusted per column: case_rate=3, death_rate=3
+      * Lags adjusted per column: case_rate=3, death_rate=3
       
 
 ---
@@ -1127,7 +1127,7 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
-      * Latency adjusted for death_rate=3
+      * Aheads adjusted for death_rate=3
       
 
 # arx_classifier snapshots
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 354d02d84..fd49b8824 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -13,10 +13,10 @@
       time_value: 1
       
       -- Operations 
-      1. Lagging: death_rate by 0, 6, 11
-      2. Lagging: case_rate by 1, 5
-      3. Leading: death_rate by 7
-      4. extend_lags: case_rate with latency set at train time
+      1. extend_lags: case_rate with latency set at train time
+      2. Lagging: death_rate by 0, 6, 11
+      3. Lagging: case_rate by 1, 5
+      4. Leading: death_rate by 7
 
 ---
 
@@ -33,7 +33,7 @@
       time_value: 1
       
       -- Operations 
-      1. Leading: death_rate by 7
-      2. extend_ahead: all previous predictors with latency set at train time
-      3. Lagging: death_rate by 0, 7, 14
+      1. Lagging: death_rate by 0, 7, 14
+      2. extend_ahead: all future predictors with latency set at train time
+      3. Leading: death_rate by 7
 
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index cad6e79bf..3ba41158a 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -80,10 +80,12 @@ test_that("Do not specify a forecast_date in `layer_add_forecast_date()`", {
 })
 
 test_that("`layer_add_forecast_date()` infers correct date when using `adjust_latency`", {
-  r_latent <- epi_recipe(jhu) %>%
+  jhu_reasonable_date <- jhu
+  attributes(jhu_reasonable_date)$metadata$as_of <- as.Date("2022-01-03")
+  r_latent <- epi_recipe(jhu_reasonable_date) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-    step_epi_ahead(death_rate, ahead = 7) %>%
     step_adjust_latency(method = "extend_ahead") %>%
+    step_epi_ahead(death_rate, ahead = 7) %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
   frost_latent <- frosting() %>%
@@ -91,7 +93,7 @@ test_that("`layer_add_forecast_date()` infers correct date when using `adjust_la
     layer_add_forecast_date() %>%
     layer_naomit(.pred)
   wf_latent <- epi_workflow(r_latent, parsnip::linear_reg()) %>%
-    fit(jhu) %>%
+    fit(jhu_reasonable_date) %>%
     add_frosting(frost_latent)
   p_latent <- predict(wf_latent, latest)
   expect_equal(
diff --git a/tests/testthat/test-layer_add_target_date.R b/tests/testthat/test-layer_add_target_date.R
index 72f3c1d7f..8bdb3a76b 100644
--- a/tests/testthat/test-layer_add_target_date.R
+++ b/tests/testthat/test-layer_add_target_date.R
@@ -42,9 +42,9 @@ test_that("Use ahead + max time value from pre, fit, post", {
 })
 test_that("latency adjust doesn't interfere with correct target date", {
   r_latent <- epi_recipe(jhu) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
   wf_latent <- epi_workflow(r_latent, parsnip::linear_reg()) %>% fit(jhu)
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 8627fe5ed..bdb0457de 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -96,9 +96,9 @@ test_that("arx_forecaster snapshots", {
   expect_false(all(arx2$predictions == arx3$predictions))
 })
 
-test_that("arx_forecasrte output format snapshots", {
+test_that("arx_forecaster output format snapshots", {
   jhu <- case_death_rate_subset %>%
-    dplyr::filter(time_value >= as.Date("2021-12-01"))
+    dplyr::filter(time_value >= as.Date("2021-12-01")) 
   out1 <- arx_forecaster(
     jhu, "death_rate",
     c("case_rate", "death_rate")
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 7a0aac51c..fd1bb9675 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -35,19 +35,44 @@ x_lagged
 attributes(x_lagged)$metadata$as_of <- testing_as_of
 
 test_that("epi_adjust_latency correctly extends the lags", {
+  expect_warning(epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_adjust_latency(method = "extend_lags"))
+
   r1 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = ahead) %>%
-    step_adjust_latency(method = "extend_lags")
+    step_epi_ahead(death_rate, ahead = ahead)
+
+  # directly checking the shifts
+  baked_x <- r1 %>% prep(real_x) %>% bake(real_x)
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date))
+  expect_equal(last_dates,
+               tribble(
+    ~name, ~last_date,
+    "lag_16_death_rate", max_time + 16,
+    "lag_11_death_rate", max_time + 11,
+    "lag_10_case_rate", max_time + 10,
+    "lag_6_case_rate", max_time + 6,
+    "lag_5_death_rate", max_time + 5,
+    "case_rate", max_time,
+    "death_rate", max_time,
+    "ahead_7_death_rate", max_time - 7,
+  ))
+
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit1 <- slm_fit(r1), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
+  expect_error(expect_warning(fit1 <- slm_fit(r1, data = x), regexp = "The latency is 1033"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit1 <- slm_fit(r1, data = real_x)
-
   expect_equal(
     names(fit1$pre$mold$predictors),
     c(
@@ -55,7 +80,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
       "lag_6_case_rate", "lag_10_case_rate"
     )
   )
-  latest <- get_test_data(r1, x)
+  latest <- get_test_data(r1, real_x)
   pred <- predict(fit1, latest)
   point_pred <- pred %>% filter(!is.na(.pred))
   expect_equal(nrow(point_pred), 1)
@@ -88,10 +113,10 @@ test_that("epi_adjust_latency correctly extends the lags", {
 
 test_that("epi_adjust_latency correctly extends the ahead", {
   r2 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = ahead) %>%
-    step_adjust_latency(method = "extend_ahead")
+    step_epi_ahead(death_rate, ahead = ahead)
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
@@ -105,7 +130,7 @@ test_that("epi_adjust_latency correctly extends the ahead", {
       "lag_1_case_rate", "lag_5_case_rate"
     )
   )
-  latest <- get_test_data(r2, x)
+  latest <- get_test_data(r2, real_x)
   pred2 <- predict(fit2, latest)
   point_pred2 <- pred2 %>% filter(!is.na(.pred))
   # max time is still the forecast date
@@ -134,10 +159,10 @@ test_that("epi_adjust_latency correctly extends the ahead", {
 test_that("epi_adjust_latency extends multiple aheads", {
   aheads <- 1:3
   r3 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = aheads) %>%
-    step_adjust_latency(method = "extend_ahead")
+    step_epi_ahead(death_rate, ahead = aheads)
   fitter <- smooth_quantile_reg(
     quantile_levels = 0.5,
     outcome_locations = aheads,
@@ -147,7 +172,7 @@ test_that("epi_adjust_latency extends multiple aheads", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit3 <- fit(epi_wf, data = x)))
+  expect_error(fit3 <- fit(epi_wf, data = x))
   # real date example
   fit3 <- fit(epi_wf, data = real_x)
   expect_equal(
@@ -200,16 +225,16 @@ test_that("epi_adjust_latency extend_ahead uses the same adjustment when predict
 
 test_that("epi_adjust_latency works for other time types", {})
 
-test_that("epi_adjust_latency insist there's steps before it", {
-  expect_error(
+test_that("epi_adjust_latency warns there's steps before it", {
+  expect_warning(
     r5 <- epi_recipe(x) %>%
-      step_epi_ahead(death_rate, ahead = ahead) %>%
+      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
       step_adjust_latency(method = "extend_lags"),
     regexp = "extend_lags"
   )
-  expect_error(
+  expect_warning(
     r5 <- epi_recipe(x) %>%
-      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+      step_epi_ahead(death_rate, ahead = ahead) %>%
       step_adjust_latency(method = "extend_ahead"),
     regexp = "extend_ahead"
   )
@@ -218,28 +243,27 @@ test_that("epi_adjust_latency insist there's steps before it", {
 test_that("epi_adjust_latency warns against removing NA's beforehand", {
   expect_error(
     r5 <- epi_recipe(x) %>%
-      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-      step_epi_lag(case_rate, lag = c(1, 5)) %>%
       step_epi_naomit() %>%
-      step_adjust_latency(method = "extend_lags"),
+      step_adjust_latency(method = "extend_lags") %>%
+      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+      step_epi_lag(case_rate, lag = c(1, 5)),
     regexp = "adjust_latency needs to occur before any `NA` removal"
   )
 })
 # todo check that epi_adjust_latency errors for nonsense `as_of`'s
 
 
-
 # todo make sure that `epi_keys_checked` works correctly for extra epi_keys
 test_that("epi_adjust_latency correctly extends the lags", {
   r5 <- epi_recipe(x_lagged) %>%
+    step_adjust_latency(method = "extend_lags", epi_keys_checked = NULL) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = ahead) %>%
-    step_adjust_latency(method = "extend_lags", epi_keys_checked = NULL)
+    step_epi_ahead(death_rate, ahead = ahead)
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The shift has been adjusted by 1033"), class = "simpleError")
+  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The latency is 1033"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = x_lagged)
@@ -251,7 +275,6 @@ test_that("epi_adjust_latency correctly extends the lags", {
     )
   )
   latest <- get_test_data(r5, x_lagged)
-  latest$time_value %>% unique()
   pred <- predict(fit5, latest)
   point_pred <- pred %>% filter(!is.na(.pred))
   expect_equal(nrow(point_pred), 1)
@@ -286,10 +309,10 @@ test_that("`step_adjust_latency` only allows one instance of itself", {})
 
 test_that("`step_adjust_latency` only uses the columns specified in the `...`", {
   r5 <- epi_recipe(x) %>%
-    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_adjust_latency(case_rate, method = "extend_lags") %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11))  %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = ahead) %>%
-    step_adjust_latency(case_rate, method = "extend_lags")
+    step_epi_ahead(death_rate, ahead = ahead)
 
   fit5 <- slm_fit(r5, data = real_x)
   expect_equal(names(fit5$fit$fit$fit$coefficients), c("(Intercept)", "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate", "lag_6_case_rate", "lag_10_case_rate"))
@@ -299,14 +322,16 @@ test_that("setting fixed_* works for `step_adjust_latency`", {})
 
 test_that("printing step_adjust_latency results in expected output", {
   r5 <- epi_recipe(x) %>%
+    step_adjust_latency(case_rate, method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
-    step_epi_ahead(death_rate, ahead = ahead) %>%
-    step_adjust_latency(case_rate, method = "extend_lags")
+    step_epi_ahead(death_rate, ahead = ahead)
   expect_snapshot(r5)
   r <- epi_recipe(case_death_rate_subset) %>%
-    step_epi_ahead(death_rate, ahead = 7) %>%
+    step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_adjust_latency(method = "extend_ahead") %>%
-    step_epi_lag(death_rate, lag = c(0, 7, 14))
+    step_epi_ahead(death_rate, ahead = 7)
   expect_snapshot(r)
 })
+
+test_that("lags of transforms (of transforms etc) work", {})
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index f52b1a950..5a5979e2a 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -22,58 +22,31 @@ old_data <- old_data %>%
 modified_data <-
   old_data %>%
   dplyr::full_join(
-    epi_shift_single(old_data, "case_rate", -4, "ahead_4_case_rate", keys),
+    epi_shift_single(old_data, "case_rate", -4, "case_rate_a", keys),
     by = keys
   ) %>%
   dplyr::full_join(
-    epi_shift_single(old_data, "case_rate", 3, "lag_3_case_rate", keys),
+    epi_shift_single(old_data, "case_rate", 3, "case_rate_b", keys),
     by = keys
   ) %>%
   dplyr::full_join(
-    epi_shift_single(old_data, "death_rate", 7, "lag_7_death_rate", keys),
+    epi_shift_single(old_data, "death_rate", 7, "death_rate_a", keys),
     by = keys
   ) %>%
   arrange(time_value)
-modified_data %>% tail()
-as_of - (modified_data %>% filter(!is.na(ahead_4_case_rate)) %>% pull(time_value) %>% max())
-all_shift_cols <- tibble::tribble(
-  ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-  "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 5, 8, "lag_8_case_rate", "numeric", "predictor",
-  "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 4, 11, "lag_11_death_rate", "numeric", "predictor",
-  "case_rate", 4, "ahead_", "ahead_4_case_rate", "case_rate", -5, 9, "ahead_9_case_rate", "numeric", "outcome"
-)
-test_recipe <- epi_recipe(modified_data) %>%
-  step_epi_lag(case_rate, lag = c(3)) %>%
-  step_epi_lag(death_rate, lag = 7) %>%
-  step_epi_ahead(case_rate, ahead = 4)
-shift_cols <- construct_shift_tibble(c("case_rate", "death_rate"), test_recipe, "step_epi_lag", "lag")
-
-
-test_that("construct_shift_tibble constructs the right tibble", {
-  expected_shift_cols <- tibble::tribble(
-    ~terms, ~shift, ~prefix,
-    "case_rate", 3, "lag_",
-    "death_rate", 7, "lag_"
-  )
-  expect_equal(shift_cols, expected_shift_cols)
-})
 
 test_that("get_latency works", {
-  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1, "geo_value"), 4)
-  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 3, 1, "geo_value"), 5)
-  # get_latency does't check the shift_amount
-  expect_equal(get_latency(modified_data, as_of, "lag_3_case_rate", 4, 1, "geo_value"), 6)
-  # ahead works correctly
-  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, -1, "geo_value"), -5)
-  # setting the wrong sign doubles the shift and gets the sign wrong
-  expect_equal(get_latency(modified_data, as_of, "ahead_4_case_rate", 4, 1, "geo_value"), 5 + 4 * 2)
-  # minimizing over everything decreases the latency
-  expect_equal(get_latency(modified_data, as_of, "lag_7_death_rate", 7, 1, NULL), 3)
+expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
+expect_equal(get_latency(modified_data, as_of, "case_rate", -1, "geo_value"), -5)
+expect_equal(get_latency(modified_data, as_of, "death_rate", 1, "geo_value"), 4)
+expect_equal(get_latency(modified_data, as_of, "case_rate_a", 1, "geo_value"), 5 + 4)
+expect_equal(get_latency(modified_data, as_of, "case_rate_b", 1, "geo_value"), 5 - 3)
+expect_equal(get_latency(modified_data, as_of, "death_rate_a", 1, "geo_value"), 4 - 7)
 })
 
 test_that("get_latency infers max_time to be the minimum `max time` across the epi_keys", {})
 
-test_that("get_asof works", {
+test_that("set_forecast_date works", {
   info <- tribble(
     ~variable, ~type, ~role, ~source,
     "time_value", "date", "time_value", "original",
@@ -87,99 +60,6 @@ test_that("get_asof works", {
   expect_equal(set_forecast_date(modified_data, info, NULL), as_of)
 })
 
-test_that("get_latent_column_tibble infers latency and works correctly", {
-  info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
-
-  case_lag <- get_latent_column_tibble(
-    shift_cols[1, ], modified_data, as_of, NULL, 1, info,
-    epi_keys_checked = "geo_value"
-  )
-  expect_equal(case_lag, all_shift_cols[1, ])
-
-  death_lag <- get_latent_column_tibble(
-    shift_cols[2, ], modified_data, as_of, NULL, 1, info,
-    epi_keys_checked = "geo_value"
-  )
-  expect_equal(death_lag, all_shift_cols[2, ])
-
-  both_lag <- get_latent_column_tibble(
-    shift_cols, modified_data, as_of, NULL, 1, info,
-    epi_keys_checked = "geo_value"
-  )
-  expect_equal(both_lag, all_shift_cols[1:2, ])
-})
-
-test_that("get_latent_column_tibble assigns given latencies", {
-  # non-null latency
-  info <- tibble(variable = c("lag_3_case_rate", "lag_7_death_rate", "ahead_4_case_rate"), type = "numeric", role = c(rep("predictor", 2), "outcome"), source = "derived")
-  both_lag <- get_latent_column_tibble(
-    shift_cols, modified_data, as_of, 50, 1, info
-  )
-  weird_latencies <- tibble::tribble(
-    ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 50, 53, "lag_53_case_rate", "numeric", "predictor",
-    "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 50, 57, "lag_57_death_rate", "numeric", "predictor",
-  )
-  expect_equal(both_lag, weird_latencies)
-
-  # supposing we add the latencies by hand, and they're different, and in a different order
-  weird_latencies <- tibble::tribble(
-    ~terms, ~shift, ~prefix, ~original_name, ~parent_name, ~latency, ~effective_shift, ~new_name, ~type, ~role,
-    "case_rate", 3, "lag_", "lag_3_case_rate", "case_rate", 70, 73, "lag_73_case_rate", "numeric", "predictor",
-    "death_rate", 7, "lag_", "lag_7_death_rate", "death_rate", 30, 37, "lag_37_death_rate", "numeric", "predictor",
-  )
-  both_lag <- get_latent_column_tibble(
-    shift_cols, modified_data, as_of, c(death_rate = 30, case_rate = 70), 1, info
-  )
-  expect_equal(both_lag, weird_latencies[1:2, ])
-
-  ahead_shift_cols <- construct_shift_tibble(c("case_rate"), test_recipe, "step_epi_ahead", "ahead")
-  case_ahead <- get_latent_column_tibble(
-    ahead_shift_cols, modified_data, as_of, NULL, -1, info, "geo_value"
-  )
-  expect_equal(case_ahead, all_shift_cols[3, ])
-})
-
-test_that("get_shifted_column_tibble objects to non-columns", {
-  non_shift_cols <- tibble(terms = "not_present", shift = 99, prefix = "lag_")
-  expect_error(
-    get_latent_column_tibble(
-      non_shift_cols, modified_data, as_of, NULL, 1, info
-    ),
-    regexp = "Can't subset elements that don't exist"
-  )
-})
-
-test_that("extend_either works", {
-  keys <- c("geo_value", "time_value")
-  # extend_either doesn't differentiate between the directions, it just moves
-  # things
-  expected_post_shift <-
-    old_data %>%
-    dplyr::full_join(
-      epi_shift_single(old_data, "case_rate", 8, "lag_8_case_rate", keys),
-      by = keys
-    ) %>%
-    dplyr::full_join(
-      epi_shift_single(old_data, "death_rate", 11, "lag_11_death_rate", keys),
-      by = keys
-    ) %>%
-    dplyr::full_join(
-      epi_shift_single(old_data, "case_rate", -9, "ahead_9_case_rate", keys),
-      by = keys
-    ) %>%
-    dplyr::bind_rows(tibble(
-      geo_value = c("place1", "place2"),
-      time_value = as.Date(c("2021-04-23", "2021-04-24")), case_rate = c(NA, NA), death_rate = c(NA, NA),
-      lag_8_case_rate = c(NA, NA), lag_11_death_rate = c(NA, NA), ahead_9_case_rate = c(NA, NA)
-    )) %>%
-    arrange(time_value, geo_value)
-  expect_equal(
-    extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value, geo_value),
-    expected_post_shift
-  )
-  extended <- extend_either(modified_data, all_shift_cols, keys) %>% arrange(time_value, geo_value)
-})
 
 
 

From 09fbfd812c9535b085fade3ad78c622f6be195a0 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 3 Jul 2024 18:50:57 -0500
Subject: [PATCH 069/387] moving locf to step_adjust_ahead instead of
 get_test_data

---
 NAMESPACE                                 |   2 +
 R/arx_classifier.R                        |   6 --
 R/arx_forecaster.R                        |  23 +----
 R/cdc_baseline_forecaster.R               |   9 +-
 R/epi_workflow.R                          |   6 +-
 R/flatline_forecaster.R                   |   9 +-
 R/get_test_data.R                         | 105 +++-------------------
 R/step_adjust_latency.R                   |  81 ++++++++++-------
 R/utils-latency.R                         |  51 ++++++++++-
 man/arx_args_list.Rd                      |  11 ---
 man/arx_class_args_list.Rd                |  11 ---
 man/cdc_baseline_args_list.Rd             |  11 ---
 man/flatline_args_list.Rd                 |  11 ---
 man/get_test_data.Rd                      |  17 +---
 man/set_forecast_date.Rd                  |   2 +-
 man/step_adjust_latency.Rd                |   2 +-
 tests/testthat/test-epi_workflow.R        |  10 +--
 tests/testthat/test-get_test_data.R       |   2 +
 tests/testthat/test-step_adjust_latency.R | 105 ++++++++++++++++++++++
 19 files changed, 228 insertions(+), 246 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 19e42fef5..899d22a1e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -240,6 +240,7 @@ importFrom(dplyr,everything)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
+importFrom(dplyr,group_by_at)
 importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
@@ -306,6 +307,7 @@ importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
 importFrom(tidyr,expand_grid)
+importFrom(tidyr,fill)
 importFrom(tidyr,unnest)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index f07ae3e71..5ee6de88a 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -67,9 +67,6 @@ arx_classifier <- function(
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   preds <- forecast(
     wf,
-    fill_locf = is.null(args_list$adjust_latency),
-    n_recent = args_list$nafill_buffer,
-    forecast_date = forecast_date
   ) %>%
     as_tibble() %>%
     select(-time_value)
@@ -294,7 +291,6 @@ arx_class_args_list <- function(
     method = c("rel_change", "linear_reg"),
     log_scale = FALSE,
     additional_gr_args = list(),
-    nafill_buffer = Inf,
     check_enough_data_n = NULL,
     check_enough_data_epi_keys = NULL,
     ...) {
@@ -312,7 +308,6 @@ arx_class_args_list <- function(
   arg_is_lgl(log_scale)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
   if (!is.list(additional_gr_args)) {
     cli_abort(c(
       "`additional_gr_args` must be a {.cls list}.",
@@ -353,7 +348,6 @@ arx_class_args_list <- function(
       method,
       log_scale,
       additional_gr_args,
-      nafill_buffer,
       check_enough_data_n,
       check_enough_data_epi_keys
     ),
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 3abbeb2eb..a67d07c11 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -51,14 +51,9 @@ arx_forecaster <- function(
   wf <- arx_fcast_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
-  preds <- forecast(
-    wf,
-    fill_locf = is.null(args_list$adjust_latency),
-    n_recent = args_list$nafill_buffer,
-    forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
-  ) %>%
-    as_tibble() %>%
-    select(-time_value)
+  preds <- forecast(wf) %>%
+    tibble::as_tibble() %>%
+    dplyr::select(-time_value)
 
   structure(
     list(
@@ -252,15 +247,6 @@ arx_fcast_epi_workflow <- function(
 #'   `character(0)` performs no grouping. This argument only applies when
 #'   residual quantiles are used. It is not applicable with
 #'   `trainer = quantile_reg()`, for example.
-#' @param nafill_buffer At predict time, recent values of the training data
-#'   are used to create a forecast. However, these can be `NA` due to, e.g.,
-#'   data latency issues. By default, any missing values will get filled with
-#'   less recent data. Setting this value to `NULL` will result in 1 extra
-#'   recent row (beyond those required for lag creation) to be used. Note that
-#'   we require at least `min(lags)` rows of recent data per `geo_value` to
-#'   create a prediction. For this reason, setting `nafill_buffer < min(lags)`
-#'   will be treated as _additional_ allowed recent data rather than the
-#'   total amount of recent data to examine.
 #' @param check_enough_data_n Integer. A lower limit for the number of rows per
 #'   epi_key that are required for training. If `NULL`, this check is ignored.
 #' @param check_enough_data_epi_keys Character vector. A character vector of
@@ -287,7 +273,6 @@ arx_args_list <- function(
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
-    nafill_buffer = Inf,
     check_enough_data_n = NULL,
     check_enough_data_epi_keys = NULL,
     ...) {
@@ -305,7 +290,6 @@ arx_args_list <- function(
   arg_is_probabilities(quantile_levels, allow_null = TRUE)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
@@ -332,7 +316,6 @@ arx_args_list <- function(
       nonneg,
       max_lags,
       quantile_by_key,
-      nafill_buffer,
       check_enough_data_n,
       check_enough_data_epi_keys
     ),
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index b2e7434e2..f3ba48794 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -79,9 +79,7 @@ cdc_baseline_forecaster <- function(
 
 
   latest <- get_test_data(
-    epi_recipe(epi_data), epi_data, TRUE, args_list$nafill_buffer,
-    forecast_date
-  )
+    epi_recipe(epi_data), epi_data)
 
   f <- frosting() %>%
     layer_predict() %>%
@@ -169,7 +167,6 @@ cdc_baseline_args_list <- function(
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = "geo_value",
-    nafill_buffer = Inf,
     ...) {
   rlang::check_dots_empty()
   arg_is_scalar(n_training, nsims, data_frequency)
@@ -183,7 +180,6 @@ cdc_baseline_args_list <- function(
   arg_is_probabilities(quantile_levels, allow_null = TRUE)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
 
   structure(
     enlist(
@@ -195,8 +191,7 @@ cdc_baseline_args_list <- function(
       nsims,
       symmetrize,
       nonneg,
-      quantile_by_key,
-      nafill_buffer
+      quantile_by_key
     ),
     class = c("cdc_baseline_fcast", "alist")
   )
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index fe9b656ba..34141b732 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -267,13 +267,9 @@ forecast.epi_workflow <- function(object, ..., fill_locf = FALSE, n_recent = NUL
       ))
     }
   }
-
   test_data <- get_test_data(
     hardhat::extract_preprocessor(object),
-    object$original_data,
-    fill_locf = fill_locf,
-    n_recent = n_recent %||% Inf,
-    forecast_date = forecast_date %||% frosting_fd %||% max(object$original_data$time_value)
+    object$original_data
   )
 
   predict(object, new_data = test_data)
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 55808b803..f2d6fa998 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -67,9 +67,7 @@ flatline_forecaster <- function(
   wf <- fit(wf, epi_data)
   preds <- suppressWarnings(forecast(
     wf,
-    fill_locf = TRUE,
-    n_recent = args_list$nafill_buffer,
-    forecast_date = forecast_date
+    fill_locf = TRUE
   )) %>%
     as_tibble() %>%
     select(-time_value)
@@ -117,7 +115,6 @@ flatline_args_list <- function(
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
-    nafill_buffer = Inf,
     ...) {
   rlang::check_dots_empty()
   arg_is_scalar(ahead, n_training)
@@ -129,7 +126,6 @@ flatline_args_list <- function(
   arg_is_probabilities(quantile_levels, allow_null = TRUE)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
@@ -149,8 +145,7 @@ flatline_args_list <- function(
       quantile_levels,
       symmetrize,
       nonneg,
-      quantile_by_key,
-      nafill_buffer
+      quantile_by_key
     ),
     class = c("flat_fcast", "alist")
   )
diff --git a/R/get_test_data.R b/R/get_test_data.R
index a491ed40d..ebd6e1b7b 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -20,13 +20,6 @@
 #' @param recipe A recipe object.
 #' @param x An epi_df. The typical usage is to
 #'   pass the same data as that used for fitting the recipe.
-#' @param fill_locf Logical. Should we use `locf` to fill in missing data?
-#' @param n_recent Integer or NULL. If filling missing data with `locf = TRUE`,
-#'   how far back are we willing to tolerate missing data? Larger values allow
-#'   more filling. The default `NULL` will determine this from the
-#'   the `recipe`. For example, suppose `n_recent = 3`, then if the
-#'   3 most recent observations in any `geo_value` are all `NA`’s, we won’t be
-#'   able to fill anything, and an error message will be thrown. (See details.)
 #' @param forecast_date By default, this is set to the maximum
 #'   `time_value` in `x`. But if there is data latency such that recent `NA`'s
 #'   should be filled, this may be _after_ the last available `time_value`.
@@ -44,18 +37,8 @@
 #' @export
 get_test_data <- function(
     recipe,
-    x,
-    fill_locf = FALSE,
-    n_recent = NULL,
-    forecast_date = max(x$time_value)) {
-  if (!is_epi_df(x)) cli_abort("`x` must be an `epi_df`.")
-  arg_is_lgl(fill_locf)
-  arg_is_scalar(fill_locf)
-  arg_is_scalar(n_recent, allow_null = TRUE)
-  if (!is.null(n_recent) && is.finite(n_recent)) {
-    arg_is_pos_int(n_recent, allow_null = TRUE)
-  }
-  if (!is.null(n_recent)) n_recent <- abs(n_recent) # in case they passed -Inf
+    x) {
+  if (!is_epi_df(x)) cli::cli_abort("`x` must be an `epi_df`.")
 
   check <- hardhat::check_column_names(x, colnames(recipe$template))
   if (!check$ok) {
@@ -64,103 +47,35 @@ get_test_data <- function(
       i = "The following required columns are missing: {check$missing_names}"
     ))
   }
-  if (class(forecast_date) != class(x$time_value)) {
-    cli_abort("`forecast_date` must be the same class as `x$time_value`.")
-  }
-  if (forecast_date < max(x$time_value)) {
-    cli_abort("`forecast_date` must be no earlier than `max(x$time_value)`")
-  }
 
   min_lags <- min(map_dbl(recipe$steps, ~ min(.x$lag %||% Inf)), Inf)
   max_lags <- max(map_dbl(recipe$steps, ~ max(.x$lag %||% 0)), 0)
   max_horizon <- max(map_dbl(recipe$steps, ~ max(.x$horizon %||% 0)), 0)
-  max_slide <- max(map_dbl(recipe$steps, ~ max(.x$before %||% 0)), 0)
-  min_required <- max_lags + max_horizon + max_slide
-  if (is.null(n_recent)) n_recent <- min_required + 1 # one extra for filling
-  if (n_recent <= min_required) n_recent <- min_required + n_recent
+  keep <- max_lags + max_horizon
 
   # CHECK: Error out if insufficient training data
   # Probably needs a fix based on the time_type of the epi_df
   avail_recent <- diff(range(x$time_value))
-  if (avail_recent < min_required) {
-    cli_abort(c(
+  if (avail_recent < keep) {
+    cli::cli_abort(c(
       "You supplied insufficient recent data for this recipe. ",
       "!" = "You need at least {min_required} days of data,",
       "!" = "but `x` contains only {avail_recent}."
     ))
   }
-
+  max_time_value <- x %>% na.omit %>% pull(time_value) %>% max
   x <- arrange(x, time_value)
   groups <- epi_keys_only(recipe)
 
   # If we skip NA completion, we remove undesirably early time values
   # Happens globally, over all groups
-  keep <- max(n_recent, min_required + 1)
-  x <- filter(x, forecast_date - time_value <= keep)
-
-  # Pad with explicit missing values up to and including the forecast_date
-  # x is grouped here
-  x <- pad_to_end(x, groups, forecast_date) %>%
-    group_by(across(all_of(groups)))
+  x <- dplyr::filter(x, max_time_value - time_value <= keep)
 
   # If all(lags > 0), then we get rid of recent data
   if (min_lags > 0 && min_lags < Inf) {
-    x <- filter(x, forecast_date - time_value >= min_lags)
-  }
-
-  # Now, fill forward missing data if requested
-  if (fill_locf) {
-    cannot_be_used <- x %>%
-      dplyr::filter(forecast_date - time_value <= n_recent) %>%
-      dplyr::mutate(fillers = forecast_date - time_value > min_required) %>%
-      dplyr::summarise(
-        dplyr::across(
-          -tidyselect::any_of(epi_keys(recipe)),
-          ~ all(is.na(.x[fillers])) & is.na(head(.x[!fillers], 1))
-        ),
-        .groups = "drop"
-      ) %>%
-      select(-fillers) %>%
-      summarise(across(-any_of(key_colnames(recipe)), ~ any(.x))) %>%
-      unlist()
-    if (any(cannot_be_used)) {
-      bad_vars <- names(cannot_be_used)[cannot_be_used]
-      if (recipes::is_trained(recipe)) {
-        cli_abort(c(
-          "The variables {.var {bad_vars}} have too many recent missing",
-          `!` = "values to be filled automatically. ",
-          i = "You should either choose `n_recent` larger than its current ",
-          i = "value {n_recent}, or perform NA imputation manually, perhaps with ",
-          i = "{.code recipes::step_impute_*()} or with {.code tidyr::fill()}."
-        ))
-      }
-    }
-    x <- tidyr::fill(x, !time_value)
+    x <- dplyr::filter(x, max_time_value - time_value >= min_lags)
   }
 
-  filter(x, forecast_date - time_value <= min_required) %>%
-    ungroup()
-}
-
-pad_to_end <- function(x, groups, end_date) {
-  itval <- guess_period(c(x$time_value, end_date), "time_value")
-  completed_time_values <- x %>%
-    group_by(across(all_of(groups))) %>%
-    summarise(
-      time_value = rlang::list2(
-        time_value = Seq(max(time_value) + itval, end_date, itval)
-      )
-    ) %>%
-    unnest("time_value") %>%
-    mutate(time_value = vctrs::vec_cast(time_value, x$time_value))
-
-  bind_rows(x, completed_time_values) %>%
-    arrange(across(all_of(c("time_value", groups))))
-}
-
-Seq <- function(from, to, by) {
-  if (from > to) {
-    return(NULL)
-  }
-  seq(from = from, to = to, by = by)
+  dplyr::filter(x, max_time_value - time_value <= keep) %>%
+    epiprocess::ungroup()
 }
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 40fd7c37d..b963ce257 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -122,6 +122,8 @@ step_adjust_latency <-
         "If `method` is {.val extend_lags} or {.val locf},
 then the previous `step_epi_lag`s won't work with modified data."
       )
+    } else if ((method == "locf") && (length(recipe$steps) > 0)) {
+      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others")
     }
     if (detect_step(recipe, "naomit")) {
       cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
@@ -205,14 +207,14 @@ construct_latency_table <- function(x, latency, training, info) {
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   sign_shift <- get_sign(x)
   latency <- x$latency
-  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked)
+  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked, latency)
   # construct the latency table
   latency_table <- names(training)[!names(training) %in% epi_keys(training)] %>%
     tibble(col_name = .)
-    if (length(recipes_eval_select(x$terms, training, info)) > 0) {
+  if (length(recipes_eval_select(x$terms, training, info)) > 0) {
     latency_table <- latency_table %>% filter(col_name %in%
       recipes_eval_select(x$terms, training, info))
-    }
+  }
 
   if (is.null(latency)) {
     latency_table <- latency_table %>%
@@ -237,30 +239,28 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       filter(role == "raw") %>%
       pull(variable)
   }
-  # get and check the max_time and forecast_date are the right kinds of dates
-  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked)
 
-    # check that the shift amount isn't too extreme
-    latency <- max(latency_table$latency)
-    time_type <- attributes(training)$metadata$time_type
-    i_latency <- which.max(latency_table$latency)
-    if (
-      (grepl("day", time_type) && (latency >= 10)) ||
-        (grepl("week", time_type) && (latency >= 4)) ||
-        ((time_type == "yearmonth") && (latency >= 2)) ||
-        ((time_type == "yearquarter") && (latency >= 1)) ||
-        ((time_type == "year") && (latency >= 1))
-    ) {
-      cli::cli_warn(paste(
-        "!" = paste(
-          "The latency is {latency}, ",
-          "which is questionable for it's `time_type` of ",
-          "{time_type}."
-        ),
-        "i" = "latency: {latency_table$latency[[i_latency]]}",
-        "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
-      ))
-    }
+  # check that the shift amount isn't too extreme
+  latency_max <- max(abs(latency_table$latency))
+  time_type <- attributes(training)$metadata$time_type
+  i_latency <- which.max(latency_table$latency)
+  if (
+    (grepl("day", time_type) && (latency_max >= 10)) ||
+      (grepl("week", time_type) && (latency_max >= 4)) ||
+      ((time_type == "yearmonth") && (latency_max >= 2)) ||
+      ((time_type == "yearquarter") && (latency_max >= 1)) ||
+      ((time_type == "year") && (latency_max >= 1))
+  ) {
+    cli::cli_warn(paste(
+      "!" = paste(
+        "The maximum latency is {latency_max}, ",
+        "which is questionable for it's `time_type` of ",
+        "{time_type}."
+      ),
+      "i" = "latency: {latency_table$latency[[i_latency]]}",
+      "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
+    ))
+  }
 
   step_adjust_latency_new(
     terms = x$terms,
@@ -279,20 +279,33 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   )
 }
 
-#' @importFrom dplyr %>% pull
+#' @importFrom dplyr %>% pull group_by_at
+#' @importFrom tidyr fill
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!isa(new_data, "epi_df")) {
+    # TODO if new_data actually has keys other than geo_value and time_value, this is going to cause problems
     new_data <- new_data %>% as_epi_df(as_of = object$forecast_date)
   }
-  attributes(new_data)$metadata$latency_method <- object$method
-  attributes(new_data)$metadata$shift_sign <- get_sign(object)
-  attributes(new_data)$metadata$latency_table <- object$latency_table
-  if ((object$method == "extend_ahead") || (object$method == "extend_lags")) {
+  if (object$method == "extend_ahead" || object$method == "extend_lags") {
+    attributes(new_data)$metadata$latency_method <- object$method
+    attributes(new_data)$metadata$shift_sign <- get_sign(object)
+    attributes(new_data)$metadata$latency_table <- object$latency_table
     keys <- object$keys
-    return(
-      new_data
-    )
+    return(new_data)
+  } else if (object$method == "locf") {
+    # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
+    rel_keys <- setdiff(epi_keys(new_data), "time_value")
+    object$forecast_date
+    unnamed_columns <- object$columns %>% unname()
+    new_data %>%
+      pad_to_end(rel_keys, object$forecast_date) %>%
+      # group_by_at(rel_keys) %>%
+      arrange(time_value) %>%
+      as_tibble() %>%
+      tidyr::fill(.direction = "down", any_of(unnamed_columns)) %>%
+      ungroup() %>%
+      return()
   }
 }
 #' @export
diff --git a/R/utils-latency.R b/R/utils-latency.R
index a567dae06..f681358d4 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -32,7 +32,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
 #' @importFrom dplyr select
-set_forecast_date <- function(new_data, info, epi_keys_checked) {
+set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   original_columns <- info %>%
     filter(source == "original") %>%
     pull(variable)
@@ -58,7 +58,11 @@ set_forecast_date <- function(new_data, info, epi_keys_checked) {
     summarise(time_value = max(time_value)) %>%
     pull(time_value) %>%
     min()
-  forecast_date <- attributes(new_data)$metadata$as_of
+  if (is.null(latency)) {
+    forecast_date <- attributes(new_data)$metadata$as_of
+  } else {
+    forecast_date <- max_time + latency
+  }
   # make sure the as_of is sane
   if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
     cli::cli_abort(paste(
@@ -142,3 +146,46 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
   }
   max_time_value
 }
+
+
+fill_locf <- function(x, forecast_date) {
+  cannot_be_used <- x %>%
+    dplyr::filter(forecast_date - time_value <= n_recent) %>%
+    dplyr::mutate(fillers = forecast_date - time_value > keep) %>%
+    dplyr::summarise(
+      dplyr::across(
+        -tidyselect::any_of(epi_keys(recipe)),
+        ~ all(is.na(.x[fillers])) & is.na(head(.x[!fillers], 1))
+      ),
+      .groups = "drop"
+    ) %>%
+    dplyr::select(-fillers) %>%
+    dplyr::summarise(dplyr::across(
+      -tidyselect::any_of(epi_keys(recipe)), ~ any(.x)
+    )) %>%
+    unlist()
+  x <- tidyr::fill(x, !time_value)
+}
+
+pad_to_end <- function(x, groups, end_date) {
+  itval <- epiprocess:::guess_period(c(x$time_value, end_date), "time_value")
+  completed_time_values <- x %>%
+    dplyr::group_by(dplyr::across(tidyselect::all_of(groups))) %>%
+    dplyr::summarise(
+      time_value = rlang::list2(
+        time_value = seq_null_swap(max(time_value) + itval, end_date, itval)
+      )
+    ) %>%
+    unnest("time_value") %>%
+    mutate(time_value = vctrs::vec_cast(time_value, x$time_value))
+
+  dplyr::bind_rows(x, completed_time_values) %>%
+    dplyr::arrange(dplyr::across(tidyselect::all_of(c("time_value", groups))))
+}
+
+seq_null_swap <- function(from, to, by) {
+  if (from > to) {
+    return(NULL)
+  }
+  seq(from = from, to = to, by = by)
+}
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 2612d4a3e..99e2f4063 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -15,7 +15,6 @@ arx_args_list(
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
-  nafill_buffer = Inf,
   check_enough_data_n = NULL,
   check_enough_data_epi_keys = NULL,
   ...
@@ -76,16 +75,6 @@ before calculating residual quantiles. See the \code{by_key} argument to
 residual quantiles are used. It is not applicable with
 \code{trainer = quantile_reg()}, for example.}
 
-\item{nafill_buffer}{At predict time, recent values of the training data
-are used to create a forecast. However, these can be \code{NA} due to, e.g.,
-data latency issues. By default, any missing values will get filled with
-less recent data. Setting this value to \code{NULL} will result in 1 extra
-recent row (beyond those required for lag creation) to be used. Note that
-we require at least \code{min(lags)} rows of recent data per \code{geo_value} to
-create a prediction. For this reason, setting \code{nafill_buffer < min(lags)}
-will be treated as \emph{additional} allowed recent data rather than the
-total amount of recent data to examine.}
-
 \item{check_enough_data_n}{Integer. A lower limit for the number of rows per
 epi_key that are required for training. If \code{NULL}, this check is ignored.}
 
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index e801dcc4e..9c1832142 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -17,7 +17,6 @@ arx_class_args_list(
   method = c("rel_change", "linear_reg"),
   log_scale = FALSE,
   additional_gr_args = list(),
-  nafill_buffer = Inf,
   check_enough_data_n = NULL,
   check_enough_data_epi_keys = NULL,
   ...
@@ -95,16 +94,6 @@ log scale.}
 calculation. See \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}} and the related Vignette for
 more details.}
 
-\item{nafill_buffer}{At predict time, recent values of the training data
-are used to create a forecast. However, these can be \code{NA} due to, e.g.,
-data latency issues. By default, any missing values will get filled with
-less recent data. Setting this value to \code{NULL} will result in 1 extra
-recent row (beyond those required for lag creation) to be used. Note that
-we require at least \code{min(lags)} rows of recent data per \code{geo_value} to
-create a prediction. For this reason, setting \code{nafill_buffer < min(lags)}
-will be treated as \emph{additional} allowed recent data rather than the
-total amount of recent data to examine.}
-
 \item{check_enough_data_n}{Integer. A lower limit for the number of rows per
 epi_key that are required for training. If \code{NULL}, this check is ignored.}
 
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 981c3c7e5..3870134fb 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -14,7 +14,6 @@ cdc_baseline_args_list(
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = "geo_value",
-  nafill_buffer = Inf,
   ...
 )
 }
@@ -65,16 +64,6 @@ before calculating residual quantiles. See the \code{by_key} argument to
 residual quantiles are used. It is not applicable with
 \code{trainer = quantile_reg()}, for example.}
 
-\item{nafill_buffer}{At predict time, recent values of the training data
-are used to create a forecast. However, these can be \code{NA} due to, e.g.,
-data latency issues. By default, any missing values will get filled with
-less recent data. Setting this value to \code{NULL} will result in 1 extra
-recent row (beyond those required for lag creation) to be used. Note that
-we require at least \code{min(lags)} rows of recent data per \code{geo_value} to
-create a prediction. For this reason, setting \code{nafill_buffer < min(lags)}
-will be treated as \emph{additional} allowed recent data rather than the
-total amount of recent data to examine.}
-
 \item{...}{Space to handle future expansions (unused).}
 }
 \value{
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 633d45020..d056ed825 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -13,7 +13,6 @@ flatline_args_list(
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
-  nafill_buffer = Inf,
   ...
 )
 }
@@ -57,16 +56,6 @@ before calculating residual quantiles. See the \code{by_key} argument to
 residual quantiles are used. It is not applicable with
 \code{trainer = quantile_reg()}, for example.}
 
-\item{nafill_buffer}{At predict time, recent values of the training data
-are used to create a forecast. However, these can be \code{NA} due to, e.g.,
-data latency issues. By default, any missing values will get filled with
-less recent data. Setting this value to \code{NULL} will result in 1 extra
-recent row (beyond those required for lag creation) to be used. Note that
-we require at least \code{min(lags)} rows of recent data per \code{geo_value} to
-create a prediction. For this reason, setting \code{nafill_buffer < min(lags)}
-will be treated as \emph{additional} allowed recent data rather than the
-total amount of recent data to examine.}
-
 \item{...}{Space to handle future expansions (unused).}
 }
 \value{
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index b18685d89..6fef23ba4 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -4,13 +4,7 @@
 \alias{get_test_data}
 \title{Get test data for prediction based on longest lag period}
 \usage{
-get_test_data(
-  recipe,
-  x,
-  fill_locf = FALSE,
-  n_recent = NULL,
-  forecast_date = max(x$time_value)
-)
+get_test_data(recipe, x)
 }
 \arguments{
 \item{recipe}{A recipe object.}
@@ -18,15 +12,6 @@ get_test_data(
 \item{x}{An epi_df. The typical usage is to
 pass the same data as that used for fitting the recipe.}
 
-\item{fill_locf}{Logical. Should we use \code{locf} to fill in missing data?}
-
-\item{n_recent}{Integer or NULL. If filling missing data with \code{locf = TRUE},
-how far back are we willing to tolerate missing data? Larger values allow
-more filling. The default \code{NULL} will determine this from the
-the \code{recipe}. For example, suppose \code{n_recent = 3}, then if the
-3 most recent observations in any \code{geo_value} are all \code{NA}’s, we won’t be
-able to fill anything, and an error message will be thrown. (See details.)}
-
 \item{forecast_date}{By default, this is set to the maximum
 \code{time_value} in \code{x}. But if there is data latency such that recent \code{NA}'s
 should be filled, this may be \emph{after} the last available \code{time_value}.}
diff --git a/man/set_forecast_date.Rd b/man/set_forecast_date.Rd
index 58682bd2b..29dd98d33 100644
--- a/man/set_forecast_date.Rd
+++ b/man/set_forecast_date.Rd
@@ -4,7 +4,7 @@
 \alias{set_forecast_date}
 \title{Extract the as_of for the forecast date, and make sure there's nothing very off about it.}
 \usage{
-set_forecast_date(new_data, info, epi_keys_checked)
+set_forecast_date(new_data, info, epi_keys_checked, latency)
 }
 \description{
 Extract the as_of for the forecast date, and make sure there's nothing very off about it.
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index fc4c55878..1006a76dc 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -123,8 +123,8 @@ jhu <- case_death_rate_subset \%>\%
 attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 
 r <- epi_recipe(case_death_rate_subset) \%>\%
-  step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_adjust_latency(method = "extend_ahead") \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
 r
 
diff --git a/tests/testthat/test-epi_workflow.R b/tests/testthat/test-epi_workflow.R
index 8bb58b0bc..ecd955cc5 100644
--- a/tests/testthat/test-epi_workflow.R
+++ b/tests/testthat/test-epi_workflow.R
@@ -79,17 +79,11 @@ test_that("forecast method works", {
     ))
   )
 
-  args <- list(
-    fill_locf = TRUE,
-    n_recent = 360 * 3,
-    forecast_date = as.Date("2024-01-01")
-  )
   expect_equal(
-    forecast(wf, !!!args),
+    forecast(wf),
     predict(wf, new_data = get_test_data(
       hardhat::extract_preprocessor(wf),
-      jhu,
-      !!!args
+      jhu
     ))
   )
 })
diff --git a/tests/testthat/test-get_test_data.R b/tests/testthat/test-get_test_data.R
index aa799150b..3fabdea2e 100644
--- a/tests/testthat/test-get_test_data.R
+++ b/tests/testthat/test-get_test_data.R
@@ -44,6 +44,7 @@ test_that("expect error that geo_value or time_value does not exist", {
 
 
 test_that("NA fill behaves as desired", {
+  testthat::skip()
   df <- tibble::tibble(
     geo_value = rep(c("ca", "ny"), each = 10),
     time_value = rep(1:10, times = 2),
@@ -81,6 +82,7 @@ test_that("NA fill behaves as desired", {
 })
 
 test_that("forecast date behaves", {
+  testthat::skip()
   df <- tibble::tibble(
     geo_value = rep(c("ca", "ny"), each = 10),
     time_value = rep(1:10, times = 2),
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index fd1bb9675..0113f2399 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -316,6 +316,33 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
 
   fit5 <- slm_fit(r5, data = real_x)
   expect_equal(names(fit5$fit$fit$fit$coefficients), c("(Intercept)", "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate", "lag_6_case_rate", "lag_10_case_rate"))
+
+  r51 <- epi_recipe(x) %>%
+    step_adjust_latency(case_rate, method = "locf") %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11))  %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+
+  baked_x <- r51 %>% prep(real_x) %>% bake(real_x)
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date)) %>%
+    mutate(locf_date = last_date - latency)
+  # iterate over all columns and make sure the latent time period has the exact same values
+  for (ii in seq(nrow(last_dates))) {
+    baked_var <- baked_x %>%
+      filter(last_dates[[ii,"locf_date"]] <= time_value, time_value <=last_dates[[ii,"last_date"]]) %>%
+      pull(last_dates[[ii,"name"]]) %>%
+      var
+    if (grepl("case_rate", last_dates[[ii, "name"]])) {
+      expect_equal(baked_var, 0)
+    } else {
+      expect_true(baked_var > 0)
+    }
+  }
 })
 
 test_that("setting fixed_* works for `step_adjust_latency`", {})
@@ -334,4 +361,82 @@ test_that("printing step_adjust_latency results in expected output", {
   expect_snapshot(r)
 })
 
+test_that("data with epi_df shorn off works", {})
 test_that("lags of transforms (of transforms etc) work", {})
+test_that("locf works as intended", {
+  expect_warning(epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_adjust_latency(method = "locf"))
+
+  r6 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "locf") %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+
+  # directly checking the shifts
+  baked_x <- r6 %>% prep(real_x) %>% bake(real_x)
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date)) %>%
+    mutate(locf_date = last_date - latency)
+  # iterate over all columns and make sure the latent time period has the exact same values
+  for (ii in seq(nrow(last_dates))) {
+    baked_x %>%
+      filter(last_dates[[ii,"locf_date"]] <= time_value, time_value <=last_dates[[ii,"last_date"]]) %>%
+      pull(last_dates[[ii,"name"]]) %>%
+      var %>%
+      expect_equal(0)
+  }
+
+  # the as_of on x is today's date, which is >970 days in the future
+  # also, there's no data >970 days in the past, so it gets an error trying to
+  # fit on no data
+  expect_warning(fit6 <- slm_fit(r6, data = x), regexp = "The maximum latency is 1033")
+
+  # now trying with the as_of a reasonable distance in the future
+  fit6 <- slm_fit(r6, data = real_x)
+  expect_equal(
+    names(fit6$pre$mold$predictors),
+    c(
+      "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate",
+      "lag_1_case_rate", "lag_5_case_rate"
+    )
+  )
+  latest <- get_test_data(r6, real_x)
+  pred <- predict(fit6, latest)
+  point_pred <- pred %>% filter(!is.na(.pred))
+  expect_equal(max(point_pred$time_value), as.Date(testing_as_of))
+
+  expect_equal(
+    names(fit6$pre$mold$outcomes),
+    glue::glue("ahead_{ahead}_death_rate")
+  )
+  latest <- get_test_data(r6, x)
+  pred1 <- predict(fit6, latest)
+  actual_solutions <- pred1 %>% filter(!is.na(.pred))
+  expect_equal(max(actual_solutions$time_value), testing_as_of)
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit6$fit$fit$fit$coefficients), 6)
+
+  # result should be equivalent to just immediately doing the adjusted lags by
+  # hand
+  #
+  hand_adjusted <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+  locf_x <- real_x %>% rbind(tibble(geo_value = rep("place", latency),
+                                    time_value = max_time + 1:latency,
+                                    case_rate = rep(real_x$case_rate[nrow(x)], latency),
+                                    death_rate = rep(real_x$death_rate[nrow(x)], latency)))
+  fit_hand_adj <- slm_fit(hand_adjusted, data = locf_x)
+  expect_equal(
+    fit6$fit$fit$fit$coefficients,
+    fit_hand_adj$fit$fit$fit$coefficients
+  )
+})

From 01dc1483669fd7d4db1c12407f2a29b30687f06e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 8 Jul 2024 13:04:09 -0500
Subject: [PATCH 070/387] hotfix from Dan

---
 R/utils-latency.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index f681358d4..9bcb7c7a2 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -121,11 +121,11 @@ get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_ch
 #'   a potentially different max_time_value
 #' @keywords internal
 get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new_data) {
-  max_time_value <- max(
+  max_time_value <- as.Date(max(
     workflow_max_time_value,
     this_recipe$max_time_value,
     max(new_data$time_value)
-  )
+  ))
   if (this_recipe %>% recipes::detect_step("adjust_latency")) {
     # get the as_of in an `adjust_latency` step, regardless of where
     handpicked_forecast_date <- map(

From a5a84a7e4f9f067e1311ef9cf5c00d5229c4da0f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 30 Jul 2024 17:37:11 -0500
Subject: [PATCH 071/387] rebase fixes, error classes, unskip latency tests

---
 NAMESPACE                                 |   1 +
 R/canned-epipred.R                        |   3 +-
 R/cdc_baseline_forecaster.R               |   3 +-
 R/epi_shift.R                             |   4 +-
 R/get_test_data.R                         |  19 +--
 R/step_adjust_latency.R                   |  26 ++--
 R/utils-latency.R                         |  52 +++++---
 man/get_test_data.Rd                      |  10 --
 man/step_epi_shift.Rd                     |   7 ++
 tests/testthat/test-snapshots.R           |   2 +-
 tests/testthat/test-step_adjust_latency.R | 147 ++++++++++++++++------
 tests/testthat/test-utils_latency.R       |  34 ++---
 12 files changed, 194 insertions(+), 114 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 899d22a1e..c6ac51064 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -288,6 +288,7 @@ importFrom(rlang,enquos)
 importFrom(rlang,expr)
 importFrom(rlang,global_env)
 importFrom(rlang,inject)
+importFrom(rlang,is_empty)
 importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
 importFrom(rlang,is_true)
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 4f95f3d22..66c66a339 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -133,7 +133,8 @@ print.canned_epipred <- function(x, name, ...) {
       purrr::map("columns") %>%
       reduce(c)
     latency_per_base_col <- latency_step$latency_table %>%
-      filter(col_name %in% valid_columns) %>% mutate(latency = abs(latency))
+      filter(col_name %in% valid_columns) %>%
+      mutate(latency = abs(latency))
     if (latency_step$method != "locf" && nrow(latency_per_base_col) > 1) {
       intro_text <- glue::glue("{type_str} adjusted per column: ")
     } else if (latency_step$method != "locf") {
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index f3ba48794..85635cfae 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -79,7 +79,8 @@ cdc_baseline_forecaster <- function(
 
 
   latest <- get_test_data(
-    epi_recipe(epi_data), epi_data)
+    epi_recipe(epi_data), epi_data
+  )
 
   f <- frosting() %>%
     layer_predict() %>%
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 43876a53a..0bd3da30c 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -46,12 +46,12 @@ add_shifted_columns <- function(new_data, object, amount) {
   shift_sign_lat <- attributes(new_data)$metadata$shift_sign
   if (!is.null(latency_table) &&
     shift_sign_lat == sign_shift) {
-    #TODO this doesn't work on lags of transforms
+    # TODO this doesn't work on lags of transforms
     rel_latency <- latency_table %>% filter(col_name %in% object$columns)
   } else {
     rel_latency <- tibble(col_name = object$columns, latency = 0L)
   }
-  grid <- expand_grid(col = object$columns, amount = sign_shift *amount) %>%
+  grid <- expand_grid(col = object$columns, amount = sign_shift * amount) %>%
     left_join(rel_latency, by = join_by(col == col_name), ) %>%
     tidyr::replace_na(list(latency = 0)) %>%
     mutate(
diff --git a/R/get_test_data.R b/R/get_test_data.R
index ebd6e1b7b..f1d83aad0 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -11,18 +11,9 @@
 #' used if growth rate calculations are requested by the recipe. This is
 #' calculated internally.
 #'
-#' It also optionally fills missing values
-#' using the last-observation-carried-forward (LOCF) method. If this
-#' is not possible (say because there would be only `NA`'s in some location),
-#' it will produce an error suggesting alternative options to handle missing
-#' values with more advanced techniques.
-#'
 #' @param recipe A recipe object.
 #' @param x An epi_df. The typical usage is to
 #'   pass the same data as that used for fitting the recipe.
-#' @param forecast_date By default, this is set to the maximum
-#'   `time_value` in `x`. But if there is data latency such that recent `NA`'s
-#'   should be filled, this may be _after_ the last available `time_value`.
 #'
 #' @return An object of the same type as `x` with columns `geo_value`, `time_value`, any additional
 #'   keys, as well other variables in the original dataset.
@@ -35,9 +26,8 @@
 #' get_test_data(recipe = rec, x = case_death_rate_subset)
 #' @importFrom rlang %@%
 #' @export
-get_test_data <- function(
-    recipe,
-    x) {
+
+get_test_data <- function(recipe, x) {
   if (!is_epi_df(x)) cli::cli_abort("`x` must be an `epi_df`.")
 
   check <- hardhat::check_column_names(x, colnames(recipe$template))
@@ -63,7 +53,10 @@ get_test_data <- function(
       "!" = "but `x` contains only {avail_recent}."
     ))
   }
-  max_time_value <- x %>% na.omit %>% pull(time_value) %>% max
+  max_time_value <- x %>%
+    na.omit() %>%
+    pull(time_value) %>%
+    max()
   x <- arrange(x, time_value)
   groups <- epi_keys_only(recipe)
 
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index b963ce257..11e1d84fd 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -86,7 +86,7 @@
 #' jhu_fit
 #'
 #' @importFrom recipes detect_step
-#' @importFrom rlang enquos
+#' @importFrom rlang enquos is_empty
 step_adjust_latency <-
   function(recipe,
            ...,
@@ -106,39 +106,43 @@ step_adjust_latency <-
            id = recipes::rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
+      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.", class = "epipredict__step_adjust_latency__epi_recipe_only")
     }
     if (!is.null(columns)) {
       cli::cli_abort(c("The `columns` argument must be `NULL`.",
         i = "Use `tidyselect` methods to choose columns to lag."
-      ))
+      ), class = "epipredict__step_adjust_latency__cols_not_null")
     }
     if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
       cli::cli_warn(
-        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified."
+        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
       cli::cli_warn(
         "If `method` is {.val extend_lags} or {.val locf},
-then the previous `step_epi_lag`s won't work with modified data."
+then the previous `step_epi_lag`s won't work with modified data.",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "locf") && (length(recipe$steps) > 0)) {
-      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others")
+      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
+      )
     }
     if (detect_step(recipe, "naomit")) {
       cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
-                      as columns may be moved around")
+                      as columns may be moved around", class = "epipredict__step_adjust_latency__post_NA_error")
     }
     if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
       cli::cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
- can be non-`NULL` at a time!")
+ can be non-`NULL` at a time!", class = "epipredict__step_adjust_latency__too_many_args_error")
     }
     if (length(fixed_latency > 1)) {
       template <- recipe$template
       data_names <- names(template)[!names(template) %in% epi_keys(template)]
       wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
       if (length(wrong_names) > 0) {
-        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}")
+        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
       }
     }
 
@@ -258,8 +262,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         "{time_type}."
       ),
       "i" = "latency: {latency_table$latency[[i_latency]]}",
-      "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
-    ))
+      "i" = "`max_time` = {max(training$time_value)} -> `forecast_date` = {forecast_date}"
+    ), class = "epipredict__prep.step_latency__very_large_latency")
   }
 
   step_adjust_latency_new(
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9bcb7c7a2..8356ae1e2 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -38,11 +38,14 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
     pull(variable)
   # make sure that there's enough column names
   if (length(original_columns) < 3) {
-    cli::cli_abort(glue::glue(
-      "The original columns of `time_value`, ",
-      "`geo_value` and at least one signal. The current colums are \n",
-      paste(capture.output(object$info), collapse = "\n\n")
-    ))
+    cli::cli_abort(
+      glue::glue(
+        "The original columns of `time_value`, ",
+        "`geo_value` and at least one signal. The current colums are \n",
+        paste(capture.output(object$info), collapse = "\n\n")
+      ),
+      class = "epipredict__set_forecast_date__too_few_data_columns"
+    )
   }
   # the source data determines the actual time_values
   # these are the non-na time_values;
@@ -65,25 +68,34 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   }
   # make sure the as_of is sane
   if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
-    cli::cli_abort(paste(
-      "the data matrix `forecast_date` value is {forecast_date}, ",
-      "and not a valid `time_type` with type ",
-      "matching `time_value`'s type of ",
-      "{class(max_time)}."
-    ))
+    cli::cli_abort(
+      paste(
+        "the data matrix `forecast_date` value is {forecast_date}, ",
+        "and not a valid `time_type` with type ",
+        "matching `time_value`'s type of ",
+        "{class(max_time)}."
+      ),
+      class = "epipredict__set_forecast_date__wrong_time_value_type_error"
+    )
   }
   if (is.null(forecast_date) || is.na(forecast_date)) {
-    cli::cli_warn(paste(
-      "epi_data's `forecast_date` was {forecast_date}, setting to ",
-      "the latest time value, {max_time}."
-    ))
+    cli::cli_warn(
+      paste(
+        "epi_data's `forecast_date` was {forecast_date}, setting to ",
+        "the latest time value, {max_time}."
+      ),
+      class = "epipredict__set_forecast_date__max_time_warning"
+    )
     forecast_date <- max_time
   } else if (forecast_date < max_time) {
-    cli::cli_abort(paste(
-      "`forecast_date` ({(forecast_date)}) is before the most ",
-      "recent data ({max_time}). Remove before ",
-      "predicting."
-    ))
+    cli::cli_abort(
+      paste(
+        "`forecast_date` ({(forecast_date)}) is before the most ",
+        "recent data ({max_time}). Remove before ",
+        "predicting."
+      ),
+      class = "epipredict__set_forecast_date__misordered_forecast_date_error"
+    )
   }
   # TODO cover the rest of the possible types for as_of and max_time...
   if (inherits(max_time, "Date")) {
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index 6fef23ba4..81649452a 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -11,10 +11,6 @@ get_test_data(recipe, x)
 
 \item{x}{An epi_df. The typical usage is to
 pass the same data as that used for fitting the recipe.}
-
-\item{forecast_date}{By default, this is set to the maximum
-\code{time_value} in \code{x}. But if there is data latency such that recent \code{NA}'s
-should be filled, this may be \emph{after} the last available \code{time_value}.}
 }
 \value{
 An object of the same type as \code{x} with columns \code{geo_value}, \code{time_value}, any additional
@@ -32,12 +28,6 @@ The minimum required (recent) data to produce a forecast is equal to
 the maximum lag requested (on any predictor) plus the longest horizon
 used if growth rate calculations are requested by the recipe. This is
 calculated internally.
-
-It also optionally fills missing values
-using the last-observation-carried-forward (LOCF) method. If this
-is not possible (say because there would be only \code{NA}'s in some location),
-it will produce an error suggesting alternative options to handle missing
-values with more advanced techniques.
 }
 \examples{
 # create recipe
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index f0f7f2a2f..ba8262c71 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -55,6 +55,13 @@ Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
 \item{id}{A unique identifier for the step}
+
+\item{latency_adjustment}{a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
+\itemize{
+\item \code{"extend_ahead"}: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
+\item \code{"locf"}: carries forward the last observed value up to the forecast date.
+\item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that the shortest lag at predict time is
+}}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index bdb0457de..7f1d46006 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -98,7 +98,7 @@ test_that("arx_forecaster snapshots", {
 
 test_that("arx_forecaster output format snapshots", {
   jhu <- case_death_rate_subset %>%
-    dplyr::filter(time_value >= as.Date("2021-12-01")) 
+    dplyr::filter(time_value >= as.Date("2021-12-01"))
   out1 <- arx_forecaster(
     jhu, "death_rate",
     c("case_rate", "death_rate")
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 0113f2399..1f8a2889f 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -1,4 +1,9 @@
 library(dplyr)
+# Test ideas that were dropped:
+# - "epi_adjust_latency works correctly when there's gaps in the timeseries"
+# - "epi_adjust_latency extend_ahead uses the same adjustment when predicting on new data after being baked"
+# - "`step_adjust_latency` only allows one instance of itself"
+# - "data with epi_df shorn off works"
 
 x <- tibble(
   geo_value = rep("place", 200),
@@ -46,30 +51,34 @@ test_that("epi_adjust_latency correctly extends the lags", {
     step_epi_ahead(death_rate, ahead = ahead)
 
   # directly checking the shifts
-  baked_x <- r1 %>% prep(real_x) %>% bake(real_x)
+  baked_x <- r1 %>%
+    prep(real_x) %>%
+    bake(real_x)
   # map each column to its last non-NA value
   last_dates <- baked_x %>%
     tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
     group_by(name) %>%
     summarise(last_date = max(time_value)) %>%
     arrange(desc(last_date))
-  expect_equal(last_dates,
-               tribble(
-    ~name, ~last_date,
-    "lag_16_death_rate", max_time + 16,
-    "lag_11_death_rate", max_time + 11,
-    "lag_10_case_rate", max_time + 10,
-    "lag_6_case_rate", max_time + 6,
-    "lag_5_death_rate", max_time + 5,
-    "case_rate", max_time,
-    "death_rate", max_time,
-    "ahead_7_death_rate", max_time - 7,
-  ))
+  expect_equal(
+    last_dates,
+    tribble(
+      ~name, ~last_date,
+      "lag_16_death_rate", max_time + 16,
+      "lag_11_death_rate", max_time + 11,
+      "lag_10_case_rate", max_time + 10,
+      "lag_6_case_rate", max_time + 6,
+      "lag_5_death_rate", max_time + 5,
+      "case_rate", max_time,
+      "death_rate", max_time,
+      "ahead_7_death_rate", max_time - 7,
+    )
+  )
 
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit1 <- slm_fit(r1, data = x), regexp = "The latency is 1033"), class = "simpleError")
+  expect_error(expect_warning(fit1 <- slm_fit(r1, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit1 <- slm_fit(r1, data = real_x)
@@ -120,7 +129,7 @@ test_that("epi_adjust_latency correctly extends the ahead", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r2)))
+  expect_error(expect_warning(fit5 <- slm_fit(r2), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
   # real date example
   fit2 <- slm_fit(r2, data = real_x)
   expect_equal(
@@ -172,7 +181,7 @@ test_that("epi_adjust_latency extends multiple aheads", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(fit3 <- fit(epi_wf, data = x))
+  expect_error(expect_warning(fit3 <- fit(epi_wf, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
   # real date example
   fit3 <- fit(epi_wf, data = real_x)
   expect_equal(
@@ -205,7 +214,6 @@ test_that("epi_adjust_latency extends multiple aheads", {
     step_epi_ahead(death_rate, ahead = ahead + latency)
   equiv_fit <- fit(epi_wf, data = real_x)
   # adjusting the ahead should do the same thing as directly adjusting the ahead
-  equiv_fit
   expect_equal(
     fit3$fit$fit$fit$rqfit,
     equiv_fit$fit$fit$fit$rqfit
@@ -215,13 +223,71 @@ test_that("epi_adjust_latency extends multiple aheads", {
   expect_equal(length(fit3$fit$fit$fit$rqfit$coefficients), 6)
 })
 
-test_that("epi_adjust_latency fixed_* work", {})
+test_that("epi_adjust_latency fixed_forecast_date works", {
+  r4 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "extend_lags", fixed_forecast_date = max_time + 14) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+  expect_warning(baked_x <- r4 %>% prep(real_x) %>% bake(real_x), class = "epipredict__prep.step_latency__very_large_latency")
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date))
+  expect_equal(
+    last_dates,
+    tribble(
+      ~name, ~last_date,
+      "lag_25_death_rate", max_time + 25,
+      "lag_20_death_rate", max_time + 20,
+      "lag_19_case_rate", max_time + 19,
+      "lag_15_case_rate", max_time + 15,
+      "lag_14_death_rate", max_time + 14,
+      "case_rate", max_time,
+      "death_rate", max_time,
+      "ahead_7_death_rate", max_time - 7,
+    )
+  )
+})
+
+test_that("epi_adjust_latency fixed_latency works", {
+  r4.1 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "extend_lags", fixed_latency = 2) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+  baked_x <- r4.1 %>%
+    prep(real_x) %>%
+    bake(real_x)
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date))
+  expect_equal(
+    last_dates,
+    tribble(
+      ~name, ~last_date,
+      "lag_13_death_rate", max_time + 13,
+      "lag_8_death_rate", max_time + 8,
+      "lag_7_case_rate", max_time + 7,
+      "lag_3_case_rate", max_time + 3,
+      "lag_2_death_rate", max_time + 2,
+      "case_rate", max_time,
+      "death_rate", max_time,
+      "ahead_7_death_rate", max_time - 7,
+    )
+  )
+})
+
+
 # todo test variants on the columns for which this is applied
 # todo need to have both on columns 1, and 2
 
-test_that("epi_adjust_latency works correctly when there's gaps in the timeseries", {})
 
-test_that("epi_adjust_latency extend_ahead uses the same adjustment when predicting on new data after being baked", {})
 
 test_that("epi_adjust_latency works for other time types", {})
 
@@ -263,7 +329,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5), regexp = "The latency is 1033"), class = "simpleError")
+  expect_error(expect_warning(fit5 <- slm_fit(r5, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
 
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = x_lagged)
@@ -305,12 +371,11 @@ test_that("epi_adjust_latency correctly extends the lags", {
   )
 })
 
-test_that("`step_adjust_latency` only allows one instance of itself", {})
 
 test_that("`step_adjust_latency` only uses the columns specified in the `...`", {
   r5 <- epi_recipe(x) %>%
     step_adjust_latency(case_rate, method = "extend_lags") %>%
-    step_epi_lag(death_rate, lag = c(0, 6, 11))  %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
 
@@ -319,11 +384,13 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
 
   r51 <- epi_recipe(x) %>%
     step_adjust_latency(case_rate, method = "locf") %>%
-    step_epi_lag(death_rate, lag = c(0, 6, 11))  %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
 
-  baked_x <- r51 %>% prep(real_x) %>% bake(real_x)
+  baked_x <- r51 %>%
+    prep(real_x) %>%
+    bake(real_x)
   # map each column to its last non-NA value
   last_dates <- baked_x %>%
     tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
@@ -334,9 +401,9 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
   # iterate over all columns and make sure the latent time period has the exact same values
   for (ii in seq(nrow(last_dates))) {
     baked_var <- baked_x %>%
-      filter(last_dates[[ii,"locf_date"]] <= time_value, time_value <=last_dates[[ii,"last_date"]]) %>%
-      pull(last_dates[[ii,"name"]]) %>%
-      var
+      filter(last_dates[[ii, "locf_date"]] <= time_value, time_value <= last_dates[[ii, "last_date"]]) %>%
+      pull(last_dates[[ii, "name"]]) %>%
+      var()
     if (grepl("case_rate", last_dates[[ii, "name"]])) {
       expect_equal(baked_var, 0)
     } else {
@@ -345,8 +412,6 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
   }
 })
 
-test_that("setting fixed_* works for `step_adjust_latency`", {})
-
 test_that("printing step_adjust_latency results in expected output", {
   r5 <- epi_recipe(x) %>%
     step_adjust_latency(case_rate, method = "extend_lags") %>%
@@ -361,8 +426,6 @@ test_that("printing step_adjust_latency results in expected output", {
   expect_snapshot(r)
 })
 
-test_that("data with epi_df shorn off works", {})
-test_that("lags of transforms (of transforms etc) work", {})
 test_that("locf works as intended", {
   expect_warning(epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
@@ -375,7 +438,9 @@ test_that("locf works as intended", {
     step_epi_ahead(death_rate, ahead = ahead)
 
   # directly checking the shifts
-  baked_x <- r6 %>% prep(real_x) %>% bake(real_x)
+  baked_x <- r6 %>%
+    prep(real_x) %>%
+    bake(real_x)
   # map each column to its last non-NA value
   last_dates <- baked_x %>%
     tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
@@ -386,9 +451,9 @@ test_that("locf works as intended", {
   # iterate over all columns and make sure the latent time period has the exact same values
   for (ii in seq(nrow(last_dates))) {
     baked_x %>%
-      filter(last_dates[[ii,"locf_date"]] <= time_value, time_value <=last_dates[[ii,"last_date"]]) %>%
-      pull(last_dates[[ii,"name"]]) %>%
-      var %>%
+      filter(last_dates[[ii, "locf_date"]] <= time_value, time_value <= last_dates[[ii, "last_date"]]) %>%
+      pull(last_dates[[ii, "name"]]) %>%
+      var() %>%
       expect_equal(0)
   }
 
@@ -430,10 +495,12 @@ test_that("locf works as intended", {
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
-  locf_x <- real_x %>% rbind(tibble(geo_value = rep("place", latency),
-                                    time_value = max_time + 1:latency,
-                                    case_rate = rep(real_x$case_rate[nrow(x)], latency),
-                                    death_rate = rep(real_x$death_rate[nrow(x)], latency)))
+  locf_x <- real_x %>% rbind(tibble(
+    geo_value = rep("place", latency),
+    time_value = max_time + 1:latency,
+    case_rate = rep(real_x$case_rate[nrow(x)], latency),
+    death_rate = rep(real_x$death_rate[nrow(x)], latency)
+  ))
   fit_hand_adj <- slm_fit(hand_adjusted, data = locf_x)
   expect_equal(
     fit6$fit$fit$fit$coefficients,
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 5a5979e2a..09f77259f 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -36,15 +36,24 @@ modified_data <-
   arrange(time_value)
 
 test_that("get_latency works", {
-expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
-expect_equal(get_latency(modified_data, as_of, "case_rate", -1, "geo_value"), -5)
-expect_equal(get_latency(modified_data, as_of, "death_rate", 1, "geo_value"), 4)
-expect_equal(get_latency(modified_data, as_of, "case_rate_a", 1, "geo_value"), 5 + 4)
-expect_equal(get_latency(modified_data, as_of, "case_rate_b", 1, "geo_value"), 5 - 3)
-expect_equal(get_latency(modified_data, as_of, "death_rate_a", 1, "geo_value"), 4 - 7)
+  expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
+  expect_equal(get_latency(modified_data, as_of, "case_rate", -1, "geo_value"), -5)
+  expect_equal(get_latency(modified_data, as_of, "death_rate", 1, "geo_value"), 4)
+  expect_equal(get_latency(modified_data, as_of, "case_rate_a", 1, "geo_value"), 5 + 4)
+  expect_equal(get_latency(modified_data, as_of, "case_rate_b", 1, "geo_value"), 5 - 3)
+  expect_equal(get_latency(modified_data, as_of, "death_rate_a", 1, "geo_value"), 4 - 7)
+})
+
+test_that("get_latency infers max_time to be the minimum `max time` across grouping the specified keys", {
+  # place 2 is already 1 day less latent than place 1, so decreasing it's
+  # latency it should have no effect
+  place2_delayed_data <- modified_data %>% mutate(time_value = time_value + 3 * (geo_value == "place2"))
+  expect_equal(get_latency(place2_delayed_data, as_of, "case_rate", 1, "geo_value"), 5)
+  # decreaseing the latency of place1 more than 1 pushes it past place2, so at most changes the latency by 1
+  place1_delayed_data <- modified_data %>% mutate(time_value = time_value + 5 * (geo_value == "place1"))
+  expect_equal(get_latency(place1_delayed_data, as_of, "case_rate", 1, "geo_value"), 4)
 })
 
-test_that("get_latency infers max_time to be the minimum `max time` across the epi_keys", {})
 
 test_that("set_forecast_date works", {
   info <- tribble(
@@ -55,16 +64,11 @@ test_that("set_forecast_date works", {
     "death_rate", "numeric", "raw", "original",
     "not_real", "numeric", "predictor", "derived"
   )
-  expect_equal(set_forecast_date(modified_data, info, "geo_value"), as_of)
-  expect_equal(set_forecast_date(modified_data, info, ""), as_of)
-  expect_equal(set_forecast_date(modified_data, info, NULL), as_of)
+  expect_equal(set_forecast_date(modified_data, info, "geo_value", NULL), as_of)
+  expect_equal(set_forecast_date(modified_data, info, "", NULL), as_of)
+  expect_equal(set_forecast_date(modified_data, info, NULL, NULL), as_of)
 })
 
-
-
-
-
-
 time_range <- as.Date("2021-01-01") + 0:199
 x_adjust_ahead <- tibble(
   geo_value = rep("place", 200),

From bfde279c9c6a8f4c0596cf300ac1a976aadc4228 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 4 Sep 2024 12:29:11 -0500
Subject: [PATCH 072/387] rebase fixes round 2

---
 NAMESPACE                          |  1 +
 R/step_adjust_latency.R            |  8 ++++----
 R/step_epi_shift.R                 | 23 ++++-------------------
 R/utils-latency.R                  |  5 +++--
 man/step_epi_shift.Rd              |  1 -
 tests/testthat/_snaps/snapshots.md | 30 ++++++++++++++----------------
 6 files changed, 26 insertions(+), 42 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index c6ac51064..6ec287dc4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -307,6 +307,7 @@ importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
 importFrom(tidyr,crossing)
+importFrom(tidyr,drop_na)
 importFrom(tidyr,expand_grid)
 importFrom(tidyr,fill)
 importFrom(tidyr,unnest)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 11e1d84fd..bb03f0237 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -139,7 +139,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
     }
     if (length(fixed_latency > 1)) {
       template <- recipe$template
-      data_names <- names(template)[!names(template) %in% epi_keys(template)]
+      data_names <- names(template)[!names(template) %in% key_colnames(template)]
       wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
       if (length(wrong_names) > 0) {
         cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
@@ -173,7 +173,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
         latency = fixed_latency,
         latency_table = NULL,
         default = default,
-        keys = epi_keys(recipe),
+        keys = key_colnames(recipe),
         columns = columns,
         skip = skip,
         id = id
@@ -213,7 +213,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   latency <- x$latency
   forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked, latency)
   # construct the latency table
-  latency_table <- names(training)[!names(training) %in% epi_keys(training)] %>%
+  latency_table <- names(training)[!names(training) %in% key_colnames(training)] %>%
     tibble(col_name = .)
   if (length(recipes_eval_select(x$terms, training, info)) > 0) {
     latency_table <- latency_table %>% filter(col_name %in%
@@ -299,7 +299,7 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     return(new_data)
   } else if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
-    rel_keys <- setdiff(epi_keys(new_data), "time_value")
+    rel_keys <- setdiff(key_colnames(new_data), "time_value")
     object$forecast_date
     unnamed_columns <- object$columns %>% unname()
     new_data %>%
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index bbb0fc93d..c1b0f51bc 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -19,10 +19,6 @@
 #'  be the lag or lead for each value in the vector. Lag integers must be
 #'  nonnegative, while ahead integers must be positive.
 #' @param prefix A character string that will be prefixed to the new column.
-#' @param latency_adjustment a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
-#'   - `"extend_ahead"`: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
-#'   - `"locf"`: carries forward the last observed value up to the forecast date.
-#'   - `"extend_lags"`: per `epi_key` and `predictor`, adjusts the lag so that the shortest lag at predict time is
 #' @param default Determines what fills empty rows
 #'   left by leading/lagging (defaults to NA).
 #' @param skip A logical. Should the step be skipped when the
@@ -71,12 +67,6 @@ step_epi_lag <-
     }
     arg_is_nonneg_int(lag)
     arg_is_chr_scalar(prefix, id)
-    if (!is.null(columns)) {
-      cli::cli_abort(c(
-        "The `columns` argument must be `NULL`.",
-        i = "Use `tidyselect` methods to choose columns to lag."
-      ))
-    }
     recipes::add_step(
       recipe,
       step_epi_lag_new(
@@ -87,8 +77,7 @@ step_epi_lag <-
         prefix = prefix,
         default = default,
         keys = key_colnames(recipe),
-        columns = columns,
-        latency_adjustment = latency_adjustment,
+        columns = NULL,
         skip = skip,
         id = id
       )
@@ -107,7 +96,6 @@ step_epi_ahead <-
            role = "outcome",
            prefix = "ahead_",
            default = NA,
-           columns = NULL,
            skip = FALSE,
            id = rand_id("epi_ahead")) {
     if (!is_epi_recipe(recipe)) {
@@ -121,7 +109,7 @@ step_epi_ahead <-
       ))
     }
     arg_is_nonneg_int(ahead)
-    arg_is_chr_scalar(prefix, id, latency_adjustment)
+    arg_is_chr_scalar(prefix, id)
     recipes::add_step(
       recipe,
       step_epi_ahead_new(
@@ -132,8 +120,7 @@ step_epi_ahead <-
         prefix = prefix,
         default = default,
         keys = key_colnames(recipe),
-        latency_adjustment = latency_adjustment,
-        columns = columns,
+        columns = NULL,
         skip = skip,
         id = id
       )
@@ -143,7 +130,7 @@ step_epi_ahead <-
 
 step_epi_lag_new <-
   function(terms, role, trained, lag, prefix, default, keys,
-           latency_adjustment, columns, skip, id) {
+           columns, skip, id) {
     recipes::step(
       subclass = "epi_lag",
       terms = terms,
@@ -189,7 +176,6 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
-    latency_adjustment = x$latency_adjustment,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
     id = x$id
@@ -206,7 +192,6 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
-    latency_adjustment = x$latency_adjustment,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
     id = x$id
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 8356ae1e2..5498e3ede 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -32,6 +32,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
 #' @importFrom dplyr select
+#' @importFrom tidyr drop_na
 set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   original_columns <- info %>%
     filter(source == "original") %>%
@@ -166,14 +167,14 @@ fill_locf <- function(x, forecast_date) {
     dplyr::mutate(fillers = forecast_date - time_value > keep) %>%
     dplyr::summarise(
       dplyr::across(
-        -tidyselect::any_of(epi_keys(recipe)),
+        -tidyselect::any_of(key_colnames(recipe)),
         ~ all(is.na(.x[fillers])) & is.na(head(.x[!fillers], 1))
       ),
       .groups = "drop"
     ) %>%
     dplyr::select(-fillers) %>%
     dplyr::summarise(dplyr::across(
-      -tidyselect::any_of(epi_keys(recipe)), ~ any(.x)
+      -tidyselect::any_of(key_colnames(recipe)), ~ any(.x)
     )) %>%
     unlist()
   x <- tidyr::fill(x, !time_value)
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index ba8262c71..e33cf7e0e 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -23,7 +23,6 @@ step_epi_ahead(
   role = "outcome",
   prefix = "ahead_",
   default = NA,
-  columns = NULL,
   skip = FALSE,
   id = rand_id("epi_ahead")
 )
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index fb11026dd..6c439dbd8 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1036,26 +1036,24 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
     0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c("5%" = 0.136509784083987, "95%" = 0.469979623951498
+        structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c("5%" = 0.364597933377326, "95%" = 0.698067773244837
-            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c("5%" = 0.422093024752224, "95%" = 0.755562864619735
-            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c("5%" = 0.821955329282474, "95%" = 1.15542516914998
-            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c("5%" = 0.628067077067883, "95%" = 0.961536916935394
-            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.821955329282474, 1.15542516914998), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067883, 
+        0.961536916935394), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c("5%" = 0.140160537291566, "95%" = 0.473630377159077
-            ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
-    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
+            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
+    18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 

From 8119e72172b44d81b688410b3d45bc74a4200790 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 4 Sep 2024 17:48:27 -0500
Subject: [PATCH 073/387] NEWS+Description, partial locf tests, docs

---
 DEVELOPMENT.md                      |  1 +
 NAMESPACE                           |  3 ++
 NEWS.md                             |  6 +--
 R/arx_forecaster.R                  | 10 ++---
 R/epi_shift.R                       |  3 +-
 R/step_adjust_latency.R             | 21 +++++----
 R/utils-latency.R                   | 55 +++++++++++++++++++----
 man/arx_args_list.Rd                | 10 ++---
 man/arx_class_args_list.Rd          | 10 ++---
 man/epi_shift_single.Rd             | 24 ++++++++++
 man/get_grouping_columns.Rd         | 11 +++++
 man/pad_to_end.Rd                   | 22 +++++++++
 man/seq_null_swap.Rd                | 12 +++++
 man/step_adjust_latency.Rd          | 15 ++++---
 tests/testthat/test-utils_latency.R | 70 +++++++++++++++++++++++++----
 15 files changed, 218 insertions(+), 55 deletions(-)
 create mode 100644 man/epi_shift_single.Rd
 create mode 100644 man/get_grouping_columns.Rd
 create mode 100644 man/pad_to_end.Rd
 create mode 100644 man/seq_null_swap.Rd

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 67f1b3003..0335f124b 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -33,6 +33,7 @@ You can also build the docs manually and launch the site with python. From the t
 
 ```bash
 R -e 'devtools::document()'
+R -e 'pkgdown::build_site()'
 python -m http.server -d docs
 ```
 
diff --git a/NAMESPACE b/NAMESPACE
index 6ec287dc4..a93bb0c19 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -292,6 +292,8 @@ importFrom(rlang,is_empty)
 importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
 importFrom(rlang,is_true)
+importFrom(rlang,is_vector)
+importFrom(rlang,list2)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
 importFrom(stats,as.formula)
@@ -311,6 +313,7 @@ importFrom(tidyr,drop_na)
 importFrom(tidyr,expand_grid)
 importFrom(tidyr,fill)
 importFrom(tidyr,unnest)
+importFrom(tidyselect,all_of)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)
diff --git a/NEWS.md b/NEWS.md
index 3c3a0f942..5b907f237 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,9 +1,6 @@
 # epipredict (development)
 
 Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.
-# epipredict 0.2
-
--   add `latency_adjustment` as an option for `add_epi_ahead`, which adjusts the `ahead` so that the prediction is `ahead` relative to the `as_of` date for the `epi_data`, rather than relative to the last day of data.
 
 # epipredict 0.1
 
@@ -64,3 +61,6 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Fix bug where `fit()` drops the `epi_workflow` class (also error if
   non-`epi_df` data is given to `epi_recipe()`), #363
 - Try to retain the `epi_df` class during baking to the extent possible, #376
+- Add `latency_adjustment` as an option for `add_epi_ahead`, which adjusts the
+  `ahead` so that the prediction is `ahead` relative to the `as_of` date for the
+  `epi_data`, rather than relative to the last day of data.
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index a67d07c11..702a259eb 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -227,11 +227,11 @@ arx_fcast_epi_workflow <- function(
 #'   difference. The options are:
 #'   - `NULL` the default, assumes the `forecast_date` is the last day of data
 #'   - `"extend_ahead"`: increase the `ahead` by the latency so it's relative to
-#'   the last day of data. If the last day of data was 3 days ago, the ahead
-#'   becomes `ahead+3`.
-#'   - `"extend_lags"`: increase the lags so they're relative to the actual forecast date. If the lags are
-#'   `c(0,7,14)` and the last day of data was 3 days ago, the lags become
-#'   `c(3,10,17)`.
+#'   the last day of data. For example, if the last day of data was 3 days ago,
+#'   the ahead becomes `ahead+3`.
+#'   - `"extend_lags"`: increase the lags so they're relative to the actual
+#'   forecast date. For example, if the lags are `c(0,7,14)` and the last day of
+#'   data was 3 days ago, the lags become `c(3,10,17)`.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
 #'   prediction intervals. These are created by computing the quantiles of
 #'   training residuals. A `NULL` value will result in point forecasts only.
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 0bd3da30c..9365331a9 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -55,7 +55,8 @@ add_shifted_columns <- function(new_data, object, amount) {
     left_join(rel_latency, by = join_by(col == col_name), ) %>%
     tidyr::replace_na(list(latency = 0)) %>%
     mutate(
-      shift_val = amount + latency) %>%
+      shift_val = amount + latency
+    ) %>%
     mutate(
       newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"), # name is always positive
       amount = NULL,
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index bb03f0237..0dd0f3600 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -3,11 +3,13 @@
 #' In the standard case, the arx models assume that the last observation is also
 #' the day from which the forecast is being made. But if the data has latency,
 #' then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
-#' to compensate. This allows the model to create bleeding-edge forecasts using
-#' the lags actually observed rather than anticipated. `step_adjust_latency`
-#' uses the `as_of` date of the `epi_df` as the `forecast_date`. This is most
-#' useful in realtime and pseudo-prospective forecasting for data where there is
-#' some delay between the day recorded and when that data is available.
+#' to compensate. This allows the user to create models on the most recent data,
+#' regardless of latency patterns. Instead of using the last observation date,
+#' `step_adjust_latency` uses the `as_of` date of the `epi_df` as the
+#' `forecast_date`, potentially using different dates depending on the
+#' `epi_keys`, such as geography. This is most useful in realtime and
+#' pseudo-prospective forecasting for data where there is some delay between the
+#' event occurring and the event being reported.
 #'
 #' @param method a character. Determines the method by which the
 #'   forecast handles latency. The options are:
@@ -17,8 +19,7 @@
 #'   `forecast_date` date for a 4 day ahead forecast, the ahead used in practice
 #'   is actually 7.
 #'   - `"locf"`: carries forward the last observed value(s) up to the forecast
-#'   date. See the Vignette TODO for equivalents using other steps and more
-#'   sophisticated methods of extrapolation.
+#'   date.
 #'   - `"extend_lags"`: per `epi_key` and `predictor`, adjusts the lag so that
 #'   the shortest lag at predict time is at the last observation. E.g. if the
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
@@ -296,11 +297,9 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     attributes(new_data)$metadata$shift_sign <- get_sign(object)
     attributes(new_data)$metadata$latency_table <- object$latency_table
     keys <- object$keys
-    return(new_data)
   } else if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
-    object$forecast_date
     unnamed_columns <- object$columns %>% unname()
     new_data %>%
       pad_to_end(rel_keys, object$forecast_date) %>%
@@ -308,9 +307,9 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
       arrange(time_value) %>%
       as_tibble() %>%
       tidyr::fill(.direction = "down", any_of(unnamed_columns)) %>%
-      ungroup() %>%
-      return()
+      ungroup()
   }
+  return(new_data)
 }
 #' @export
 print.step_adjust_latency <-
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 5498e3ede..b32edf7b5 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -180,22 +180,59 @@ fill_locf <- function(x, forecast_date) {
   x <- tidyr::fill(x, !time_value)
 }
 
-pad_to_end <- function(x, groups, end_date) {
+#' pad every group at the right interval
+#' @description
+#' Perform last observation carried forward on a group by group basis. It uses
+#'   `guess_period` to find the appropriate interval to fill-forward by. It
+#'   maintains the grouping structure it recieves. It does *not* fill any
+#'   "interior" `NA` values occurring in the data beforehand.
+#' @param x an epi_df to be filled forward.
+#' @param columns_to_complete which columns to apply completion to. By default every non-key column of an epi_df
+#' @param groups the grouping by which to fill forward
+#' @importFrom dplyr across, arrange, bind_rows, group_by, summarise
+#' @importFrom tidyselect all_of
+#' @importFrom rlang list2
+#' @importFrom vctrs vec_cast
+#' @keywords internal
+pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
+  if (is.null(columns_to_complete)) {
+    columns_to_complete <- setdiff(names(x), key_colnames(x))
+  }
   itval <- epiprocess:::guess_period(c(x$time_value, end_date), "time_value")
-  completed_time_values <- x %>%
-    dplyr::group_by(dplyr::across(tidyselect::all_of(groups))) %>%
-    dplyr::summarise(
-      time_value = rlang::list2(
-        time_value = seq_null_swap(max(time_value) + itval, end_date, itval)
+  # get the time values we need to fill in
+  completed_time_values <-
+    x %>%
+    group_by(across(all_of(groups))) %>%
+    summarise(
+      time_value = list2(
+        time_value = seq_null_swap(from = max(time_value) + itval, to = end_date, by = itval)
       )
     ) %>%
     unnest("time_value") %>%
-    mutate(time_value = vctrs::vec_cast(time_value, x$time_value))
+    mutate(time_value = vec_cast(time_value, x$time_value))
+  # pull the last value in each group and fill forward
+  filled_values <- x %>%
+    group_by(across(all_of(groups))) %>%
+    slice_tail() %>%
+    bind_rows(completed_time_values) %>%
+    arrange(across(all_of(c("time_value", groups)))) %>%
+    fill(all_of(columns_to_complete), .direction = "down") %>%
+    slice(-1)
 
-  dplyr::bind_rows(x, completed_time_values) %>%
-    dplyr::arrange(dplyr::across(tidyselect::all_of(c("time_value", groups))))
+  bind_rows(x, filled_values) %>%
+    arrange(across(all_of(key_colnames(x)))) %>%
+    ungroup() %>%
+    group_by(across(all_of(get_grouping_columns(x))))
 }
 
+#' return the names of the grouped columns, or `NULL`
+get_grouping_columns <- function(x) {
+  group_names <- names(attributes(x)$groups)
+  head(group_names, -1)
+}
+
+#' seq, but returns null if from is larger
+#' @keywords internal
 seq_null_swap <- function(from, to, by) {
   if (from > to) {
     return(NULL)
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 99e2f4063..84e8c72d9 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -49,11 +49,11 @@ difference. The options are:
 \itemize{
 \item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
-the last day of data. If the last day of data was 3 days ago, the ahead
-becomes \code{ahead+3}.
-\item \code{"extend_lags"}: increase the lags so they're relative to the actual forecast date. If the lags are
-\code{c(0,7,14)} and the last day of data was 3 days ago, the lags become
-\code{c(3,10,17)}.
+the last day of data. For example, if the last day of data was 3 days ago,
+the ahead becomes \code{ahead+3}.
+\item \code{"extend_lags"}: increase the lags so they're relative to the actual
+forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
+data was 3 days ago, the lags become \code{c(3,10,17)}.
 }}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 9c1832142..3502508cb 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -51,11 +51,11 @@ difference. The options are:
 \itemize{
 \item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
-the last day of data. If the last day of data was 3 days ago, the ahead
-becomes \code{ahead+3}.
-\item \code{"extend_lags"}: increase the lags so they're relative to the actual forecast date. If the lags are
-\code{c(0,7,14)} and the last day of data was 3 days ago, the lags become
-\code{c(3,10,17)}.
+the last day of data. For example, if the last day of data was 3 days ago,
+the ahead becomes \code{ahead+3}.
+\item \code{"extend_lags"}: increase the lags so they're relative to the actual
+forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
+data was 3 days ago, the lags become \code{c(3,10,17)}.
 }}
 
 \item{outcome_transform}{Scalar character. Whether the outcome should
diff --git a/man/epi_shift_single.Rd b/man/epi_shift_single.Rd
new file mode 100644
index 000000000..871879004
--- /dev/null
+++ b/man/epi_shift_single.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/epi_shift.R
+\name{epi_shift_single}
+\alias{epi_shift_single}
+\title{Shift predictors while maintaining grouping and time_value ordering}
+\usage{
+epi_shift_single(x, col, shift_val, newname, key_cols)
+}
+\arguments{
+\item{x}{Data frame.}
+
+\item{shift_val}{a single integer. Negative values produce leads.}
+
+\item{newname}{the name for the newly shifted column}
+
+\item{key_cols}{vector, or \code{NULL}. Additional grouping vars.}
+}
+\value{
+a list of tibbles
+}
+\description{
+This is a lower-level function. As such it performs no error checking.
+}
+\keyword{internal}
diff --git a/man/get_grouping_columns.Rd b/man/get_grouping_columns.Rd
new file mode 100644
index 000000000..6b653628d
--- /dev/null
+++ b/man/get_grouping_columns.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_grouping_columns}
+\alias{get_grouping_columns}
+\title{return the names of the grouped columns, or \code{NULL}}
+\usage{
+get_grouping_columns(x)
+}
+\description{
+return the names of the grouped columns, or \code{NULL}
+}
diff --git a/man/pad_to_end.Rd b/man/pad_to_end.Rd
new file mode 100644
index 000000000..b9cde372e
--- /dev/null
+++ b/man/pad_to_end.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{pad_to_end}
+\alias{pad_to_end}
+\title{pad every group at the right interval}
+\usage{
+pad_to_end(x, groups, end_date, columns_to_complete = NULL)
+}
+\arguments{
+\item{x}{an epi_df to be filled forward.}
+
+\item{groups}{the grouping by which to fill forward}
+
+\item{columns_to_complete}{which columns to apply completion to. By default every non-key column of an epi_df}
+}
+\description{
+Perform last observation carried forward on a group by group basis. It uses
+\code{guess_period} to find the appropriate interval to fill-forward by. It
+maintains the grouping structure it recieves. It does \emph{not} fill any
+"interior" \code{NA} values occurring in the data beforehand.
+}
+\keyword{internal}
diff --git a/man/seq_null_swap.Rd b/man/seq_null_swap.Rd
new file mode 100644
index 000000000..7ad5a8954
--- /dev/null
+++ b/man/seq_null_swap.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{seq_null_swap}
+\alias{seq_null_swap}
+\title{seq, but returns null if from is larger}
+\usage{
+seq_null_swap(from, to, by)
+}
+\description{
+seq, but returns null if from is larger
+}
+\keyword{internal}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 1006a76dc..3da958045 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -42,8 +42,7 @@ E.g. if there are 3 days of latency between the last observation and the
 \code{forecast_date} date for a 4 day ahead forecast, the ahead used in practice
 is actually 7.
 \item \code{"locf"}: carries forward the last observed value(s) up to the forecast
-date. See the Vignette TODO for equivalents using other steps and more
-sophisticated methods of extrapolation.
+date.
 \item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that
 the shortest lag at predict time is at the last observation. E.g. if the
 lags are \code{c(0,7,14)} for data that is 3 days latent, the actual lags used
@@ -98,11 +97,13 @@ sequence of any existing operations.
 In the standard case, the arx models assume that the last observation is also
 the day from which the forecast is being made. But if the data has latency,
 then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
-to compensate. This allows the model to create bleeding-edge forecasts using
-the lags actually observed rather than anticipated. \code{step_adjust_latency}
-uses the \code{as_of} date of the \code{epi_df} as the \code{forecast_date}. This is most
-useful in realtime and pseudo-prospective forecasting for data where there is
-some delay between the day recorded and when that data is available.
+to compensate. This allows the user to create models on the most recent data,
+regardless of latency patterns. Instead of using the last observation date,
+\code{step_adjust_latency} uses the \code{as_of} date of the \code{epi_df} as the
+\code{forecast_date}, potentially using different dates depending on the
+\code{epi_keys}, such as geography. This is most useful in realtime and
+pseudo-prospective forecasting for data where there is some delay between the
+event occurring and the event being reported.
 }
 \details{
 The step assumes that the pipeline has already applied either
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 09f77259f..a23be628b 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -34,6 +34,14 @@ modified_data <-
     by = keys
   ) %>%
   arrange(time_value)
+time_range <- as.Date("2021-01-01") + 0:199
+x_adjust_ahead <- tibble(
+  geo_value = rep("place", 200),
+  time_value = time_range,
+  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
+  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
+) %>%
+  as_epi_df(as_of = max(time_range) + 3)
 
 test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
@@ -69,16 +77,7 @@ test_that("set_forecast_date works", {
   expect_equal(set_forecast_date(modified_data, info, NULL, NULL), as_of)
 })
 
-time_range <- as.Date("2021-01-01") + 0:199
-x_adjust_ahead <- tibble(
-  geo_value = rep("place", 200),
-  time_value = time_range,
-  case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
-  death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
-) %>%
-  as_epi_df(as_of = max(time_range) + 3)
 # confirm the delay is right
-
 test_that("adjust_latency extend_ahead works", {
   # testing that POSIXct converts correctly (as well as basic types)
   expect_equal(
@@ -100,4 +99,57 @@ test_that("extend_ahead warns in case of extreme adjustment", {
   testthat::expect_warning(adjust_latency(object, x_adjust_ahead), regexp = "The ahead has been adjusted by 100")
 })
 
+test_that("pad_to_end works correctly", {
+  single_ex <- tribble(
+    ~geo_value, ~time_value, ~a, ~b,
+    "1", as.Date("1066-10-13"), 2, -.6,
+    "1", as.Date("1066-10-14"), NA, NA,
+    "1", as.Date("1066-10-15"), 1, -.5,
+    "2", as.Date("1066-10-13"), 3, .9
+  ) %>%
+    as_epi_df(as_of = "1066-10-16")
+  expect_equal(
+    single_ex %>% pad_to_end("geo_value", as.Date("1066-10-16")),
+    rbind(
+      single_ex,
+      tibble(geo_value = "1", time_value = as.Date("1066-10-16"), a = 1, b = -.5),
+      tibble(
+        geo_value = "2",
+        time_value = seq.Date(
+          from = as.Date("1066-10-14"),
+          to = as.Date("1066-10-16"),
+          by = 1
+        ),
+        a = 3, b = .9
+      )
+    ) %>% arrange(geo_value, time_value)
+  )
+})
+
+
+test_that("pad_to_end handles weeks", {
+  single_ex <- tribble(
+    ~geo_value, ~time_value, ~a, ~b,
+    "1", as.Date("1066-10-14"), 2, -.6,
+    "1", as.Date("1066-10-21"), 1, -.5,
+    "2", as.Date("1066-10-14"), 3, .9
+  ) %>%
+    as_epi_df(as_of = "1066-10-28")
+  expect_equal(
+    single_ex %>% pad_to_end("geo_value", as.Date("1066-10-28")),
+    rbind(
+      single_ex,
+      tibble(geo_value = "1", time_value = as.Date("1066-10-28"), a = 1, b = -.5),
+      tibble(
+        geo_value = "2",
+        time_value = seq.Date(
+          from = as.Date("1066-10-21"),
+          to = as.Date("1066-10-28"),
+          by = 7
+        ),
+        a = 3, b = .9
+      )
+    ) %>% arrange(time_value, geo_value)
+  )
+})
 # todo case where somehow columns of different roles are selected

From 7816d132ee4564fe31a5026e61e719e6d81b135f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 4 Sep 2024 18:05:58 -0500
Subject: [PATCH 074/387] testing the step

---
 R/step_adjust_latency.R                   |  2 +-
 tests/testthat/test-step_adjust_latency.R | 52 +++++++++++++++++++++++
 2 files changed, 53 insertions(+), 1 deletion(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 0dd0f3600..cdef74565 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -301,7 +301,7 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
     unnamed_columns <- object$columns %>% unname()
-    new_data %>%
+    new_data <- new_data %>%
       pad_to_end(rel_keys, object$forecast_date) %>%
       # group_by_at(rel_keys) %>%
       arrange(time_value) %>%
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 1f8a2889f..a557fc146 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -165,6 +165,58 @@ test_that("epi_adjust_latency correctly extends the ahead", {
   expect_equal(length(fit2$fit$fit$fit$coefficients), 6)
 })
 
+test_that("epi_adjust_latency correctly locfs", {
+  r1 <- epi_recipe(x) %>%
+    step_adjust_latency(method = "locf") %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+
+  # directly checking the shifts
+  baked_x <- r1 %>%
+    prep(real_x) %>%
+    bake(real_x)
+  # map each column to its last non-NA value
+  last_dates <- baked_x %>%
+    tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
+    group_by(name) %>%
+    summarise(last_date = max(time_value)) %>%
+    arrange(desc(last_date))
+  expect_equal(
+    last_dates,
+    tribble(
+      ~name, ~last_date,
+      "lag_11_death_rate", max_time + 11,
+      "lag_6_death_rate", max_time + 6,
+      "lag_5_case_rate", max_time + 5,
+      "lag_1_case_rate", max_time + 1,
+      "case_rate", max_time,
+      "death_rate", max_time,
+      "lag_0_death_rate", max_time + 0,
+      "ahead_7_death_rate", max_time - 7,
+    )
+  )
+  # we expect a 5-fold repetition of the last values found in the original
+  # epi_df
+  last_real <- real_x %>%
+    group_by(geo_value) %>%
+    arrange(time_value) %>%
+    slice_tail() %>%
+    ungroup() %>%
+    select(case_rate, death_rate) %>%
+    uncount(5)
+  # pulling just the region between the last day and the prediction day
+  filled_values <-
+    baked_x %>%
+    filter(
+      time_value > max(real_x$time_value),
+      time_value <= attributes(real_x)$metadata$as_of
+    ) %>%
+    ungroup() %>%
+    select(case_rate, death_rate)
+  expect_equal(last_real, filled_values)
+})
+
 test_that("epi_adjust_latency extends multiple aheads", {
   aheads <- 1:3
   r3 <- epi_recipe(x) %>%

From edc2b3f641c5f2cd66a01d05f861ea3278a47b14 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 5 Sep 2024 11:43:05 -0500
Subject: [PATCH 075/387] step locf tests passing, grf pkgdown

---
 NAMESPACE                                 |  3 +++
 R/get_test_data.R                         |  2 +-
 R/step_adjust_latency.R                   |  2 +-
 R/utils-latency.R                         | 23 ++++----------------
 man/get_grouping_columns.Rd               |  4 ++++
 tests/testthat/_snaps/snapshots.md        | 26 +++++++++++++++++++++++
 tests/testthat/test-snapshots.R           | 13 ++++++++++++
 tests/testthat/test-step_adjust_latency.R | 20 ++++++++---------
 tests/testthat/test-utils_latency.R       |  2 +-
 9 files changed, 63 insertions(+), 32 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index a93bb0c19..bc83aa2f8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -301,6 +301,7 @@ importFrom(stats,family)
 importFrom(stats,lm)
 importFrom(stats,median)
 importFrom(stats,model.frame)
+importFrom(stats,na.omit)
 importFrom(stats,poly)
 importFrom(stats,predict)
 importFrom(stats,qnorm)
@@ -314,6 +315,8 @@ importFrom(tidyr,expand_grid)
 importFrom(tidyr,fill)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
+importFrom(utils,capture.output)
+importFrom(utils,head)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)
diff --git a/R/get_test_data.R b/R/get_test_data.R
index f1d83aad0..5e74da5a1 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -25,8 +25,8 @@
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14))
 #' get_test_data(recipe = rec, x = case_death_rate_subset)
 #' @importFrom rlang %@%
+#' @importFrom stats na.omit
 #' @export
-
 get_test_data <- function(recipe, x) {
   if (!is_epi_df(x)) cli::cli_abort("`x` must be an `epi_df`.")
 
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index cdef74565..1275c3948 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -302,7 +302,7 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
     unnamed_columns <- object$columns %>% unname()
     new_data <- new_data %>%
-      pad_to_end(rel_keys, object$forecast_date) %>%
+      pad_to_end(rel_keys, object$forecast_date, unnamed_columns) %>%
       # group_by_at(rel_keys) %>%
       arrange(time_value) %>%
       as_tibble() %>%
diff --git a/R/utils-latency.R b/R/utils-latency.R
index b32edf7b5..35db0b484 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -33,6 +33,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @keywords internal
 #' @importFrom dplyr select
 #' @importFrom tidyr drop_na
+#' @importFrom utils capture.output
 set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   original_columns <- info %>%
     filter(source == "original") %>%
@@ -161,25 +162,6 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
 }
 
 
-fill_locf <- function(x, forecast_date) {
-  cannot_be_used <- x %>%
-    dplyr::filter(forecast_date - time_value <= n_recent) %>%
-    dplyr::mutate(fillers = forecast_date - time_value > keep) %>%
-    dplyr::summarise(
-      dplyr::across(
-        -tidyselect::any_of(key_colnames(recipe)),
-        ~ all(is.na(.x[fillers])) & is.na(head(.x[!fillers], 1))
-      ),
-      .groups = "drop"
-    ) %>%
-    dplyr::select(-fillers) %>%
-    dplyr::summarise(dplyr::across(
-      -tidyselect::any_of(key_colnames(recipe)), ~ any(.x)
-    )) %>%
-    unlist()
-  x <- tidyr::fill(x, !time_value)
-}
-
 #' pad every group at the right interval
 #' @description
 #' Perform last observation carried forward on a group by group basis. It uses
@@ -226,6 +208,9 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
 }
 
 #' return the names of the grouped columns, or `NULL`
+#' @param x an epi_df
+#' @keywords internal
+#' @importFrom utils head
 get_grouping_columns <- function(x) {
   group_names <- names(attributes(x)$groups)
   head(group_names, -1)
diff --git a/man/get_grouping_columns.Rd b/man/get_grouping_columns.Rd
index 6b653628d..f8b61af42 100644
--- a/man/get_grouping_columns.Rd
+++ b/man/get_grouping_columns.Rd
@@ -6,6 +6,10 @@
 \usage{
 get_grouping_columns(x)
 }
+\arguments{
+\item{x}{an epi_df}
+}
 \description{
 return the names of the grouped columns, or \code{NULL}
 }
+\keyword{internal}
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 6c439dbd8..6b337c056 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1057,6 +1057,32 @@
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
+---
+
+    structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
+    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
+    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c(0.136509784083987, 0.469979623951498
+        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.821955329282474, 1.15542516914998), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067883, 
+        0.961536916935394), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
+    18997, 18997), class = "Date"), target_date = structure(c(18998, 
+    18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
+    -6L), class = c("tbl_df", "tbl", "data.frame"))
+
 # arx_forecaster output format snapshots
 
     Code
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 7f1d46006..003fc8319 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -94,6 +94,19 @@ test_that("arx_forecaster snapshots", {
   )
   # not the same predictions
   expect_false(all(arx2$predictions == arx3$predictions))
+
+
+  arx4 <- arx_forecaster(
+    train_data,
+    "death_rate_7d_av",
+    c("death_rate_7d_av", "case_rate_7d_av"),
+    args_list = arx_args_list(
+      ahead = 1L,
+      adjust_latency = "locf"
+    )
+  )
+  # consistency check
+  expect_snapshot_tibble(arx3$predictions)
 })
 
 test_that("arx_forecaster output format snapshots", {
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index a557fc146..75656989a 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -186,14 +186,14 @@ test_that("epi_adjust_latency correctly locfs", {
     last_dates,
     tribble(
       ~name, ~last_date,
-      "lag_11_death_rate", max_time + 11,
-      "lag_6_death_rate", max_time + 6,
-      "lag_5_case_rate", max_time + 5,
-      "lag_1_case_rate", max_time + 1,
-      "case_rate", max_time,
-      "death_rate", max_time,
-      "lag_0_death_rate", max_time + 0,
-      "ahead_7_death_rate", max_time - 7,
+      "lag_11_death_rate", max_time + 16,
+      "lag_6_death_rate", max_time + 11,
+      "lag_5_case_rate", max_time + 10,
+      "lag_1_case_rate", max_time + 6,
+      "case_rate", max_time + 5,
+      "death_rate", max_time + 5,
+      "lag_0_death_rate", max_time + 5,
+      "ahead_7_death_rate", max_time - 2,
     )
   )
   # we expect a 5-fold repetition of the last values found in the original
@@ -204,7 +204,7 @@ test_that("epi_adjust_latency correctly locfs", {
     slice_tail() %>%
     ungroup() %>%
     select(case_rate, death_rate) %>%
-    uncount(5)
+    tidyr::uncount(5)
   # pulling just the region between the last day and the prediction day
   filled_values <-
     baked_x %>%
@@ -450,7 +450,7 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
     summarise(last_date = max(time_value)) %>%
     arrange(desc(last_date)) %>%
     mutate(locf_date = last_date - latency)
-  # iterate over all columns and make sure the latent time period has the exact same values
+  # iterate over all columns and make sure the latent time period has the exact same values (so the variance is zero)
   for (ii in seq(nrow(last_dates))) {
     baked_var <- baked_x %>%
       filter(last_dates[[ii, "locf_date"]] <= time_value, time_value <= last_dates[[ii, "last_date"]]) %>%
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index a23be628b..78f294564 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -149,7 +149,7 @@ test_that("pad_to_end handles weeks", {
         ),
         a = 3, b = .9
       )
-    ) %>% arrange(time_value, geo_value)
+    ) %>% arrange(geo_value, time_value)
   )
 })
 # todo case where somehow columns of different roles are selected

From acc5fa0fdddc4ee00be827f5fe199838a7b72896 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 5 Sep 2024 23:01:40 -0500
Subject: [PATCH 076/387] locf correct on NA at end columns

---
 R/step_adjust_latency.R             |  8 ++-----
 R/utils-latency.R                   | 35 +++++++++++++++++++++++------
 tests/testthat/test-utils_latency.R | 17 ++++++++++++--
 3 files changed, 45 insertions(+), 15 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 1275c3948..defbd9262 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -101,7 +101,6 @@ step_adjust_latency <-
            epi_keys_checked = c("geo_value"),
            fixed_latency = NULL,
            fixed_forecast_date = NULL,
-           default = NA,
            skip = FALSE,
            columns = NULL,
            id = recipes::rand_id("adjust_latency")) {
@@ -173,7 +172,6 @@ then the previous `step_epi_lag`s won't work with modified data.",
         forecast_date = fixed_forecast_date,
         latency = fixed_latency,
         latency_table = NULL,
-        default = default,
         keys = key_colnames(recipe),
         columns = columns,
         skip = skip,
@@ -183,8 +181,8 @@ then the previous `step_epi_lag`s won't work with modified data.",
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, forecast_date, latency, latency_table, time_type, default,
-           keys, method, epi_keys_checked, columns, skip, id) {
+  function(terms, role, trained, forecast_date, latency, latency_table,
+           time_type, keys, method, epi_keys_checked, columns, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
@@ -195,7 +193,6 @@ step_adjust_latency_new <-
       forecast_date = forecast_date,
       latency = latency,
       latency_table = latency_table,
-      default = default,
       keys = keys,
       columns = columns,
       skip = skip,
@@ -274,7 +271,6 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     forecast_date = forecast_date,
     latency = unique(latency_table$latency),
     latency_table = latency_table,
-    default = x$default,
     keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 35db0b484..9148c7c2b 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -171,10 +171,10 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
 #' @param x an epi_df to be filled forward.
 #' @param columns_to_complete which columns to apply completion to. By default every non-key column of an epi_df
 #' @param groups the grouping by which to fill forward
-#' @importFrom dplyr across, arrange, bind_rows, group_by, summarise
 #' @importFrom tidyselect all_of
 #' @importFrom rlang list2
 #' @importFrom vctrs vec_cast
+#' @importFrom dplyr across arrange bind_rows group_by summarise
 #' @keywords internal
 pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   if (is.null(columns_to_complete)) {
@@ -193,15 +193,20 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
     unnest("time_value") %>%
     mutate(time_value = vec_cast(time_value, x$time_value))
   # pull the last value in each group and fill forward
-  filled_values <- x %>%
-    group_by(across(all_of(groups))) %>%
-    slice_tail() %>%
+  grouped_and_arranged <- x %>%
+    arrange(across(all_of(c("time_value", groups)))) %>%
+    group_by(across(all_of(groups)))
+  values_to_fill <- grouped_and_arranged %>%
+    slice(min(across(columns_to_complete, count_single_column)):n())
+
+  filled_values <- values_to_fill %>%
     bind_rows(completed_time_values) %>%
     arrange(across(all_of(c("time_value", groups)))) %>%
     fill(all_of(columns_to_complete), .direction = "down") %>%
-    slice(-1)
-
-  bind_rows(x, filled_values) %>%
+    slice(-1) # remove the oirginal rows
+  grouped_and_arranged %>%
+    slice(1:min(across(columns_to_complete, count_single_column))) %>%
+    bind_rows(filled_values) %>%
     arrange(across(all_of(key_colnames(x)))) %>%
     ungroup() %>%
     group_by(across(all_of(get_grouping_columns(x))))
@@ -216,6 +221,22 @@ get_grouping_columns <- function(x) {
   head(group_names, -1)
 }
 
+#' get the location of the last real value
+#' @param col the relevant column
+#' @param from_last instead of the number of columns
+#' @keywords internal
+count_single_column <- function(col, from_last) {
+  run_lengths <- rle(is.na(col))
+  n_el <- length(col)
+  if (tail(run_lengths$values, 1)) {
+    n_at_end <- tail(run_lengths$lengths, 1)
+    return(n_el - n_at_end)
+  } else {
+    return(n_el)
+  }
+}
+
+
 #' seq, but returns null if from is larger
 #' @keywords internal
 seq_null_swap <- function(from, to, by) {
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 78f294564..e0f35d7fa 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -103,15 +103,19 @@ test_that("pad_to_end works correctly", {
   single_ex <- tribble(
     ~geo_value, ~time_value, ~a, ~b,
     "1", as.Date("1066-10-13"), 2, -.6,
+    # internal NA
     "1", as.Date("1066-10-14"), NA, NA,
     "1", as.Date("1066-10-15"), 1, -.5,
-    "2", as.Date("1066-10-13"), 3, .9
+    "2", as.Date("1066-10-13"), 3, .9,
+    # note these are intentionally out of order
+    "3", as.Date("1066-10-14"), 2.5, NA,
+    "3", as.Date("1066-10-13"), 2, -.6,
   ) %>%
     as_epi_df(as_of = "1066-10-16")
   expect_equal(
     single_ex %>% pad_to_end("geo_value", as.Date("1066-10-16")),
     rbind(
-      single_ex,
+      single_ex[-5, ],
       tibble(geo_value = "1", time_value = as.Date("1066-10-16"), a = 1, b = -.5),
       tibble(
         geo_value = "2",
@@ -121,6 +125,15 @@ test_that("pad_to_end works correctly", {
           by = 1
         ),
         a = 3, b = .9
+      ),
+      tibble(
+        geo_value = "3",
+        time_value = seq.Date(
+          from = as.Date("1066-10-14"),
+          to = as.Date("1066-10-16"),
+          by = 1
+        ),
+        a = 2.5, b = -0.6
       )
     ) %>% arrange(geo_value, time_value)
   )

From a11fa5f2593226e59e033dc0f5b6513ce82a63c4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 9 Sep 2024 14:47:41 -0500
Subject: [PATCH 077/387] docs along with more extensive tests

---
 NAMESPACE                                     |   4 +-
 R/arx_forecaster.R                            |  20 +-
 R/epi_shift.R                                 |   5 +-
 R/epi_workflow.R                              |   1 -
 R/step_adjust_latency.R                       | 209 +++++++++++-----
 R/utils-latency.R                             |  53 +++-
 man/arx_args_list.Rd                          |  22 +-
 man/arx_class_args_list.Rd                    |  22 +-
 man/cdc_baseline_args_list.Rd                 |  12 +-
 man/flatline_args_list.Rd                     |  17 +-
 man/step_adjust_latency.Rd                    | 228 +++++++++++++++---
 tests/testthat/test-layer_add_forecast_date.R |   4 +-
 tests/testthat/test-step_adjust_latency.R     |  93 +++++--
 tests/testthat/test-utils_latency.R           |  21 ++
 14 files changed, 555 insertions(+), 156 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index bc83aa2f8..ba319cecd 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -244,13 +244,16 @@ importFrom(dplyr,group_by_at)
 importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,n)
 importFrom(dplyr,pull)
 importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
+importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
 importFrom(dplyr,tibble)
+importFrom(dplyr,tribble)
 importFrom(dplyr,ungroup)
 importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
@@ -273,7 +276,6 @@ importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
-importFrom(recipes,recipes_eval_select)
 importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 702a259eb..42729ed85 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -213,18 +213,18 @@ arx_fcast_epi_workflow <- function(
 #' @param n_training Integer. An upper limit for the number of rows per
 #'   key that are used for training
 #'   (in the time unit of the `epi_df`).
-#' @param forecast_date Date. The date on which the forecast is created.  The
-#'   default `NULL` will attempt to determine this automatically either as the
-#'   max time value if there is no latency adjustment, or as the `as_of` of
-#'   `epi_data` if `adjust_latency` is non-`NULL`.
-#' @param target_date Date. The date for which the forecast is intended. The
-#'   default `NULL` will attempt to determine this automatically as
-#'   `forecast_date + ahead`.
+#' @param forecast_date Date. The date from which the forecast is occurring.
+#'   The default `NULL` will determine this automatically from either
+#'   1. the maximum time value for which there's data if there is no latency
+#'   adjustment (the default case), or
+#'   2. the `as_of` date of `epi_data` if `adjust_latency` is
+#'   non-`NULL`.
+#' @param target_date Date. The date that is being forecast. The default `NULL`
+#'   will determine this automatically as `forecast_date + ahead`.
 #' @param adjust_latency Character or `NULL`. One of the `method`s of
 #'   [step_adjust_latency()], or `NULL` (in which case there is no adjustment).
-#'   If there is a difference between the `forecast_date` and the last day of
-#'   data, this determines how to shift the model to account for this
-#'   difference. The options are:
+#'   If the `forecast_date` is after the last day of data, this determines how
+#'   to shift the model to account for this difference. The options are:
 #'   - `NULL` the default, assumes the `forecast_date` is the last day of data
 #'   - `"extend_ahead"`: increase the `ahead` by the latency so it's relative to
 #'   the last day of data. For example, if the last day of data was 3 days ago,
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 9365331a9..e0b68b579 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -46,9 +46,12 @@ add_shifted_columns <- function(new_data, object, amount) {
   shift_sign_lat <- attributes(new_data)$metadata$shift_sign
   if (!is.null(latency_table) &&
     shift_sign_lat == sign_shift) {
-    # TODO this doesn't work on lags of transforms
+    # are we adding an unreasonable amount of latency?
+    check_interminable_latency(new_data, latency_table, object$columns, attributes(new_data)$metadata$forecast_date)
+    # get the actually used latencies
     rel_latency <- latency_table %>% filter(col_name %in% object$columns)
   } else {
+    # adding zero if there's no latency table
     rel_latency <- tibble(col_name = object$columns, latency = 0L)
   }
   grid <- expand_grid(col = object$columns, amount = sign_shift * amount) %>%
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 34141b732..19a30d73a 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -43,7 +43,6 @@ epi_workflow <- function(preprocessor = NULL, spec = NULL, postprocessor = NULL)
   if (!is_null(postprocessor)) {
     out <- add_postprocessor(out, postprocessor)
   }
-
   out
 }
 
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index defbd9262..e4968920e 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -1,16 +1,133 @@
 #' Adapt the model to latent data
 #'
+#' @description
 #' In the standard case, the arx models assume that the last observation is also
 #' the day from which the forecast is being made. But if the data has latency,
 #' then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
-#' to compensate. This allows the user to create models on the most recent data,
-#' regardless of latency patterns. Instead of using the last observation date,
-#' `step_adjust_latency` uses the `as_of` date of the `epi_df` as the
-#' `forecast_date`, potentially using different dates depending on the
-#' `epi_keys`, such as geography. This is most useful in realtime and
+#' to compensate.
+#' This is most useful in realtime and
 #' pseudo-prospective forecasting for data where there is some delay between the
 #' event occurring and the event being reported.
 #'
+#' @details
+#' This step allows the user to create models on the most recent
+#' data, automatically accounting for latency patterns. Instead of using the last observation
+#' date, `step_adjust_latency` uses the `as_of` date of the `epi_df` as the
+#' `forecast_date`, and adjusts the model so that there is data available. To
+#'   demonstrate some of the subtleties, let's consider a toy dataset:
+#' ```{r toy_df}
+#' toy_df <- tribble(
+#'  ~geo_value, ~time_value, ~a, ~b,
+#'  "ma", as.Date("2015-01-11"), 20, 6,
+#'  "ma", as.Date("2015-01-12"), 23, NA,
+#'  "ma", as.Date("2015-01-13"), 25, NA,
+#'  "ca", as.Date("2015-01-11"), 100, 5,
+#'  "ca", as.Date("2015-01-12"), 103, 10,
+#' ) %>%
+#'    as_epi_df(as_of = as.Date("2015-01-14"))
+#' ```
+#' If we're looking to predict the value on the 15th, forecasting from the 14th (the `as_of` date above),
+#'   there are two issues we will need to address:
+#' 1. `"ca"` is latent by 2 days, whereas `"ma"` is latent by 1
+#' 2. if we want to use `b` as an exogenous variable, for `"ma"` it is latent by 3 days instead of just 1.
+#'
+#' Regardless of `method`, `epi_keys_checked="geo_value"` guarantees tha the
+#'   difference between `"ma"` and `"ca"` is accounted for by making  the
+#'   latency adjustment at least 2. For some comparison, here's what the various
+#'   methods will do:
+#'
+#' ## `locf`
+#' Short for "last observation carried forward", `locf` assumes that every day
+#'   between the last observation and the forecast day is exactly the same.
+#'   This is a very straightforward assumption, but wrecks any features that
+#'   depend on changes in value over time, such as the growth rate, or even
+#'   adjacent lags. A more robust version of this falls under the heading of
+#'   nowcasting, an eventual aim for this package. On the toy dataset, it
+#'   doesn't matter which day we're trying to predict, since it just fills
+#'   forward to the `forecast_date`:
+#'   ```{r toy_df}
+#'   toy_recipe <- epi_recipe(toy_df) %>%
+#'     step_adjust_latency(method="locf")
+#'
+#'   toy_recipe %>%
+#'     prep(toy_df) %>%
+#'     bake(toy_df) %>%
+#'     arrange(geo_value, time_value)
+#'   ```
+#'
+#' ## `extend_lags`
+#' `extend_lags` increases the lags so that they are guaranteed to have
+#'   data. This has the advantage of being applicable on
+#'   a per-column basis; if cases and deaths are reported at different
+#'   latencies, the lags for each are adjusted separately. In the toy example:
+#'   ```{r toy_df}
+#'   toy_recipe <- epi_recipe(toy_df) %>%
+#'     step_adjust_latency(method="extend_lags") %>%
+#'     step_epi_lag(a,lag=1) %>%
+#'     step_epi_lag(b,lag=1) %>%
+#'     step_epi_ahead(a, ahead=1)
+#'
+#'   toy_recipe %>%
+#'     prep(toy_df) %>%
+#'     bake(toy_df) %>%
+#'     arrange(geo_value, time_value)
+#'   ```
+#' The maximum latency in column `a` is 2 days, so the lag is increased to 3,
+#'   while the max latency in column `b` is 3, so the same lag is increased to
+#'   4; both of these changes are reflected in the column names. Meanwhile the
+#'   ahead is uneffected.
+#'
+#'   As a side-note, lag/ahead can be somewhat ambiguous about direction. Here,
+#'   the values are brought forward in time, so that for a given row, column
+#'   `lag_3_a` represents the value 3 days before.
+#'
+#' ## `extend_ahead`
+#' `extend_ahead` increases the ahead, turning a 3 day ahead forecast
+#'   into a 7 day one; this has the advantage of simplicity and is reflective of
+#'   the actual modelling task, but potentially leaves information unused if
+#'   different data sources have different latencies; it must use the latency of
+#'   the most latent data to insure there is data available. In the toy example:
+#'   ```{r toy_df}
+#'   toy_recipe <- epi_recipe(toy_df) %>%
+#'     step_adjust_latency(method="extend_ahead") %>%
+#'     step_epi_lag(a,lag=0) %>%
+#'     step_epi_ahead(a, ahead=1)
+#'
+#'   toy_recipe %>%
+#'     prep(toy_df) %>%
+#'     bake(toy_df) %>%
+#'     arrange(geo_value, time_value)
+#'   ```
+#'   Even though we're doing a 1 day ahead forecast, because our worst latency
+#'   is 3 days from column `b`'s `"ma"` data, our outcome column is `ahead_4_a`
+#'   (so 4 days ahead). If we want to ignore any latency in column `b`, we need
+#'   to explicitly set the columns to consider while adjusting like this:
+#'   `step_adjust_latency(a, method="extend_ahead")`.
+#'
+#' # Programmatic details
+#' `step_adjust_latency` uses the metadata, such as `time_type` and `as_of`, of
+#'   the `epi_df` used in the initial prep step, rather than baking or
+#'   prediction. This means reusing the same forecaster on new data is not
+#'   advised, though typically it is not advised in general.
+#'
+#' The latency adjustment only applies to columns created after this step, so
+#'   this step should go before both `step_epi_ahead` and `step_epi_lag`. This will work:
+#'  ```{r}
+#'  toy_recipe <- epi_recipe(toy_df) %>%
+#'     # non-lag steps
+#'     step_adjust_latency(a, method = "extend_lags") %>%
+#'     step_epi_lag(a, lag=0) # other steps
+#'  ```
+#'  while this will not:
+#'  ```{r}
+#'  toy_recipe <- epi_recipe(toy_df) %>%
+#'     step_epi_lag(a, lag=0) %>%
+#'     step_adjust_latency(a, method = "extend_lags")
+#'  ```
+#' If you create columns that you then apply lags to (such as
+#'   `step_growth_rate()`), these should be created before
+#'   `step_adjust_latency`, so any subseqent latency can be addressed.
+#'
 #' @param method a character. Determines the method by which the
 #'   forecast handles latency. The options are:
 #'   - `"extend_ahead"`: Lengthen the ahead so that forecasting from the last
@@ -25,22 +142,22 @@
 #'   lags are `c(0,7,14)` for data that is 3 days latent, the actual lags used
 #'   become `c(3,10,17)`.
 #' @param epi_keys_checked a character vector. A list of keys to group by before
-#'   finding the `max_time_value`.  The default value of this is
-#'   `c("geo_value")`, but it can be any collection of `epi_keys`.  Different
-#'   locations may have different latencies; to produce a forecast at every
-#'   location, we need to use the largest latency across every location; this
-#'   means taking `max_time_value` to be the minimum of the `max_time_value`s
-#'   for each `geo_value` (or whichever collection of keys are specified).  If
-#'   `NULL` or an empty character vector, it will take the maximum across all
-#'   values, irrespective of any keys.
+#'   finding the `max_time_value` (the last day of data), defaulting to
+#'   `geo_value`. Different locations may have different latencies; to produce a
+#'   forecast at every location, we need to guarantee data at every location by
+#'   using the largest latency across every location; this means taking
+#'   `max_time_value` to be the minimum of the `max_time_value`s for each set of
+#'   key values (so the earliest date).  If `NULL` or an empty character vector,
+#'   it will take the maximum across all values, irrespective of any keys.
+#'
+#'   Note that this is a separate concern from different latencies across
+#'   different *data columns*, which is only handled by the choice of `method`.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
 #'   vector. Cannot be set at the same time as `fixed_forecast_date`. If
 #'   non-`NULL`, the amount to offset the ahead or lag by. If a single integer,
 #'   this is used for all columns; if a labeled vector, the labels must
 #'   correspond to the base column names (before lags/aheads).  If `NULL`, the
-#'   latency is the distance between the `epi_df`'s `max_time_value` and either
-#'   the `fixed_forecast_date` or the `epi_df`'s `as_of` field (the default for
-#'   `forecast_date`).
+#'   latency is the distance between the `epi_df`'s `max_time_value` and the `forecast_date`.
 #' @param fixed_forecast_date either a date of the same kind used in the
 #'   `epi_df`, or `NULL`. Exclusive with `fixed_latency`. If a date, it gives
 #'   the date from which the forecast is actually occurring. If `NULL`, the
@@ -49,21 +166,9 @@
 #' @param role For model terms created by this step, what analysis role should
 #'   they be assigned? `lag` is a predictor while `ahead` is an outcome.  It
 #'   should be correctly inferred and not need setting
-#' @param default Determines what fills empty rows
-#'   left by leading/lagging (defaults to NA).
 #' @template step-return
 #' @inheritParams recipes::step_lag
 #'
-#' @details The step assumes that the pipeline has already applied either
-#'   `step_epi_ahead` or `step_epi_lag` depending on the value of `"method"`,
-#'   and that `step_epi_naomit` has NOT been run.  By default, the latency will
-#'   be determined using the arguments below, but can be set explicitly using
-#'   either `fixed_latency` or `fixed_forecast_date`.
-#'
-#' The `prefix` and `id` arguments are unchangeable to ensure that the code runs
-#' properly and to avoid inconsistency with naming. For `step_epi_ahead`, they
-#' are always set to `"ahead_"` and `"epi_ahead"` respectively, while for
-#' `step_epi_lag`, they are set to `"lag_"` and `"epi_lag`, respectively.
 #'
 #' @family row operation steps
 #' @rdname step_adjust_latency
@@ -88,6 +193,7 @@
 #'
 #' @importFrom recipes detect_step
 #' @importFrom rlang enquos is_empty
+#' @importFrom dplyr tribble n
 step_adjust_latency <-
   function(recipe,
            ...,
@@ -172,6 +278,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
         forecast_date = fixed_forecast_date,
         latency = fixed_latency,
         latency_table = NULL,
+        metadata = NULL,
         keys = key_colnames(recipe),
         columns = columns,
         skip = skip,
@@ -182,7 +289,8 @@ then the previous `step_epi_lag`s won't work with modified data.",
 
 step_adjust_latency_new <-
   function(terms, role, trained, forecast_date, latency, latency_table,
-           time_type, keys, method, epi_keys_checked, columns, skip, id) {
+           metadata, time_type, keys, method, epi_keys_checked, columns, skip,
+           id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
@@ -193,19 +301,17 @@ step_adjust_latency_new <-
       forecast_date = forecast_date,
       latency = latency,
       latency_table = latency_table,
+      metadata = metadata,
       keys = keys,
       columns = columns,
       skip = skip,
       id = id
     )
   }
-#' @importFrom recipes recipes_eval_select
-construct_latency_table <- function(x, latency, training, info) {
-  return(latency_table)
-}
 # lags introduces max(lags) NA's after the max_time_value.
 #' @export
 #' @importFrom glue glue
+#' @importFrom dplyr rowwise
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   sign_shift <- get_sign(x)
   latency <- x$latency
@@ -220,6 +326,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 
   if (is.null(latency)) {
     latency_table <- latency_table %>%
+      rowwise() %>%
       mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, x$epi_keys_checked))
   } else if (length(latency) > 1) {
     # if latency has a length, it must also have named elements. We assign based on comparing the name in the list
@@ -242,27 +349,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       pull(variable)
   }
 
-  # check that the shift amount isn't too extreme
-  latency_max <- max(abs(latency_table$latency))
-  time_type <- attributes(training)$metadata$time_type
-  i_latency <- which.max(latency_table$latency)
-  if (
-    (grepl("day", time_type) && (latency_max >= 10)) ||
-      (grepl("week", time_type) && (latency_max >= 4)) ||
-      ((time_type == "yearmonth") && (latency_max >= 2)) ||
-      ((time_type == "yearquarter") && (latency_max >= 1)) ||
-      ((time_type == "year") && (latency_max >= 1))
-  ) {
-    cli::cli_warn(paste(
-      "!" = paste(
-        "The maximum latency is {latency_max}, ",
-        "which is questionable for it's `time_type` of ",
-        "{time_type}."
-      ),
-      "i" = "latency: {latency_table$latency[[i_latency]]}",
-      "i" = "`max_time` = {max(training$time_value)} -> `forecast_date` = {forecast_date}"
-    ), class = "epipredict__prep.step_latency__very_large_latency")
-  }
+
 
   step_adjust_latency_new(
     terms = x$terms,
@@ -271,6 +358,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     forecast_date = forecast_date,
     latency = unique(latency_table$latency),
     latency_table = latency_table,
+    metadata = attributes(training)$metadata,
     keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
@@ -286,23 +374,28 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!isa(new_data, "epi_df")) {
     # TODO if new_data actually has keys other than geo_value and time_value, this is going to cause problems
-    new_data <- new_data %>% as_epi_df(as_of = object$forecast_date)
+    new_data <- as_epi_df(new_data)
+    attributes(new_data)$metadata <- object$metadata
+    attributes(new_data)$metadata$as_of <- object$forecast_date
   }
   if (object$method == "extend_ahead" || object$method == "extend_lags") {
     attributes(new_data)$metadata$latency_method <- object$method
     attributes(new_data)$metadata$shift_sign <- get_sign(object)
     attributes(new_data)$metadata$latency_table <- object$latency_table
+    attributes(new_data)$metadata$forecast_date <- object$forecast_date
     keys <- object$keys
   } else if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
-    unnamed_columns <- object$columns %>% unname()
+    modified_columns <- object$columns %>% unname()
+    check_interminable_latency(new_data, object$latency_table, modified_columns, object$forecast_date)
+
     new_data <- new_data %>%
-      pad_to_end(rel_keys, object$forecast_date, unnamed_columns) %>%
+      pad_to_end(rel_keys, object$forecast_date, modified_columns) %>%
       # group_by_at(rel_keys) %>%
       arrange(time_value) %>%
       as_tibble() %>%
-      tidyr::fill(.direction = "down", any_of(unnamed_columns)) %>%
+      tidyr::fill(.direction = "down", any_of(modified_columns)) %>%
       ungroup()
   }
   return(new_data)
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9148c7c2b..bd0e4803e 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -197,15 +197,15 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
     arrange(across(all_of(c("time_value", groups)))) %>%
     group_by(across(all_of(groups)))
   values_to_fill <- grouped_and_arranged %>%
-    slice(min(across(columns_to_complete, count_single_column)):n())
-
+    slice(min(across(all_of(columns_to_complete), count_single_column)):n())
   filled_values <- values_to_fill %>%
     bind_rows(completed_time_values) %>%
     arrange(across(all_of(c("time_value", groups)))) %>%
     fill(all_of(columns_to_complete), .direction = "down") %>%
     slice(-1) # remove the oirginal rows
+
   grouped_and_arranged %>%
-    slice(1:min(across(columns_to_complete, count_single_column))) %>%
+    slice(1:min(across(all_of(columns_to_complete), count_single_column))) %>%
     bind_rows(filled_values) %>%
     arrange(across(all_of(key_colnames(x)))) %>%
     ungroup() %>%
@@ -245,3 +245,50 @@ seq_null_swap <- function(from, to, by) {
   }
   seq(from = from, to = to, by = by)
 }
+
+
+#' warn when the latency is larger than would be reasonable
+#' @param dataset the epi_df
+#' @param latency_table the whole collection of latencies
+#' @param target_columns the names of the columns that we're adjusting, and whether its unreasonably latent
+#' @keywords internal
+check_interminable_latency <- function(dataset, latency_table, target_columns, forecast_date, call = caller_env()) {
+  # check that the shift amount isn't too extreme
+  rel_latency_table <- latency_table %>%
+    filter(col_name %in% target_columns)
+  # no relevant columns, so this error definitely isn't happening
+  if (nrow(rel_latency_table) == 0) {
+    return()
+  }
+  latency_max <- rel_latency_table %>%
+    pull(latency) %>%
+    abs() %>%
+    max()
+  time_type <- attributes(dataset)$metadata$time_type
+  i_latency <- which.max(latency_table$latency)
+  if (
+    (grepl("day", time_type) && (latency_max >= 28)) ||
+      (grepl("week", time_type) && (latency_max >= 4)) ||
+      ((time_type == "yearmonth") && (latency_max >= 2)) ||
+      ((time_type == "yearquarter") && (latency_max >= 1)) ||
+      ((time_type == "year") && (latency_max >= 1))
+  ) {
+    max_time_value <- dataset %>%
+      filter(!is.na(!!(latency_table[[i_latency, "col_name"]]))) %>%
+      pull(time_value) %>%
+      max()
+    cli::cli_warn(
+      message = c(
+        paste(
+          "The maximum latency is {latency_max}, ",
+          "which is questionable for it's `time_type` of ",
+          "{time_type}."
+        ),
+        "i" = "latency: {latency_table$latency[[i_latency]]}",
+        "i" = "`max_time` = {max_time_value} -> `forecast_date` = {forecast_date}"
+      ),
+      class = "epipredict__prep.step_latency__very_large_latency",
+      call = call
+    )
+  }
+}
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 84e8c72d9..214726fde 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -32,20 +32,22 @@ date for which forecasts should be produced.}
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.  The
-default \code{NULL} will attempt to determine this automatically either as the
-max time value if there is no latency adjustment, or as the \code{as_of} of
-\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
+\item{forecast_date}{Date. The date from which the forecast is occurring.
+The default \code{NULL} will determine this automatically from either
+\enumerate{
+\item the maximum time value for which there's data if there is no latency
+adjustment (the default case), or
+\item the \code{as_of} date of \code{epi_data} if \code{adjust_latency} is
+non-\code{NULL}.
+}}
 
-\item{target_date}{Date. The date for which the forecast is intended. The
-default \code{NULL} will attempt to determine this automatically as
-\code{forecast_date + ahead}.}
+\item{target_date}{Date. The date that is being forecast. The default \code{NULL}
+will determine this automatically as \code{forecast_date + ahead}.}
 
 \item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
 \code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
-If there is a difference between the \code{forecast_date} and the last day of
-data, this determines how to shift the model to account for this
-difference. The options are:
+If the \code{forecast_date} is after the last day of data, this determines how
+to shift the model to account for this difference. The options are:
 \itemize{
 \item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 3502508cb..185b868e5 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -34,20 +34,22 @@ date for which forecasts should be produced.}
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.  The
-default \code{NULL} will attempt to determine this automatically either as the
-max time value if there is no latency adjustment, or as the \code{as_of} of
-\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
+\item{forecast_date}{Date. The date from which the forecast is occurring.
+The default \code{NULL} will determine this automatically from either
+\enumerate{
+\item the maximum time value for which there's data if there is no latency
+adjustment (the default case), or
+\item the \code{as_of} date of \code{epi_data} if \code{adjust_latency} is
+non-\code{NULL}.
+}}
 
-\item{target_date}{Date. The date for which the forecast is intended. The
-default \code{NULL} will attempt to determine this automatically as
-\code{forecast_date + ahead}.}
+\item{target_date}{Date. The date that is being forecast. The default \code{NULL}
+will determine this automatically as \code{forecast_date + ahead}.}
 
 \item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
 \code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
-If there is a difference between the \code{forecast_date} and the last day of
-data, this determines how to shift the model to account for this
-difference. The options are:
+If the \code{forecast_date} is after the last day of data, this determines how
+to shift the model to account for this difference. The options are:
 \itemize{
 \item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 3870134fb..4a8c13113 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -33,10 +33,14 @@ set of prediction horizons for \code{\link[=layer_cdc_flatline_quantiles]{layer_
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.  The
-default \code{NULL} will attempt to determine this automatically either as the
-max time value if there is no latency adjustment, or as the \code{as_of} of
-\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
+\item{forecast_date}{Date. The date from which the forecast is occurring.
+The default \code{NULL} will determine this automatically from either
+\enumerate{
+\item the maximum time value for which there's data if there is no latency
+adjustment (the default case), or
+\item the \code{as_of} date of \code{epi_data} if \code{adjust_latency} is
+non-\code{NULL}.
+}}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index d056ed825..401850efe 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -28,14 +28,17 @@ So for example, \code{ahead = 7} will create residuals by comparing values
 key that are used for training
 (in the time unit of the \code{epi_df}).}
 
-\item{forecast_date}{Date. The date on which the forecast is created.  The
-default \code{NULL} will attempt to determine this automatically either as the
-max time value if there is no latency adjustment, or as the \code{as_of} of
-\code{epi_data} if \code{adjust_latency} is non-\code{NULL}.}
+\item{forecast_date}{Date. The date from which the forecast is occurring.
+The default \code{NULL} will determine this automatically from either
+\enumerate{
+\item the maximum time value for which there's data if there is no latency
+adjustment (the default case), or
+\item the \code{as_of} date of \code{epi_data} if \code{adjust_latency} is
+non-\code{NULL}.
+}}
 
-\item{target_date}{Date. The date for which the forecast is intended. The
-default \code{NULL} will attempt to determine this automatically as
-\code{forecast_date + ahead}.}
+\item{target_date}{Date. The date that is being forecast. The default \code{NULL}
+will determine this automatically as \code{forecast_date + ahead}.}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 3da958045..2b5f282d9 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -13,7 +13,6 @@ step_adjust_latency(
   epi_keys_checked = c("geo_value"),
   fixed_latency = NULL,
   fixed_forecast_date = NULL,
-  default = NA,
   skip = FALSE,
   columns = NULL,
   id = recipes::rand_id("adjust_latency")
@@ -50,23 +49,23 @@ become \code{c(3,10,17)}.
 }}
 
 \item{epi_keys_checked}{a character vector. A list of keys to group by before
-finding the \code{max_time_value}.  The default value of this is
-\code{c("geo_value")}, but it can be any collection of \code{epi_keys}.  Different
-locations may have different latencies; to produce a forecast at every
-location, we need to use the largest latency across every location; this
-means taking \code{max_time_value} to be the minimum of the \code{max_time_value}s
-for each \code{geo_value} (or whichever collection of keys are specified).  If
-\code{NULL} or an empty character vector, it will take the maximum across all
-values, irrespective of any keys.}
+finding the \code{max_time_value} (the last day of data), defaulting to
+\code{geo_value}. Different locations may have different latencies; to produce a
+forecast at every location, we need to guarantee data at every location by
+using the largest latency across every location; this means taking
+\code{max_time_value} to be the minimum of the \code{max_time_value}s for each set of
+key values (so the earliest date).  If \code{NULL} or an empty character vector,
+it will take the maximum across all values, irrespective of any keys.
+
+Note that this is a separate concern from different latencies across
+different \emph{data columns}, which is only handled by the choice of \code{method}.}
 
 \item{fixed_latency}{either a positive integer, or a labeled positive integer
 vector. Cannot be set at the same time as \code{fixed_forecast_date}. If
 non-\code{NULL}, the amount to offset the ahead or lag by. If a single integer,
 this is used for all columns; if a labeled vector, the labels must
 correspond to the base column names (before lags/aheads).  If \code{NULL}, the
-latency is the distance between the \code{epi_df}'s \code{max_time_value} and either
-the \code{fixed_forecast_date} or the \code{epi_df}'s \code{as_of} field (the default for
-\code{forecast_date}).}
+latency is the distance between the \code{epi_df}'s \code{max_time_value} and the \code{forecast_date}.}
 
 \item{fixed_forecast_date}{either a date of the same kind used in the
 \code{epi_df}, or \code{NULL}. Exclusive with \code{fixed_latency}. If a date, it gives
@@ -74,9 +73,6 @@ the date from which the forecast is actually occurring. If \code{NULL}, the
 \code{forecast_date} is determined either via the \code{fixed_latency}, or is set to
 the \code{epi_df}'s \code{as_of} value if \code{fixed_latency} is also \code{NULL}.}
 
-\item{default}{Determines what fills empty rows
-left by leading/lagging (defaults to NA).}
-
 \item{skip}{A logical. Should the step be skipped when the
 recipe is baked by \code{\link[recipes:bake]{bake()}}? While all operations are baked
 when \code{\link[recipes:prep]{prep()}} is run, some operations may not be able to be
@@ -97,26 +93,198 @@ sequence of any existing operations.
 In the standard case, the arx models assume that the last observation is also
 the day from which the forecast is being made. But if the data has latency,
 then you may wish to adjust the predictors (lags) and/or the outcome (ahead)
-to compensate. This allows the user to create models on the most recent data,
-regardless of latency patterns. Instead of using the last observation date,
-\code{step_adjust_latency} uses the \code{as_of} date of the \code{epi_df} as the
-\code{forecast_date}, potentially using different dates depending on the
-\code{epi_keys}, such as geography. This is most useful in realtime and
+to compensate.
+This is most useful in realtime and
 pseudo-prospective forecasting for data where there is some delay between the
 event occurring and the event being reported.
 }
 \details{
-The step assumes that the pipeline has already applied either
-\code{step_epi_ahead} or \code{step_epi_lag} depending on the value of \code{"method"},
-and that \code{step_epi_naomit} has NOT been run.  By default, the latency will
-be determined using the arguments below, but can be set explicitly using
-either \code{fixed_latency} or \code{fixed_forecast_date}.
-
-The \code{prefix} and \code{id} arguments are unchangeable to ensure that the code runs
-properly and to avoid inconsistency with naming. For \code{step_epi_ahead}, they
-are always set to \code{"ahead_"} and \code{"epi_ahead"} respectively, while for
-\code{step_epi_lag}, they are set to \code{"lag_"} and \verb{"epi_lag}, respectively.
+This step allows the user to create models on the most recent
+data, automatically accounting for latency patterns. Instead of using the last observation
+date, \code{step_adjust_latency} uses the \code{as_of} date of the \code{epi_df} as the
+\code{forecast_date}, and adjusts the model so that there is data available. To
+demonstrate some of the subtleties, let's consider a toy dataset:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_df <- tribble(
+ ~geo_value, ~time_value, ~a, ~b,
+ "ma", as.Date("2015-01-11"), 20, 6,
+ "ma", as.Date("2015-01-12"), 23, NA,
+ "ma", as.Date("2015-01-13"), 25, NA,
+ "ca", as.Date("2015-01-11"), 100, 5,
+ "ca", as.Date("2015-01-12"), 103, 10,
+) \%>\%
+   as_epi_df(as_of = as.Date("2015-01-14"))
+}\if{html}{\out{</div>}}
+
+If we're looking to predict the value on the 15th, forecasting from the 14th (the \code{as_of} date above),
+there are two issues we will need to address:
+\enumerate{
+\item \code{"ca"} is latent by 2 days, whereas \code{"ma"} is latent by 1
+\item if we want to use \code{b} as an exogenous variable, for \code{"ma"} it is latent by 3 days instead of just 1.
+}
+
+Regardless of \code{method}, \code{epi_keys_checked="geo_value"} guarantees tha the
+difference between \code{"ma"} and \code{"ca"} is accounted for by making  the
+latency adjustment at least 2. For some comparison, here's what the various
+methods will do:
+\subsection{\code{locf}}{
+
+Short for "last observation carried forward", \code{locf} assumes that every day
+between the last observation and the forecast day is exactly the same.
+This is a very straightforward assumption, but wrecks any features that
+depend on changes in value over time, such as the growth rate, or even
+adjacent lags. A more robust version of this falls under the heading of
+nowcasting, an eventual aim for this package. On the toy dataset, it
+doesn't matter which day we're trying to predict, since it just fills
+forward to the \code{forecast_date}:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
+  step_adjust_latency(method="locf")
+
+toy_recipe \%>\%
+  prep(toy_df) \%>\%
+  bake(toy_df) \%>\%
+  arrange(geo_value, time_value)
+#> An `epi_df` object, 8 x 4 with metadata:
+#> * geo_type  = state
+#> * time_type = day
+#> * as_of     = 2015-01-14
+#> 
+#> # A tibble: 8 x 4
+#>   geo_value time_value     a     b
+#> * <chr>     <date>     <dbl> <dbl>
+#> 1 ca        2015-01-11   100     5
+#> 2 ca        2015-01-12   103    10
+#> 3 ca        2015-01-13   103    10
+#> 4 ca        2015-01-14   103    10
+#> 5 ma        2015-01-11    20     6
+#> 6 ma        2015-01-12    23     6
+#> 7 ma        2015-01-13    25     6
+#> 8 ma        2015-01-14    25     6
+}\if{html}{\out{</div>}}
+}
+
+\subsection{\code{extend_lags}}{
+
+\code{extend_lags} increases the lags so that they are guaranteed to have
+data. This has the advantage of being applicable on
+a per-column basis; if cases and deaths are reported at different
+latencies, the lags for each are adjusted separately. In the toy example:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
+  step_adjust_latency(method="extend_lags") \%>\%
+  step_epi_lag(a,lag=1) \%>\%
+  step_epi_lag(b,lag=1) \%>\%
+  step_epi_ahead(a, ahead=1)
+
+toy_recipe \%>\%
+  prep(toy_df) \%>\%
+  bake(toy_df) \%>\%
+  arrange(geo_value, time_value)
+#> An `epi_df` object, 21 x 7 with metadata:
+#> * geo_type  = state
+#> * time_type = day
+#> * as_of     = 2015-01-14
+#> 
+#> # A tibble: 21 x 7
+#>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
+#>  * <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>  1 ca        2015-01-10    NA    NA      NA      NA       100
+#>  2 ca        2015-01-11   100     5      NA      NA       103
+#>  3 ca        2015-01-12   103    10      NA      NA        NA
+#>  4 ca        2015-01-13    NA    NA      NA      NA        NA
+#>  5 ca        2015-01-14    NA    NA     100      NA        NA
+#>  6 ca        2015-01-15    NA    NA     103       5        NA
+#>  7 ca        2015-01-16    NA    NA      NA      10        NA
+#>  8 ca        2015-01-17    NA    NA      NA      NA        NA
+#>  9 ca        2015-01-18    NA    NA      NA      NA        NA
+#> 10 ca        2015-01-19    NA    NA      NA      NA        NA
+#> # i 11 more rows
+}\if{html}{\out{</div>}}
+
+The maximum latency in column \code{a} is 2 days, so the lag is increased to 3,
+while the max latency in column \code{b} is 3, so the same lag is increased to
+4; both of these changes are reflected in the column names. Meanwhile the
+ahead is uneffected.
+
+As a side-note, lag/ahead can be somewhat ambiguous about direction. Here,
+the values are brought forward in time, so that for a given row, column
+\code{lag_3_a} represents the value 3 days before.
+}
+
+\subsection{\code{extend_ahead}}{
+
+\code{extend_ahead} increases the ahead, turning a 3 day ahead forecast
+into a 7 day one; this has the advantage of simplicity and is reflective of
+the actual modelling task, but potentially leaves information unused if
+different data sources have different latencies; it must use the latency of
+the most latent data to insure there is data available. In the toy example:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
+  step_adjust_latency(method="extend_ahead") \%>\%
+  step_epi_lag(a,lag=0) \%>\%
+  step_epi_ahead(a, ahead=1)
+
+toy_recipe \%>\%
+  prep(toy_df) \%>\%
+  bake(toy_df) \%>\%
+  arrange(geo_value, time_value)
+#> An `epi_df` object, 10 x 6 with metadata:
+#> * geo_type  = state
+#> * time_type = day
+#> * as_of     = 2015-01-14
+#> 
+#> # A tibble: 10 x 6
+#>    geo_value time_value     a     b lag_0_a ahead_3_a
+#>  * <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>  1 ca        2015-01-08    NA    NA      NA       100
+#>  2 ca        2015-01-09    NA    NA      NA       103
+#>  3 ca        2015-01-11   100     5     100        NA
+#>  4 ca        2015-01-12   103    10     103        NA
+#>  5 ma        2015-01-08    NA    NA      NA        20
+#>  6 ma        2015-01-09    NA    NA      NA        23
+#>  7 ma        2015-01-10    NA    NA      NA        25
+#>  8 ma        2015-01-11    20     6      20        NA
+#>  9 ma        2015-01-12    23    NA      23        NA
+#> 10 ma        2015-01-13    25    NA      25        NA
+}\if{html}{\out{</div>}}
+
+Even though we're doing a 1 day ahead forecast, because our worst latency
+is 3 days from column \code{b}'s \code{"ma"} data, our outcome column is \code{ahead_4_a}
+(so 4 days ahead). If we want to ignore any latency in column \code{b}, we need
+to explicitly set the columns to consider while adjusting like this:
+\code{step_adjust_latency(a, method="extend_ahead")}.
+}
 }
+\section{Programmatic details}{
+\code{step_adjust_latency} uses the metadata, such as \code{time_type} and \code{as_of}, of
+the \code{epi_df} used in the initial prep step, rather than baking or
+prediction. This means reusing the same forecaster on new data is not
+advised, though typically it is not advised in general.
+
+The latency adjustment only applies to columns created after this step, so
+this step should go before both \code{step_epi_ahead} and \code{step_epi_lag}. This will work:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
+   # non-lag steps
+   step_adjust_latency(a, method = "extend_lags") \%>\%
+   step_epi_lag(a, lag=0) # other steps
+}\if{html}{\out{</div>}}
+
+while this will not:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
+   step_epi_lag(a, lag=0) \%>\%
+   step_adjust_latency(a, method = "extend_lags")
+#> Warning: If `method` is "extend_lags" or "locf", then the previous
+#> `step_epi_lag`s won't work with modified data.
+}\if{html}{\out{</div>}}
+
+If you create columns that you then apply lags to (such as
+\code{step_growth_rate()}), these should be created before
+\code{step_adjust_latency}, so any subseqent latency can be addressed.
+}
+
 \examples{
 jhu <- case_death_rate_subset \%>\%
   dplyr::filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 3ba41158a..49a5e7820 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -95,7 +95,9 @@ test_that("`layer_add_forecast_date()` infers correct date when using `adjust_la
   wf_latent <- epi_workflow(r_latent, parsnip::linear_reg()) %>%
     fit(jhu_reasonable_date) %>%
     add_frosting(frost_latent)
-  p_latent <- predict(wf_latent, latest)
+  reasonable_date <- jhu %>%
+    dplyr::filter(time_value >= max(time_value) - 14)
+  p_latent <- predict(wf_latent, reasonable_date)
   expect_equal(
     p_latent$forecast_date,
     rep(as.Date("2022-01-03"), times = 3)
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 75656989a..1e9ac4d15 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -11,7 +11,7 @@ x <- tibble(
   case_rate = sqrt(1:200) + atan(0.1 * 1:200) + sin(5 * 1:200) + 1,
   death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
 ) %>%
-  as_epi_df(as_of = as.POSIXct("2024-05-17"))
+  as_epi_df(as_of = as.POSIXct("2024-09-17"))
 max_time <- max(x$time_value)
 class(attributes(x)$metadata$as_of)
 as_of <- attributes(x)$metadata$as_of
@@ -78,7 +78,16 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit1 <- slm_fit(r1, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
+  expect_error(
+    expect_warning(
+      expect_warning(
+        fit1 <- slm_fit(r1, data = x),
+        class = "epipredict__prep.step_latency__very_large_latency"
+      ),
+      class = "epipredict__prep.step_latency__very_large_latency"
+    ),
+    class = "simpleError"
+  )
 
   # now trying with the as_of a reasonable distance in the future
   fit1 <- slm_fit(r1, data = real_x)
@@ -281,7 +290,9 @@ test_that("epi_adjust_latency fixed_forecast_date works", {
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
-  expect_warning(baked_x <- r4 %>% prep(real_x) %>% bake(real_x), class = "epipredict__prep.step_latency__very_large_latency")
+  baked_x <- r4 %>%
+    prep(real_x) %>%
+    bake(real_x)
   # map each column to its last non-NA value
   last_dates <- baked_x %>%
     tidyr::pivot_longer(cols = contains("rate"), values_drop_na = TRUE) %>%
@@ -341,7 +352,7 @@ test_that("epi_adjust_latency fixed_latency works", {
 
 
 
-test_that("epi_adjust_latency works for other time types", {})
+# test_that("epi_adjust_latency works for other time types", {})
 
 test_that("epi_adjust_latency warns there's steps before it", {
   expect_warning(
@@ -368,35 +379,31 @@ test_that("epi_adjust_latency warns against removing NA's beforehand", {
     regexp = "adjust_latency needs to occur before any `NA` removal"
   )
 })
-# todo check that epi_adjust_latency errors for nonsense `as_of`'s
+# TODO check that epi_adjust_latency errors for nonsense `as_of`'s
 
 
-# todo make sure that `epi_keys_checked` works correctly for extra epi_keys
-test_that("epi_adjust_latency correctly extends the lags", {
+# TODO make sure that `epi_keys_checked` works correctly for extra epi_keys
+
+test_that("epi_adjust_latency correctly extends the lags when there are different delays per-geo", {
   r5 <- epi_recipe(x_lagged) %>%
-    step_adjust_latency(method = "extend_lags", epi_keys_checked = NULL) %>%
+    step_adjust_latency(method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
-  # the as_of on x is today's date, which is >970 days in the future
-  # also, there's no data >970 days in the past, so it gets an error trying to
-  # fit on no data
-  expect_error(expect_warning(fit5 <- slm_fit(r5, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
-
   # now trying with the as_of a reasonable distance in the future
   fit5 <- slm_fit(r5, data = x_lagged)
   expect_equal(
     names(fit5$pre$mold$predictors),
     c(
-      "lag_5_death_rate", "lag_11_death_rate", "lag_16_death_rate",
-      "lag_6_case_rate", "lag_10_case_rate"
+      "lag_6_death_rate", "lag_12_death_rate", "lag_17_death_rate",
+      "lag_7_case_rate", "lag_11_case_rate"
     )
   )
   latest <- get_test_data(r5, x_lagged)
   pred <- predict(fit5, latest)
   point_pred <- pred %>% filter(!is.na(.pred))
   expect_equal(nrow(point_pred), 1)
-  expect_equal(point_pred$time_value, as.Date(testing_as_of))
+  expect_equal(point_pred$time_value, as.Date(testing_as_of) + 1)
 
   expect_equal(
     names(fit5$pre$mold$outcomes),
@@ -405,7 +412,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
   latest <- get_test_data(r5, x)
   pred <- predict(fit5, latest)
   actual_solutions <- pred %>% filter(!is.na(.pred))
-  expect_equal(actual_solutions$time_value, testing_as_of)
+  expect_equal(actual_solutions$time_value, testing_as_of + 1)
 
   # should have four predictors, including the intercept
   expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
@@ -413,8 +420,8 @@ test_that("epi_adjust_latency correctly extends the lags", {
   # result should be equivalent to just immediately doing the adjusted lags by
   # hand
   hand_adjusted <- epi_recipe(x) %>%
-    step_epi_lag(death_rate, lag = c(5, 11, 16)) %>%
-    step_epi_lag(case_rate, lag = c(6, 10)) %>%
+    step_epi_lag(death_rate, lag = c(6, 12, 17)) %>%
+    step_epi_lag(case_rate, lag = c(7, 11)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
   fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
   expect_equal(
@@ -423,6 +430,50 @@ test_that("epi_adjust_latency correctly extends the lags", {
   )
 })
 
+test_that("epi_adjust_latency correctly extends the ahead when there are different delays per-geo", {
+  r5 <- epi_recipe(x_lagged) %>%
+    step_adjust_latency(method = "extend_ahead") %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead)
+
+  fit5 <- slm_fit(r5, data = x_lagged)
+  expect_equal(
+    names(fit5$pre$mold$predictors),
+    c(
+      "lag_0_death_rate", "lag_6_death_rate", "lag_11_death_rate",
+      "lag_1_case_rate", "lag_5_case_rate"
+    )
+  )
+  latest <- get_test_data(r5, x_lagged)
+  pred <- predict(fit5, latest)
+  point_pred <- pred %>% filter(!is.na(.pred))
+  expect_equal(nrow(point_pred), 1)
+  expect_equal(point_pred$time_value, as.Date(max_time))
+  joint_latency <- latency + 1
+  expect_equal(
+    names(fit5$pre$mold$outcomes),
+    glue::glue("ahead_{ahead+joint_latency}_death_rate")
+  )
+  actual_solutions <- pred %>% filter(!is.na(.pred))
+  expect_equal(actual_solutions$time_value, as.Date(max_time))
+
+  # should have four predictors, including the intercept
+  expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
+
+  # result should be equivalent to just immediately doing the adjusted lags by
+  # hand
+  hand_adjusted <- epi_recipe(x) %>%
+    step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
+    step_epi_lag(case_rate, lag = c(1, 5)) %>%
+    step_epi_ahead(death_rate, ahead = ahead + joint_latency)
+
+  fit_hand_adj <- slm_fit(hand_adjusted, data = real_x)
+  expect_equal(
+    fit5$fit$fit$fit$coefficients,
+    fit_hand_adj$fit$fit$fit$coefficients
+  )
+})
 
 test_that("`step_adjust_latency` only uses the columns specified in the `...`", {
   r5 <- epi_recipe(x) %>%
@@ -512,7 +563,9 @@ test_that("locf works as intended", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_warning(fit6 <- slm_fit(r6, data = x), regexp = "The maximum latency is 1033")
+  expect_warning(fit6 <- slm_fit(r6, data = x),
+    class = "epipredict__prep.step_latency__very_large_latency"
+  )
 
   # now trying with the as_of a reasonable distance in the future
   fit6 <- slm_fit(r6, data = real_x)
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index e0f35d7fa..6d71ee6eb 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -43,6 +43,23 @@ x_adjust_ahead <- tibble(
 ) %>%
   as_epi_df(as_of = max(time_range) + 3)
 
+modified_data %>% arrange(geo_value, desc(time_value))
+modified_data %>%
+  group_by(geo_value) %>%
+  filter(!is.na(case_rate)) %>%
+  summarise(max(time_value))
+as_of
+
+toy_df <- tribble(
+  ~geo_value, ~time_value, ~a, ~b,
+  "ma", as.Date("2015-01-11"), 20, 6,
+  "ma", as.Date("2015-01-12"), 23, NA,
+  "ma", as.Date("2015-01-13"), 25, NA,
+  "ca", as.Date("2015-01-11"), 100, 5,
+  "ca", as.Date("2015-01-12"), 103, 10,
+) %>%
+  as_epi_df(as_of = as.Date("2015-01-14"))
+
 test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
   expect_equal(get_latency(modified_data, as_of, "case_rate", -1, "geo_value"), -5)
@@ -50,6 +67,10 @@ test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "case_rate_a", 1, "geo_value"), 5 + 4)
   expect_equal(get_latency(modified_data, as_of, "case_rate_b", 1, "geo_value"), 5 - 3)
   expect_equal(get_latency(modified_data, as_of, "death_rate_a", 1, "geo_value"), 4 - 7)
+  expect_equal(get_latency(toy_df, as.Date("2015-01-14"), "a", 1, "geo_value"), 2)
+  expect_equal(get_latency(toy_df, as.Date("2015-01-14"), "a", -1, "geo_value"), -2)
+  expect_equal(get_latency(toy_df, as.Date("2015-01-14"), "b", 1, "geo_value"), 3)
+  expect_equal(get_latency(toy_df, as.Date("2015-01-14"), "b", -1, "geo_value"), -3)
 })
 
 test_that("get_latency infers max_time to be the minimum `max time` across grouping the specified keys", {

From 71694d717b9e8936f81cc2e76662ae85088f2d04 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 9 Sep 2024 17:23:07 -0500
Subject: [PATCH 078/387] non-timezone dependent printing tests

---
 tests/testthat/_snaps/snapshots.md | 6 +++---
 tests/testthat/test-snapshots.R    | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 6b337c056..236632c15 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1095,7 +1095,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1118,7 +1118,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1142,7 +1142,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31 14:08:25.
+      * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 003fc8319..2cfce9521 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -112,6 +112,7 @@ test_that("arx_forecaster snapshots", {
 test_that("arx_forecaster output format snapshots", {
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value >= as.Date("2021-12-01"))
+  attributes(jhu)$metadata$as_of <- as.Date(attributes(jhu)$metadata$as_of)
   out1 <- arx_forecaster(
     jhu, "death_rate",
     c("case_rate", "death_rate")

From aa9f1e30e90866b51eb7a770a8222cbc0f5458f5 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 9 Sep 2024 18:26:56 -0500
Subject: [PATCH 079/387] arx_forecaster consistency check and tests

---
 R/arx_forecaster.R                   | 19 +++++++------------
 tests/testthat/test-arx_forecaster.R | 26 ++++++++++++++++++++++++++
 2 files changed, 33 insertions(+), 12 deletions(-)
 create mode 100644 tests/testthat/test-arx_forecaster.R

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 42729ed85..89a3f418a 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -120,7 +120,10 @@ arx_fcast_epi_workflow <- function(
   if (is.null(args_list$adjust_latency)) {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+      cli::cli_warn(
+        "The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is actually occurring {forecast_date_default}.",
+        class = "epipredict__arx_forecaster__forecast_date_defaulting"
+      )
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -128,10 +131,11 @@ arx_fcast_epi_workflow <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   if (forecast_date + args_list$ahead != target_date) {
-    cli::cli_warn(c(
+    cli::cli_abort(c(
       "`forecast_date` + `ahead` must equal `target_date`.",
       i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-    ))
+    ),
+    class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date")
   }
 
   lags <- arx_lags_validator(predictors, args_list$lags)
@@ -293,15 +297,6 @@ arx_args_list <- function(
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
-  if (!is.null(forecast_date) && !is.null(target_date)) {
-    if (forecast_date + ahead != target_date) {
-      cli_warn(c(
-        "`forecast_date` + `ahead` must equal `target_date`.",
-        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-      ))
-    }
-  }
-
   max_lags <- max(lags)
   structure(
     enlist(
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
new file mode 100644
index 000000000..bb4dbd085
--- /dev/null
+++ b/tests/testthat/test-arx_forecaster.R
@@ -0,0 +1,26 @@
+train_data <- jhu_csse_daily_subset
+test_that("arx_forecaster warns if forecast date beyond the implicit one", {
+  bad_date <- max(train_data$time_value)+300
+  expect_warning(
+    arx1 <- arx_forecaster(
+    train_data,
+    "death_rate_7d_av",
+    c("death_rate_7d_av", "case_rate_7d_av"),
+    args_list = (arx_args_list(forecast_date = bad_date))
+    ),
+    class = "epipredict__arx_forecaster__forecast_date_defaulting"
+  )
+})
+
+test_that("arx_forecaster errors if forecast date, target date, and ahead are inconsistent", {
+  max_date <- max(train_data$time_value)
+  expect_error(
+    arx1 <- arx_forecaster(
+    train_data,
+    "death_rate_7d_av",
+    c("death_rate_7d_av", "case_rate_7d_av"),
+    args_list = (arx_args_list(ahead = 5, target_date = max_date, forecast_date = max_date))
+    ),
+    class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
+  )
+})

From 899ea5143c99ab1e77274c73495822f08953d4ab Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 10 Sep 2024 16:25:29 -0500
Subject: [PATCH 080/387] arx_forecaster updates

---
 R/arx_forecaster.R                   |  9 +++++++++
 R/step_adjust_latency.R              | 10 ++--------
 tests/testthat/test-arx_args_list.R  |  7 ++++---
 tests/testthat/test-arx_forecaster.R |  2 +-
 4 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 89a3f418a..fed506a7e 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -297,6 +297,15 @@ arx_args_list <- function(
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
+  if (!is.null(forecast_date) && !is.null(target_date)) {
+    if (forecast_date + ahead != target_date) {
+      cli_abort(c(
+        "`forecast_date` + `ahead` must equal `target_date`.",
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."),
+        class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
+    }
+  }
+
   max_lags <- max(lags)
   structure(
     enlist(
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index e4968920e..9c1d015a2 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -212,7 +212,8 @@ step_adjust_latency <-
            id = recipes::rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.", class = "epipredict__step_adjust_latency__epi_recipe_only")
+      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
+                     class = "epipredict__step_adjust_latency__epi_recipe_only")
     }
     if (!is.null(columns)) {
       cli::cli_abort(c("The `columns` argument must be `NULL`.",
@@ -251,14 +252,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
         cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
       }
     }
-
     method <- rlang::arg_match(method)
-    terms_used <- recipes_eval_select(enquos(...), recipe$template, recipe$term_info)
-    if (is_empty(terms_used)) {
-      terms_used <- recipe$term_info %>%
-        filter(role == "raw") %>%
-        pull(variable)
-    }
     if (method == "extend_ahead") {
       rel_step_type <- "step_epi_ahead"
       shift_name <- "ahead"
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 03cbc0025..4ae8ec3f5 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -26,11 +26,12 @@ test_that("arx_args checks inputs", {
   expect_snapshot(error = TRUE, arx_args_list(n_training_min = "de"))
   expect_snapshot(error = TRUE, arx_args_list(epi_keys = 1))
 
-  expect_warning(arx_args_list(
+  expect_error(arx_args_list(
     forecast_date = as.Date("2022-01-01"),
-    target_date = as.Date("2022-01-03"),
+    target_date = as.Date("2022-01-04"),
     ahead = 1L
-  ))
+  ),
+  class =  "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
 })
 
 test_that("arx forecaster disambiguates quantiles", {
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index bb4dbd085..d5603aebe 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -21,6 +21,6 @@ test_that("arx_forecaster errors if forecast date, target date, and ahead are in
     c("death_rate_7d_av", "case_rate_7d_av"),
     args_list = (arx_args_list(ahead = 5, target_date = max_date, forecast_date = max_date))
     ),
-    class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
+    class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
   )
 })

From af45eaf2c9c8dd5aca4454f8abad5c0f0dd3e06b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 10 Sep 2024 16:41:07 -0500
Subject: [PATCH 081/387] arx_classifier addition

---
 R/arx_classifier.R                 | 26 +++++++++++++++-----------
 man/check_interminable_latency.Rd  | 25 +++++++++++++++++++++++++
 man/count_single_column.Rd         | 17 +++++++++++++++++
 tests/testthat/_snaps/snapshots.md | 27 +++++++++++++++++++++++++++
 tests/testthat/test-snapshots.R    |  9 +++++++++
 5 files changed, 93 insertions(+), 11 deletions(-)
 create mode 100644 man/check_interminable_latency.Rd
 create mode 100644 man/count_single_column.Rd

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 5ee6de88a..45d432bb7 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -158,13 +158,13 @@ arx_class_epi_workflow <- function(
       additional_gr_args_list = args_list$additional_gr_args
     )
   for (l in seq_along(lags)) {
-    p <- predictors[l]
-    p <- as.character(glue::glue_data(args_list, "gr_{horizon}_{method}_{p}"))
-    r <- step_epi_lag(r, !!p, lag = lags[[l]])
+    pred_names <- predictors[l]
+    pred_names <- as.character(glue::glue_data(args_list, "gr_{horizon}_{method}_{pred_names}"))
+    r <- step_epi_lag(r, !!pred_names, lag = lags[[l]])
   }
   # ------- outcome
   if (args_list$outcome_transform == "lag_difference") {
-    o <- as.character(
+    pre_out_name <- as.character(
       glue::glue_data(args_list, "lag_diff_{horizon}_{outcome}")
     )
     r <- r %>%
@@ -175,7 +175,7 @@ arx_class_epi_workflow <- function(
       )
   }
   if (args_list$outcome_transform == "growth_rate") {
-    o <- as.character(
+    pre_out_name <- as.character(
       glue::glue_data(args_list, "gr_{horizon}_{method}_{outcome}")
     )
     if (!(outcome %in% predictors)) {
@@ -190,20 +190,24 @@ arx_class_epi_workflow <- function(
         )
     }
   }
-  o2 <- rlang::sym(paste0("ahead_", args_list$ahead, "_", o))
+  ahead_out_name <- rlang::sym(paste0("ahead_", args_list$ahead, "_", pre_out_name))
   method_adjust_latency <- args_list$adjust_latency
   if (!is.null(method_adjust_latency)) {
     # only extend_ahead is supported atm
-    r <- r %>% step_adjust_latency(all_outcomes(),
+    r <- r %>% step_adjust_latency(!!pre_out_name,
       fixed_forecast_date = forecast_date,
       method = method_adjust_latency
     )
   }
   r <- r %>%
-    step_epi_ahead(!!o, ahead = args_list$ahead, role = "pre-outcome")
+    step_epi_ahead(!!pre_out_name, ahead = args_list$ahead, role = "pre-outcome")
   r <- r %>%
     step_mutate(
-      outcome_class = cut(!!o2, breaks = args_list$breaks),
+      across(
+        starts_with("ahead"),
+        ~cut(.x, breaks = args_list$breaks),
+        .names = "outcome_class",
+        .unpack = TRUE),
       role = "outcome"
     ) %>%
     step_epi_naomit() %>%
@@ -322,8 +326,8 @@ arx_class_args_list <- function(
     if (forecast_date + ahead != target_date) {
       cli::cli_warn(c(
         "`forecast_date` + `ahead` must equal `target_date`.",
-        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-      ))
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."),
+      class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
     }
   }
 
diff --git a/man/check_interminable_latency.Rd b/man/check_interminable_latency.Rd
new file mode 100644
index 000000000..8ae9ec6df
--- /dev/null
+++ b/man/check_interminable_latency.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{check_interminable_latency}
+\alias{check_interminable_latency}
+\title{warn when the latency is larger than would be reasonable}
+\usage{
+check_interminable_latency(
+  dataset,
+  latency_table,
+  target_columns,
+  forecast_date,
+  call = caller_env()
+)
+}
+\arguments{
+\item{dataset}{the epi_df}
+
+\item{latency_table}{the whole collection of latencies}
+
+\item{target_columns}{the names of the columns that we're adjusting, and whether its unreasonably latent}
+}
+\description{
+warn when the latency is larger than would be reasonable
+}
+\keyword{internal}
diff --git a/man/count_single_column.Rd b/man/count_single_column.Rd
new file mode 100644
index 000000000..090f064e4
--- /dev/null
+++ b/man/count_single_column.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{count_single_column}
+\alias{count_single_column}
+\title{get the location of the last real value}
+\usage{
+count_single_column(col, from_last)
+}
+\arguments{
+\item{col}{the relevant column}
+
+\item{from_last}{instead of the number of columns}
+}
+\description{
+get the location of the last real value
+}
+\keyword{internal}
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 236632c15..9ce6502d1 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1181,3 +1181,30 @@
         18999, 18999, 18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -53L), class = c("tbl_df", "tbl", "data.frame"))
 
+---
+
+    structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
+    "ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in", 
+    "ks", "ky", "la", "ma", "me", "mi", "mn", "mo", "mp", "ms", "mt", 
+    "nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", 
+    "pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", 
+    "wi", "wv", "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
+    1L), levels = c("(-Inf,0.25]", "(0.25, Inf]"), class = "factor"), 
+        forecast_date = structure(c(18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+        18994, 18994, 18994), class = "Date"), target_date = structure(c(19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+        19001, 19001, 19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
+    -53L), class = c("tbl_df", "tbl", "data.frame"))
+
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 2cfce9521..d8b4a7734 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -153,4 +153,13 @@ test_that("arx_classifier snapshots", {
     c("case_rate", "death_rate")
   )
   expect_snapshot_tibble(arc1$predictions)
+  max_date <-case_death_rate_subset$time_value %>% max
+  arc2 <- arx_classifier(
+    case_death_rate_subset %>%
+      dplyr::filter(time_value >= as.Date("2021-11-01")),
+    "death_rate",
+    c("case_rate", "death_rate"),
+    args_list = arx_class_args_list(adjust_latency = "extend_ahead", forecast_date = max_date+2)
+  )
+  expect_snapshot_tibble(arc2$predictions)
 })

From 1155d30c4387547aeb5b2385671fb41afc1c5378 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 10 Sep 2024 17:18:27 -0500
Subject: [PATCH 082/387] formatting

---
 R/arx_classifier.R                   | 16 ++++++++++------
 R/arx_forecaster.R                   | 23 ++++++++++++++---------
 R/step_adjust_latency.R              |  3 ++-
 tests/testthat/test-arx_args_list.R  | 14 ++++++++------
 tests/testthat/test-arx_forecaster.R | 18 +++++++++---------
 tests/testthat/test-snapshots.R      |  4 ++--
 6 files changed, 45 insertions(+), 33 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 45d432bb7..32a58cd04 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -205,9 +205,10 @@ arx_class_epi_workflow <- function(
     step_mutate(
       across(
         starts_with("ahead"),
-        ~cut(.x, breaks = args_list$breaks),
+        ~ cut(.x, breaks = args_list$breaks),
         .names = "outcome_class",
-        .unpack = TRUE),
+        .unpack = TRUE
+      ),
       role = "outcome"
     ) %>%
     step_epi_naomit() %>%
@@ -324,10 +325,13 @@ arx_class_args_list <- function(
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
-      cli::cli_warn(c(
-        "`forecast_date` + `ahead` must equal `target_date`.",
-        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."),
-      class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
+      cli::cli_warn(
+        c(
+          "`forecast_date` + `ahead` must equal `target_date`.",
+          i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+        ),
+        class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
+      )
     }
   }
 
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index fed506a7e..fdc1e88a1 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -131,11 +131,13 @@ arx_fcast_epi_workflow <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   if (forecast_date + args_list$ahead != target_date) {
-    cli::cli_abort(c(
-      "`forecast_date` + `ahead` must equal `target_date`.",
-      i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-    ),
-    class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date")
+    cli::cli_abort(
+      c(
+        "`forecast_date` + `ahead` must equal `target_date`.",
+        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+      ),
+      class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
+    )
   }
 
   lags <- arx_lags_validator(predictors, args_list$lags)
@@ -299,10 +301,13 @@ arx_args_list <- function(
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
-      cli_abort(c(
-        "`forecast_date` + `ahead` must equal `target_date`.",
-        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."),
-        class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
+      cli_abort(
+        c(
+          "`forecast_date` + `ahead` must equal `target_date`.",
+          i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+        ),
+        class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
+      )
     }
   }
 
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 9c1d015a2..4caf17d8e 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -213,7 +213,8 @@ step_adjust_latency <-
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
       cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
-                     class = "epipredict__step_adjust_latency__epi_recipe_only")
+        class = "epipredict__step_adjust_latency__epi_recipe_only"
+      )
     }
     if (!is.null(columns)) {
       cli::cli_abort(c("The `columns` argument must be `NULL`.",
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 4ae8ec3f5..9bbff0135 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -26,12 +26,14 @@ test_that("arx_args checks inputs", {
   expect_snapshot(error = TRUE, arx_args_list(n_training_min = "de"))
   expect_snapshot(error = TRUE, arx_args_list(epi_keys = 1))
 
-  expect_error(arx_args_list(
-    forecast_date = as.Date("2022-01-01"),
-    target_date = as.Date("2022-01-04"),
-    ahead = 1L
-  ),
-  class =  "epipredict__arx_args__inconsistent_target_ahead_forecaste_date")
+  expect_error(
+    arx_args_list(
+      forecast_date = as.Date("2022-01-01"),
+      target_date = as.Date("2022-01-04"),
+      ahead = 1L
+    ),
+    class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
+  )
 })
 
 test_that("arx forecaster disambiguates quantiles", {
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index d5603aebe..4145a064d 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,12 +1,12 @@
 train_data <- jhu_csse_daily_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
-  bad_date <- max(train_data$time_value)+300
+  bad_date <- max(train_data$time_value) + 300
   expect_warning(
     arx1 <- arx_forecaster(
-    train_data,
-    "death_rate_7d_av",
-    c("death_rate_7d_av", "case_rate_7d_av"),
-    args_list = (arx_args_list(forecast_date = bad_date))
+      train_data,
+      "death_rate_7d_av",
+      c("death_rate_7d_av", "case_rate_7d_av"),
+      args_list = (arx_args_list(forecast_date = bad_date))
     ),
     class = "epipredict__arx_forecaster__forecast_date_defaulting"
   )
@@ -16,10 +16,10 @@ test_that("arx_forecaster errors if forecast date, target date, and ahead are in
   max_date <- max(train_data$time_value)
   expect_error(
     arx1 <- arx_forecaster(
-    train_data,
-    "death_rate_7d_av",
-    c("death_rate_7d_av", "case_rate_7d_av"),
-    args_list = (arx_args_list(ahead = 5, target_date = max_date, forecast_date = max_date))
+      train_data,
+      "death_rate_7d_av",
+      c("death_rate_7d_av", "case_rate_7d_av"),
+      args_list = (arx_args_list(ahead = 5, target_date = max_date, forecast_date = max_date))
     ),
     class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
   )
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index d8b4a7734..fc3ee890b 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -153,13 +153,13 @@ test_that("arx_classifier snapshots", {
     c("case_rate", "death_rate")
   )
   expect_snapshot_tibble(arc1$predictions)
-  max_date <-case_death_rate_subset$time_value %>% max
+  max_date <- case_death_rate_subset$time_value %>% max()
   arc2 <- arx_classifier(
     case_death_rate_subset %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate"),
-    args_list = arx_class_args_list(adjust_latency = "extend_ahead", forecast_date = max_date+2)
+    args_list = arx_class_args_list(adjust_latency = "extend_ahead", forecast_date = max_date + 2)
   )
   expect_snapshot_tibble(arc2$predictions)
 })

From 343551f0e5294b7f0141d7fdd42845def2335937 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 11 Sep 2024 12:32:56 -0500
Subject: [PATCH 083/387] various minor fixes caught in pre-review

---
 R/arx_forecaster.R                            | 11 ++++++++++-
 R/epi_recipe.R                                |  3 ++-
 R/epi_workflow.R                              |  8 +++++---
 R/flatline_forecaster.R                       |  6 +++---
 R/frosting.R                                  |  3 ++-
 R/get_test_data.R                             |  5 +++--
 R/step_epi_shift.R                            |  4 ++++
 R/step_growth_rate.R                          |  3 ++-
 R/step_lag_difference.R                       |  3 ++-
 man/forecast.epi_workflow.Rd                  |  4 +---
 man/step_adjust_latency.Rd                    |  4 ++--
 tests/testthat/test-layer_add_forecast_date.R |  7 -------
 tests/testthat/test-population_scaling.R      |  1 -
 tests/testthat/test-utils-shift.R             |  0
 14 files changed, 36 insertions(+), 26 deletions(-)
 delete mode 100644 tests/testthat/test-utils-shift.R

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index fdc1e88a1..291413b5b 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -51,7 +51,16 @@ arx_forecaster <- function(
   wf <- arx_fcast_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
-  preds <- forecast(wf) %>%
+  # get the forecast date for the forecast function
+  if (is.null(args_list$adjust_latency)) {
+    forecast_date_default <- max(epi_data$time_value)
+  } else {
+    forecast_date_default <- attributes(epi_data)$metadata$as_of
+  }
+  forecast_date <- args_list$forecast_date %||% forecast_date_default
+
+
+  preds <- forecast(wf, forecast_date = forecast_date) %>%
     tibble::as_tibble() %>%
     dplyr::select(-time_value)
 
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index edb7f352b..684642075 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -431,7 +431,7 @@ prep.epi_recipe <- function(
     x, training = NULL, fresh = FALSE, verbose = FALSE,
     retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ...) {
   if (is.null(training)) {
-    cli::cli_warn(paste(
+    cli::cli_warn(c(
       "!" = "No training data was supplied to {.fn prep}.",
       "!" = "Unlike a {.cls recipe}, an {.cls epi_recipe} does not ",
       "!" = "store the full template data in the object.",
@@ -583,6 +583,7 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
   new_data
 }
 
+
 kill_levels <- function(x, keys) {
   for (i in which(names(x) %in% keys)) x[[i]] <- list(values = NA, ordered = NA)
   x
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 19a30d73a..619fb6d55 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -43,6 +43,7 @@ epi_workflow <- function(preprocessor = NULL, spec = NULL, postprocessor = NULL)
   if (!is_null(postprocessor)) {
     out <- add_postprocessor(out, postprocessor)
   }
+
   out
 }
 
@@ -154,9 +155,10 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' preds <- predict(wf, latest)
 #' preds
+#' @importFrom cli cli_abort
 predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), ...) {
   if (!workflows::is_trained_workflow(object)) {
-    cli::cli_abort(c(
+    cli_abort(c(
       "Can't predict on an untrained epi_workflow.",
       i = "Do you need to call `fit()`?"
     ))
@@ -232,7 +234,6 @@ print.epi_workflow <- function(x, ...) {
 #'
 #' @param object An epi workflow.
 #' @param ... Not used.
-#' @param fill_locf Logical. Should we use locf to fill in missing data?
 #' @param n_recent Integer or NULL. If filling missing data with locf = TRUE,
 #' how far back are we willing to tolerate missing data? Larger values allow
 #' more filling. The default NULL will determine this from the the recipe. For
@@ -246,7 +247,7 @@ print.epi_workflow <- function(x, ...) {
 #' @return A forecast tibble.
 #'
 #' @export
-forecast.epi_workflow <- function(object, ..., fill_locf = FALSE, n_recent = NULL, forecast_date = NULL) {
+forecast.epi_workflow <- function(object, ..., n_recent = NULL, forecast_date = NULL) {
   rlang::check_dots_empty()
 
   if (!object$trained) {
@@ -266,6 +267,7 @@ forecast.epi_workflow <- function(object, ..., fill_locf = FALSE, n_recent = NUL
       ))
     }
   }
+
   test_data <- get_test_data(
     hardhat::extract_preprocessor(object),
     object$original_data
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index f2d6fa998..fe072a42b 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -66,8 +66,7 @@ flatline_forecaster <- function(
   wf <- epi_workflow(r, eng, f)
   wf <- fit(wf, epi_data)
   preds <- suppressWarnings(forecast(
-    wf,
-    fill_locf = TRUE
+    wf
   )) %>%
     as_tibble() %>%
     select(-time_value)
@@ -106,6 +105,7 @@ flatline_forecaster <- function(
 #' flatline_args_list()
 #' flatline_args_list(symmetrize = FALSE)
 #' flatline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
+#' @importFrom cli cli_abort cli_warn
 flatline_args_list <- function(
     ahead = 7L,
     n_training = Inf,
@@ -129,7 +129,7 @@ flatline_args_list <- function(
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
-      cli::cli_warn(c(
+      cli_warn(c(
         "`forecast_date` + `ahead` must equal `target_date`.",
         i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
       ))
diff --git a/R/frosting.R b/R/frosting.R
index 13d2293a6..6bc103465 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -356,6 +356,7 @@ apply_frosting.default <- function(workflow, components, ...) {
 
 #' @rdname apply_frosting
 #' @importFrom rlang is_null
+#' @importFrom cli cli_abort cli_warn
 #' @param type,opts forwarded (along with `...`) to [`predict.model_fit()`] and
 #'   [`slather()`] for supported layers
 #' @export
@@ -374,7 +375,7 @@ apply_frosting.epi_workflow <-
     }
 
     if (!has_postprocessor_frosting(workflow)) {
-      cli::cli_warn(paste(
+      cli_warn(paste(
         "Only postprocessors of class {.cls frosting} are allowed.",
         "Returning unpostprocessed predictions."
       ))
diff --git a/R/get_test_data.R b/R/get_test_data.R
index 5e74da5a1..f070e97bb 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -26,9 +26,10 @@
 #' get_test_data(recipe = rec, x = case_death_rate_subset)
 #' @importFrom rlang %@%
 #' @importFrom stats na.omit
+#' @importFrom cli cli_abort cli_warn
 #' @export
 get_test_data <- function(recipe, x) {
-  if (!is_epi_df(x)) cli::cli_abort("`x` must be an `epi_df`.")
+  if (!is_epi_df(x)) cli_abort("`x` must be an `epi_df`.")
 
   check <- hardhat::check_column_names(x, colnames(recipe$template))
   if (!check$ok) {
@@ -47,7 +48,7 @@ get_test_data <- function(recipe, x) {
   # Probably needs a fix based on the time_type of the epi_df
   avail_recent <- diff(range(x$time_value))
   if (avail_recent < keep) {
-    cli::cli_abort(c(
+    cli_abort(c(
       "You supplied insufficient recent data for this recipe. ",
       "!" = "You need at least {min_required} days of data,",
       "!" = "but `x` contains only {avail_recent}."
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index c1b0f51bc..f09e8ae29 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -67,6 +67,7 @@ step_epi_lag <-
     }
     arg_is_nonneg_int(lag)
     arg_is_chr_scalar(prefix, id)
+
     recipes::add_step(
       recipe,
       step_epi_lag_new(
@@ -110,6 +111,7 @@ step_epi_ahead <-
     }
     arg_is_nonneg_int(ahead)
     arg_is_chr_scalar(prefix, id)
+
     recipes::add_step(
       recipe,
       step_epi_ahead_new(
@@ -204,6 +206,7 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
 bake.step_epi_lag <- function(object, new_data, ...) {
   add_shifted_columns(new_data, object, object$lag)
 }
+
 #' @export
 bake.step_epi_ahead <- function(object, new_data, ...) {
   add_shifted_columns(new_data, object, object$ahead)
@@ -218,6 +221,7 @@ print.step_epi_lag <- function(x, width = max(20, options()$width - 30), ...) {
   invisible(x)
 }
 
+
 #' @export
 print.step_epi_ahead <- function(x, width = max(20, options()$width - 30), ...) {
   print_epi_step(x$columns, x$terms, x$trained, "Leading",
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 70d0ac2ab..d9d52684d 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -30,6 +30,7 @@
 #'
 #' @family row operation steps
 #' @importFrom epiprocess growth_rate
+#' @importFrom cli cli_abort
 #' @export
 #' @examples
 #' r <- epi_recipe(case_death_rate_subset) %>%
@@ -58,7 +59,7 @@ step_growth_rate <-
     arg_is_pos_int(horizon)
     arg_is_scalar(horizon)
     if (!is.null(replace_Inf)) {
-      if (length(replace_Inf) != 1L) cli::cli_abort("`replace_Inf` must be a scalar.")
+      if (length(replace_Inf) != 1L) cli_abort("`replace_Inf` must be a scalar.")
       if (!is.na(replace_Inf)) arg_is_numeric(replace_Inf)
     }
     arg_is_chr(role)
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index e8c7c36f7..bee5f5c40 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -13,6 +13,7 @@
 #'
 #'
 #' @family row operation steps
+#' @importFrom cli cli_abort cli_warn
 #' @export
 #' @examples
 #' r <- epi_recipe(case_death_rate_subset) %>%
@@ -32,7 +33,7 @@ step_lag_difference <-
            skip = FALSE,
            id = rand_id("lag_diff")) {
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
+      cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
     arg_is_pos_int(horizon)
     arg_is_chr(role)
diff --git a/man/forecast.epi_workflow.Rd b/man/forecast.epi_workflow.Rd
index b9f6870b8..22f8cf4bb 100644
--- a/man/forecast.epi_workflow.Rd
+++ b/man/forecast.epi_workflow.Rd
@@ -4,15 +4,13 @@
 \alias{forecast.epi_workflow}
 \title{Produce a forecast from an epi workflow}
 \usage{
-\method{forecast}{epi_workflow}(object, ..., fill_locf = FALSE, n_recent = NULL, forecast_date = NULL)
+\method{forecast}{epi_workflow}(object, ..., n_recent = NULL, forecast_date = NULL)
 }
 \arguments{
 \item{object}{An epi workflow.}
 
 \item{...}{Not used.}
 
-\item{fill_locf}{Logical. Should we use locf to fill in missing data?}
-
 \item{n_recent}{Integer or NULL. If filling missing data with locf = TRUE,
 how far back are we willing to tolerate missing data? Larger values allow
 more filling. The default NULL will determine this from the the recipe. For
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 2b5f282d9..9b10d1822 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -276,8 +276,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous
-#> `step_epi_lag`s won't work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with modified
+#> data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 49a5e7820..491bf5e20 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -64,13 +64,6 @@ test_that("Do not specify a forecast_date in `layer_add_forecast_date()`", {
     layer_naomit(.pred)
   wf3 <- wf %>% add_frosting(f3)
 
-  # this warning has been removed
-  # expect_warning(
-  #   p3 <- predict(wf3, latest),
-  #   "forecast_date is less than the most recent update date of the data."
-  # )
-  p3 <- predict(wf3, latest)
-  p3
   expect_silent(p3 <- predict(wf3, latest))
   expect_equal(ncol(p3), 4L)
   expect_s3_class(p3, "epi_df")
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index fbae404ad..d18be65f5 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -193,7 +193,6 @@ test_that("Postprocessing to get cases from case rate", {
 
 
 test_that("test joining by default columns", {
-  skip()
   jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
diff --git a/tests/testthat/test-utils-shift.R b/tests/testthat/test-utils-shift.R
deleted file mode 100644
index e69de29bb..000000000

From 60827a36d638df3ddf994fa0bd74db8524ca16ab Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 11 Sep 2024 13:46:26 -0500
Subject: [PATCH 084/387] spurious join_by tests removed

---
 tests/testthat/_snaps/population_scaling.md | 32 ---------------------
 tests/testthat/test-population_scaling.R    | 10 +++----
 2 files changed, 5 insertions(+), 37 deletions(-)
 delete mode 100644 tests/testthat/_snaps/population_scaling.md

diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
deleted file mode 100644
index 6d75fcc22..000000000
--- a/tests/testthat/_snaps/population_scaling.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# test joining by default columns
-
-    Code
-      prep <- prep(r, jhu)
-    Message
-      Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
-
----
-
-    Code
-      b <- bake(prep, jhu)
-    Message
-      Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
-
----
-
-    Code
-      wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
-    Message
-      Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
-
----
-
-    Code
-      p <- predict(wf, latest)
-    Message
-      Joining with `by = join_by(geo_value)`
-      Joining with `by = join_by(geo_value)`
-
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index d18be65f5..646afdf76 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -214,9 +214,9 @@ test_that("test joining by default columns", {
     recipes::step_naomit(recipes::all_predictors()) %>%
     recipes::step_naomit(recipes::all_outcomes(), skip = TRUE)
 
-  expect_snapshot(prep <- prep(r, jhu))
+  prep <- prep(r, jhu)
 
-  expect_snapshot(b <- bake(prep, jhu))
+  b <- bake(prep, jhu)
 
   f <- frosting() %>%
     layer_predict() %>%
@@ -228,12 +228,12 @@ test_that("test joining by default columns", {
       df_pop_col = "values"
     )
 
-  expect_snapshot(wf <- epi_workflow(
+  wf <- epi_workflow(
     r,
     parsnip::linear_reg()
   ) %>%
     fit(jhu) %>%
-    add_frosting(f))
+    add_frosting(f)
 
   latest <- get_test_data(
     recipe = r,
@@ -246,7 +246,7 @@ test_that("test joining by default columns", {
   )
 
 
-  expect_snapshot(p <- predict(wf, latest))
+  p <- predict(wf, latest)
 
 
 

From 60b3fad8ff06714121299fff379751cbfdb36bfd Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 11 Sep 2024 16:07:47 -0500
Subject: [PATCH 085/387] vignettes: no more fill_locf, some missing data

---
 vignettes/articles/sliding.Rmd   | 4 ++--
 vignettes/articles/smooth-qr.Rmd | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/articles/sliding.Rmd b/vignettes/articles/sliding.Rmd
index 1556c4a72..908caf307 100644
--- a/vignettes/articles/sliding.Rmd
+++ b/vignettes/articles/sliding.Rmd
@@ -328,7 +328,7 @@ confirmed_incidence_prop <- pub_covidcast(
   geo_type = "state",
   time_values = epirange(20200301, 20211231),
   geo_values = states,
-  issues = epirange(20000101, 20211231)
+  issues = epirange(20000101, 20221231)
 ) %>%
   select(geo_value, time_value, version = issue, case_rate = value) %>%
   arrange(geo_value, time_value) %>%
@@ -341,7 +341,7 @@ deaths_incidence_prop <- pub_covidcast(
   geo_type = "state",
   time_values = epirange(20200301, 20211231),
   geo_values = states,
-  issues = epirange(20000101, 20211231)
+  issues = epirange(20000101, 20221231)
 ) %>%
   select(geo_value, time_value, version = issue, death_rate = value) %>%
   arrange(geo_value, time_value) %>%
diff --git a/vignettes/articles/smooth-qr.Rmd b/vignettes/articles/smooth-qr.Rmd
index 3d626b2e1..b93c726f6 100644
--- a/vignettes/articles/smooth-qr.Rmd
+++ b/vignettes/articles/smooth-qr.Rmd
@@ -195,7 +195,7 @@ smooth_fc <- function(x, aheads = 1:28, degree = 3L, quantiles = 0.5, fd) {
 
   the_fit <- ewf %>% fit(x)
 
-  latest <- get_test_data(rec, x, fill_locf = TRUE)
+  latest <- get_test_data(rec, x)
 
   preds <- predict(the_fit, new_data = latest) %>%
     mutate(forecast_date = fd, target_date = fd + ahead) %>%

From 28c5863fc88e6c3c6344160db0e874ec44a53072 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 12 Sep 2024 15:45:06 -0500
Subject: [PATCH 086/387] drop :: for cli, many dplyr commands

---
 R/arx_classifier.R               |  8 ++---
 R/arx_forecaster.R               |  6 ++--
 R/autoplot.R                     |  2 +-
 R/compat-purrr.R                 |  4 +--
 R/compat-recipes.R               |  2 +-
 R/create-layer.R                 |  2 +-
 R/dist_quantiles.R               | 12 ++++----
 R/epi_recipe.R                   | 52 +++++++++++++++-----------------
 R/epi_shift.R                    |  7 ++---
 R/epi_workflow.R                 |  7 ++---
 R/flatline_forecaster.R          |  1 -
 R/flusight_hub_formatter.R       |  4 +--
 R/frosting.R                     |  9 +++---
 R/get_test_data.R                |  7 ++---
 R/layer_cdc_flatline_quantiles.R |  8 ++---
 R/layer_residual_quantiles.R     | 14 ++++-----
 R/layers.R                       |  4 +--
 R/make_quantile_reg.R            |  2 +-
 R/pivot_quantiles.R              |  6 ++--
 R/step_adjust_latency.R          | 16 +++++-----
 R/step_growth_rate.R             |  1 -
 R/step_lag_difference.R          |  1 -
 R/step_population_scaling.R      |  2 +-
 R/tidy.R                         |  2 +-
 R/utils-enframer.R               |  2 +-
 R/utils-latency.R                | 10 +++---
 R/utils-misc.R                   |  4 +--
 R/utils-shift.R                  | 12 ++++----
 28 files changed, 98 insertions(+), 109 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 32a58cd04..bbb3433c1 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -58,7 +58,7 @@ arx_classifier <- function(
   if (is.null(args_list$adjust_latency)) {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+      cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -136,7 +136,7 @@ arx_class_epi_workflow <- function(
   if (is.null(args_list$adjust_latency)) {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli::cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+      cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -150,7 +150,7 @@ arx_class_epi_workflow <- function(
   # ------- predictors
   r <- epi_recipe(epi_data) %>%
     step_growth_rate(
-      dplyr::all_of(predictors),
+      all_of(predictors),
       role = "grp",
       horizon = args_list$horizon,
       method = args_list$method,
@@ -325,7 +325,7 @@ arx_class_args_list <- function(
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
-      cli::cli_warn(
+      cli_warn(
         c(
           "`forecast_date` + `ahead` must equal `target_date`.",
           i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 291413b5b..a3363e689 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -62,7 +62,7 @@ arx_forecaster <- function(
 
   preds <- forecast(wf, forecast_date = forecast_date) %>%
     tibble::as_tibble() %>%
-    dplyr::select(-time_value)
+    select(-time_value)
 
   structure(
     list(
@@ -129,7 +129,7 @@ arx_fcast_epi_workflow <- function(
   if (is.null(args_list$adjust_latency)) {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli::cli_warn(
+      cli_warn(
         "The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is actually occurring {forecast_date_default}.",
         class = "epipredict__arx_forecaster__forecast_date_defaulting"
       )
@@ -140,7 +140,7 @@ arx_fcast_epi_workflow <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   if (forecast_date + args_list$ahead != target_date) {
-    cli::cli_abort(
+    cli_abort(
       c(
         "`forecast_date` + `ahead` must equal `target_date`.",
         i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
diff --git a/R/autoplot.R b/R/autoplot.R
index 648c74e33..8bded03a3 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -217,7 +217,7 @@ autoplot.canned_epipred <- function(
 
   ewf <- object$epi_workflow
   predictions <- object$predictions %>%
-    dplyr::rename(time_value = target_date)
+    rename(time_value = target_date)
 
   autoplot(ewf, predictions,
     .color_by = .color_by, .facet_by = .facet_by,
diff --git a/R/compat-purrr.R b/R/compat-purrr.R
index e06038e44..6ec7df02f 100644
--- a/R/compat-purrr.R
+++ b/R/compat-purrr.R
@@ -11,11 +11,11 @@ map_vec <- function(.x, .f, ...) {
 map_dfr <- function(.x, .f, ..., .id = NULL) {
   .f <- rlang::as_function(.f, env = rlang::global_env())
   res <- map(.x, .f, ...)
-  dplyr::bind_rows(res, .id = .id)
+  bind_rows(res, .id = .id)
 }
 
 map2_dfr <- function(.x, .y, .f, ..., .id = NULL) {
   .f <- rlang::as_function(.f, env = rlang::global_env())
   res <- map2(.x, .y, .f, ...)
-  dplyr::bind_rows(res, .id = .id)
+  bind_rows(res, .id = .id)
 }
diff --git a/R/compat-recipes.R b/R/compat-recipes.R
index 0e4a557cb..f90367497 100644
--- a/R/compat-recipes.R
+++ b/R/compat-recipes.R
@@ -18,7 +18,7 @@ inline_check <- function(x) {
   funs <- fun_calls(x)
   funs <- funs[!(funs %in% c("~", "+", "-"))]
   if (length(funs) > 0) {
-    cli::cli_abort(paste0(
+    cli_abort(paste0(
       "No in-line functions should be used here; ",
       "use steps to define baking actions."
     ))
diff --git a/R/create-layer.R b/R/create-layer.R
index 0268a906f..7179cb7e3 100644
--- a/R/create-layer.R
+++ b/R/create-layer.R
@@ -22,7 +22,7 @@ create_layer <- function(name = NULL, open = rlang::is_interactive()) {
   if (substr(name, 1, 5) == "layer") {
     nn <- substring(name, 6)
     if (substr(nn, 1, 1) == "_") nn <- substring(nn, 2)
-    cli::cli_abort(
+    cli_abort(
       c('`name` should not begin with "layer" or "layer_".',
         i = 'Did you mean to use `create_layer("{ nn }")`?'
       )
diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index dd97ec809..8930bdeaa 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -23,7 +23,7 @@ new_quantiles <- function(values = double(1), quantile_levels = double(1)) {
     quantile_levels <- quantile_levels[o]
   }
   if (is.unsorted(values, na.rm = TRUE)) {
-    cli::cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
+    cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
   }
 
   new_rcrd(list(values = values, quantile_levels = quantile_levels),
@@ -102,14 +102,14 @@ validate_dist_quantiles <- function(values, quantile_levels) {
   )
   length_diff <- vctrs::list_sizes(values) != vctrs::list_sizes(quantile_levels)
   if (any(length_diff)) {
-    cli::cli_abort(c(
+    cli_abort(c(
       "`values` and `quantile_levels` must have common length.",
       i = "Mismatches found at position(s): {.val {which(length_diff)}}."
     ))
   }
   level_duplication <- map_lgl(quantile_levels, vctrs::vec_duplicate_any)
   if (any(level_duplication)) {
-    cli::cli_abort(c(
+    cli_abort(c(
       "`quantile_levels` must not be duplicated.",
       i = "Duplicates found at position(s): {.val {which(level_duplication)}}."
     ))
@@ -171,7 +171,7 @@ quantile_extrapolate <- function(x, tau_out, middle) {
     return(qvals[match(tau_out, tau)])
   }
   if (length(tau) < 2) {
-    cli::cli_abort(
+    cli_abort(
       "Quantile extrapolation is not possible with fewer than 2 quantiles."
     )
     return(qvals_out)
@@ -209,7 +209,7 @@ quantile_extrapolate <- function(x, tau_out, middle) {
       v = c(qvals, qvals_out[indm])
     ) %>%
       dplyr::distinct(q, .keep_all = TRUE) %>%
-      dplyr::arrange(q)
+      arrange(q)
   }
   if (any(indl)) {
     qvals_out[indl] <- tail_extrapolate(tau_out[indl], utils::head(qv, 2))
@@ -267,7 +267,7 @@ Ops.dist_quantiles <- function(e1, e2) {
   }
   tau <- union(tau1, tau2)
   if (all(is_dist)) {
-    cli::cli_abort(
+    cli_abort(
       "You can't perform arithmetic between two distributions like this."
     )
   } else {
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 684642075..7db9b9179 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -61,15 +61,13 @@ epi_recipe.epi_df <-
     attr(x, "decay_to_tibble") <- FALSE
     if (!is.null(formula)) {
       if (!is.null(vars)) {
-        cli::cli_abort(
-          paste0(
-            "This `vars` specification will be ignored ",
-            "when a formula is used"
-          )
-        )
+        cli_abort(paste0(
+          "This `vars` specification will be ignored ",
+          "when a formula is used"
+        ))
       }
       if (!is.null(roles)) {
-        cli::cli_abort(
+        cli_abort(
           paste0(
             "This `roles` specification will be ignored ",
             "when a formula is used"
@@ -82,10 +80,10 @@ epi_recipe.epi_df <-
     }
     if (is.null(vars)) vars <- colnames(x)
     if (any(table(vars) > 1)) {
-      cli::cli_abort("`vars` should have unique members")
+      cli_abort("`vars` should have unique members")
     }
     if (any(!(vars %in% colnames(x)))) {
-      cli::cli_abort("1 or more elements of `vars` are not in the data")
+      cli_abort("1 or more elements of `vars` are not in the data")
     }
 
     keys <- key_colnames(x) # we know x is an epi_df
@@ -96,14 +94,14 @@ epi_recipe.epi_df <-
     ## Check and add roles when available
     if (!is.null(roles)) {
       if (length(roles) != length(vars)) {
-        cli::cli_abort(paste(
+        cli_abort(paste(
           "The number of roles should be the same as the number of ",
           "variables."
         ))
       }
       var_info$role <- roles
     } else {
-      var_info <- var_info %>% dplyr::filter(!(variable %in% keys))
+      var_info <- var_info %>% filter(!(variable %in% keys))
       var_info$role <- "raw"
     }
     ## Now we add the keys when necessary
@@ -113,12 +111,12 @@ epi_recipe.epi_df <-
     )
 
     ## Add types
-    var_info <- dplyr::full_join(recipes:::get_types(x), var_info, by = "variable")
+    var_info <- full_join(recipes:::get_types(x), var_info, by = "variable")
     var_info$source <- "original"
 
     ## arrange to easy order
     var_info <- var_info %>%
-      dplyr::arrange(factor(
+      arrange(factor(
         role,
         levels = union(
           c("predictor", "outcome", "time_value", "geo_value", "key"),
@@ -394,7 +392,7 @@ adjust_epi_recipe.epi_workflow <- function(x, which_step, ..., blueprint = defau
 #' @export
 adjust_epi_recipe.epi_recipe <- function(x, which_step, ..., blueprint = default_epi_recipe_blueprint()) {
   if (!(is.numeric(which_step) || is.character(which_step))) {
-    cli::cli_abort(
+    cli_abort(
       c("`which_step` must be a number or a character.",
         i = "`which_step` has class {.cls {class(which_step)[1]}}."
       )
@@ -407,7 +405,7 @@ adjust_epi_recipe.epi_recipe <- function(x, which_step, ..., blueprint = default
     if (!starts_with_step) which_step <- paste0("step_", which_step)
 
     if (!(which_step %in% step_names)) {
-      cli::cli_abort(c(
+      cli_abort(c(
         "`which_step` does not appear in the available `epi_recipe` step names. ",
         i = "The step names are {.val {step_names}}."
       ))
@@ -416,7 +414,7 @@ adjust_epi_recipe.epi_recipe <- function(x, which_step, ..., blueprint = default
     if (length(which_step_idx) == 1) {
       x$steps[[which_step_idx]] <- update(x$steps[[which_step_idx]], ...)
     } else {
-      cli::cli_abort(c(
+      cli_abort(c(
         "`which_step` is not unique. Matches steps: {.val {which_step_idx}}.",
         i = "Please use the step number instead for precise alterations."
       ))
@@ -431,7 +429,7 @@ prep.epi_recipe <- function(
     x, training = NULL, fresh = FALSE, verbose = FALSE,
     retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ...) {
   if (is.null(training)) {
-    cli::cli_warn(c(
+    cli_warn(c(
       "!" = "No training data was supplied to {.fn prep}.",
       "!" = "Unlike a {.cls recipe}, an {.cls epi_recipe} does not ",
       "!" = "store the full template data in the object.",
@@ -441,7 +439,7 @@ prep.epi_recipe <- function(
   }
   training <- recipes:::check_training_set(training, x, fresh)
   training <- epi_check_training_set(training, x)
-  training <- dplyr::relocate(training, dplyr::all_of(key_colnames(training)))
+  training <- relocate(training, all_of(key_colnames(training)))
   tr_data <- recipes:::train_info(training)
   keys <- key_colnames(x)
 
@@ -456,7 +454,7 @@ prep.epi_recipe <- function(
   }
   skippers <- map_lgl(x$steps, recipes:::is_skipable)
   if (any(skippers) & !retain) {
-    cli::cli_warn(paste(
+    cli_warn(paste(
       "Since some operations have `skip = TRUE`, using ",
       "`retain = TRUE` will allow those steps results to ",
       "be accessible."
@@ -464,7 +462,7 @@ prep.epi_recipe <- function(
   }
   if (fresh) x$term_info <- x$var_info
 
-  running_info <- x$term_info %>% dplyr::mutate(number = 0, skip = FALSE)
+  running_info <- x$term_info %>% mutate(number = 0, skip = FALSE)
   for (i in seq(along.with = x$steps)) {
     needs_tuning <- map_lgl(x$steps[[i]], recipes:::is_tune)
     if (any(needs_tuning)) {
@@ -474,7 +472,7 @@ prep.epi_recipe <- function(
         "You cannot `prep()` a tuneable recipe. Argument(s) with `tune()`: ",
         arg, ". Do you want to use a tuning function such as `tune_grid()`?"
       )
-      cli::cli_abort(msg)
+      cli_abort(msg)
     }
     note <- paste("oper", i, gsub("_", " ", class(x$steps[[i]])[1]))
     if (!x$steps[[i]]$trained | fresh) {
@@ -489,7 +487,7 @@ prep.epi_recipe <- function(
       )
       training <- bake(x$steps[[i]], new_data = training)
       if (!tibble::is_tibble(training)) {
-        cli::cli_abort("`bake()` methods should always return {.cls tibble}.")
+        cli_abort("`bake()` methods should always return {.cls tibble}.")
       }
       if (!is_epi_df(training)) {
         # tidymodels killed our class
@@ -501,7 +499,7 @@ prep.epi_recipe <- function(
           other_keys = metadata$other_keys %||% character()
         )
       }
-      training <- dplyr::relocate(training, all_of(key_colnames(training)))
+      training <- relocate(training, all_of(key_colnames(training)))
       x$term_info <- recipes:::merge_term_info(get_types(training), x$term_info)
       if (!is.na(x$steps[[i]]$role)) {
         new_vars <- setdiff(x$term_info$variable, running_info$variable)
@@ -514,7 +512,7 @@ prep.epi_recipe <- function(
       recipes:::changelog(log_changes, before_nms, names(training), x$steps[[i]])
       running_info <- rbind(
         running_info,
-        dplyr::mutate(x$term_info, number = i, skip = x$steps[[i]]$skip)
+        mutate(x$term_info, number = i, skip = x$steps[[i]]$skip)
       )
     } else {
       if (verbose) cat(note, "[pre-trained]\n")
@@ -546,9 +544,9 @@ prep.epi_recipe <- function(
   x$orig_lvls <- orig_lvls
   x$retained <- retain
   x$last_term_info <- running_info %>%
-    dplyr::group_by(variable) %>%
-    dplyr::arrange(dplyr::desc(number)) %>%
-    dplyr::summarise(
+    group_by(variable) %>%
+    arrange(dplyr::desc(number)) %>%
+    summarise(
       type = list(dplyr::first(type)),
       role = list(unique(unlist(role))),
       source = dplyr::first(source),
diff --git a/R/epi_shift.R b/R/epi_shift.R
index e0b68b579..815e1e48f 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -37,9 +37,8 @@ get_sign <- function(object) {
 #' backend for both `bake.step_epi_ahead` and `bake.step_epi_lag`, performs the
 #' checks missing in `epi_shift_single`
 #' @keywords internal
-#' @importFrom cli cli_abort
 #' @importFrom tidyr expand_grid
-#' @importFrom dplyr mutate left_join join_by
+#' @importFrom dplyr join_by
 add_shifted_columns <- function(new_data, object, amount) {
   sign_shift <- get_sign(object)
   latency_table <- attributes(new_data)$metadata$latency_table
@@ -78,12 +77,12 @@ add_shifted_columns <- function(new_data, object, amount) {
   ok <- object$keys
   shifted <- reduce(
     pmap(grid, epi_shift_single, x = new_data, key_cols = ok),
-    dplyr::full_join,
+    full_join,
     by = ok
   )
   processed <- new_data %>%
     full_join(shifted, by = ok) %>%
-    group_by(dplyr::across(dplyr::all_of(kill_time_value(ok)))) %>%
+    group_by(across(all_of(kill_time_value(ok)))) %>%
     arrange(time_value) %>%
     ungroup() %>%
     as_epi_df()
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 619fb6d55..7819a6d73 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -155,7 +155,6 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' preds <- predict(wf, latest)
 #' preds
-#' @importFrom cli cli_abort
 predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), ...) {
   if (!workflows::is_trained_workflow(object)) {
     cli_abort(c(
@@ -188,12 +187,10 @@ augment.epi_workflow <- function(x, new_data, ...) {
   if (is_epi_df(predictions)) {
     join_by <- key_colnames(predictions)
   } else {
-    cli::cli_abort(
-      c(
+    cli_abort(c(
         "Cannot determine how to join `new_data` with the `predictions`.",
         "Try converting `new_data` to an {.cls epi_df} with `as_epi_df(new_data)`."
-      )
-    )
+      ))
   }
   complete_overlap <- intersect(names(new_data), join_by)
   if (length(complete_overlap) < length(join_by)) {
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index fe072a42b..b26e3c2c1 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -105,7 +105,6 @@ flatline_forecaster <- function(
 #' flatline_args_list()
 #' flatline_args_list(symmetrize = FALSE)
 #' flatline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
-#' @importFrom cli cli_abort cli_warn
 flatline_args_list <- function(
     ahead = 7L,
     n_training = Inf,
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index 3e0eb1aaa..c1aa00b82 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -1,7 +1,7 @@
 location_to_abbr <- function(location) {
   dictionary <-
     state_census %>%
-    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
+    mutate(fips = sprintf("%02d", fips)) %>%
     dplyr::transmute(
       location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
       abbr
@@ -12,7 +12,7 @@ location_to_abbr <- function(location) {
 abbr_to_location <- function(abbr) {
   dictionary <-
     state_census %>%
-    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
+    mutate(fips = sprintf("%02d", fips)) %>%
     dplyr::transmute(
       location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
       abbr
diff --git a/R/frosting.R b/R/frosting.R
index 6bc103465..2672bcdd1 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -89,7 +89,7 @@ validate_has_postprocessor <- function(x, ..., call = caller_env()) {
       "The workflow must have a frosting postprocessor.",
       i = "Provide one with `add_frosting()`."
     )
-    cli::cli_abort(message, call = call)
+    cli_abort(message, call = call)
   }
   invisible(x)
 }
@@ -288,7 +288,7 @@ new_frosting <- function() {
 #' p
 frosting <- function(layers = NULL, requirements = NULL) {
   if (!is_null(layers) || !is_null(requirements)) {
-    cli::cli_abort(
+    cli_abort(
       "Currently, no arguments to `frosting()` are allowed to be non-null."
     )
   }
@@ -356,7 +356,6 @@ apply_frosting.default <- function(workflow, components, ...) {
 
 #' @rdname apply_frosting
 #' @importFrom rlang is_null
-#' @importFrom cli cli_abort cli_warn
 #' @param type,opts forwarded (along with `...`) to [`predict.model_fit()`] and
 #'   [`slather()`] for supported layers
 #' @export
@@ -368,7 +367,7 @@ apply_frosting.epi_workflow <-
       components$predictions <- predict(
         the_fit, components$forged$predictors, ...
       )
-      components$predictions <- dplyr::bind_cols(
+      components$predictions <- bind_cols(
         components$keys, components$predictions
       )
       return(components)
@@ -382,7 +381,7 @@ apply_frosting.epi_workflow <-
       components$predictions <- predict(
         the_fit, components$forged$predictors, type, opts, ...
       )
-      components$predictions <- dplyr::bind_cols(
+      components$predictions <- bind_cols(
         components$keys, components$predictions
       )
       return(components)
diff --git a/R/get_test_data.R b/R/get_test_data.R
index f070e97bb..af43fc012 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -26,7 +26,6 @@
 #' get_test_data(recipe = rec, x = case_death_rate_subset)
 #' @importFrom rlang %@%
 #' @importFrom stats na.omit
-#' @importFrom cli cli_abort cli_warn
 #' @export
 get_test_data <- function(recipe, x) {
   if (!is_epi_df(x)) cli_abort("`x` must be an `epi_df`.")
@@ -63,13 +62,13 @@ get_test_data <- function(recipe, x) {
 
   # If we skip NA completion, we remove undesirably early time values
   # Happens globally, over all groups
-  x <- dplyr::filter(x, max_time_value - time_value <= keep)
+  x <- filter(x, max_time_value - time_value <= keep)
 
   # If all(lags > 0), then we get rid of recent data
   if (min_lags > 0 && min_lags < Inf) {
-    x <- dplyr::filter(x, max_time_value - time_value >= min_lags)
+    x <- filter(x, max_time_value - time_value >= min_lags)
   }
 
-  dplyr::filter(x, max_time_value - time_value <= keep) %>%
+  filter(x, max_time_value - time_value <= keep) %>%
     epiprocess::ungroup()
 }
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index daeaa1a3e..fd61c4045 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -163,7 +163,7 @@ slather.layer_cdc_flatline_quantiles <-
     }
     the_fit <- workflows::extract_fit_parsnip(workflow)
     if (!inherits(the_fit, "_flatline")) {
-      cli::cli_warn(c(
+      cli_warn(c(
         "Predictions for this workflow were not produced by the {.cls flatline}",
         "{.pkg parsnip} engine. Results may be unexpected. See {.fn epipredict::flatline}."
       ))
@@ -176,7 +176,7 @@ slather.layer_cdc_flatline_quantiles <-
     if (length(object$by_key) > 0L) {
       cols_in_preds <- hardhat::check_column_names(p, object$by_key)
       if (!cols_in_preds$ok) {
-        cli::cli_warn(paste(
+        cli_warn(paste(
           "Predicted values are missing key columns: {.val {cols_in_preds$missing_names}}.",
           "Ignoring these."
         ))
@@ -184,7 +184,7 @@ slather.layer_cdc_flatline_quantiles <-
       if (inherits(the_fit, "_flatline")) {
         cols_in_resids <- hardhat::check_column_names(r, object$by_key)
         if (!cols_in_resids$ok) {
-          cli::cli_warn(paste(
+          cli_warn(paste(
             "Existing residuals are missing key columns: {.val {cols_in_resids$missing_names}}.",
             "Ignoring these."
           ))
@@ -201,7 +201,7 @@ slather.layer_cdc_flatline_quantiles <-
         )
         cols_in_resids <- hardhat::check_column_names(key_cols, object$by_key)
         if (!cols_in_resids$ok) {
-          cli::cli_warn(paste(
+          cli_warn(paste(
             "Requested residuals are missing key columns: {.val {cols_in_resids$missing_names}}.",
             "Ignoring these."
           ))
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index b21bdcfcc..1b623adfa 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -102,7 +102,7 @@ slather.layer_residual_quantiles <-
       common <- intersect(object$by_key, names(key_cols))
       excess <- setdiff(object$by_key, names(key_cols))
       if (length(excess) > 0L) {
-        cli::cli_warn(paste(
+        cli_warn(paste(
           "Requested residual grouping key(s) {.val {excess}} are unavailable ",
           "in the original data. Grouping by the remainder: {.val {common}}."
         ))
@@ -113,7 +113,7 @@ slather.layer_residual_quantiles <-
         if (length(common_in_r) == length(common)) {
           r <- left_join(key_cols, r, by = common_in_r)
         } else {
-          cli::cli_warn(paste(
+          cli_warn(paste(
             "Some grouping keys are not in data.frame returned by the",
             "`residuals()` method. Groupings may not be correct."
           ))
@@ -124,7 +124,7 @@ slather.layer_residual_quantiles <-
     }
 
     r <- r %>%
-      dplyr::summarise(
+      summarise(
         dstn = list(quantile(
           c(.resid, s * .resid),
           probs = object$quantile_levels, na.rm = TRUE
@@ -132,7 +132,7 @@ slather.layer_residual_quantiles <-
       )
     # Check for NA
     if (any(sapply(r$dstn, is.na))) {
-      cli::cli_abort(c(
+      cli_abort(c(
         "Residual quantiles could not be calculated due to missing residuals.",
         i = "This may be due to `n_train` < `ahead` in your {.cls epi_recipe}."
       ))
@@ -149,7 +149,7 @@ slather.layer_residual_quantiles <-
 
 grab_residuals <- function(the_fit, components) {
   if (the_fit$spec$mode != "regression") {
-    cli::cli_abort("For meaningful residuals, the predictor should be a regression model.")
+    cli_abort("For meaningful residuals, the predictor should be a regression model.")
   }
   r <- stats::residuals(the_fit$fit)
   if (!is.null(r)) { # Got something from the method
@@ -157,7 +157,7 @@ grab_residuals <- function(the_fit, components) {
       if (".resid" %in% names(r)) { # success
         return(r)
       } else { # failure
-        cli::cli_warn(c(
+        cli_warn(c(
           "The `residuals()` method for objects of class {.cls {cl}} results in",
           "a data frame without a column named `.resid`.",
           i = "Residual quantiles will be calculated directly from the",
@@ -168,7 +168,7 @@ grab_residuals <- function(the_fit, components) {
     } else if (is.vector(drop(r))) { # also success
       return(tibble(.resid = drop(r)))
     } else { # failure
-      cli::cli_warn(paste(
+      cli_warn(paste(
         "The `residuals()` method for objects of class {.cls {cl}} results in an",
         "object that is neither a data frame with a column named `.resid`,",
         "nor something coercible to a vector.",
diff --git a/R/layers.R b/R/layers.R
index aa515a917..2a7568d51 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -148,8 +148,8 @@ validate_layer <- function(x, ..., arg = rlang::caller_arg(x),
                            call = caller_env()) {
   rlang::check_dots_empty()
   if (!is_layer(x)) {
-    cli::cli_abort(
-      "{arg} must be a frosting layer, not a {.cls {class(x)[[1]]}}.",
+-    cli_abort(
+-      "{arg} must be a frosting layer, not a {.cls {class(x)[[1]]}}.",
       .call = call
     )
   }
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 2157aa470..9e653184c 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -37,7 +37,7 @@ quantile_reg <- function(mode = "regression", engine = "rq", quantile_levels = 0
   if (any(quantile_levels > 1)) cli_abort("All `quantile_levels` must be less than 1.")
   if (any(quantile_levels < 0)) cli_abort("All `quantile_levels` must be greater than 0.")
   if (is.unsorted(quantile_levels)) {
-    cli::cli_warn("Sorting `quantile_levels` to increasing order.")
+    cli_warn("Sorting `quantile_levels` to increasing order.")
     quantile_levels <- sort(quantile_levels)
   }
   args <- list(quantile_levels = rlang::enquo(quantile_levels), method = rlang::enquo(method))
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 4abf9d257..b01dc392c 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -71,7 +71,7 @@ pivot_quantiles_longer <- function(.data, ..., .ignore_length_check = FALSE) {
           .data <- .data %>% tidyr::unnest(all_of(col), names_sep = "_")
         }
       } else {
-        cli::cli_abort(paste(
+        cli_abort(paste(
           "Some selected columns contain different numbers of quantiles.",
           "The result would be a {.emph very} long {.cls tibble}.",
           "To do this anyway, rerun with `.ignore_length_check = TRUE`."
@@ -115,7 +115,7 @@ pivot_quantiles_wider <- function(.data, ...) {
   checks <- map_lgl(cols, ~ diff(range(vctrs::list_sizes(.data[[.x]]))) == 0L)
   if (!all(checks)) {
     nms <- cols[!checks]
-    cli::cli_abort(c(
+    cli_abort(c(
       "Quantiles must be the same length and have the same set of taus.",
       i = "Check failed for variables(s) {.var {nms}}."
     ))
@@ -157,7 +157,7 @@ validate_pivot_quantiles <- function(.data, ...) {
   dqs <- map_lgl(cols, ~ is_dist_quantiles(.data[[.x]]))
   if (!all(dqs)) {
     nms <- cols[!dqs]
-    cli::cli_abort(
+    cli_abort(
       "Variables(s) {.var {nms}} are not `dist_quantiles`. Cannot pivot them."
     )
   }
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 4caf17d8e..be359fc9c 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -212,37 +212,37 @@ step_adjust_latency <-
            id = recipes::rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
+      cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
         class = "epipredict__step_adjust_latency__epi_recipe_only"
       )
     }
     if (!is.null(columns)) {
-      cli::cli_abort(c("The `columns` argument must be `NULL`.",
+      cli_abort(c("The `columns` argument must be `NULL`.",
         i = "Use `tidyselect` methods to choose columns to lag."
       ), class = "epipredict__step_adjust_latency__cols_not_null")
     }
     if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
-      cli::cli_warn(
+      cli_warn(
         "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
         class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
-      cli::cli_warn(
+      cli_warn(
         "If `method` is {.val extend_lags} or {.val locf},
 then the previous `step_epi_lag`s won't work with modified data.",
         class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "locf") && (length(recipe$steps) > 0)) {
-      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others",
+      cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others",
         class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     }
     if (detect_step(recipe, "naomit")) {
-      cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
+      cli_abort("adjust_latency needs to occur before any `NA` removal,
                       as columns may be moved around", class = "epipredict__step_adjust_latency__post_NA_error")
     }
     if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
-      cli::cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
+      cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
  can be non-`NULL` at a time!", class = "epipredict__step_adjust_latency__too_many_args_error")
     }
     if (length(fixed_latency > 1)) {
@@ -250,7 +250,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
       data_names <- names(template)[!names(template) %in% key_colnames(template)]
       wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
       if (length(wrong_names) > 0) {
-        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
+        cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
       }
     }
     method <- rlang::arg_match(method)
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index d9d52684d..00bf9bd87 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -30,7 +30,6 @@
 #'
 #' @family row operation steps
 #' @importFrom epiprocess growth_rate
-#' @importFrom cli cli_abort
 #' @export
 #' @examples
 #' r <- epi_recipe(case_death_rate_subset) %>%
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index bee5f5c40..39ae1ba59 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -13,7 +13,6 @@
 #'
 #'
 #' @family row operation steps
-#' @importFrom cli cli_abort cli_warn
 #' @export
 #' @examples
 #' r <- epi_recipe(case_death_rate_subset) %>%
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 4e4d3aa26..76068109c 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -165,7 +165,7 @@ bake.step_population_scaling <- function(object, new_data, ...) {
   hardhat::validate_column_names(object$df, joinby$y)
 
   if (object$suffix != "_scaled" && object$create_new == FALSE) {
-    cli::cli_warn(c(
+    cli_warn(c(
       "Custom `suffix` {.val {object$suffix}} was ignored in `step_population_scaling`.",
       i = "Perhaps `create_new` should be {.val {TRUE}}?"
     ))
diff --git a/R/tidy.R b/R/tidy.R
index 61b298411..8fc06398a 100644
--- a/R/tidy.R
+++ b/R/tidy.R
@@ -96,7 +96,7 @@ tidy.frosting <- function(x, number = NA, id = NA, ...) {
 
 #' @export
 tidy.layer <- function(x, ...) {
-  cli::cli_abort(
+  cli_abort(
     "No `tidy()` method exists for a layer with class: {.cls {class(x)}}."
   )
 }
diff --git a/R/utils-enframer.R b/R/utils-enframer.R
index 0a8152906..723bd6a9c 100644
--- a/R/utils-enframer.R
+++ b/R/utils-enframer.R
@@ -8,7 +8,7 @@ enframer <- function(df, x, fill = NA) {
   if (any(names(df) %in% x)) {
     stop("In enframer: some new cols match existing column names")
   }
-  for (v in x) df <- dplyr::mutate(df, !!v := fill)
+  for (v in x) df <- mutate(df, !!v := fill)
   df
 }
 
diff --git a/R/utils-latency.R b/R/utils-latency.R
index bd0e4803e..9398f8188 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -40,7 +40,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
     pull(variable)
   # make sure that there's enough column names
   if (length(original_columns) < 3) {
-    cli::cli_abort(
+    cli_abort(
       glue::glue(
         "The original columns of `time_value`, ",
         "`geo_value` and at least one signal. The current colums are \n",
@@ -70,7 +70,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   }
   # make sure the as_of is sane
   if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
-    cli::cli_abort(
+    cli_abort(
       paste(
         "the data matrix `forecast_date` value is {forecast_date}, ",
         "and not a valid `time_type` with type ",
@@ -81,7 +81,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
     )
   }
   if (is.null(forecast_date) || is.na(forecast_date)) {
-    cli::cli_warn(
+    cli_warn(
       paste(
         "epi_data's `forecast_date` was {forecast_date}, setting to ",
         "the latest time value, {max_time}."
@@ -90,7 +90,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
     )
     forecast_date <- max_time
   } else if (forecast_date < max_time) {
-    cli::cli_abort(
+    cli_abort(
       paste(
         "`forecast_date` ({(forecast_date)}) is before the most ",
         "recent data ({max_time}). Remove before ",
@@ -277,7 +277,7 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
       filter(!is.na(!!(latency_table[[i_latency, "col_name"]]))) %>%
       pull(time_value) %>%
       max()
-    cli::cli_warn(
+    cli_warn(
       message = c(
         paste(
           "The maximum latency is {latency_max}, ",
diff --git a/R/utils-misc.R b/R/utils-misc.R
index 8fc16a968..5cc8b364d 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -34,7 +34,7 @@ check_pname <- function(res, preds, object, newname = NULL) {
 
 grab_forged_keys <- function(forged, workflow, new_data) {
   forged_roles <- names(forged$extras$roles)
-  extras <- dplyr::bind_cols(forged$extras$roles[forged_roles %in% c("geo_value", "time_value", "key")])
+  extras <- bind_cols(forged$extras$roles[forged_roles %in% c("geo_value", "time_value", "key")])
   # 1. these are the keys in the test data after prep/bake
   new_keys <- names(extras)
   # 2. these are the keys in the training data
@@ -42,7 +42,7 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   # 3. these are the keys in the test data as input
   new_df_keys <- key_colnames(new_data, extra_keys = setdiff(new_keys, c("geo_value", "time_value")))
   if (!(setequal(old_keys, new_df_keys) && setequal(new_keys, new_df_keys))) {
-    cli::cli_warn(paste(
+    cli_warn(paste(
       "Not all epi keys that were present in the training data are available",
       "in `new_data`. Predictions will have only the available keys."
     ))
diff --git a/R/utils-shift.R b/R/utils-shift.R
index 75a9a6bd1..7b9de6a08 100644
--- a/R/utils-shift.R
+++ b/R/utils-shift.R
@@ -12,7 +12,7 @@ adjust_latency <- function(object, new_data) {
   } else if (method == "extend_ahead") {
     as_of <- attributes(new_data)$metadata$as_of
     if (FALSE && (typeof(as_of) != typeof(new_data$time_value))) {
-      cli::cli_abort(paste(
+      cli_abort(paste(
         "the data matrix `as_of` value is {as_of}, ",
         "and not a valid `time_type` with type ",
         "matching `time_value`'s type of ",
@@ -27,13 +27,13 @@ adjust_latency <- function(object, new_data) {
       max_time <- max(time_values)
       shift_amount <- as.Date(as_of) - max_time
       if (is.null(as_of) || is.na(as_of)) {
-        cli::cli_warn(paste(
+        cli_warn(paste(
           "epi_data's `as_of` was {as_of}, setting to ",
           "the latest time value, {max_time}."
         ))
         as_of <- max_time
       } else if (as_of < max_time) {
-        cli::cli_abort(paste(
+        cli_abort(paste(
           "`as_of` ({(as_of)}) is before the most ",
           "recent data ({max_time}). Remove before ",
           "predicting."
@@ -47,7 +47,7 @@ adjust_latency <- function(object, new_data) {
         ((time_type == "yearmonth") && (shift_amount >= 2)) ||
         ((time_type == "yearquarter") && (shift_amount >= 1)) ||
         ((time_type == "year") && (shift_amount >= 1))) {
-        cli::cli_warn(paste(
+        cli_warn(paste(
           "!" = glue::glue(
             "The ahead has been adjusted by {shift_amount}, ",
             "which is questionable for it's `time_type` of ",
@@ -60,10 +60,10 @@ adjust_latency <- function(object, new_data) {
       }
       return(effective_ahead)
     } else {
-      cli::cli_abort("the `time_value` column of `new_data` is empty")
+      cli_abort("the `time_value` column of `new_data` is empty")
     }
   } else {
-    cli::cli_abort(paste(
+    cli_abort(paste(
       "Latency adjustment method {method} has not yet ",
       "been implemented for `step_epi_ahead`."
     ))

From 63db4d5802f1af54d43b2931a36d5e140d5d1aa6 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 13 Sep 2024 10:55:58 -0500
Subject: [PATCH 087/387] various recommendations

---
 DEVELOPMENT.md                      |  1 +
 R/arx_classifier.R                  |  2 +-
 R/arx_forecaster.R                  | 15 +++---
 R/canned-epipred.R                  |  4 +-
 R/cdc_baseline_forecaster.R         |  4 +-
 R/epi_shift.R                       |  6 +--
 R/epi_workflow.R                    |  6 +--
 R/flatline_forecaster.R             |  8 ++--
 R/import-standalone-purrr.R         |  2 +-
 R/layers.R                          |  6 +--
 R/step_adjust_latency.R             | 42 ++++++++---------
 R/utils-latency.R                   | 12 +----
 R/utils-shift.R                     | 71 -----------------------------
 man/adjust_latency.Rd               | 14 ------
 man/arx_args_list.Rd                |  8 ++--
 man/arx_class_args_list.Rd          |  6 +--
 man/count_single_column.Rd          |  4 +-
 man/step_adjust_latency.Rd          | 30 +++---------
 tests/testthat/test-utils_latency.R | 22 ---------
 19 files changed, 60 insertions(+), 203 deletions(-)
 delete mode 100644 R/utils-shift.R
 delete mode 100644 man/adjust_latency.Rd

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 0335f124b..8bc251e77 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -32,6 +32,7 @@ The `main` version is available at `file:///<local path>/epidatr/epipredict/inde
 You can also build the docs manually and launch the site with python. From the terminal, this looks like
 
 ```bash
+R -e 'pkgdown::clean_site()'
 R -e 'devtools::document()'
 R -e 'pkgdown::build_site()'
 python -m http.server -d docs
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index bbb3433c1..2bd2c30a1 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -204,7 +204,7 @@ arx_class_epi_workflow <- function(
   r <- r %>%
     step_mutate(
       across(
-        starts_with("ahead"),
+        starts_with("ahead_"),
         ~ cut(.x, breaks = args_list$breaks),
         .names = "outcome_class",
         .unpack = TRUE
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index a3363e689..14d8b112b 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -126,7 +126,7 @@ arx_fcast_epi_workflow <- function(
   # forecast_date is above all what they set;
   # if they don't and they're not adjusting latency, it defaults to the max time_value
   # if they're adjusting, it defaults to the as_of
-  if (is.null(args_list$adjust_latency)) {
+  if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
       cli_warn(
@@ -236,11 +236,11 @@ arx_fcast_epi_workflow <- function(
 #'   non-`NULL`.
 #' @param target_date Date. The date that is being forecast. The default `NULL`
 #'   will determine this automatically as `forecast_date + ahead`.
-#' @param adjust_latency Character or `NULL`. One of the `method`s of
-#'   [step_adjust_latency()], or `NULL` (in which case there is no adjustment).
+#' @param adjust_latency Character. One of the `method`s of
+#'   [step_adjust_latency()], or `"none"` (in which case there is no adjustment).
 #'   If the `forecast_date` is after the last day of data, this determines how
 #'   to shift the model to account for this difference. The options are:
-#'   - `NULL` the default, assumes the `forecast_date` is the last day of data
+#'   - `"none"` the default, assumes the `forecast_date` is the last day of data
 #'   - `"extend_ahead"`: increase the `ahead` by the latency so it's relative to
 #'   the last day of data. For example, if the last day of data was 3 days ago,
 #'   the ahead becomes `ahead+3`.
@@ -283,7 +283,7 @@ arx_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    adjust_latency = NULL,
+    adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
     quantile_levels = c(0.05, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
@@ -296,9 +296,10 @@ arx_args_list <- function(
   .lags <- lags
   if (is.list(lags)) lags <- unlist(lags)
 
-  arg_is_scalar(ahead, n_training, symmetrize, nonneg)
+  adjust_latency <- rlang::arg_match(adjust_latency)
+  arg_is_scalar(ahead, n_training, symmetrize, nonneg, adjust_latency)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
-  arg_is_scalar(forecast_date, target_date, adjust_latency, allow_null = TRUE)
+  arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags)
   arg_is_lgl(symmetrize, nonneg)
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 66c66a339..1e088426e 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -113,7 +113,7 @@ print.canned_epipred <- function(x, name, ...) {
   ))
   fit_recipe <- extract_recipe(x$epi_workflow)
   if (detect_step(fit_recipe, "adjust_latency")) {
-    is_adj_latency <- map_lgl(fit_recipe$steps, \(x) inherits(x, "step_adjust_latency"))
+    is_adj_latency <- map_lgl(fit_recipe$steps, function(x) inherits(x, "step_adjust_latency"))
     latency_step <- fit_recipe$steps[is_adj_latency][[1]]
     # all steps after adjust_latency
     later_steps <- fit_recipe$steps[-(1:which(is_adj_latency))]
@@ -129,7 +129,7 @@ print.canned_epipred <- function(x, name, ...) {
     }
     later_steps[[1]]$columns
     valid_columns <- later_steps %>%
-      keep(\(x) inherits(x, step_names)) %>%
+      keep(function(x) inherits(x, step_names)) %>%
       purrr::map("columns") %>%
       reduce(c)
     latency_per_base_col <- latency_step$latency_table %>%
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index 85635cfae..3352c5159 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -78,9 +78,7 @@ cdc_baseline_forecaster <- function(
   # target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
 
 
-  latest <- get_test_data(
-    epi_recipe(epi_data), epi_data
-  )
+  latest <- get_test_data(epi_recipe(epi_data), epi_data)
 
   f <- frosting() %>%
     layer_predict() %>%
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 815e1e48f..7088fb341 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -45,8 +45,6 @@ add_shifted_columns <- function(new_data, object, amount) {
   shift_sign_lat <- attributes(new_data)$metadata$shift_sign
   if (!is.null(latency_table) &&
     shift_sign_lat == sign_shift) {
-    # are we adding an unreasonable amount of latency?
-    check_interminable_latency(new_data, latency_table, object$columns, attributes(new_data)$metadata$forecast_date)
     # get the actually used latencies
     rel_latency <- latency_table %>% filter(col_name %in% object$columns)
   } else {
@@ -56,9 +54,7 @@ add_shifted_columns <- function(new_data, object, amount) {
   grid <- expand_grid(col = object$columns, amount = sign_shift * amount) %>%
     left_join(rel_latency, by = join_by(col == col_name), ) %>%
     tidyr::replace_na(list(latency = 0)) %>%
-    mutate(
-      shift_val = amount + latency
-    ) %>%
+    mutate(shift_val = amount + latency) %>%
     mutate(
       newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"), # name is always positive
       amount = NULL,
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 7819a6d73..ff2393ecc 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -188,9 +188,9 @@ augment.epi_workflow <- function(x, new_data, ...) {
     join_by <- key_colnames(predictions)
   } else {
     cli_abort(c(
-        "Cannot determine how to join `new_data` with the `predictions`.",
-        "Try converting `new_data` to an {.cls epi_df} with `as_epi_df(new_data)`."
-      ))
+      "Cannot determine how to join `new_data` with the `predictions`.",
+      "Try converting `new_data` to an {.cls epi_df} with `as_epi_df(new_data)`."
+    ))
   }
   complete_overlap <- intersect(names(new_data), join_by)
   if (length(complete_overlap) < length(join_by)) {
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index b26e3c2c1..59f54bd86 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -63,11 +63,9 @@ flatline_forecaster <- function(
 
   eng <- linear_reg(engine = "flatline")
 
-  wf <- epi_workflow(r, eng, f)
-  wf <- fit(wf, epi_data)
-  preds <- suppressWarnings(forecast(
-    wf
-  )) %>%
+  wf <- epi_workflow(r, eng, f) %>%
+    fit(epi_data)
+  preds <- suppressWarnings(forecast(wf)) %>%
     as_tibble() %>%
     select(-time_value)
 
diff --git a/R/import-standalone-purrr.R b/R/import-standalone-purrr.R
index e4e83f428..623142a0e 100644
--- a/R/import-standalone-purrr.R
+++ b/R/import-standalone-purrr.R
@@ -123,7 +123,7 @@ map_if <- function(.x, .p, .f, ...) {
   .x
 }
 .rlang_purrr_probe <- function(.x, .p, ...) {
-  if (rlang::is_logical(.p)) {
+  if (is_logical(.p)) {
     stopifnot(length(.p) == length(.x))
     .p
   } else {
diff --git a/R/layers.R b/R/layers.R
index 2a7568d51..538fcad1b 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -148,9 +148,9 @@ validate_layer <- function(x, ..., arg = rlang::caller_arg(x),
                            call = caller_env()) {
   rlang::check_dots_empty()
   if (!is_layer(x)) {
--    cli_abort(
--      "{arg} must be a frosting layer, not a {.cls {class(x)[[1]]}}.",
-      .call = call
+    cli_abort(
+      "{arg} must be a frosting layer, not a {.cls {class(x)[[1]]}}.",
+      call = call
     )
   }
   invisible(x)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index be359fc9c..d5776940e 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -163,9 +163,9 @@
 #'   the date from which the forecast is actually occurring. If `NULL`, the
 #'   `forecast_date` is determined either via the `fixed_latency`, or is set to
 #'   the `epi_df`'s `as_of` value if `fixed_latency` is also `NULL`.
-#' @param role For model terms created by this step, what analysis role should
-#'   they be assigned? `lag` is a predictor while `ahead` is an outcome.  It
-#'   should be correctly inferred and not need setting
+#' @param check_latency_length bool, determines whether to warn if the latency
+#'   is unusually high. Turn off if you know your forecast is going to be far
+#'   into the future.
 #' @template step-return
 #' @inheritParams recipes::step_lag
 #'
@@ -197,30 +197,22 @@
 step_adjust_latency <-
   function(recipe,
            ...,
-           role = NA,
-           trained = FALSE,
            method = c(
              "extend_ahead",
              "locf",
              "extend_lags"
            ),
-           epi_keys_checked = c("geo_value"),
+           epi_keys_checked = NULL,
            fixed_latency = NULL,
            fixed_forecast_date = NULL,
-           skip = FALSE,
-           columns = NULL,
-           id = recipes::rand_id("adjust_latency")) {
+           check_latency_length = TRUE,
+           id = rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
       cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
         class = "epipredict__step_adjust_latency__epi_recipe_only"
       )
     }
-    if (!is.null(columns)) {
-      cli_abort(c("The `columns` argument must be `NULL`.",
-        i = "Use `tidyselect` methods to choose columns to lag."
-      ), class = "epipredict__step_adjust_latency__cols_not_null")
-    }
     if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
       cli_warn(
         "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
@@ -261,22 +253,25 @@ then the previous `step_epi_lag`s won't work with modified data.",
       rel_step_type <- "step_epi_lag"
       shift_name <- "lag"
     }
-
+    if (is.null(epi_keys_checked)) {
+      epi_keys_checked <- kill_time_value(key_colnames(recipe$template))
+    }
     recipes::add_step(
       recipe,
       step_adjust_latency_new(
         terms = enquos(...),
-        role = role,
+        role = NA,
         method = method,
         epi_keys_checked = epi_keys_checked,
-        trained = trained,
+        check_latency_length = check_latency_length,
+        trained = FALSE,
         forecast_date = fixed_forecast_date,
         latency = fixed_latency,
         latency_table = NULL,
         metadata = NULL,
         keys = key_colnames(recipe),
-        columns = columns,
-        skip = skip,
+        columns = NULL,
+        skip = FALSE,
         id = id
       )
     )
@@ -284,7 +279,7 @@ then the previous `step_epi_lag`s won't work with modified data.",
 
 step_adjust_latency_new <-
   function(terms, role, trained, forecast_date, latency, latency_table,
-           metadata, time_type, keys, method, epi_keys_checked, columns, skip,
+           metadata, time_type, keys, method, epi_keys_checked, check_latency_length, columns, skip,
            id) {
     step(
       subclass = "adjust_latency",
@@ -292,6 +287,7 @@ step_adjust_latency_new <-
       role = role,
       method = method,
       epi_keys_checked = epi_keys_checked,
+      check_latency_length = check_latency_length,
       trained = trained,
       forecast_date = forecast_date,
       latency = latency,
@@ -337,7 +333,6 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   attributes(training)$metadata$latency_table <- latency_table
   # get the columns used, even if it's all of them
   terms_used <- x$terms
-  # TODO replace with is_empty as in bake.recipe
   if (is_empty(terms_used)) {
     terms_used <- info %>%
       filter(role == "raw") %>%
@@ -357,6 +352,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
+    check_latency_length = x$check_latency_length,
     columns = recipes_eval_select(latency_table$col_name, training, info),
     skip = x$skip,
     id = x$id
@@ -383,7 +379,9 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
     modified_columns <- object$columns %>% unname()
-    check_interminable_latency(new_data, object$latency_table, modified_columns, object$forecast_date)
+    if (object$check_latency_length) {
+      check_interminable_latency(new_data, object$latency_table, modified_columns, object$forecast_date)
+    }
 
     new_data <- new_data %>%
       pad_to_end(rel_keys, object$forecast_date, modified_columns) %>%
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 9398f8188..f53f01ca3 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -223,17 +223,9 @@ get_grouping_columns <- function(x) {
 
 #' get the location of the last real value
 #' @param col the relevant column
-#' @param from_last instead of the number of columns
 #' @keywords internal
-count_single_column <- function(col, from_last) {
-  run_lengths <- rle(is.na(col))
-  n_el <- length(col)
-  if (tail(run_lengths$values, 1)) {
-    n_at_end <- tail(run_lengths$lengths, 1)
-    return(n_el - n_at_end)
-  } else {
-    return(n_el)
-  }
+count_single_column <- function(col) {
+  max(which(!is.na(col)))
 }
 
 
diff --git a/R/utils-shift.R b/R/utils-shift.R
deleted file mode 100644
index 7b9de6a08..000000000
--- a/R/utils-shift.R
+++ /dev/null
@@ -1,71 +0,0 @@
-#' various ways of handling differences between the `as_of` date and the maximum
-#' time value
-#' @description
-#' adjust the ahead so that we will be predicting `ahead` days after the `as_of`
-#'   date, rather than relative to the last day of data
-#' @keywords internal
-adjust_latency <- function(object, new_data) {
-  method <- object$latency_adjustment
-  ahead <- object$ahead
-  if (is.na(method) || is.null(method) || method == "None") {
-    return(object$ahead)
-  } else if (method == "extend_ahead") {
-    as_of <- attributes(new_data)$metadata$as_of
-    if (FALSE && (typeof(as_of) != typeof(new_data$time_value))) {
-      cli_abort(paste(
-        "the data matrix `as_of` value is {as_of}, ",
-        "and not a valid `time_type` with type ",
-        "matching `time_value`'s type of ",
-        "{typeof(new_data$time_value)}."
-      ))
-    }
-    # adjust the ahead so that we're predicting relative to the as_of date,
-    # rather
-    # than the last day of data
-    time_values <- new_data$time_value
-    if (length(time_values) > 0) {
-      max_time <- max(time_values)
-      shift_amount <- as.Date(as_of) - max_time
-      if (is.null(as_of) || is.na(as_of)) {
-        cli_warn(paste(
-          "epi_data's `as_of` was {as_of}, setting to ",
-          "the latest time value, {max_time}."
-        ))
-        as_of <- max_time
-      } else if (as_of < max_time) {
-        cli_abort(paste(
-          "`as_of` ({(as_of)}) is before the most ",
-          "recent data ({max_time}). Remove before ",
-          "predicting."
-        ))
-      }
-      effective_ahead <- as.integer(shift_amount + ahead)
-      time_type <- attributes(new_data)$metadata$time_type
-
-      if ((grepl("day", time_type) && (shift_amount >= 10)) ||
-        (grepl("week", time_type) && (shift_amount >= 4)) ||
-        ((time_type == "yearmonth") && (shift_amount >= 2)) ||
-        ((time_type == "yearquarter") && (shift_amount >= 1)) ||
-        ((time_type == "year") && (shift_amount >= 1))) {
-        cli_warn(paste(
-          "!" = glue::glue(
-            "The ahead has been adjusted by {shift_amount}, ",
-            "which is questionable for it's `time_type` of ",
-            "{time_type}"
-          ),
-          "i" = "input ahead: {ahead}",
-          "i" = "shifted ahead: {effective_ahead}",
-          "i" = "max_time = {max_time} -> as_of = {as_of}"
-        ))
-      }
-      return(effective_ahead)
-    } else {
-      cli_abort("the `time_value` column of `new_data` is empty")
-    }
-  } else {
-    cli_abort(paste(
-      "Latency adjustment method {method} has not yet ",
-      "been implemented for `step_epi_ahead`."
-    ))
-  }
-}
diff --git a/man/adjust_latency.Rd b/man/adjust_latency.Rd
deleted file mode 100644
index eaebd5c29..000000000
--- a/man/adjust_latency.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-shift.R
-\name{adjust_latency}
-\alias{adjust_latency}
-\title{various ways of handling differences between the \code{as_of} date and the maximum
-time value}
-\usage{
-adjust_latency(object, new_data)
-}
-\description{
-adjust the ahead so that we will be predicting \code{ahead} days after the \code{as_of}
-date, rather than relative to the last day of data
-}
-\keyword{internal}
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 214726fde..39ef23e09 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -10,7 +10,7 @@ arx_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
-  adjust_latency = NULL,
+  adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
   quantile_levels = c(0.05, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
@@ -44,12 +44,12 @@ non-\code{NULL}.
 \item{target_date}{Date. The date that is being forecast. The default \code{NULL}
 will determine this automatically as \code{forecast_date + ahead}.}
 
-\item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
-\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
+\item{adjust_latency}{Character. One of the \code{method}s of
+\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{"none"} (in which case there is no adjustment).
 If the \code{forecast_date} is after the last day of data, this determines how
 to shift the model to account for this difference. The options are:
 \itemize{
-\item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
+\item \code{"none"} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 185b868e5..4873d59ae 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -46,12 +46,12 @@ non-\code{NULL}.
 \item{target_date}{Date. The date that is being forecast. The default \code{NULL}
 will determine this automatically as \code{forecast_date + ahead}.}
 
-\item{adjust_latency}{Character or \code{NULL}. One of the \code{method}s of
-\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{NULL} (in which case there is no adjustment).
+\item{adjust_latency}{Character. One of the \code{method}s of
+\code{\link[=step_adjust_latency]{step_adjust_latency()}}, or \code{"none"} (in which case there is no adjustment).
 If the \code{forecast_date} is after the last day of data, this determines how
 to shift the model to account for this difference. The options are:
 \itemize{
-\item \code{NULL} the default, assumes the \code{forecast_date} is the last day of data
+\item \code{"none"} the default, assumes the \code{forecast_date} is the last day of data
 \item \code{"extend_ahead"}: increase the \code{ahead} by the latency so it's relative to
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
diff --git a/man/count_single_column.Rd b/man/count_single_column.Rd
index 090f064e4..7922511e7 100644
--- a/man/count_single_column.Rd
+++ b/man/count_single_column.Rd
@@ -4,12 +4,10 @@
 \alias{count_single_column}
 \title{get the location of the last real value}
 \usage{
-count_single_column(col, from_last)
+count_single_column(col)
 }
 \arguments{
 \item{col}{the relevant column}
-
-\item{from_last}{instead of the number of columns}
 }
 \description{
 get the location of the last real value
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 9b10d1822..80d6aa3ef 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -7,15 +7,12 @@
 step_adjust_latency(
   recipe,
   ...,
-  role = NA,
-  trained = FALSE,
   method = c("extend_ahead", "locf", "extend_lags"),
-  epi_keys_checked = c("geo_value"),
+  epi_keys_checked = NULL,
   fixed_latency = NULL,
   fixed_forecast_date = NULL,
-  skip = FALSE,
-  columns = NULL,
-  id = recipes::rand_id("adjust_latency")
+  check_latency_length = TRUE,
+  id = rand_id("adjust_latency")
 )
 }
 \arguments{
@@ -25,13 +22,6 @@ sequence of operations for this recipe.}
 \item{...}{One or more selector functions to choose variables
 for this step. See \code{\link[recipes:selections]{selections()}} for more details.}
 
-\item{role}{For model terms created by this step, what analysis role should
-they be assigned? \code{lag} is a predictor while \code{ahead} is an outcome.  It
-should be correctly inferred and not need setting}
-
-\item{trained}{A logical to indicate if the quantities for
-preprocessing have been estimated.}
-
 \item{method}{a character. Determines the method by which the
 forecast handles latency. The options are:
 \itemize{
@@ -73,15 +63,9 @@ the date from which the forecast is actually occurring. If \code{NULL}, the
 \code{forecast_date} is determined either via the \code{fixed_latency}, or is set to
 the \code{epi_df}'s \code{as_of} value if \code{fixed_latency} is also \code{NULL}.}
 
-\item{skip}{A logical. Should the step be skipped when the
-recipe is baked by \code{\link[recipes:bake]{bake()}}? While all operations are baked
-when \code{\link[recipes:prep]{prep()}} is run, some operations may not be able to be
-conducted on new data (e.g. processing the outcome variable(s)).
-Care should be taken when using \code{skip = TRUE} as it may affect
-the computations for subsequent operations.}
-
-\item{columns}{A character string of the selected variable names. This field
-is a placeholder and will be populated once \code{\link[recipes:prep]{prep()}} is used.}
+\item{check_latency_length}{bool, determines whether to warn if the latency
+is unusually high. Turn off if you know your forecast is going to be far
+into the future.}
 
 \item{id}{A character string that is unique to this step to identify it.}
 }
@@ -276,8 +260,6 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with modified
-#> data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 6d71ee6eb..d408ba7d8 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -98,28 +98,6 @@ test_that("set_forecast_date works", {
   expect_equal(set_forecast_date(modified_data, info, NULL, NULL), as_of)
 })
 
-# confirm the delay is right
-test_that("adjust_latency extend_ahead works", {
-  # testing that POSIXct converts correctly (as well as basic types)
-  expect_equal(
-    attributes(x_adjust_ahead)$metadata$as_of - max(x_adjust_ahead$time_value),
-    as.difftime(3, units = "days")
-  )
-  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
-  expect_no_error(adjusted_ahead <- adjust_latency(object, x_adjust_ahead))
-  expect_type(adjusted_ahead, "integer")
-  expect_equal(adjusted_ahead, 3 + 7)
-})
-
-test_that("extend_ahead warns in case of extreme adjustment", {
-  # warns if the ahead is relatively small
-  attributes(x_adjust_ahead)$metadata$as_of <-
-    max(x_adjust_ahead$time_value) + 100
-  object <- list(latency_adjustment = "extend_ahead", ahead = 7)
-  attributes(x_adjust_ahead)$metadata$time_type
-  testthat::expect_warning(adjust_latency(object, x_adjust_ahead), regexp = "The ahead has been adjusted by 100")
-})
-
 test_that("pad_to_end works correctly", {
   single_ex <- tribble(
     ~geo_value, ~time_value, ~a, ~b,

From b5ed1b3a16a52dbf11fb887eb5b5788cf66b40a6 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 13 Sep 2024 17:54:09 -0500
Subject: [PATCH 088/387] remaining recs besides metadata to term_info

---
 DESCRIPTION                                   |   1 -
 NAMESPACE                                     |   1 -
 R/arx_classifier.R                            |  18 +--
 R/arx_forecaster.R                            |   8 +-
 R/step_adjust_latency.R                       |  91 ++++++++-------
 R/utils-latency.R                             | 104 ++++++++++++------
 man/arx_args_list.Rd                          |   4 +
 man/arx_class_args_list.Rd                    |   6 +-
 ..._forecast_date.Rd => get_forecast_date.Rd} |   6 +-
 man/get_grouping_columns.Rd                   |  15 ---
 man/get_latency_table.Rd                      |  25 +++++
 man/step_adjust_latency.Rd                    |   2 +
 tests/testthat/test-utils_latency.R           |   8 +-
 13 files changed, 179 insertions(+), 110 deletions(-)
 rename man/{set_forecast_date.Rd => get_forecast_date.Rd} (71%)
 delete mode 100644 man/get_grouping_columns.Rd
 create mode 100644 man/get_latency_table.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 552857a36..d89f9b276 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -41,7 +41,6 @@ Imports:
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
     purrr,
-    smoothqr,
     stats,
     tibble,
     tidyr,
diff --git a/NAMESPACE b/NAMESPACE
index ba319cecd..4c5ec75ac 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -318,7 +318,6 @@ importFrom(tidyr,fill)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
-importFrom(utils,head)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 2bd2c30a1..9cc76052f 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -55,7 +55,7 @@ arx_classifier <- function(
   wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
   wf <- fit(wf, epi_data)
 
-  if (is.null(args_list$adjust_latency)) {
+  if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
       cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
@@ -133,7 +133,7 @@ arx_class_epi_workflow <- function(
     cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
   }
 
-  if (is.null(args_list$adjust_latency)) {
+  if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
       cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
@@ -190,9 +190,9 @@ arx_class_epi_workflow <- function(
         )
     }
   }
-  ahead_out_name <- rlang::sym(paste0("ahead_", args_list$ahead, "_", pre_out_name))
+  ahead_out_name <- glue::glue("ahead_[0-9]*_{pre_out_name}")
   method_adjust_latency <- args_list$adjust_latency
-  if (!is.null(method_adjust_latency)) {
+  if (method_adjust_latency   != "none") {
     # only extend_ahead is supported atm
     r <- r %>% step_adjust_latency(!!pre_out_name,
       fixed_forecast_date = forecast_date,
@@ -204,7 +204,7 @@ arx_class_epi_workflow <- function(
   r <- r %>%
     step_mutate(
       across(
-        starts_with("ahead_"),
+        matches(ahead_out_name),
         ~ cut(.x, breaks = args_list$breaks),
         .names = "outcome_class",
         .unpack = TRUE
@@ -289,7 +289,8 @@ arx_class_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    adjust_latency = NULL,
+    adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
+    warn_latency = TRUE,
     outcome_transform = c("growth_rate", "lag_difference"),
     breaks = 0.25,
     horizon = 7L,
@@ -305,8 +306,9 @@ arx_class_args_list <- function(
   method <- rlang::arg_match(method)
   outcome_transform <- rlang::arg_match(outcome_transform)
 
-  arg_is_scalar(ahead, n_training, horizon, log_scale)
-  arg_is_scalar(forecast_date, target_date, adjust_latency, allow_null = TRUE)
+  adjust_latency <- rlang::arg_match(adjust_latency)
+  arg_is_scalar(ahead, n_training, horizon, log_scale, adjust_latency, warn_latency)
+  arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags, horizon)
   arg_is_numeric(breaks)
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 14d8b112b..4d7dab60e 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -52,7 +52,7 @@ arx_forecaster <- function(
   wf <- fit(wf, epi_data)
 
   # get the forecast date for the forecast function
-  if (is.null(args_list$adjust_latency)) {
+  if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -247,6 +247,8 @@ arx_fcast_epi_workflow <- function(
 #'   - `"extend_lags"`: increase the lags so they're relative to the actual
 #'   forecast date. For example, if the lags are `c(0,7,14)` and the last day of
 #'   data was 3 days ago, the lags become `c(3,10,17)`.
+#' @param warn_latency by default, `step_adjust_latency` warns the user if the
+#'   latency is large. If this is `FALSE`, that warning is turned off.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
 #'   prediction intervals. These are created by computing the quantiles of
 #'   training residuals. A `NULL` value will result in point forecasts only.
@@ -284,6 +286,7 @@ arx_args_list <- function(
     forecast_date = NULL,
     target_date = NULL,
     adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
+    warn_latency = TRUE,
     quantile_levels = c(0.05, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
@@ -297,7 +300,7 @@ arx_args_list <- function(
   if (is.list(lags)) lags <- unlist(lags)
 
   adjust_latency <- rlang::arg_match(adjust_latency)
-  arg_is_scalar(ahead, n_training, symmetrize, nonneg, adjust_latency)
+  arg_is_scalar(ahead, n_training, symmetrize, nonneg, adjust_latency, warn_latency)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
   arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
@@ -331,6 +334,7 @@ arx_args_list <- function(
       forecast_date,
       target_date,
       adjust_latency,
+      warn_latency,
       symmetrize,
       nonneg,
       max_lags,
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index d5776940e..78aaafe7e 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -265,11 +265,11 @@ then the previous `step_epi_lag`s won't work with modified data.",
         epi_keys_checked = epi_keys_checked,
         check_latency_length = check_latency_length,
         trained = FALSE,
-        forecast_date = fixed_forecast_date,
+        fixed_forecast_date = fixed_forecast_date,
+        forecast_date = NULL,
         latency = fixed_latency,
         latency_table = NULL,
         metadata = NULL,
-        keys = key_colnames(recipe),
         columns = NULL,
         skip = FALSE,
         id = id
@@ -278,8 +278,8 @@ then the previous `step_epi_lag`s won't work with modified data.",
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, forecast_date, latency, latency_table,
-           metadata, time_type, keys, method, epi_keys_checked, check_latency_length, columns, skip,
+  function(terms, role, trained, fixed_forecast_date, forecast_date, latency, latency_table,
+           metadata, time_type, method, epi_keys_checked, check_latency_length, columns, skip,
            id) {
     step(
       subclass = "adjust_latency",
@@ -289,47 +289,28 @@ step_adjust_latency_new <-
       epi_keys_checked = epi_keys_checked,
       check_latency_length = check_latency_length,
       trained = trained,
+      fixed_forecast_date = fixed_forecast_date,
       forecast_date = forecast_date,
       latency = latency,
       latency_table = latency_table,
       metadata = metadata,
-      keys = keys,
       columns = columns,
       skip = skip,
       id = id
     )
   }
+
 # lags introduces max(lags) NA's after the max_time_value.
 #' @export
 #' @importFrom glue glue
 #' @importFrom dplyr rowwise
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
-  sign_shift <- get_sign(x)
   latency <- x$latency
-  forecast_date <- x$forecast_date %||% set_forecast_date(training, info, x$epi_keys_checked, latency)
-  # construct the latency table
-  latency_table <- names(training)[!names(training) %in% key_colnames(training)] %>%
-    tibble(col_name = .)
-  if (length(recipes_eval_select(x$terms, training, info)) > 0) {
-    latency_table <- latency_table %>% filter(col_name %in%
-      recipes_eval_select(x$terms, training, info))
-  }
+  forecast_date <- x$fixed_forecast_date %||% get_forecast_date(training, info, x$epi_keys_checked, latency)
 
-  if (is.null(latency)) {
-    latency_table <- latency_table %>%
-      rowwise() %>%
-      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, x$epi_keys_checked))
-  } else if (length(latency) > 1) {
-    # if latency has a length, it must also have named elements. We assign based on comparing the name in the list
-    # with the column names, and drop any which don't have a latency assigned
-    latency_table <- latency_table %>%
-      filter(col_name %in% names(latency)) %>%
-      rowwise() %>%
-      mutate(latency = unname(latency[names(latency) == col_name])) %>%
-      ungroup()
-  } else {
-    latency_table <- latency_table %>% mutate(latency = latency)
-  }
+  latency_table <- get_latency_table(
+    training, NULL, forecast_date, latency,
+    get_sign(x), x$epi_keys_checked, info, x$terms)
   attributes(training)$metadata$latency_table <- latency_table
   # get the columns used, even if it's all of them
   terms_used <- x$terms
@@ -338,18 +319,16 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       filter(role == "raw") %>%
       pull(variable)
   }
-
-
-
+  
   step_adjust_latency_new(
     terms = x$terms,
     role = x$role,
     trained = TRUE,
+    fixed_forecast_date = x$fixed_forecast_date,
     forecast_date = forecast_date,
-    latency = unique(latency_table$latency),
+    latency = x$latency,
     latency_table = latency_table,
     metadata = attributes(training)$metadata,
-    keys = x$keys,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
     check_latency_length = x$check_latency_length,
@@ -363,18 +342,50 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @importFrom tidyr fill
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
-  if (!isa(new_data, "epi_df")) {
-    # TODO if new_data actually has keys other than geo_value and time_value, this is going to cause problems
+  if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
     new_data <- as_epi_df(new_data)
     attributes(new_data)$metadata <- object$metadata
     attributes(new_data)$metadata$as_of <- object$forecast_date
+  } else {
+    latency <- object$latency
+    current_forecast_date <- object$fixed_forecast_date %||%
+      get_forecast_date(
+        new_data, NULL, object$epi_keys_checked, latency, c(key_colnames(new_data), object$columns)
+      )
+    local_latency_table <- get_latency_table(
+      new_data, object$columns, current_forecast_date, latency,
+      get_sign(object), object$epi_keys_checked, NULL, NULL)
+    comparison_table <- local_latency_table %>%
+      ungroup() %>%
+      dplyr::full_join(object$latency_table %>% ungroup(), by = join_by(col_name), suffix = c(".bake",".prep")) %>%
+      mutate(bakeMprep = latency.bake - latency.prep)
+    if (any(comparison_table$bakeMprep > 0)) {
+      cli_abort(paste0(
+        "There is more latency at bake time than there was at prep time.",
+        " You will need to fit a model with more latency to predict on this dataset."),
+        class = "epipredict__latency__bake_prep_difference_error",
+        latency_table = comparison_table)
+    }
+    if (any(comparison_table$bakeMprep < 0)) {
+      cli_warn(paste0(
+        "There is less latency at bake time than there was at prep time.",
+        " This will still fit, but will discard the most recent data."),
+        class = "epipredict__latency__bake_prep_difference_warn",
+        latency_table = comparison_table)
+    }
+    if (current_forecast_date != object$forecast_date){
+      cli_warn(paste0(
+        "The forecast date differs from the one set at train time; ",
+        " this means any dates added by `layer_forecast_date` will be inaccurate."),
+        class = "epipredict__latency__bake_prep_forecast_date_warn"
+      )
+    }
   }
   if (object$method == "extend_ahead" || object$method == "extend_lags") {
     attributes(new_data)$metadata$latency_method <- object$method
     attributes(new_data)$metadata$shift_sign <- get_sign(object)
     attributes(new_data)$metadata$latency_table <- object$latency_table
     attributes(new_data)$metadata$forecast_date <- object$forecast_date
-    keys <- object$keys
   } else if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
@@ -399,13 +410,13 @@ print.step_adjust_latency <-
     if (!is.null(x$forecast_date)) {
       conj <- "with forecast date"
       extra_text <- x$forecast_date
-    } else if (!is.null(x$latency)) {
-      conj <- if (length(x$latency == 1)) {
+    } else if (!is.null(x$latency_table)) {
+      conj <- if (nrow(x$latency) == 1) {
         "with latency"
       } else {
         "with latencies"
       }
-      extra_text <- x$latency
+      extra_text <- unique(x$latency_table$latency)
     } else {
       conj <- "with latency"
       extra_text <- "set at train time"
diff --git a/R/utils-latency.R b/R/utils-latency.R
index f53f01ca3..8e8655e2a 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -15,7 +15,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
     return(NULL)
   }
   rel_list <- recipe$steps %>%
-    purrr::map(extract_named_rates) %>%
+    map(extract_named_rates) %>%
     unlist(recursive = FALSE) %>%
     split(c("term", "shift", "prefix"))
   relevant_shifts <- tibble(
@@ -34,26 +34,28 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @importFrom dplyr select
 #' @importFrom tidyr drop_na
 #' @importFrom utils capture.output
-set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
-  original_columns <- info %>%
-    filter(source == "original") %>%
-    pull(variable)
-  # make sure that there's enough column names
-  if (length(original_columns) < 3) {
-    cli_abort(
-      glue::glue(
-        "The original columns of `time_value`, ",
-        "`geo_value` and at least one signal. The current colums are \n",
-        paste(capture.output(object$info), collapse = "\n\n")
-      ),
-      class = "epipredict__set_forecast_date__too_few_data_columns"
-    )
+get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns = NULL) {
+  if (is.null(columns)) {
+    columns <- info %>%
+      filter(source == "original") %>%
+      pull(variable)
+    # make sure that there's enough column names
+    if (length(columns) < 3) {
+      cli_abort(
+        glue::glue(
+          "The original columns of `time_value`, ",
+          "`geo_value` and at least one signal. The current colums are \n",
+          paste(capture.output(object$info), collapse = "\n\n")
+        ),
+        class = "epipredict__get_forecast_date__too_few_data_columns"
+      )
+    }
   }
   # the source data determines the actual time_values
   # these are the non-na time_values;
   # get the minimum value across the checked epi_keys' maximum time values
   max_time <- new_data %>%
-    select(all_of(original_columns)) %>%
+    select(all_of(columns)) %>%
     drop_na()
   # null and "" don't work in `group_by`
   if (!is.null(epi_keys_checked) && (epi_keys_checked != "")) {
@@ -77,7 +79,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
         "matching `time_value`'s type of ",
         "{class(max_time)}."
       ),
-      class = "epipredict__set_forecast_date__wrong_time_value_type_error"
+      class = "epipredict__get_forecast_date__wrong_time_value_type_error"
     )
   }
   if (is.null(forecast_date) || is.na(forecast_date)) {
@@ -86,7 +88,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
         "epi_data's `forecast_date` was {forecast_date}, setting to ",
         "the latest time value, {max_time}."
       ),
-      class = "epipredict__set_forecast_date__max_time_warning"
+      class = "epipredict__get_forecast_date__max_time_warning"
     )
     forecast_date <- max_time
   } else if (forecast_date < max_time) {
@@ -96,7 +98,7 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
         "recent data ({max_time}). Remove before ",
         "predicting."
       ),
-      class = "epipredict__set_forecast_date__misordered_forecast_date_error"
+      class = "epipredict__get_forecast_date__misordered_forecast_date_error"
     )
   }
   # TODO cover the rest of the possible types for as_of and max_time...
@@ -135,7 +137,7 @@ get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_ch
 #'   a potentially different max_time_value
 #' @keywords internal
 get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new_data) {
-  max_time_value <- as.Date(max(
+  forecast_date <- as.Date(max(
     workflow_max_time_value,
     this_recipe$max_time_value,
     max(new_data$time_value)
@@ -149,16 +151,16 @@ get_forecast_date_in_layer <- function(this_recipe, workflow_max_time_value, new
       }
     ) %>% Filter(Negate(is.null), .)
     if (length(handpicked_forecast_date) > 0) {
-      max_time_value <- handpicked_forecast_date[[1]]
+      forecast_date <- handpicked_forecast_date[[1]]
     } else {
       # if we haven't chosen one, use either the max_time_value or the as_of
-      max_time_value <- max(
-        max_time_value,
+      forecast_date <- max(
+        forecast_date,
         attributes(new_data)$metadata$as_of
       )
     }
   }
-  max_time_value
+  forecast_date
 }
 
 
@@ -208,17 +210,7 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
     slice(1:min(across(all_of(columns_to_complete), count_single_column))) %>%
     bind_rows(filled_values) %>%
     arrange(across(all_of(key_colnames(x)))) %>%
-    ungroup() %>%
-    group_by(across(all_of(get_grouping_columns(x))))
-}
-
-#' return the names of the grouped columns, or `NULL`
-#' @param x an epi_df
-#' @keywords internal
-#' @importFrom utils head
-get_grouping_columns <- function(x) {
-  group_names <- names(attributes(x)$groups)
-  head(group_names, -1)
+    ungroup()
 }
 
 #' get the location of the last real value
@@ -284,3 +276,45 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
     )
   }
 }
+
+#' create the latency table
+#' This is a table of column names and the latency adjustment necessary for that column. An example:
+#'
+#'   col_name   latency
+#'   <chr>        <int>
+#' 1 case_rate        5
+#' 2 death_rate       5
+#' @keywords internal
+#' @importFrom dplyr rowwise
+get_latency_table <- function(training, columns, forecast_date, latency, sign_shift, epi_keys_checked, info, terms) {
+  if (is.null(columns)) {
+    columns <- recipes_eval_select(terms, training, info)
+  }
+  # construct the latency table
+  latency_table <- names(training)[!names(training) %in% key_colnames(training)] %>%
+    tibble(col_name = .)
+  if (length(columns) > 0) {
+    latency_table <- latency_table %>% filter(col_name %in%
+      columns)
+  }
+
+  if (is.null(latency)) {
+    latency_table <- latency_table %>%
+      rowwise() %>%
+      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, epi_keys_checked))
+  } else if (length(latency) > 1) {
+    # if latency has a length, it must also have named elements. We assign based on comparing the name in the list
+    # with the column names, and drop any which don't have a latency assigned
+    latency_table <- latency_table %>%
+      filter(col_name %in% names(latency)) %>%
+      rowwise() %>%
+      mutate(latency = unname(latency[names(latency) == col_name]))
+  } else {
+    tmp_latency_table <- latency_table %>%
+      rowwise() %>%
+      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, epi_keys_checked))
+    if (latency )
+    latency_table <- latency_table %>% mutate(latency = latency)
+  }
+  return(latency_table %>% ungroup())
+}
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 39ef23e09..f28cdefab 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -11,6 +11,7 @@ arx_args_list(
   forecast_date = NULL,
   target_date = NULL,
   adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
+  warn_latency = TRUE,
   quantile_levels = c(0.05, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
@@ -58,6 +59,9 @@ forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
 data was 3 days ago, the lags become \code{c(3,10,17)}.
 }}
 
+\item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
+latency is large. If this is \code{FALSE}, that warning is turned off.}
+
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
 training residuals. A \code{NULL} value will result in point forecasts only.}
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 4873d59ae..a229b67c0 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -10,7 +10,8 @@ arx_class_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
-  adjust_latency = NULL,
+  adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
+  warn_latency = TRUE,
   outcome_transform = c("growth_rate", "lag_difference"),
   breaks = 0.25,
   horizon = 7L,
@@ -60,6 +61,9 @@ forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
 data was 3 days ago, the lags become \code{c(3,10,17)}.
 }}
 
+\item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
+latency is large. If this is \code{FALSE}, that warning is turned off.}
+
 \item{outcome_transform}{Scalar character. Whether the outcome should
 be created using growth rates (as the predictors are) or lagged
 differences. The second case is closer to the requirements for the
diff --git a/man/set_forecast_date.Rd b/man/get_forecast_date.Rd
similarity index 71%
rename from man/set_forecast_date.Rd
rename to man/get_forecast_date.Rd
index 29dd98d33..6a35fff04 100644
--- a/man/set_forecast_date.Rd
+++ b/man/get_forecast_date.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/utils-latency.R
-\name{set_forecast_date}
-\alias{set_forecast_date}
+\name{get_forecast_date}
+\alias{get_forecast_date}
 \title{Extract the as_of for the forecast date, and make sure there's nothing very off about it.}
 \usage{
-set_forecast_date(new_data, info, epi_keys_checked, latency)
+get_forecast_date(new_data, info, epi_keys_checked, latency, columns = NULL)
 }
 \description{
 Extract the as_of for the forecast date, and make sure there's nothing very off about it.
diff --git a/man/get_grouping_columns.Rd b/man/get_grouping_columns.Rd
deleted file mode 100644
index f8b61af42..000000000
--- a/man/get_grouping_columns.Rd
+++ /dev/null
@@ -1,15 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-latency.R
-\name{get_grouping_columns}
-\alias{get_grouping_columns}
-\title{return the names of the grouped columns, or \code{NULL}}
-\usage{
-get_grouping_columns(x)
-}
-\arguments{
-\item{x}{an epi_df}
-}
-\description{
-return the names of the grouped columns, or \code{NULL}
-}
-\keyword{internal}
diff --git a/man/get_latency_table.Rd b/man/get_latency_table.Rd
new file mode 100644
index 000000000..ae309c944
--- /dev/null
+++ b/man/get_latency_table.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{get_latency_table}
+\alias{get_latency_table}
+\title{create the latency table
+This is a table of column names and the latency adjustment necessary for that column. An example:}
+\usage{
+get_latency_table(
+  training,
+  columns,
+  forecast_date,
+  latency,
+  sign_shift,
+  epi_keys_checked,
+  info,
+  terms
+)
+}
+\description{
+col_name   latency
+\if{html}{\out{<chr>}}        \if{html}{\out{<int>}}
+1 case_rate        5
+2 death_rate       5
+}
+\keyword{internal}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 80d6aa3ef..6ea9581c0 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -260,6 +260,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with modified
+#> data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index d408ba7d8..3ba6453a0 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -84,7 +84,7 @@ test_that("get_latency infers max_time to be the minimum `max time` across group
 })
 
 
-test_that("set_forecast_date works", {
+test_that("get_forecast_date works", {
   info <- tribble(
     ~variable, ~type, ~role, ~source,
     "time_value", "date", "time_value", "original",
@@ -93,9 +93,9 @@ test_that("set_forecast_date works", {
     "death_rate", "numeric", "raw", "original",
     "not_real", "numeric", "predictor", "derived"
   )
-  expect_equal(set_forecast_date(modified_data, info, "geo_value", NULL), as_of)
-  expect_equal(set_forecast_date(modified_data, info, "", NULL), as_of)
-  expect_equal(set_forecast_date(modified_data, info, NULL, NULL), as_of)
+  expect_equal(get_forecast_date(modified_data, info, "geo_value", NULL), as_of)
+  expect_equal(get_forecast_date(modified_data, info, "", NULL), as_of)
+  expect_equal(get_forecast_date(modified_data, info, NULL, NULL), as_of)
 })
 
 test_that("pad_to_end works correctly", {

From 50cecaefd7227819ba16320fe3b8996b02c675ee Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 16 Sep 2024 14:49:38 -0500
Subject: [PATCH 089/387] moving step checks to a separate function, styler

---
 R/arx_classifier.R         |  2 +-
 R/step_adjust_latency.R    | 83 +++++++++++++-------------------------
 R/utils-latency.R          | 57 +++++++++++++++++++++++++-
 man/step_adjust_latency.Rd |  4 +-
 4 files changed, 87 insertions(+), 59 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 9cc76052f..c5166a176 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -192,7 +192,7 @@ arx_class_epi_workflow <- function(
   }
   ahead_out_name <- glue::glue("ahead_[0-9]*_{pre_out_name}")
   method_adjust_latency <- args_list$adjust_latency
-  if (method_adjust_latency   != "none") {
+  if (method_adjust_latency != "none") {
     # only extend_ahead is supported atm
     r <- r %>% step_adjust_latency(!!pre_out_name,
       fixed_forecast_date = forecast_date,
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 78aaafe7e..1ac3a4fc8 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -207,44 +207,9 @@ step_adjust_latency <-
            fixed_forecast_date = NULL,
            check_latency_length = TRUE,
            id = rand_id("adjust_latency")) {
-    arg_is_chr_scalar(id, method)
-    if (!is_epi_recipe(recipe)) {
-      cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
-        class = "epipredict__step_adjust_latency__epi_recipe_only"
-      )
-    }
-    if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
-      cli_warn(
-        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
-        class = "epipredict__step_adjust_latency__misordered_step_warning"
-      )
-    } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
-      cli_warn(
-        "If `method` is {.val extend_lags} or {.val locf},
-then the previous `step_epi_lag`s won't work with modified data.",
-        class = "epipredict__step_adjust_latency__misordered_step_warning"
-      )
-    } else if ((method == "locf") && (length(recipe$steps) > 0)) {
-      cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others",
-        class = "epipredict__step_adjust_latency__misordered_step_warning"
-      )
-    }
-    if (detect_step(recipe, "naomit")) {
-      cli_abort("adjust_latency needs to occur before any `NA` removal,
-                      as columns may be moved around", class = "epipredict__step_adjust_latency__post_NA_error")
-    }
-    if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
-      cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
- can be non-`NULL` at a time!", class = "epipredict__step_adjust_latency__too_many_args_error")
-    }
-    if (length(fixed_latency > 1)) {
-      template <- recipe$template
-      data_names <- names(template)[!names(template) %in% key_colnames(template)]
-      wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
-      if (length(wrong_names) > 0) {
-        cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
-      }
-    }
+    step_adjust_latency_checks(
+      id, method, recipe, fixed_latency, fixed_forecast_date
+    )
     method <- rlang::arg_match(method)
     if (method == "extend_ahead") {
       rel_step_type <- "step_epi_ahead"
@@ -310,7 +275,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 
   latency_table <- get_latency_table(
     training, NULL, forecast_date, latency,
-    get_sign(x), x$epi_keys_checked, info, x$terms)
+    get_sign(x), x$epi_keys_checked, info, x$terms
+  )
   attributes(training)$metadata$latency_table <- latency_table
   # get the columns used, even if it's all of them
   terms_used <- x$terms
@@ -319,7 +285,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
       filter(role == "raw") %>%
       pull(variable)
   }
-  
+
   step_adjust_latency_new(
     terms = x$terms,
     role = x$role,
@@ -354,29 +320,38 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
       )
     local_latency_table <- get_latency_table(
       new_data, object$columns, current_forecast_date, latency,
-      get_sign(object), object$epi_keys_checked, NULL, NULL)
+      get_sign(object), object$epi_keys_checked, NULL, NULL
+    )
     comparison_table <- local_latency_table %>%
       ungroup() %>%
-      dplyr::full_join(object$latency_table %>% ungroup(), by = join_by(col_name), suffix = c(".bake",".prep")) %>%
+      dplyr::full_join(object$latency_table %>% ungroup(), by = join_by(col_name), suffix = c(".bake", ".prep")) %>%
       mutate(bakeMprep = latency.bake - latency.prep)
     if (any(comparison_table$bakeMprep > 0)) {
-      cli_abort(paste0(
-        "There is more latency at bake time than there was at prep time.",
-        " You will need to fit a model with more latency to predict on this dataset."),
+      cli_abort(
+        paste0(
+          "There is more latency at bake time than there was at prep time.",
+          " You will need to fit a model with more latency to predict on this dataset."
+        ),
         class = "epipredict__latency__bake_prep_difference_error",
-        latency_table = comparison_table)
+        latency_table = comparison_table
+      )
     }
     if (any(comparison_table$bakeMprep < 0)) {
-      cli_warn(paste0(
-        "There is less latency at bake time than there was at prep time.",
-        " This will still fit, but will discard the most recent data."),
+      cli_warn(
+        paste0(
+          "There is less latency at bake time than there was at prep time.",
+          " This will still fit, but will discard the most recent data."
+        ),
         class = "epipredict__latency__bake_prep_difference_warn",
-        latency_table = comparison_table)
+        latency_table = comparison_table
+      )
     }
-    if (current_forecast_date != object$forecast_date){
-      cli_warn(paste0(
-        "The forecast date differs from the one set at train time; ",
-        " this means any dates added by `layer_forecast_date` will be inaccurate."),
+    if (current_forecast_date != object$forecast_date) {
+      cli_warn(
+        paste0(
+          "The forecast date differs from the one set at train time; ",
+          " this means any dates added by `layer_forecast_date` will be inaccurate."
+        ),
         class = "epipredict__latency__bake_prep_forecast_date_warn"
       )
     }
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 8e8655e2a..e3df8bb61 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -313,8 +313,61 @@ get_latency_table <- function(training, columns, forecast_date, latency, sign_sh
     tmp_latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, epi_keys_checked))
-    if (latency )
-    latency_table <- latency_table %>% mutate(latency = latency)
+    if (latency) {
+      latency_table <- latency_table %>% mutate(latency = latency)
+    }
   }
   return(latency_table %>% ungroup())
 }
+
+
+#' checks: the recipe type, whether a previous step is the relevant epi_shift,
+#' that either `fixed_latency` or `fixed_forecast_date` is non-null, and that
+#' `fixed_latency` only references columns that exist at the time of the step
+#' inclusion
+#' @keywords internal
+step_adjust_latency_checks <- function(id, method, recipe, fixed_latency, fixed_forecast_date, call = caller_env()) {
+  arg_is_chr_scalar(id, method)
+  if (!is_epi_recipe(recipe)) {
+    cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
+      class = "epipredict__step_adjust_latency__epi_recipe_only"
+    )
+  }
+  if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
+    cli_warn(
+      "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
+      class = "epipredict__step_adjust_latency__misordered_step_warning"
+    )
+  } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
+    cli_warn(
+      "If `method` is {.val extend_lags} or {.val locf},
+then the previous `step_epi_lag`s won't work with modified data.",
+      class = "epipredict__step_adjust_latency__misordered_step_warning"
+    )
+  } else if ((method == "locf") && (length(recipe$steps) > 0)) {
+    cli_warn(
+      paste0(
+        "There are steps before `step_adjust_latency`.",
+        " With the method {.val locf}, it is recommended to include this step before any others"
+      ),
+      class = "epipredict__step_adjust_latency__misordered_step_warning"
+    )
+  }
+  if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
+    cli_abort(
+      "Only one of `fixed_latency` and `fixed_forecast_date` can be non-`NULL` at a time!",
+      class = "epipredict__step_adjust_latency__too_many_args_error"
+    )
+  }
+  if (length(fixed_latency > 1)) {
+    template <- recipe$template
+    data_names <- names(template)[!names(template) %in% key_colnames(template)]
+    wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
+    if (length(wrong_names) > 0) {
+      cli_abort(
+        "{.val fixed_latency} contains names not in the template dataset: {wrong_names}",
+        class = "epipredict__step_adjust_latency__undefined_names_error"
+      )
+    }
+  }
+}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 6ea9581c0..e1e516a3c 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -260,8 +260,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with modified
-#> data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous
+#> `step_epi_lag`s won't work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as

From 7f18662a5622d5052f23e54b1f7f848e2f34afd8 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 16 Sep 2024 15:08:16 -0500
Subject: [PATCH 090/387] NAomit can happen before (but probably shouldn't)

---
 tests/testthat/test-step_adjust_latency.R | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 1e9ac4d15..93d4b0660 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -369,16 +369,6 @@ test_that("epi_adjust_latency warns there's steps before it", {
   )
 })
 
-test_that("epi_adjust_latency warns against removing NA's beforehand", {
-  expect_error(
-    r5 <- epi_recipe(x) %>%
-      step_epi_naomit() %>%
-      step_adjust_latency(method = "extend_lags") %>%
-      step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-      step_epi_lag(case_rate, lag = c(1, 5)),
-    regexp = "adjust_latency needs to occur before any `NA` removal"
-  )
-})
 # TODO check that epi_adjust_latency errors for nonsense `as_of`'s
 
 

From 4b37f7ef9fed757c503f43eae9d0bbaf6d2535e6 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 16 Sep 2024 15:33:17 -0700
Subject: [PATCH 091/387] draft implementation

Strategy:
1. Add the grid created in ahead/lag steps to the object
2. At bake time, directly modify these if needed.
---
 R/epi_recipe.R          |  4 +++
 R/epi_shift.R           | 11 ++------
 R/step_adjust_latency.R | 50 ++++++++++++++++++----------------
 R/step_epi_shift.R      | 22 ++++++++++++---
 R/utils-latency.R       | 60 ++++++++++++++++++++++++++++++++++-------
 5 files changed, 102 insertions(+), 45 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 7db9b9179..44b7b5a5b 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -568,6 +568,10 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
     }
     composition <- "tibble"
   }
+
+  ## Latency checks/adjustments
+  object <- adjust_recipe_latency_before_bake(object)
+
   new_data <- NextMethod("bake")
   if (!is.null(meta)) {
     # Baking should have dropped epi_df-ness and metadata. Re-infer some
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 7088fb341..2787d33fd 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -51,15 +51,8 @@ add_shifted_columns <- function(new_data, object, amount) {
     # adding zero if there's no latency table
     rel_latency <- tibble(col_name = object$columns, latency = 0L)
   }
-  grid <- expand_grid(col = object$columns, amount = sign_shift * amount) %>%
-    left_join(rel_latency, by = join_by(col == col_name), ) %>%
-    tidyr::replace_na(list(latency = 0)) %>%
-    mutate(shift_val = amount + latency) %>%
-    mutate(
-      newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"), # name is always positive
-      amount = NULL,
-      latency = NULL
-    )
+  grid <- object$shift_grid %>%
+    mutate(newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"))
 
   ## ensure no name clashes
   new_data_names <- colnames(new_data)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 1ac3a4fc8..bbeb07932 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -211,13 +211,6 @@ step_adjust_latency <-
       id, method, recipe, fixed_latency, fixed_forecast_date
     )
     method <- rlang::arg_match(method)
-    if (method == "extend_ahead") {
-      rel_step_type <- "step_epi_ahead"
-      shift_name <- "ahead"
-    } else if (method == "extend_lags") {
-      rel_step_type <- "step_epi_lag"
-      shift_name <- "lag"
-    }
     if (is.null(epi_keys_checked)) {
       epi_keys_checked <- kill_time_value(key_colnames(recipe$template))
     }
@@ -226,15 +219,15 @@ step_adjust_latency <-
       step_adjust_latency_new(
         terms = enquos(...),
         role = NA,
-        method = method,
-        epi_keys_checked = epi_keys_checked,
-        check_latency_length = check_latency_length,
         trained = FALSE,
         fixed_forecast_date = fixed_forecast_date,
         forecast_date = NULL,
         latency = fixed_latency,
         latency_table = NULL,
         metadata = NULL,
+        method = method,
+        epi_keys_checked = epi_keys_checked,
+        check_latency_length = check_latency_length,
         columns = NULL,
         skip = FALSE,
         id = id
@@ -243,22 +236,22 @@ step_adjust_latency <-
   }
 
 step_adjust_latency_new <-
-  function(terms, role, trained, fixed_forecast_date, forecast_date, latency, latency_table,
-           metadata, time_type, method, epi_keys_checked, check_latency_length, columns, skip,
-           id) {
+  function(terms, role, trained, fixed_forecast_date, forecast_date, latency,
+           latency_table, metadata, method, epi_keys_checked,
+           check_latency_length, columns, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
       role = role,
-      method = method,
-      epi_keys_checked = epi_keys_checked,
-      check_latency_length = check_latency_length,
       trained = trained,
       fixed_forecast_date = fixed_forecast_date,
       forecast_date = forecast_date,
       latency = latency,
       latency_table = latency_table,
       metadata = metadata,
+      method = method,
+      epi_keys_checked = epi_keys_checked,
+      check_latency_length = check_latency_length,
       columns = columns,
       skip = skip,
       id = id
@@ -271,13 +264,13 @@ step_adjust_latency_new <-
 #' @importFrom dplyr rowwise
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   latency <- x$latency
-  forecast_date <- x$fixed_forecast_date %||% get_forecast_date(training, info, x$epi_keys_checked, latency)
+  forecast_date <- x$fixed_forecast_date %||%
+    get_forecast_date(training, info, x$epi_keys_checked, latency)
 
   latency_table <- get_latency_table(
     training, NULL, forecast_date, latency,
     get_sign(x), x$epi_keys_checked, info, x$terms
   )
-  attributes(training)$metadata$latency_table <- latency_table
   # get the columns used, even if it's all of them
   terms_used <- x$terms
   if (is_empty(terms_used)) {
@@ -310,13 +303,14 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
     new_data <- as_epi_df(new_data)
-    attributes(new_data)$metadata <- object$metadata
+    attributes(new_data)$metadata <- object$metadata # DJM: why? detected above?
     attributes(new_data)$metadata$as_of <- object$forecast_date
   } else {
     latency <- object$latency
     current_forecast_date <- object$fixed_forecast_date %||%
       get_forecast_date(
-        new_data, NULL, object$epi_keys_checked, latency, c(key_colnames(new_data), object$columns)
+        new_data, NULL, object$epi_keys_checked, latency,
+        c(key_colnames(new_data), object$columns)
       )
     local_latency_table <- get_latency_table(
       new_data, object$columns, current_forecast_date, latency,
@@ -324,7 +318,11 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     )
     comparison_table <- local_latency_table %>%
       ungroup() %>%
-      dplyr::full_join(object$latency_table %>% ungroup(), by = join_by(col_name), suffix = c(".bake", ".prep")) %>%
+      dplyr::full_join(
+        object$latency_table %>% ungroup(),
+        by = join_by(col_name),
+        suffix = c(".bake", ".prep")
+      ) %>%
       mutate(bakeMprep = latency.bake - latency.prep)
     if (any(comparison_table$bakeMprep > 0)) {
       cli_abort(
@@ -362,11 +360,14 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
     attributes(new_data)$metadata$latency_table <- object$latency_table
     attributes(new_data)$metadata$forecast_date <- object$forecast_date
   } else if (object$method == "locf") {
-    # locf doesn't need to mess with the metadata at all, it just forward-fills the requested columns
+    # locf doesn't need to mess with the metadata at all, it just forward-fills
+    # the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")
     modified_columns <- object$columns %>% unname()
     if (object$check_latency_length) {
-      check_interminable_latency(new_data, object$latency_table, modified_columns, object$forecast_date)
+      check_interminable_latency(
+        new_data, object$latency_table, modified_columns, object$forecast_date
+      )
     }
 
     new_data <- new_data %>%
@@ -379,6 +380,9 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
   }
   return(new_data)
 }
+
+
+
 #' @export
 print.step_adjust_latency <-
   function(x, width = max(20, options$width - 35), ...) {
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index f09e8ae29..6006fac19 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -79,6 +79,7 @@ step_epi_lag <-
         default = default,
         keys = key_colnames(recipe),
         columns = NULL,
+        shift_grid = NULL,
         skip = skip,
         id = id
       )
@@ -123,6 +124,7 @@ step_epi_ahead <-
         default = default,
         keys = key_colnames(recipe),
         columns = NULL,
+        shift_grid = NULL,
         skip = skip,
         id = id
       )
@@ -132,7 +134,7 @@ step_epi_ahead <-
 
 step_epi_lag_new <-
   function(terms, role, trained, lag, prefix, default, keys,
-           columns, skip, id) {
+           columns, shift_grid, skip, id) {
     recipes::step(
       subclass = "epi_lag",
       terms = terms,
@@ -143,6 +145,7 @@ step_epi_lag_new <-
       default = default,
       keys = keys,
       columns = columns,
+      shift_grid = shift_grid,
       skip = skip,
       id = id
     )
@@ -150,7 +153,7 @@ step_epi_lag_new <-
 
 step_epi_ahead_new <-
   function(terms, role, trained, ahead, prefix, default, keys,
-           columns, skip, id) {
+           columns, shift_grid, skip, id) {
     recipes::step(
       subclass = "epi_ahead",
       terms = terms,
@@ -161,6 +164,7 @@ step_epi_ahead_new <-
       default = default,
       keys = keys,
       columns = columns,
+      shift_grid = shift_grid,
       skip = skip,
       id = id
     )
@@ -170,6 +174,10 @@ step_epi_ahead_new <-
 
 #' @export
 prep.step_epi_lag <- function(x, training, info = NULL, ...) {
+  columns <- recipes::recipes_eval_select(x$terms, training, info)
+  sgn <- get_sign(x)
+  shift_grid <- expand_grid(col = columns, shift_val = sgn * x$lag)
+
   step_epi_lag_new(
     terms = x$terms,
     role = x$role,
@@ -178,7 +186,8 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
-    columns = recipes::recipes_eval_select(x$terms, training, info),
+    columns = columns,
+    shift_grid = shift_grid,
     skip = x$skip,
     id = x$id
   )
@@ -186,6 +195,10 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
 
 #' @export
 prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
+  columns <- recipes::recipes_eval_select(x$terms, training, info)
+  sgn <- get_sign(x)
+  shift_grid <- expand_grid(col = columns, shift_val = sgn * x$ahead)
+
   step_epi_ahead_new(
     terms = x$terms,
     role = x$role,
@@ -194,7 +207,8 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
     prefix = x$prefix,
     default = x$default,
     keys = x$keys,
-    columns = recipes::recipes_eval_select(x$terms, training, info),
+    columns = columns,
+    shift_grid = shift_grid,
     skip = x$skip,
     id = x$id
   )
diff --git a/R/utils-latency.R b/R/utils-latency.R
index e3df8bb61..d56a163a3 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -29,6 +29,35 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 }
 
 
+adjust_recipe_latency_before_bake <- function(object) {
+  if (detect_step(object, "adjust_latency")) {
+    latency_step <- object$steps[[which(tidy(object)$type == "adjust_latency")]]
+    if (length(latency_step) > 1) {
+      cli_abort("Only one `step_adjust_latency()` is allowed.")
+    }
+    latency_table <- latency_step$latency_table
+    method <- latency_step$method
+    if (method == "extend_ahead") {
+      loc <- which(tidy(object)$type == "epi_ahead")
+      sign_shift <- -1
+    } else if (method == "extend_lags") {
+      loc <- which(tidy(object))$type == "epi_lag"
+      sign_shift <- 1
+    }
+    if (method != "locf") {
+      for (s in seq_along(loc)) {
+        sgrid <- object$steps[[s]]$shift_grid
+        sgrid <- sgrid %>%
+          left_join(latency_table, by = join_by(col == col_name)) %>%
+          tidyr::replace_na(list(latency = 0)) %>%
+          mutate(shift_val = shift_val + latency, amount = NULL)
+        object$steps[[s]]$shift_grid <- sgrid
+      }
+    }
+  }
+  object
+}
+
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
 #' @importFrom dplyr select
@@ -277,6 +306,10 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
   }
 }
 
+`%nin%` <- function(x, table) {
+  !(x %in% table)
+}
+
 #' create the latency table
 #' This is a table of column names and the latency adjustment necessary for that column. An example:
 #'
@@ -286,33 +319,38 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
 #' 2 death_rate       5
 #' @keywords internal
 #' @importFrom dplyr rowwise
-get_latency_table <- function(training, columns, forecast_date, latency, sign_shift, epi_keys_checked, info, terms) {
+get_latency_table <- function(training, columns, forecast_date, latency,
+                              sign_shift, epi_keys_checked, info, terms) {
   if (is.null(columns)) {
     columns <- recipes_eval_select(terms, training, info)
   }
   # construct the latency table
-  latency_table <- names(training)[!names(training) %in% key_colnames(training)] %>%
-    tibble(col_name = .)
+  latency_table <- tibble(col_name = names(training)) %>%
+    filter(col_name %nin% key_colnames(training))
   if (length(columns) > 0) {
-    latency_table <- latency_table %>% filter(col_name %in%
-      columns)
+    latency_table <- latency_table %>% filter(col_name %in% columns)
   }
 
   if (is.null(latency)) {
     latency_table <- latency_table %>%
       rowwise() %>%
-      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, epi_keys_checked))
+      mutate(latency = get_latency(
+        training, forecast_date, col_name, sign_shift, epi_keys_checked
+      ))
   } else if (length(latency) > 1) {
-    # if latency has a length, it must also have named elements. We assign based on comparing the name in the list
+    # if latency has a length, it must also have named elements.
+    # We assign based on comparing the name in the list
     # with the column names, and drop any which don't have a latency assigned
     latency_table <- latency_table %>%
       filter(col_name %in% names(latency)) %>%
       rowwise() %>%
       mutate(latency = unname(latency[names(latency) == col_name]))
   } else {
-    tmp_latency_table <- latency_table %>%
+    latency_table <- latency_table %>%
       rowwise() %>%
-      mutate(latency = get_latency(training, forecast_date, col_name, sign_shift, epi_keys_checked))
+      mutate(latency = get_latency(
+        training, forecast_date, col_name, sign_shift, epi_keys_checked
+      ))
     if (latency) {
       latency_table <- latency_table %>% mutate(latency = latency)
     }
@@ -328,6 +366,10 @@ get_latency_table <- function(training, columns, forecast_date, latency, sign_sh
 #' @keywords internal
 step_adjust_latency_checks <- function(id, method, recipe, fixed_latency, fixed_forecast_date, call = caller_env()) {
   arg_is_chr_scalar(id, method)
+  if (detect_step(recipe, "adjust_latency")) {
+    cli_abort("Only one `step_adjust_latency()` can be included in a recipe.",
+      class = "epipredict__step_adjust_latency__multiple_steps")
+  }
   if (!is_epi_recipe(recipe)) {
     cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",
       class = "epipredict__step_adjust_latency__epi_recipe_only"

From 9a7abc1c02b02e62bcdc36e3005be321d148912b Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 16 Sep 2024 15:34:10 -0700
Subject: [PATCH 092/387] refactor the utility fun

---
 R/utils-latency.R | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index d56a163a3..a3c0c679e 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -28,34 +28,33 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
   return(relevant_shifts)
 }
 
-
-adjust_recipe_latency_before_bake <- function(object) {
-  if (detect_step(object, "adjust_latency")) {
-    latency_step <- object$steps[[which(tidy(object)$type == "adjust_latency")]]
-    if (length(latency_step) > 1) {
-      cli_abort("Only one `step_adjust_latency()` is allowed.")
-    }
+#
+adjust_recipe_latency_before_bake <- function(rec) {
+  if (detect_step(rec, "adjust_latency")) {
+    step_types <- tidy(rec)$type
+    # can only be 1, or we would have aborted on recipe creation
+    latency_step <- rec$steps[[which(step_types == "adjust_latency")]]
     latency_table <- latency_step$latency_table
     method <- latency_step$method
     if (method == "extend_ahead") {
-      loc <- which(tidy(object)$type == "epi_ahead")
+      loc <- which(step_types == "epi_ahead")
       sign_shift <- -1
     } else if (method == "extend_lags") {
-      loc <- which(tidy(object))$type == "epi_lag"
+      loc <- which(step_types == "epi_lag")
       sign_shift <- 1
     }
     if (method != "locf") {
       for (s in seq_along(loc)) {
-        sgrid <- object$steps[[s]]$shift_grid
+        sgrid <- rec$steps[[s]]$shift_grid
         sgrid <- sgrid %>%
           left_join(latency_table, by = join_by(col == col_name)) %>%
           tidyr::replace_na(list(latency = 0)) %>%
           mutate(shift_val = shift_val + latency, amount = NULL)
-        object$steps[[s]]$shift_grid <- sgrid
+        rec$steps[[s]]$shift_grid <- sgrid
       }
     }
   }
-  object
+  rec
 }
 
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.

From 82f1165a50af1a4127b8145520ebbe6a7363cf51 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 16 Sep 2024 15:41:50 -0700
Subject: [PATCH 093/387] only do processing if locf

---
 R/step_adjust_latency.R | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index bbeb07932..afd991a07 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -354,12 +354,7 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
       )
     }
   }
-  if (object$method == "extend_ahead" || object$method == "extend_lags") {
-    attributes(new_data)$metadata$latency_method <- object$method
-    attributes(new_data)$metadata$shift_sign <- get_sign(object)
-    attributes(new_data)$metadata$latency_table <- object$latency_table
-    attributes(new_data)$metadata$forecast_date <- object$forecast_date
-  } else if (object$method == "locf") {
+  if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills
     # the requested columns
     rel_keys <- setdiff(key_colnames(new_data), "time_value")

From 9044d5a5564139e34176a84dc9967539c2d10262 Mon Sep 17 00:00:00 2001
From: Daniel McDonald <dajmcdon@gmail.com>
Date: Mon, 16 Sep 2024 17:09:27 -0700
Subject: [PATCH 094/387] Update R/utils-latency.R

Co-authored-by: David Weber <david.weber2@pm.me>
---
 R/utils-latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index a3c0c679e..6ebcd04e2 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -44,7 +44,7 @@ adjust_recipe_latency_before_bake <- function(rec) {
       sign_shift <- 1
     }
     if (method != "locf") {
-      for (s in seq_along(loc)) {
+      for (s in loc) {
         sgrid <- rec$steps[[s]]$shift_grid
         sgrid <- sgrid %>%
           left_join(latency_table, by = join_by(col == col_name)) %>%

From 4a4a90cd7247105ee930c95e77235ba6255d79fb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 16 Sep 2024 17:43:47 -0700
Subject: [PATCH 095/387] fix needles/haystack bug

---
 R/epi_shift.R      | 15 ++-------------
 R/step_epi_shift.R | 13 +++++++++++--
 R/utils-latency.R  |  3 ++-
 3 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/R/epi_shift.R b/R/epi_shift.R
index 2787d33fd..f9d5501cc 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -40,19 +40,8 @@ get_sign <- function(object) {
 #' @importFrom tidyr expand_grid
 #' @importFrom dplyr join_by
 add_shifted_columns <- function(new_data, object, amount) {
-  sign_shift <- get_sign(object)
-  latency_table <- attributes(new_data)$metadata$latency_table
-  shift_sign_lat <- attributes(new_data)$metadata$shift_sign
-  if (!is.null(latency_table) &&
-    shift_sign_lat == sign_shift) {
-    # get the actually used latencies
-    rel_latency <- latency_table %>% filter(col_name %in% object$columns)
-  } else {
-    # adding zero if there's no latency table
-    rel_latency <- tibble(col_name = object$columns, latency = 0L)
-  }
-  grid <- object$shift_grid %>%
-    mutate(newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"))
+
+  grid <- object$shift_grid
 
   ## ensure no name clashes
   new_data_names <- colnames(new_data)
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 6006fac19..098dd7eff 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -176,7 +176,12 @@ step_epi_ahead_new <-
 prep.step_epi_lag <- function(x, training, info = NULL, ...) {
   columns <- recipes::recipes_eval_select(x$terms, training, info)
   sgn <- get_sign(x)
-  shift_grid <- expand_grid(col = columns, shift_val = sgn * x$lag)
+  shift_grid <- expand_grid(
+    col = columns,
+    shift_val = sgn * x$lag,
+    newname = glue::glue("{x$prefix}{abs(shift_val)}_{col}")
+  )
+
 
   step_epi_lag_new(
     terms = x$terms,
@@ -197,7 +202,11 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
 prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
   columns <- recipes::recipes_eval_select(x$terms, training, info)
   sgn <- get_sign(x)
-  shift_grid <- expand_grid(col = columns, shift_val = sgn * x$ahead)
+  shift_grid <- expand_grid(
+    col = columns,
+    shift_val = sgn * x$ahead,
+    newname = glue::glue("{x$prefix}{abs(shift_val)}_{col}")
+  )
 
   step_epi_ahead_new(
     terms = x$terms,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 6ebcd04e2..1c3b5ea2b 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -45,11 +45,12 @@ adjust_recipe_latency_before_bake <- function(rec) {
     }
     if (method != "locf") {
       for (s in loc) {
+        pfx <- rec$steps[[s]]$prefix
         sgrid <- rec$steps[[s]]$shift_grid
         sgrid <- sgrid %>%
           left_join(latency_table, by = join_by(col == col_name)) %>%
           tidyr::replace_na(list(latency = 0)) %>%
-          mutate(shift_val = shift_val + latency, amount = NULL)
+          mutate(shift_val = shift_val + latency, amount = NULL, latency = NULL)
         rec$steps[[s]]$shift_grid <- sgrid
       }
     }

From b1298299244dfbecfca0a3e7c00ad1bb44277343 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 17 Sep 2024 15:51:12 -0500
Subject: [PATCH 096/387] some formatting

---
 R/epi_shift.R     | 1 -
 R/utils-latency.R | 3 ++-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/epi_shift.R b/R/epi_shift.R
index f9d5501cc..3d82a321f 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -40,7 +40,6 @@ get_sign <- function(object) {
 #' @importFrom tidyr expand_grid
 #' @importFrom dplyr join_by
 add_shifted_columns <- function(new_data, object, amount) {
-
   grid <- object$shift_grid
 
   ## ensure no name clashes
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 1c3b5ea2b..fd01280bc 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -368,7 +368,8 @@ step_adjust_latency_checks <- function(id, method, recipe, fixed_latency, fixed_
   arg_is_chr_scalar(id, method)
   if (detect_step(recipe, "adjust_latency")) {
     cli_abort("Only one `step_adjust_latency()` can be included in a recipe.",
-      class = "epipredict__step_adjust_latency__multiple_steps")
+      class = "epipredict__step_adjust_latency__multiple_steps"
+    )
   }
   if (!is_epi_recipe(recipe)) {
     cli_abort("This recipe step can only operate on an {.cls epi_recipe}.",

From 6e5d6fc5bc67ced3764b16549671078cb6ed12b5 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 17 Sep 2024 18:07:28 -0500
Subject: [PATCH 097/387] fixed

---
 R/epi_recipe.R                    |  10 ++-
 R/epi_shift.R                     |   2 +-
 R/step_adjust_latency.R           |  55 ++-------------
 R/step_epi_shift.R                |  57 +++++++++++-----
 R/utils-latency.R                 | 108 +++++++++++++++++++++---------
 man/add_shifted_columns.Rd        |   2 +-
 man/step_adjust_latency_checks.Rd |  25 +++++++
 7 files changed, 158 insertions(+), 101 deletions(-)
 create mode 100644 man/step_adjust_latency_checks.Rd

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 44b7b5a5b..3d72b7d19 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -498,6 +498,13 @@ prep.epi_recipe <- function(
           as_of = metadata$as_of,
           other_keys = metadata$other_keys %||% character()
         )
+      } else if (
+        inherits(x$steps[[i]], "step_adjust_latency") &&
+          (x$steps[[i]]$method == "extend_ahead" || x$steps[[i]]$method == "extend_lags")
+      ) {
+        # pass along the latency table and shift sign from the latency adjustment step
+        attributes(training)$metadata$latency_table <- x$steps[[i]]$latency_table
+        attributes(training)$metadata$latency_sign <- x$steps[[i]]$latency_sign
       }
       training <- relocate(training, all_of(key_colnames(training)))
       x$term_info <- recipes:::merge_term_info(get_types(training), x$term_info)
@@ -569,9 +576,6 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
     composition <- "tibble"
   }
 
-  ## Latency checks/adjustments
-  object <- adjust_recipe_latency_before_bake(object)
-
   new_data <- NextMethod("bake")
   if (!is.null(meta)) {
     # Baking should have dropped epi_df-ness and metadata. Re-infer some
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 3d82a321f..3113d0bf7 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -39,7 +39,7 @@ get_sign <- function(object) {
 #' @keywords internal
 #' @importFrom tidyr expand_grid
 #' @importFrom dplyr join_by
-add_shifted_columns <- function(new_data, object, amount) {
+add_shifted_columns <- function(new_data, object) {
   grid <- object$shift_grid
 
   ## ensure no name clashes
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index afd991a07..3b6d21467 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -224,6 +224,7 @@ step_adjust_latency <-
         forecast_date = NULL,
         latency = fixed_latency,
         latency_table = NULL,
+        latency_sign = NULL,
         metadata = NULL,
         method = method,
         epi_keys_checked = epi_keys_checked,
@@ -237,7 +238,7 @@ step_adjust_latency <-
 
 step_adjust_latency_new <-
   function(terms, role, trained, fixed_forecast_date, forecast_date, latency,
-           latency_table, metadata, method, epi_keys_checked,
+           latency_table, latency_sign, metadata, method, epi_keys_checked,
            check_latency_length, columns, skip, id) {
     step(
       subclass = "adjust_latency",
@@ -248,6 +249,7 @@ step_adjust_latency_new <-
       forecast_date = forecast_date,
       latency = latency,
       latency_table = latency_table,
+      latency_sign = latency_sign,
       metadata = metadata,
       method = method,
       epi_keys_checked = epi_keys_checked,
@@ -287,6 +289,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     forecast_date = forecast_date,
     latency = x$latency,
     latency_table = latency_table,
+    latency_sign = get_sign(x),
     metadata = attributes(training)$metadata,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
@@ -303,56 +306,10 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
     new_data <- as_epi_df(new_data)
-    attributes(new_data)$metadata <- object$metadata # DJM: why? detected above?
+    attributes(new_data)$metadata <- object$metadata
     attributes(new_data)$metadata$as_of <- object$forecast_date
   } else {
-    latency <- object$latency
-    current_forecast_date <- object$fixed_forecast_date %||%
-      get_forecast_date(
-        new_data, NULL, object$epi_keys_checked, latency,
-        c(key_colnames(new_data), object$columns)
-      )
-    local_latency_table <- get_latency_table(
-      new_data, object$columns, current_forecast_date, latency,
-      get_sign(object), object$epi_keys_checked, NULL, NULL
-    )
-    comparison_table <- local_latency_table %>%
-      ungroup() %>%
-      dplyr::full_join(
-        object$latency_table %>% ungroup(),
-        by = join_by(col_name),
-        suffix = c(".bake", ".prep")
-      ) %>%
-      mutate(bakeMprep = latency.bake - latency.prep)
-    if (any(comparison_table$bakeMprep > 0)) {
-      cli_abort(
-        paste0(
-          "There is more latency at bake time than there was at prep time.",
-          " You will need to fit a model with more latency to predict on this dataset."
-        ),
-        class = "epipredict__latency__bake_prep_difference_error",
-        latency_table = comparison_table
-      )
-    }
-    if (any(comparison_table$bakeMprep < 0)) {
-      cli_warn(
-        paste0(
-          "There is less latency at bake time than there was at prep time.",
-          " This will still fit, but will discard the most recent data."
-        ),
-        class = "epipredict__latency__bake_prep_difference_warn",
-        latency_table = comparison_table
-      )
-    }
-    if (current_forecast_date != object$forecast_date) {
-      cli_warn(
-        paste0(
-          "The forecast date differs from the one set at train time; ",
-          " this means any dates added by `layer_forecast_date` will be inaccurate."
-        ),
-        class = "epipredict__latency__bake_prep_forecast_date_warn"
-      )
-    }
+    compare_bake_prep_latencies(object, new_data)
   }
   if (object$method == "locf") {
     # locf doesn't need to mess with the metadata at all, it just forward-fills
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 098dd7eff..8c6b8803a 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -80,6 +80,7 @@ step_epi_lag <-
         keys = key_colnames(recipe),
         columns = NULL,
         shift_grid = NULL,
+        latency_adjusted = FALSE,
         skip = skip,
         id = id
       )
@@ -125,6 +126,7 @@ step_epi_ahead <-
         keys = key_colnames(recipe),
         columns = NULL,
         shift_grid = NULL,
+        latency_adjusted = FALSE,
         skip = skip,
         id = id
       )
@@ -134,7 +136,7 @@ step_epi_ahead <-
 
 step_epi_lag_new <-
   function(terms, role, trained, lag, prefix, default, keys,
-           columns, shift_grid, skip, id) {
+           columns, shift_grid, latency_adjusted, skip, id) {
     recipes::step(
       subclass = "epi_lag",
       terms = terms,
@@ -146,6 +148,7 @@ step_epi_lag_new <-
       keys = keys,
       columns = columns,
       shift_grid = shift_grid,
+      latency_adjusted = latency_adjusted,
       skip = skip,
       id = id
     )
@@ -153,7 +156,7 @@ step_epi_lag_new <-
 
 step_epi_ahead_new <-
   function(terms, role, trained, ahead, prefix, default, keys,
-           columns, shift_grid, skip, id) {
+           columns, shift_grid, latency_adjusted, skip, id) {
     recipes::step(
       subclass = "epi_ahead",
       terms = terms,
@@ -165,6 +168,7 @@ step_epi_ahead_new <-
       keys = keys,
       columns = columns,
       shift_grid = shift_grid,
+      latency_adjusted = latency_adjusted,
       skip = skip,
       id = id
     )
@@ -175,13 +179,20 @@ step_epi_ahead_new <-
 #' @export
 prep.step_epi_lag <- function(x, training, info = NULL, ...) {
   columns <- recipes::recipes_eval_select(x$terms, training, info)
-  sgn <- get_sign(x)
-  shift_grid <- expand_grid(
-    col = columns,
-    shift_val = sgn * x$lag,
-    newname = glue::glue("{x$prefix}{abs(shift_val)}_{col}")
-  )
-
+  if (!x$latency_adjusted) {
+    tmp <- create_shift_grid(
+      x$prefix,
+      x$lag,
+      get_sign(x),
+      columns,
+      attributes(training)$metadata$latency_table,
+      attributes(training)$metadata$latency_sign
+    )
+    shift_grid <- tmp[[1]]
+    latency_adjusted <- tmp[[2]]
+  } else {
+    shift_grid <- x$shift_grid
+  }
 
   step_epi_lag_new(
     terms = x$terms,
@@ -193,6 +204,7 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
     keys = x$keys,
     columns = columns,
     shift_grid = shift_grid,
+    latency_adjusted = latency_adjusted,
     skip = x$skip,
     id = x$id
   )
@@ -201,12 +213,20 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
 #' @export
 prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
   columns <- recipes::recipes_eval_select(x$terms, training, info)
-  sgn <- get_sign(x)
-  shift_grid <- expand_grid(
-    col = columns,
-    shift_val = sgn * x$ahead,
-    newname = glue::glue("{x$prefix}{abs(shift_val)}_{col}")
-  )
+  if (!x$latency_adjusted) {
+    tmp <- create_shift_grid(
+      x$prefix,
+      x$ahead,
+      get_sign(x),
+      columns,
+      attributes(training)$metadata$latency_table,
+      attributes(training)$metadata$latency_sign
+    )
+    shift_grid <- tmp[[1]]
+    latency_adjusted <- tmp[[2]]
+  } else {
+    shift_grid <- x$shift_grid
+  }
 
   step_epi_ahead_new(
     terms = x$terms,
@@ -218,6 +238,7 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
     keys = x$keys,
     columns = columns,
     shift_grid = shift_grid,
+    latency_adjusted = latency_adjusted,
     skip = x$skip,
     id = x$id
   )
@@ -227,12 +248,14 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_epi_lag <- function(object, new_data, ...) {
-  add_shifted_columns(new_data, object, object$lag)
+  names(object)
+  object$shift_grid
+  add_shifted_columns(new_data, object)
 }
 
 #' @export
 bake.step_epi_ahead <- function(object, new_data, ...) {
-  add_shifted_columns(new_data, object, object$ahead)
+  add_shifted_columns(new_data, object)
 }
 
 #' @export
diff --git a/R/utils-latency.R b/R/utils-latency.R
index fd01280bc..dc5fa9af8 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -28,36 +28,6 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
   return(relevant_shifts)
 }
 
-#
-adjust_recipe_latency_before_bake <- function(rec) {
-  if (detect_step(rec, "adjust_latency")) {
-    step_types <- tidy(rec)$type
-    # can only be 1, or we would have aborted on recipe creation
-    latency_step <- rec$steps[[which(step_types == "adjust_latency")]]
-    latency_table <- latency_step$latency_table
-    method <- latency_step$method
-    if (method == "extend_ahead") {
-      loc <- which(step_types == "epi_ahead")
-      sign_shift <- -1
-    } else if (method == "extend_lags") {
-      loc <- which(step_types == "epi_lag")
-      sign_shift <- 1
-    }
-    if (method != "locf") {
-      for (s in loc) {
-        pfx <- rec$steps[[s]]$prefix
-        sgrid <- rec$steps[[s]]$shift_grid
-        sgrid <- sgrid %>%
-          left_join(latency_table, by = join_by(col == col_name)) %>%
-          tidyr::replace_na(list(latency = 0)) %>%
-          mutate(shift_val = shift_val + latency, amount = NULL, latency = NULL)
-        rec$steps[[s]]$shift_grid <- sgrid
-      }
-    }
-  }
-  rec
-}
-
 #' Extract the as_of for the forecast date, and make sure there's nothing very off about it.
 #' @keywords internal
 #' @importFrom dplyr select
@@ -414,3 +384,81 @@ then the previous `step_epi_lag`s won't work with modified data.",
     }
   }
 }
+
+compare_bake_prep_latencies <- function(object, new_data, call = caller_env()) {
+  latency <- object$latency
+  current_forecast_date <- object$fixed_forecast_date %||%
+    get_forecast_date(
+      new_data, NULL, object$epi_keys_checked, latency,
+      c(key_colnames(new_data), object$columns)
+    )
+  local_latency_table <- get_latency_table(
+    new_data, object$columns, current_forecast_date, latency,
+    get_sign(object), object$epi_keys_checked, NULL, NULL
+  )
+  comparison_table <- local_latency_table %>%
+    ungroup() %>%
+    dplyr::full_join(
+      object$latency_table %>% ungroup(),
+      by = join_by(col_name),
+      suffix = c(".bake", ".prep")
+    ) %>%
+    mutate(bakeMprep = latency.bake - latency.prep)
+  if (any(comparison_table$bakeMprep > 0)) {
+    cli_abort(
+      paste0(
+        "There is more latency at bake time than there was at prep time.",
+        " You will need to fit a model with more latency to predict on this dataset."
+      ),
+      class = "epipredict__latency__bake_prep_difference_error",
+      latency_table = comparison_table,
+      call = call
+    )
+  }
+  if (any(comparison_table$bakeMprep < 0)) {
+    cli_warn(
+      paste0(
+        "There is less latency at bake time than there was at prep time.",
+        " This will still fit, but will discard the most recent data."
+      ),
+      class = "epipredict__latency__bake_prep_difference_warn",
+      latency_table = comparison_table,
+      call = call
+    )
+  }
+  if (current_forecast_date != object$forecast_date) {
+    cli_warn(
+      paste0(
+        "The forecast date differs from the one set at train time; ",
+        " this means any dates added by `layer_forecast_date` will be inaccurate."
+      ),
+      class = "epipredict__latency__bake_prep_forecast_date_warn",
+      call = call
+    )
+  }
+}
+
+
+#' @keywords internal
+create_shift_grid <- function(prefix, amount, target_sign, columns, latency_table, latency_sign) {
+  if (!is.null(latency_table) &&
+    latency_sign == target_sign) {
+    # get the actually used latencies
+    rel_latency <- latency_table %>% filter(col_name %in% columns)
+    latency_adjusted <- TRUE
+  } else {
+    # adding zero if there's no latency table
+    rel_latency <- tibble(col_name = columns, latency = 0L)
+    latency_adjusted <- FALSE
+  }
+  shift_grid <- expand_grid(col = columns, amount = target_sign * amount) %>%
+    left_join(rel_latency, by = join_by(col == col_name), ) %>%
+    tidyr::replace_na(list(latency = 0)) %>%
+    mutate(shift_val = amount + latency) %>%
+    mutate(
+      newname = glue::glue("{prefix}{abs(shift_val)}_{col}"), # name is always positive
+      amount = NULL,
+      latency = NULL
+    )
+  return(list(shift_grid, latency_adjusted))
+}
diff --git a/man/add_shifted_columns.Rd b/man/add_shifted_columns.Rd
index d7aba745b..aad22e805 100644
--- a/man/add_shifted_columns.Rd
+++ b/man/add_shifted_columns.Rd
@@ -5,7 +5,7 @@
 \title{backend for both \code{bake.step_epi_ahead} and \code{bake.step_epi_lag}, performs the
 checks missing in \code{epi_shift_single}}
 \usage{
-add_shifted_columns(new_data, object, amount)
+add_shifted_columns(new_data, object)
 }
 \description{
 backend for both \code{bake.step_epi_ahead} and \code{bake.step_epi_lag}, performs the
diff --git a/man/step_adjust_latency_checks.Rd b/man/step_adjust_latency_checks.Rd
new file mode 100644
index 000000000..baed1fb9b
--- /dev/null
+++ b/man/step_adjust_latency_checks.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{step_adjust_latency_checks}
+\alias{step_adjust_latency_checks}
+\title{checks: the recipe type, whether a previous step is the relevant epi_shift,
+that either \code{fixed_latency} or \code{fixed_forecast_date} is non-null, and that
+\code{fixed_latency} only references columns that exist at the time of the step
+inclusion}
+\usage{
+step_adjust_latency_checks(
+  id,
+  method,
+  recipe,
+  fixed_latency,
+  fixed_forecast_date,
+  call = caller_env()
+)
+}
+\description{
+checks: the recipe type, whether a previous step is the relevant epi_shift,
+that either \code{fixed_latency} or \code{fixed_forecast_date} is non-null, and that
+\code{fixed_latency} only references columns that exist at the time of the step
+inclusion
+}
+\keyword{internal}

From 388ccc1992e5943ed9866d198224489d62bf96ee Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 17 Sep 2024 18:07:35 -0500
Subject: [PATCH 098/387] single letter variables are impossible to search

---
 R/epi_recipe.R | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 3d72b7d19..d287937ee 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -463,8 +463,8 @@ prep.epi_recipe <- function(
   if (fresh) x$term_info <- x$var_info
 
   running_info <- x$term_info %>% mutate(number = 0, skip = FALSE)
-  for (i in seq(along.with = x$steps)) {
-    needs_tuning <- map_lgl(x$steps[[i]], recipes:::is_tune)
+  for (ii in seq(along.with = x$steps)) {
+    needs_tuning <- map_lgl(x$steps[[ii]], recipes:::is_tune)
     if (any(needs_tuning)) {
       arg <- names(needs_tuning)[needs_tuning]
       arg <- paste0("'", arg, "'", collapse = ", ")
@@ -474,18 +474,18 @@ prep.epi_recipe <- function(
       )
       cli_abort(msg)
     }
-    note <- paste("oper", i, gsub("_", " ", class(x$steps[[i]])[1]))
-    if (!x$steps[[i]]$trained | fresh) {
+    note <- paste("oper", ii, gsub("_", " ", class(x$steps[[ii]])[1]))
+    if (!x$steps[[ii]]$trained || fresh) {
       if (verbose) {
         cat(note, "[training]", "\n")
       }
       before_nms <- names(training)
       before_template <- training[1, ]
-      x$steps[[i]] <- prep(x$steps[[i]],
+      x$steps[[ii]] <- prep(x$steps[[ii]],
         training = training,
         info = x$term_info
       )
-      training <- bake(x$steps[[i]], new_data = training)
+      training <- bake(x$steps[[ii]], new_data = training)
       if (!tibble::is_tibble(training)) {
         cli_abort("`bake()` methods should always return {.cls tibble}.")
       }
@@ -499,27 +499,27 @@ prep.epi_recipe <- function(
           other_keys = metadata$other_keys %||% character()
         )
       } else if (
-        inherits(x$steps[[i]], "step_adjust_latency") &&
-          (x$steps[[i]]$method == "extend_ahead" || x$steps[[i]]$method == "extend_lags")
+        inherits(x$steps[[ii]], "step_adjust_latency") &&
+          (x$steps[[ii]]$method == "extend_ahead" || x$steps[[ii]]$method == "extend_lags")
       ) {
         # pass along the latency table and shift sign from the latency adjustment step
-        attributes(training)$metadata$latency_table <- x$steps[[i]]$latency_table
-        attributes(training)$metadata$latency_sign <- x$steps[[i]]$latency_sign
+        attributes(training)$metadata$latency_table <- x$steps[[ii]]$latency_table
+        attributes(training)$metadata$latency_sign <- x$steps[[ii]]$latency_sign
       }
       training <- relocate(training, all_of(key_colnames(training)))
       x$term_info <- recipes:::merge_term_info(get_types(training), x$term_info)
-      if (!is.na(x$steps[[i]]$role)) {
+      if (!is.na(x$steps[[ii]]$role)) {
         new_vars <- setdiff(x$term_info$variable, running_info$variable)
         pos_new_var <- x$term_info$variable %in% new_vars
         pos_new_and_na_role <- pos_new_var & is.na(x$term_info$role)
         pos_new_and_na_source <- pos_new_var & is.na(x$term_info$source)
-        x$term_info$role[pos_new_and_na_role] <- x$steps[[i]]$role
+        x$term_info$role[pos_new_and_na_role] <- x$steps[[ii]]$role
         x$term_info$source[pos_new_and_na_source] <- "derived"
       }
       recipes:::changelog(log_changes, before_nms, names(training), x$steps[[i]])
       running_info <- rbind(
         running_info,
-        mutate(x$term_info, number = i, skip = x$steps[[i]]$skip)
+        mutate(x$term_info, number = ii, skip = x$steps[[ii]]$skip)
       )
     } else {
       if (verbose) cat(note, "[pre-trained]\n")

From 54993d25fb62d413facb40921789d6aad001e301 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 17 Sep 2024 18:28:44 -0500
Subject: [PATCH 099/387] lat adj tag for steps which have been modified

---
 R/step_epi_shift.R                           | 16 ++++++-
 tests/testthat/_snaps/step_adjust_latency.md | 47 +++++++++++++++++++-
 tests/testthat/test-step_adjust_latency.R    |  7 +--
 3 files changed, 64 insertions(+), 6 deletions(-)

diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 8c6b8803a..b04efd06f 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -260,9 +260,15 @@ bake.step_epi_ahead <- function(object, new_data, ...) {
 
 #' @export
 print.step_epi_lag <- function(x, width = max(20, options()$width - 30), ...) {
+  if (x$latency_adjusted && x$trained) {
+    lag <- x$shift_grid$shift_val
+    lag <- c(lag, "(lat adj)")
+  } else {
+    lag <- x$lag
+  }
   print_epi_step(x$columns, x$terms, x$trained, "Lagging",
     conjunction = "by",
-    extra_text = x$lag
+    extra_text = lag
   )
   invisible(x)
 }
@@ -270,9 +276,15 @@ print.step_epi_lag <- function(x, width = max(20, options()$width - 30), ...) {
 
 #' @export
 print.step_epi_ahead <- function(x, width = max(20, options()$width - 30), ...) {
+  if (x$latency_adjusted && x$trained) {
+    ahead <- x$shift_grid$shift_val
+    ahead <- c(ahead, "(lat adj)")
+  } else {
+    ahead <- x$ahead
+  }
   print_epi_step(x$columns, x$terms, x$trained, "Leading",
     conjunction = "by",
-    extra_text = x$ahead
+    extra_text = ahead
   )
   invisible(x)
 }
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index fd49b8824..e37ae07ea 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -21,7 +21,30 @@
 ---
 
     Code
-      r
+      prep(r5, real_x)
+    Message
+      
+      -- Epi Recipe ------------------------------------------------------------------
+      
+      -- Inputs 
+      Number of variables by role
+      raw:        2
+      geo_value:  1
+      time_value: 1
+      
+      -- Training information 
+      Training data contained 200 data points and no incomplete rows.
+      
+      -- Operations 
+      1. extend_lags: case_rate with forecast date 2021-07-24 | Trained
+      2. Lagging: death_rate by 0, 6, 11, (lat adj) | Trained
+      3. Lagging: case_rate by 6, 10, (lat adj) | Trained
+      4. Leading: death_rate by 7 | Trained
+
+---
+
+    Code
+      r6
     Message
       
       -- Epi Recipe ------------------------------------------------------------------
@@ -37,3 +60,25 @@
       2. extend_ahead: all future predictors with latency set at train time
       3. Leading: death_rate by 7
 
+---
+
+    Code
+      prep(r6, case_death_rate_subset)
+    Message
+      
+      -- Epi Recipe ------------------------------------------------------------------
+      
+      -- Inputs 
+      Number of variables by role
+      raw:        2
+      geo_value:  1
+      time_value: 1
+      
+      -- Training information 
+      Training data contained 20496 data points and no incomplete rows.
+      
+      -- Operations 
+      1. Lagging: death_rate by 0, 7, 14 | Trained
+      2. extend_ahead: case_rate, ... with forecast date 2022-05-31 | Trained
+      3. Leading: death_rate by -158, (lat adj) | Trained
+
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 93d4b0660..2a9ea4419 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -49,7 +49,6 @@ test_that("epi_adjust_latency correctly extends the lags", {
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
-
   # directly checking the shifts
   baked_x <- r1 %>%
     prep(real_x) %>%
@@ -512,11 +511,13 @@ test_that("printing step_adjust_latency results in expected output", {
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
   expect_snapshot(r5)
-  r <- epi_recipe(case_death_rate_subset) %>%
+  expect_snapshot(prep(r5, real_x))
+  r6 <- epi_recipe(case_death_rate_subset) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_adjust_latency(method = "extend_ahead") %>%
     step_epi_ahead(death_rate, ahead = 7)
-  expect_snapshot(r)
+  expect_snapshot(r6)
+  expect_snapshot(prep(r6, case_death_rate_subset))
 })
 
 test_that("locf works as intended", {

From e70d5530bb1b6dfe11838b55c6bebe067d5b831e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 20 Sep 2024 12:02:58 -0500
Subject: [PATCH 100/387] remove actual changes to prep.epi_recipe

---
 R/epi_recipe.R          | 7 -------
 R/step_adjust_latency.R | 3 +++
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index d287937ee..769229b39 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -498,13 +498,6 @@ prep.epi_recipe <- function(
           as_of = metadata$as_of,
           other_keys = metadata$other_keys %||% character()
         )
-      } else if (
-        inherits(x$steps[[ii]], "step_adjust_latency") &&
-          (x$steps[[ii]]$method == "extend_ahead" || x$steps[[ii]]$method == "extend_lags")
-      ) {
-        # pass along the latency table and shift sign from the latency adjustment step
-        attributes(training)$metadata$latency_table <- x$steps[[ii]]$latency_table
-        attributes(training)$metadata$latency_sign <- x$steps[[ii]]$latency_sign
       }
       training <- relocate(training, all_of(key_colnames(training)))
       x$term_info <- recipes:::merge_term_info(get_types(training), x$term_info)
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 3b6d21467..433fbb328 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -329,6 +329,9 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
       as_tibble() %>%
       tidyr::fill(.direction = "down", any_of(modified_columns)) %>%
       ungroup()
+  } else if (object$method == "extend_lags" || object$method == "extend_ahead") {
+    attributes(new_data)$metadata$latency_table <- object$latency_table
+    attributes(new_data)$metadata$latency_sign <- object$latency_sign
   }
   return(new_data)
 }

From b3c96b346a0b7a6d4e0ad9ed52dc88aa07bdc031 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 27 Sep 2024 17:47:14 -0500
Subject: [PATCH 101/387] various requests and rebasing on dev

---
 R/arx_classifier.R |  8 ++++----
 R/arx_forecaster.R | 14 +++-----------
 R/epi_recipe.R     |  2 +-
 R/utils-enframer.R | 23 -----------------------
 4 files changed, 8 insertions(+), 39 deletions(-)
 delete mode 100644 R/utils-enframer.R

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index c5166a176..6dcffde88 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -190,10 +190,10 @@ arx_class_epi_workflow <- function(
         )
     }
   }
+  # regex that will match any amount of adjustment for the ahead
   ahead_out_name <- glue::glue("ahead_[0-9]*_{pre_out_name}")
   method_adjust_latency <- args_list$adjust_latency
   if (method_adjust_latency != "none") {
-    # only extend_ahead is supported atm
     r <- r %>% step_adjust_latency(!!pre_out_name,
       fixed_forecast_date = forecast_date,
       method = method_adjust_latency
@@ -328,9 +328,9 @@ arx_class_args_list <- function(
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
       cli_warn(
-        c(
-          "`forecast_date` + `ahead` must equal `target_date`.",
-          i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
+        paste0(
+          "`forecast_date`  {.val {forecast_date}} +",
+          " `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}."
         ),
         class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
       )
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 4d7dab60e..bfd5eaec1 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -61,7 +61,7 @@ arx_forecaster <- function(
 
 
   preds <- forecast(wf, forecast_date = forecast_date) %>%
-    tibble::as_tibble() %>%
+    as_tibble() %>%
     select(-time_value)
 
   structure(
@@ -140,11 +140,7 @@ arx_fcast_epi_workflow <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   if (forecast_date + args_list$ahead != target_date) {
-    cli_abort(
-      c(
-        "`forecast_date` + `ahead` must equal `target_date`.",
-        i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-      ),
+    cli_abort("`forecast_date` {.val {forecast_date}} + `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}.",
       class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
     )
   }
@@ -314,11 +310,7 @@ arx_args_list <- function(
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
-      cli_abort(
-        c(
-          "`forecast_date` + `ahead` must equal `target_date`.",
-          i = "{.val {forecast_date}} + {.val {ahead}} != {.val {target_date}}."
-        ),
+      cli_abort("`forecast_date` {.val {forecast_date}} + `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}.",
         class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
       )
     }
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 769229b39..311b9d073 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -94,7 +94,7 @@ epi_recipe.epi_df <-
     ## Check and add roles when available
     if (!is.null(roles)) {
       if (length(roles) != length(vars)) {
-        cli_abort(paste(
+        cli_abort(paste0(
           "The number of roles should be the same as the number of ",
           "variables."
         ))
diff --git a/R/utils-enframer.R b/R/utils-enframer.R
deleted file mode 100644
index 723bd6a9c..000000000
--- a/R/utils-enframer.R
+++ /dev/null
@@ -1,23 +0,0 @@
-enframer <- function(df, x, fill = NA) {
-  stopifnot(is.data.frame(df))
-  stopifnot(length(fill) == 1 || length(fill) == nrow(df))
-  arg_is_chr(x, allow_null = TRUE)
-  if (is.null(x)) {
-    return(df)
-  }
-  if (any(names(df) %in% x)) {
-    stop("In enframer: some new cols match existing column names")
-  }
-  for (v in x) df <- mutate(df, !!v := fill)
-  df
-}
-
-enlist <- function(...) {
-  # converted to thin wrapper around
-  rlang::dots_list(
-    ...,
-    .homonyms = "error",
-    .named = TRUE,
-    .check_assign = TRUE
-  )
-}

From 86c46a457f72ccbd6829f2b305368ab685b26761 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 30 Sep 2024 11:23:09 -0500
Subject: [PATCH 102/387] updating after rebase

---
 R/arx_classifier.R                       |  2 --
 R/utils-latency.R                        |  8 ++---
 R/utils-misc.R                           | 11 +++++++
 man/{seq_null_swap.Rd => seq_forward.Rd} |  6 ++--
 man/step_adjust_latency.Rd               |  4 +--
 tests/testthat/test-get_test_data.R      |  2 +-
 tests/testthat/test-pad_to_end.R         | 37 ------------------------
 tests/testthat/test-step_epi_naomit.R    |  6 ++--
 8 files changed, 24 insertions(+), 52 deletions(-)
 rename man/{seq_null_swap.Rd => seq_forward.Rd} (76%)
 delete mode 100644 tests/testthat/test-pad_to_end.R

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 6dcffde88..3d2daa978 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -370,5 +370,3 @@ print.arx_class <- function(x, ...) {
   name <- "ARX Classifier"
   NextMethod(name = name, ...)
 }
-
-# this is a trivial change to induce a check
diff --git a/R/utils-latency.R b/R/utils-latency.R
index dc5fa9af8..311656ac1 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -183,12 +183,11 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   }
   itval <- epiprocess:::guess_period(c(x$time_value, end_date), "time_value")
   # get the time values we need to fill in
-  completed_time_values <-
-    x %>%
+  completed_time_values <- x %>%
     group_by(across(all_of(groups))) %>%
     summarise(
       time_value = list2(
-        time_value = seq_null_swap(from = max(time_value) + itval, to = end_date, by = itval)
+        time_value = seq_forward(from = max(time_value) + itval, to = end_date, by = itval)
       )
     ) %>%
     unnest("time_value") %>%
@@ -197,6 +196,7 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   grouped_and_arranged <- x %>%
     arrange(across(all_of(c("time_value", groups)))) %>%
     group_by(across(all_of(groups)))
+
   values_to_fill <- grouped_and_arranged %>%
     slice(min(across(all_of(columns_to_complete), count_single_column)):n())
   filled_values <- values_to_fill %>%
@@ -222,7 +222,7 @@ count_single_column <- function(col) {
 
 #' seq, but returns null if from is larger
 #' @keywords internal
-seq_null_swap <- function(from, to, by) {
+seq_forward <- function(from, to, by) {
   if (from > to) {
     return(NULL)
   }
diff --git a/R/utils-misc.R b/R/utils-misc.R
index 5cc8b364d..a1e0f025f 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -75,3 +75,14 @@ is_classification <- function(trainer) {
 is_regression <- function(trainer) {
   get_parsnip_mode(trainer) %in% c("regression", "unknown")
 }
+
+
+enlist <- function(...) {
+  # converted to thin wrapper around
+  rlang::dots_list(
+    ...,
+    .homonyms = "error",
+    .named = TRUE,
+    .check_assign = TRUE
+  )
+}
diff --git a/man/seq_null_swap.Rd b/man/seq_forward.Rd
similarity index 76%
rename from man/seq_null_swap.Rd
rename to man/seq_forward.Rd
index 7ad5a8954..9b3da6e55 100644
--- a/man/seq_null_swap.Rd
+++ b/man/seq_forward.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/utils-latency.R
-\name{seq_null_swap}
-\alias{seq_null_swap}
+\name{seq_forward}
+\alias{seq_forward}
 \title{seq, but returns null if from is larger}
 \usage{
-seq_null_swap(from, to, by)
+seq_forward(from, to, by)
 }
 \description{
 seq, but returns null if from is larger
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index e1e516a3c..f0ee41390 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -260,8 +260,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous
-#> `step_epi_lag`s won't work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-get_test_data.R b/tests/testthat/test-get_test_data.R
index 3fabdea2e..5f315c499 100644
--- a/tests/testthat/test-get_test_data.R
+++ b/tests/testthat/test-get_test_data.R
@@ -1,4 +1,4 @@
-library(dplyr)
+suppressPackageStartupMessages(library(dplyr))
 test_that("return expected number of rows and returned dataset is ungrouped", {
   r <- epi_recipe(case_death_rate_subset) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
diff --git a/tests/testthat/test-pad_to_end.R b/tests/testthat/test-pad_to_end.R
deleted file mode 100644
index 6949f06ac..000000000
--- a/tests/testthat/test-pad_to_end.R
+++ /dev/null
@@ -1,37 +0,0 @@
-test_that("test set padding works", {
-  dat <- tibble::tibble(
-    gr1 = rep(c("a", "b"), times = c(3, 4)),
-    time_value = c(1:3, 1:4),
-    value = 1:7
-  ) %>% arrange(time_value, gr1)
-  expect_identical(pad_to_end(dat, "gr1", 3), dat)
-  expect_equal(nrow(pad_to_end(dat, "gr1", 4)), 8L)
-  p <- pad_to_end(dat, "gr1", 5)
-  expect_equal(nrow(p), 10L)
-  expect_identical(p$gr1, rep(c("a", "b"), times = 5))
-  expect_identical(p$time_value, rep(1:5, each = 2))
-  expect_identical(p$value, as.integer(c(1, 4, 2, 5, 3, 6, NA, 7, NA, NA)))
-
-  dat <- dat %>% arrange(gr1)
-  dat$gr2 <- c("c", "c", "d", "c", "c", "d", "d")
-  dat <- dat %>% arrange(time_value)
-  # don't treat it as a group
-  p <- pad_to_end(dat, "gr1", 4)
-  expect_identical(nrow(p), 8L)
-  expect_identical(p$gr2, c(rep("c", 4), "d", "d", NA, "d"))
-
-  # treat it as a group (needs different time_value)
-  dat$time_value <- c(1, 1, 2, 2, 1, 1, 2) # double
-  p <- pad_to_end(dat, c("gr1", "gr2"), 2)
-  expect_equal(nrow(p), 8L)
-  expect_identical(p$gr1, rep(c("a", "a", "b", "b"), times = 2))
-  expect_identical(p$gr2, rep(c("c", "d"), times = 4))
-  expect_identical(p$time_value, rep(c(1, 2), each = 4))
-  expect_identical(p$value, as.integer(c(1, 3, 4, 6, 2, NA, 5, 7)))
-
-  # make sure it maintains the epi_df
-  dat <- dat %>%
-    dplyr::rename(geo_value = gr1) %>%
-    as_epi_df(other_keys = "gr2")
-  expect_s3_class(pad_to_end(dat, "geo_value", 2), "epi_df")
-})
diff --git a/tests/testthat/test-step_epi_naomit.R b/tests/testthat/test-step_epi_naomit.R
index 0e5e1750f..2f361ec98 100644
--- a/tests/testthat/test-step_epi_naomit.R
+++ b/tests/testthat/test-step_epi_naomit.R
@@ -1,6 +1,6 @@
-library(dplyr)
-library(parsnip)
-library(workflows)
+suppressPackageStartupMessages(library(dplyr))
+suppressPackageStartupMessages(library(parsnip))
+suppressPackageStartupMessages(library(workflows))
 
 # Random generated dataset
 x <- tibble(

From 90edb465ef109b1790543a2aea214d02995710f4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 30 Sep 2024 11:28:11 -0500
Subject: [PATCH 103/387] final requests

---
 R/arx_classifier.R              |  9 +++++++--
 tests/testthat/test-snapshots.R | 20 ++++++++++++++++++++
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 3d2daa978..0aec0e362 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -191,9 +191,14 @@ arx_class_epi_workflow <- function(
     }
   }
   # regex that will match any amount of adjustment for the ahead
-  ahead_out_name <- glue::glue("ahead_[0-9]*_{pre_out_name}")
+  ahead_out_name_regex <- glue::glue("ahead_[0-9]*_{pre_out_name}")
   method_adjust_latency <- args_list$adjust_latency
   if (method_adjust_latency != "none") {
+    if (method_adjust_latency != "extend_ahead") {
+      cli_abort("only extend_ahead is currently supported",
+        class = "epipredict__arx_classifier__adjust_latency_unsupported_method"
+      )
+    }
     r <- r %>% step_adjust_latency(!!pre_out_name,
       fixed_forecast_date = forecast_date,
       method = method_adjust_latency
@@ -204,7 +209,7 @@ arx_class_epi_workflow <- function(
   r <- r %>%
     step_mutate(
       across(
-        matches(ahead_out_name),
+        matches(ahead_out_name_regex),
         ~ cut(.x, breaks = args_list$breaks),
         .names = "outcome_class",
         .unpack = TRUE
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index fc3ee890b..da8635ae0 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -162,4 +162,24 @@ test_that("arx_classifier snapshots", {
     args_list = arx_class_args_list(adjust_latency = "extend_ahead", forecast_date = max_date + 2)
   )
   expect_snapshot_tibble(arc2$predictions)
+  expect_error(
+    arc3 <- arx_classifier(
+      case_death_rate_subset %>%
+        dplyr::filter(time_value >= as.Date("2021-11-01")),
+      "death_rate",
+      c("case_rate", "death_rate"),
+      args_list = arx_class_args_list(adjust_latency = "extend_lags", forecast_date = max_date + 2)
+    ),
+    class = "epipredict__arx_classifier__adjust_latency_unsupported_method"
+  )
+  expect_error(
+    arc4 <- arx_classifier(
+      case_death_rate_subset %>%
+        dplyr::filter(time_value >= as.Date("2021-11-01")),
+      "death_rate",
+      c("case_rate", "death_rate"),
+      args_list = arx_class_args_list(adjust_latency = "locf", forecast_date = max_date + 2)
+    ),
+    class = "epipredict__arx_classifier__adjust_latency_unsupported_method"
+  )
 })

From ce99138f08802dd6369837fa0704addae108ba6c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 30 Sep 2024 12:57:29 -0500
Subject: [PATCH 104/387] only adding metadata if given an epi_df originally

---
 .Rbuildignore      |  3 ++-
 R/epi_shift.R      | 13 +++++++++----
 R/step_epi_shift.R |  2 --
 3 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 510725267..dc41e6223 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -20,4 +20,5 @@
 ^doc$
 ^Meta$
 ^.lintr$
-^.venv$
\ No newline at end of file
+^.venv$
+^inst/templates$
diff --git a/R/epi_shift.R b/R/epi_shift.R
index 3113d0bf7..367e26285 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -60,9 +60,14 @@ add_shifted_columns <- function(new_data, object) {
   processed <- new_data %>%
     full_join(shifted, by = ok) %>%
     group_by(across(all_of(kill_time_value(ok)))) %>%
-    arrange(time_value) %>%
-    ungroup() %>%
-    as_epi_df()
-  attributes(processed)$metadata <- attributes(new_data)$metadata
+    arrange(time_value)
+  if (inherits(new_data, "epi_df")) {
+    processed <- processed %>%
+      ungroup() %>%
+      as_epi_df(
+        as_of = attributes(new_data)$metadata$as_of,
+        other_keys = attributes(new_data)$metadata$other_keys
+      )
+  }
   return(processed)
 }
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index b04efd06f..a4bcee52e 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -248,8 +248,6 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_epi_lag <- function(object, new_data, ...) {
-  names(object)
-  object$shift_grid
   add_shifted_columns(new_data, object)
 }
 

From c6800bbcf4cda861b65d8eb820da2ee21fd65f9d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 30 Sep 2024 17:19:24 -0500
Subject: [PATCH 105/387] snapshot updates

---
 tests/testthat/_snaps/step_epi_shift.md   | 2 +-
 tests/testthat/_snaps/step_growth_rate.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/_snaps/step_epi_shift.md b/tests/testthat/_snaps/step_epi_shift.md
index 44c828118..eaf495995 100644
--- a/tests/testthat/_snaps/step_epi_shift.md
+++ b/tests/testthat/_snaps/step_epi_shift.md
@@ -30,7 +30,7 @@
     Code
       slm_fit(r4)
     Condition
-      Error in `bake()`:
+      Error in `add_shifted_columns()`:
       ! Name collision occured in <step_epi_lag>
       The following variable name already exists: "lag_7_death_rate".
 
diff --git a/tests/testthat/_snaps/step_growth_rate.md b/tests/testthat/_snaps/step_growth_rate.md
index 5a3ac6f44..0912977e3 100644
--- a/tests/testthat/_snaps/step_growth_rate.md
+++ b/tests/testthat/_snaps/step_growth_rate.md
@@ -117,5 +117,5 @@
       step_growth_rate(r, value, replace_Inf = c(1, 2))
     Condition
       Error in `step_growth_rate()`:
-      ! replace_Inf must be a scalar.
+      ! `replace_Inf` must be a scalar.
 

From 561570e35aec7155d8422523764730a0473dfeca Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 1 Oct 2024 16:05:19 -0500
Subject: [PATCH 106/387] description and News

---
 DESCRIPTION |  2 +-
 NEWS.md     | 10 +++++++---
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d89f9b276..d3369cf23 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 5b907f237..ef71394a2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,13 @@
 
 Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.
 
+# epipredict 0.2
+
+## features
+- Add `step_adjust_latency`, which give several methods to adjust the forecast if the `forecast_date` is after the last day of data.
+
+## bugfixes
+
 # epipredict 0.1
 
 - simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
@@ -61,6 +68,3 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Fix bug where `fit()` drops the `epi_workflow` class (also error if
   non-`epi_df` data is given to `epi_recipe()`), #363
 - Try to retain the `epi_df` class during baking to the extent possible, #376
-- Add `latency_adjustment` as an option for `add_epi_ahead`, which adjusts the
-  `ahead` so that the prediction is `ahead` relative to the `as_of` date for the
-  `epi_data`, rather than relative to the last day of data.

From 053b501ba216c861f2e9d0e5c073afbb2aaae038 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 1 Oct 2024 16:21:10 -0500
Subject: [PATCH 107/387] rerererebase

---
 NAMESPACE                                |  1 -
 R/get_test_data.R                        |  2 ++
 man/step_epi_shift.Rd                    |  7 ------
 tests/testthat/_snaps/enframer.md        | 32 ------------------------
 tests/testthat/_snaps/pivot_quantiles.md |  4 +--
 tests/testthat/test-enframer.R           | 13 ----------
 6 files changed, 3 insertions(+), 56 deletions(-)
 delete mode 100644 tests/testthat/_snaps/enframer.md
 delete mode 100644 tests/testthat/test-enframer.R

diff --git a/NAMESPACE b/NAMESPACE
index 4c5ec75ac..86b77716b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -294,7 +294,6 @@ importFrom(rlang,is_empty)
 importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
 importFrom(rlang,is_true)
-importFrom(rlang,is_vector)
 importFrom(rlang,list2)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
diff --git a/R/get_test_data.R b/R/get_test_data.R
index af43fc012..8de698301 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -41,6 +41,8 @@ get_test_data <- function(recipe, x) {
   min_lags <- min(map_dbl(recipe$steps, ~ min(.x$lag %||% Inf)), Inf)
   max_lags <- max(map_dbl(recipe$steps, ~ max(.x$lag %||% 0)), 0)
   max_horizon <- max(map_dbl(recipe$steps, ~ max(.x$horizon %||% 0)), 0)
+  max_slide <- max(map_dbl(recipe$steps, ~ max(.x$before %||% 0)), 0)
+  min_required <- max_lags + max_horizon + max_slide
   keep <- max_lags + max_horizon
 
   # CHECK: Error out if insufficient training data
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index e33cf7e0e..30ac05d16 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -54,13 +54,6 @@ Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
 \item{id}{A unique identifier for the step}
-
-\item{latency_adjustment}{a character. Determines the method by which the forecast handles data that doesn't extend to the day the forecast is made. The options are:
-\itemize{
-\item \code{"extend_ahead"}: actually forecasts from the last date. E.g. if there are 3 days of latency for a 4 day ahead forecast, the ahead used in practice is actually 7.
-\item \code{"locf"}: carries forward the last observed value up to the forecast date.
-\item \code{"extend_lags"}: per \code{epi_key} and \code{predictor}, adjusts the lag so that the shortest lag at predict time is
-}}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
diff --git a/tests/testthat/_snaps/enframer.md b/tests/testthat/_snaps/enframer.md
deleted file mode 100644
index 4b05dbff3..000000000
--- a/tests/testthat/_snaps/enframer.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# enframer errors/works as needed
-
-    Code
-      enframer(1:5, letters[1])
-    Condition
-      Error in `enframer()`:
-      ! is.data.frame(df) is not TRUE
-
----
-
-    Code
-      enframer(data.frame(a = 1:5), 1:3)
-    Condition
-      Error in `enframer()`:
-      ! `x` must be of type <character>.
-
----
-
-    Code
-      enframer(data.frame(a = 1:5), letters[1:3])
-    Condition
-      Error in `enframer()`:
-      ! In enframer: some new cols match existing column names
-
----
-
-    Code
-      enframer(data.frame(aa = 1:5), letters[1:2], fill = 1:4)
-    Condition
-      Error in `enframer()`:
-      ! length(fill) == 1 || length(fill) == nrow(df) is not TRUE
-
diff --git a/tests/testthat/_snaps/pivot_quantiles.md b/tests/testthat/_snaps/pivot_quantiles.md
index 184eb62a6..ca775a18f 100644
--- a/tests/testthat/_snaps/pivot_quantiles.md
+++ b/tests/testthat/_snaps/pivot_quantiles.md
@@ -45,7 +45,5 @@
       pivot_quantiles_longer(tib, d1, d3)
     Condition
       Error in `pivot_quantiles_longer()`:
-      ! Some selected columns contain different numbers of quantiles.
-      The result would be a very long <tibble>.
-      To do this anyway, rerun with `.ignore_length_check = TRUE`.
+      ! Some selected columns contain different numbers of quantiles. The result would be a very long <tibble>. To do this anyway, rerun with `.ignore_length_check = TRUE`.
 
diff --git a/tests/testthat/test-enframer.R b/tests/testthat/test-enframer.R
deleted file mode 100644
index 0926c587b..000000000
--- a/tests/testthat/test-enframer.R
+++ /dev/null
@@ -1,13 +0,0 @@
-test_that("enframer errors/works as needed", {
-  template1 <- data.frame(aa = 1:5, a = NA, b = NA, c = NA)
-  template2 <- data.frame(aa = 1:5, a = 2:6, b = 2:6, c = 2:6)
-  expect_snapshot(error = TRUE, enframer(1:5, letters[1]))
-  expect_snapshot(error = TRUE, enframer(data.frame(a = 1:5), 1:3))
-  expect_snapshot(error = TRUE, enframer(data.frame(a = 1:5), letters[1:3]))
-  expect_identical(enframer(data.frame(aa = 1:5), letters[1:3]), template1)
-  expect_snapshot(error = TRUE, enframer(data.frame(aa = 1:5), letters[1:2], fill = 1:4))
-  expect_identical(
-    enframer(data.frame(aa = 1:5), letters[1:3], fill = 2:6),
-    template2
-  )
-})

From 0a5819ec23bc5564844b6d1b2c3ab418a293d89d Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 2 Oct 2024 09:51:35 -0400
Subject: [PATCH 108/387] replace case_death_rate_subset with
 epidatasets::covid_case_death_rates in tests

---
 R/step_adjust_latency.R                      |  4 ++--
 man/covid_case_death_rates.Rd                |  4 ++--
 man/step_adjust_latency.Rd                   |  4 ++--
 tests/testthat/_snaps/get_test_data.md       |  2 +-
 tests/testthat/_snaps/step_adjust_latency.md |  2 +-
 tests/testthat/test-population_scaling.R     |  4 ++--
 tests/testthat/test-snapshots.R              | 10 +++++-----
 tests/testthat/test-step_adjust_latency.R    |  4 ++--
 8 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 433fbb328..75560e6de 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -174,12 +174,12 @@
 #' @rdname step_adjust_latency
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' # setting the `as_of` to something realistic
 #' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 #'
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_adjust_latency(method = "extend_ahead") %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
index 38988e5d7..b6b109869 100644
--- a/man/covid_case_death_rates.Rd
+++ b/man/covid_case_death_rates.Rd
@@ -5,7 +5,7 @@
 \alias{covid_case_death_rates}
 \title{JHU daily COVID-19 cases and deaths rates from all states}
 \format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 22176 rows and 4 columns.
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 20496 rows and 4 columns.
 }
 \source{
 This object contains a modified part of the
@@ -36,7 +36,7 @@ made available by the Center for Systems Science and Engineering at Johns
 Hopkins University, as downloaded from the CMU Delphi COVIDcast Epidata
 API. This example data is a snapshot as of March 20, 2024, and
 ranges from December 31, 2020 to December 31, 2021. It
-includes all states. It is used in the {epiprocess} correlation vignette.
+includes all states.
 }
 \section{Data dictionary}{
 
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index e1e516a3c..59e09f4ff 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -270,12 +270,12 @@ If you create columns that you then apply lags to (such as
 }
 
 \examples{
-jhu <- case_death_rate_subset \%>\%
+jhu <- covid_case_death_rates \%>\%
   dplyr::filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 # setting the `as_of` to something realistic
 attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 
-r <- epi_recipe(case_death_rate_subset) \%>\%
+r <- epi_recipe(covid_case_death_rates) \%>\%
   step_adjust_latency(method = "extend_ahead") \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
diff --git a/tests/testthat/_snaps/get_test_data.md b/tests/testthat/_snaps/get_test_data.md
index e65b0715c..22d0c942a 100644
--- a/tests/testthat/_snaps/get_test_data.md
+++ b/tests/testthat/_snaps/get_test_data.md
@@ -1,7 +1,7 @@
 # expect insufficient training data error
 
     Code
-      get_test_data(recipe = r, x = case_death_rate_subset)
+      get_test_data(recipe = r, x = covid_case_death_rates)
     Condition
       Error in `get_test_data()`:
       ! You supplied insufficient recent data for this recipe.
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index e37ae07ea..8d09248f3 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -63,7 +63,7 @@
 ---
 
     Code
-      prep(r6, case_death_rate_subset)
+      prep(r6, covid_case_death_rates)
     Message
       
       -- Epi Recipe ------------------------------------------------------------------
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index cccc8ea5d..3e1546a4e 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -237,7 +237,7 @@ test_that("test joining by default columns", {
 
   latest <- get_test_data(
     recipe = r,
-    x = case_death_rate_subset %>%
+    x = covid_case_death_rates %>%
       dplyr::filter(
         time_value > "2021-11-01",
         geo_value %in% c("ca", "ny")
@@ -250,7 +250,7 @@ test_that("test joining by default columns", {
 
 
 
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index f7767007b..cf5c80338 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -110,7 +110,7 @@ test_that("arx_forecaster snapshots", {
 })
 
 test_that("arx_forecaster output format snapshots", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value >= as.Date("2021-12-01"))
   attributes(jhu)$metadata$as_of <- as.Date(attributes(jhu)$metadata$as_of)
   out1 <- arx_forecaster(
@@ -153,9 +153,9 @@ test_that("arx_classifier snapshots", {
     c("case_rate", "death_rate")
   )
   expect_snapshot_tibble(arc1$predictions)
-  max_date <- case_death_rate_subset$time_value %>% max()
+  max_date <- covid_case_death_rates$time_value %>% max()
   arc2 <- arx_classifier(
-    case_death_rate_subset %>%
+    covid_case_death_rates %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate"),
@@ -164,7 +164,7 @@ test_that("arx_classifier snapshots", {
   expect_snapshot_tibble(arc2$predictions)
   expect_error(
     arc3 <- arx_classifier(
-      case_death_rate_subset %>%
+      covid_case_death_rates %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),
@@ -174,7 +174,7 @@ test_that("arx_classifier snapshots", {
   )
   expect_error(
     arc4 <- arx_classifier(
-      case_death_rate_subset %>%
+      covid_case_death_rates %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 2a9ea4419..136c45e64 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -512,12 +512,12 @@ test_that("printing step_adjust_latency results in expected output", {
     step_epi_ahead(death_rate, ahead = ahead)
   expect_snapshot(r5)
   expect_snapshot(prep(r5, real_x))
-  r6 <- epi_recipe(case_death_rate_subset) %>%
+  r6 <- epi_recipe(covid_case_death_rates) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
     step_adjust_latency(method = "extend_ahead") %>%
     step_epi_ahead(death_rate, ahead = 7)
   expect_snapshot(r6)
-  expect_snapshot(prep(r6, case_death_rate_subset))
+  expect_snapshot(prep(r6, covid_case_death_rates))
 })
 
 test_that("locf works as intended", {

From 00d0dd72b133a84bfe73f805f5e7e7b15b65799f Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 2 Oct 2024 17:57:20 -0400
Subject: [PATCH 109/387] attribution

---
 DESCRIPTION | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b26cde268..2f5a24d9b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,13 +6,19 @@ Authors@R: c(
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
     person("Dmitry", "Shemetov", email = "dshemeto@andrew.cmu.edu", role = "aut"),
     person("David", "Weber", email = "davidweb@andrew.cmu.edu", role = "aut"),
-    person("CMU's Delphi Research Group", role = c("cph", "fnd")),
+    person("CMU's Delphi Research Group", role = c("cph", "fnd", "dtc"),
+        comment = "Owner of masking, social-distancing, and CLI data from the COVID-19 Trends and Impacts Survey. Owner of claims-based CLI data from the Delphi Epidata API"),
     person("Logan", "Brooks", role = "aut"),
     person("Rachel", "Lobay", role = "aut"),
     person("Maggie", "Liu", role = "ctb"),
     person("Ken", "Mawer", role = "ctb"),
     person("Chloe", "You", role = "ctb"),
-    person("Jacob", "Bien", role = "ctb")
+    person("Jacob", "Bien", role = "ctb"),
+    person("Johns Hopkins University Center for Systems Science and Engineering", role = "dtc", comment = "Owner of COVID-19 cases and deaths data from the COVID-19 Data Repository"),
+    person("Johns Hopkins University", role = "cph", comment = "Copyright holder of COVID-19 cases and deaths data from the COVID-19 Data Repository"),
+    person("The COVID-19 Canada Open Data Working Group", role = "dtc", comment = "Owner of Canadian COVID-19 cases rates from the Covid19Canada data repository"),
+    person("Statistics Canada", role = "dtc", comment = "Owner of Canadian graduate employment income data from the Statistics Canada website"),
+    person("Google", role = "dtc", comment = "Collaborator on CLI data from the Google symptom surveys")
   )
 Description: A forecasting "framework" for creating epidemiological
     forecasts from versioned data. The framework is designed to be modular

From f85d6d8ed50b9129ef28cbb4a9f89d771eb466de Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 2 Oct 2024 18:17:35 -0400
Subject: [PATCH 110/387] news and version

---
 DESCRIPTION |  2 +-
 NEWS.md     | 13 +++++++++++--
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2f5a24d9b..822d05d60 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.1
+Version: 0.1.2
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index ef71394a2..a79fa3a74 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,10 +4,19 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 # epipredict 0.2
 
-## features
+## Breaking changes
+
+- Moved example datasets from being hosted in the package to being reexported
+  from the `epidatasets` package. The datasets can no longer be loaded with
+  `data()` but can be accessed with `epipredict::` or, after loading the package,
+  the name of the dataset alone (#382).
+
+## Improvements
+
 - Add `step_adjust_latency`, which give several methods to adjust the forecast if the `forecast_date` is after the last day of data.
 
-## bugfixes
+## Bug fixes
+
 
 # epipredict 0.1
 

From c86ac10fdef6323f7830d2bd9a4b6322f9cf45bd Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 2 Oct 2024 18:57:16 -0700
Subject: [PATCH 111/387] refactor: remove Suggests dependence on covidcast *
 just download the state data file, don't import covidcast * covidcast depends
 on sf which is tough to install

---
 DESCRIPTION                       | 1 -
 data-raw/case_death_rate_subset.R | 7 ++-----
 data-raw/state_census.R           | 7 +++++--
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3369cf23..0834489d6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -49,7 +49,6 @@ Imports:
     vctrs,
     workflows (>= 1.0.0)
 Suggests:
-    covidcast,
     data.table,
     epidatr (>= 1.0.0),
     fs,
diff --git a/data-raw/case_death_rate_subset.R b/data-raw/case_death_rate_subset.R
index 4bf416dbf..4fa3e64a7 100644
--- a/data-raw/case_death_rate_subset.R
+++ b/data-raw/case_death_rate_subset.R
@@ -1,9 +1,8 @@
 library(tidyverse)
-library(covidcast)
 library(epidatr)
 library(epiprocess)
 
-x <- covidcast(
+x <- pub_covidcast(
   data_source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
   time_type = "day",
@@ -11,10 +10,9 @@ x <- covidcast(
   time_values = epirange(20201231, 20211231),
   geo_values = "*"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, case_rate = value)
 
-y <- covidcast(
+y <- pub_covidcast(
   data_source = "jhu-csse",
   signals = "deaths_7dav_incidence_prop",
   time_type = "day",
@@ -22,7 +20,6 @@ y <- covidcast(
   time_values = epirange(20201231, 20211231),
   geo_values = "*"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, death_rate = value)
 
 case_death_rate_subset <- x %>%
diff --git a/data-raw/state_census.R b/data-raw/state_census.R
index 22dde5a41..df9b1b3db 100644
--- a/data-raw/state_census.R
+++ b/data-raw/state_census.R
@@ -1,11 +1,14 @@
 library(dplyr)
 library(tidyr)
 
-state_census <- covidcast::state_census %>%
+remote_file <- "https://github.com/cmu-delphi/covidcast/raw/refs/heads/main/R-packages/covidcast/data/state_census.rda"
+download.file(remote_file, "state_census.rda")
+load("state_census.rda")
+state_census <- state_census %>%
   select(STATE, NAME, POPESTIMATE2019, ABBR) %>%
   rename(abbr = ABBR, name = NAME, pop = POPESTIMATE2019, fips = STATE) %>%
   mutate(abbr = tolower(abbr)) %>%
   as_tibble()
-
+file.remove("state_census.rda")
 
 usethis::use_data(state_census, overwrite = TRUE)

From 65448b5969f4a7d8fc3c168041d8878d4c1aa513 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 2 Oct 2024 19:17:46 -0700
Subject: [PATCH 112/387] fix: simplify

---
 data-raw/state_census.R | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/data-raw/state_census.R b/data-raw/state_census.R
index df9b1b3db..9cd495ce9 100644
--- a/data-raw/state_census.R
+++ b/data-raw/state_census.R
@@ -1,14 +1,10 @@
 library(dplyr)
 library(tidyr)
 
-remote_file <- "https://github.com/cmu-delphi/covidcast/raw/refs/heads/main/R-packages/covidcast/data/state_census.rda"
-download.file(remote_file, "state_census.rda")
-load("state_census.rda")
-state_census <- state_census %>%
+state_census <- read_csv("https://github.com/cmu-delphi/covidcast/raw/c89e4d295550ba1540d64d2cc991badf63ad04e5/Python-packages/covidcast-py/covidcast/geo_mappings/state_census.csv") %>%
   select(STATE, NAME, POPESTIMATE2019, ABBR) %>%
   rename(abbr = ABBR, name = NAME, pop = POPESTIMATE2019, fips = STATE) %>%
   mutate(abbr = tolower(abbr)) %>%
   as_tibble()
-file.remove("state_census.rda")
 
 usethis::use_data(state_census, overwrite = TRUE)

From 6805d98ce2e6fd2123c59005e795da2852940a72 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 2 Oct 2024 19:32:08 -0700
Subject: [PATCH 113/387] fix: some borked tests

---
 tests/testthat/_snaps/population_scaling.md | 16 ++++++++++++++++
 tests/testthat/_snaps/step_epi_slide.md     |  4 ++--
 tests/testthat/test-grf_quantiles.R         |  2 +-
 tests/testthat/test-snapshots.R             |  6 +++---
 4 files changed, 22 insertions(+), 6 deletions(-)
 create mode 100644 tests/testthat/_snaps/population_scaling.md

diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
new file mode 100644
index 000000000..9263e8e1e
--- /dev/null
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -0,0 +1,16 @@
+# expect error if `by` selector does not match
+
+    Code
+      wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
+    Condition
+      Error in `hardhat::validate_column_names()`:
+      ! The following required columns are missing: 'a'.
+
+---
+
+    Code
+      forecast(wf)
+    Condition
+      Error in `hardhat::validate_column_names()`:
+      ! The following required columns are missing: 'nothere'.
+
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index a4b9d64c8..7493a7fea 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 2570c247d..5bbaed01f 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -10,7 +10,7 @@ test_that("quantile_rand_forest defaults work", {
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
   manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, quantiles = c(0.1, 0.5, 0.9))
-  expect_identical(pars$quantiles.orig, manual$quantiles)
+  expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 
   fseed <- 12345
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index da8635ae0..5af492378 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -117,7 +117,7 @@ test_that("arx_forecaster output format snapshots", {
     jhu, "death_rate",
     c("case_rate", "death_rate")
   )
-  expect_equal(as.Date(out1$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out1$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out1$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out1)
   out2 <- arx_forecaster(jhu, "case_rate",
@@ -129,7 +129,7 @@ test_that("arx_forecaster output format snapshots", {
       forecast_date = as.Date("2022-01-03")
     )
   )
-  expect_equal(as.Date(out2$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out2$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out2$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out2)
   out3 <- arx_forecaster(jhu, "death_rate",
@@ -140,7 +140,7 @@ test_that("arx_forecaster output format snapshots", {
       forecast_date = as.Date("2022-01-03")
     )
   )
-  expect_equal(as.Date(out3$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out3$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out3$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out3)
 })

From 57df54c2fa5903ca3c9aae9b6cf37a3c8a367364 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 2 Oct 2024 20:00:27 -0700
Subject: [PATCH 114/387] fix: add readr

---
 DESCRIPTION             | 1 +
 data-raw/state_census.R | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 0834489d6..2bd433f6f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -60,6 +60,7 @@ Suggests:
     quantreg,
     ranger,
     RcppRoll,
+    readr,
     rmarkdown,
     smoothqr,
     testthat (>= 3.0.0),
diff --git a/data-raw/state_census.R b/data-raw/state_census.R
index 9cd495ce9..cfa74d38b 100644
--- a/data-raw/state_census.R
+++ b/data-raw/state_census.R
@@ -1,7 +1,7 @@
 library(dplyr)
 library(tidyr)
 
-state_census <- read_csv("https://github.com/cmu-delphi/covidcast/raw/c89e4d295550ba1540d64d2cc991badf63ad04e5/Python-packages/covidcast-py/covidcast/geo_mappings/state_census.csv") %>%
+state_census <- readr::read_csv("https://github.com/cmu-delphi/covidcast/raw/c89e4d295550ba1540d64d2cc991badf63ad04e5/Python-packages/covidcast-py/covidcast/geo_mappings/state_census.csv") %>% # nolint: line_length_linter
   select(STATE, NAME, POPESTIMATE2019, ABBR) %>%
   rename(abbr = ABBR, name = NAME, pop = POPESTIMATE2019, fips = STATE) %>%
   mutate(abbr = tolower(abbr)) %>%

From cec2f8ba3aed68243856562016b3a6926a7bffa8 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Thu, 3 Oct 2024 15:54:46 -0700
Subject: [PATCH 115/387] ci: fix pkgdown

---
 .github/workflows/pkgdown.yaml | 36 +++++++++++++++++++++++++---------
 1 file changed, 27 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index cc940bc8b..eb776b2eb 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -1,10 +1,15 @@
 # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 #
-# Created with usethis + edited to run on PRs to dev, use API key.
+# Modifications:
+#   * workflow_dispatch added to allow manual triggering of the workflow
+#   * trigger branches changed
+#   * API key secrets.SECRET_EPIPREDICT_GHACTIONS_DELPHI_EPIDATA_KEY
 on:
   push:
     branches: [main, dev]
+  pull_request:
+    branches: [main, dev]
   release:
     types: [published]
   workflow_dispatch:
@@ -21,8 +26,9 @@ jobs:
       group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      DELPHI_EPIDATA_KEY: ${{ secrets.SECRET_EPIPREDICT_GHACTIONS_DELPHI_EPIDATA_KEY }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -32,19 +38,31 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::pkgdown, local::.
+          extra-packages: any::pkgdown, local::., any::cli
           needs: website
 
       - name: Build site
-        env:
-          DELPHI_EPIDATA_KEY: ${{ secrets.SECRET_EPIPROCESS_GHACTIONS_DELPHI_EPIDATA_KEY }}
+        # - target_ref gets the ref from a different variable, depending on the event
+        # - override allows us to set the pkgdown mode and version_label
+        #   - mode: release is the standard build mode, devel places the site in /dev
+        #   - version_label: 'light' and 'success' are CSS labels for Bootswatch: Cosmo
+        #                    https://bootswatch.com/cosmo/
+        # - we use pkgdown:::build_github_pages to build the site because of an issue in pkgdown
+        #   https://github.com/r-lib/pkgdown/issues/2257
         run: |
-          if (startsWith("${{ github.event_name }}", "pull_request")) {
-              mode <- ifelse("${{ github.base_ref }}" == "main", "release", "devel")
+          target_ref <- "${{ github.event_name == 'pull_request' && github.base_ref || github.ref }}"
+          override <- if (target_ref == "main" || target_ref == "refs/heads/main") {
+            list(development = list(mode = "release", version_label = "light"))
+          } else if (target_ref == "dev" || target_ref == "refs/heads/dev") {
+            list(development = list(mode = "devel", version_label = "success"))
           } else {
-              mode <- ifelse("${{ github.ref_name }}" == "main", "release", "devel")
+            stop("Unexpected target_ref: ", target_ref)
           }
-          pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE, override=list(PKGDOWN_DEV_MODE=mode))
+          pkg <- pkgdown::as_pkgdown(".", override = override)
+          cli::cli_rule("Cleaning files from old site...")
+          pkgdown::clean_site(pkg)
+          pkgdown::build_site(pkg, preview = FALSE, install = FALSE, new_process = FALSE)
+          pkgdown:::build_github_pages(pkg)
         shell: Rscript {0}
 
       - name: Deploy to GitHub pages 🚀

From 42ea8ffca74fdf2a1734dd457a156f54821b49fe Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Thu, 3 Oct 2024 15:56:30 -0700
Subject: [PATCH 116/387] ci: don't run on PR, since pkgdown takes so long

---
 .github/workflows/pkgdown.yaml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index eb776b2eb..d86e1485e 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -8,8 +8,6 @@
 on:
   push:
     branches: [main, dev]
-  pull_request:
-    branches: [main, dev]
   release:
     types: [published]
   workflow_dispatch:

From 6165a89af006e70dd92cf30bde33db69a81eade9 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 2 Oct 2024 19:32:08 -0700
Subject: [PATCH 117/387] fix: some borked tests

---
 tests/testthat/_snaps/population_scaling.md | 16 ++++++++++++++++
 tests/testthat/_snaps/step_epi_slide.md     |  4 ++--
 tests/testthat/test-grf_quantiles.R         |  2 +-
 tests/testthat/test-snapshots.R             |  6 +++---
 4 files changed, 22 insertions(+), 6 deletions(-)
 create mode 100644 tests/testthat/_snaps/population_scaling.md

diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
new file mode 100644
index 000000000..9263e8e1e
--- /dev/null
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -0,0 +1,16 @@
+# expect error if `by` selector does not match
+
+    Code
+      wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
+    Condition
+      Error in `hardhat::validate_column_names()`:
+      ! The following required columns are missing: 'a'.
+
+---
+
+    Code
+      forecast(wf)
+    Condition
+      Error in `hardhat::validate_column_names()`:
+      ! The following required columns are missing: 'nothere'.
+
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index a4b9d64c8..7493a7fea 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 2570c247d..5bbaed01f 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -10,7 +10,7 @@ test_that("quantile_rand_forest defaults work", {
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
   manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, quantiles = c(0.1, 0.5, 0.9))
-  expect_identical(pars$quantiles.orig, manual$quantiles)
+  expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 
   fseed <- 12345
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index da8635ae0..5af492378 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -117,7 +117,7 @@ test_that("arx_forecaster output format snapshots", {
     jhu, "death_rate",
     c("case_rate", "death_rate")
   )
-  expect_equal(as.Date(out1$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out1$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out1$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out1)
   out2 <- arx_forecaster(jhu, "case_rate",
@@ -129,7 +129,7 @@ test_that("arx_forecaster output format snapshots", {
       forecast_date = as.Date("2022-01-03")
     )
   )
-  expect_equal(as.Date(out2$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out2$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out2$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out2)
   out3 <- arx_forecaster(jhu, "death_rate",
@@ -140,7 +140,7 @@ test_that("arx_forecaster output format snapshots", {
       forecast_date = as.Date("2022-01-03")
     )
   )
-  expect_equal(as.Date(out3$metadata$forecast_created), Sys.Date())
+  expect_equal(as.Date(format(out3$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out3$metadata$forecast_created <- as.Date("0999-01-01")
   expect_snapshot(out3)
 })

From d7a8670347b88c91ca228b590f63198b737057a4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 4 Oct 2024 14:56:47 -0500
Subject: [PATCH 118/387] fix for multiple epi_keys_checked

---
 R/utils-latency.R                   |  8 ++++----
 tests/testthat/test-utils_latency.R | 26 ++++++++++++++++++++++++++
 2 files changed, 30 insertions(+), 4 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 311656ac1..64197d284 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -57,8 +57,8 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
     select(all_of(columns)) %>%
     drop_na()
   # null and "" don't work in `group_by`
-  if (!is.null(epi_keys_checked) && (epi_keys_checked != "")) {
-    max_time <- max_time %>% group_by(get(epi_keys_checked))
+  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
+    max_time <- max_time %>% group_by(across(all_of(epi_keys_checked)))
   }
   max_time <- max_time %>%
     summarise(time_value = max(time_value)) %>%
@@ -115,8 +115,8 @@ get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_ch
   shift_max_date <- new_data %>%
     drop_na(all_of(column))
   # null and "" don't work in `group_by`
-  if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
-    shift_max_date <- shift_max_date %>% group_by(get(epi_keys_checked))
+  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
+    shift_max_date <- shift_max_date %>% group_by(across(all_of(epi_keys_checked)))
   }
   shift_max_date <- shift_max_date %>%
     summarise(time_value = max(time_value)) %>%
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 3ba6453a0..6e7484dbc 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -59,6 +59,20 @@ toy_df <- tribble(
   "ca", as.Date("2015-01-12"), 103, 10,
 ) %>%
   as_epi_df(as_of = as.Date("2015-01-14"))
+toy_df_src <- tribble(
+  ~geo_value, ~source, ~time_value, ~a, ~b,
+  "ma", "new", as.Date("2015-01-11"), 20, 6,
+  "ma", "new", as.Date("2015-01-12"), 23, NA,
+  "ma", "new", as.Date("2015-01-13"), 25, NA,
+  "ca", "new", as.Date("2015-01-11"), 100, 5,
+  "ca", "new", as.Date("2015-01-12"), 103, 10,
+  "ma", "old", as.Date("2013-01-01"), 19, 4,
+  "ma", "old", as.Date("2013-01-02"), 20, 2,
+  "ca", "old", as.Date("2013-01-03"), 28, 11,
+  "na", "new", as.Date("2013-01-05"), 28, 11,
+  "ma", "older", as.Date("2010-01-05"), 28, 11,
+) %>%
+  as_epi_df(as_of = as.Date("2015-01-14"), other_keys = "source")
 
 test_that("get_latency works", {
   expect_equal(get_latency(modified_data, as_of, "case_rate", 1, "geo_value"), 5)
@@ -97,6 +111,18 @@ test_that("get_forecast_date works", {
   expect_equal(get_forecast_date(modified_data, info, "", NULL), as_of)
   expect_equal(get_forecast_date(modified_data, info, NULL, NULL), as_of)
 })
+test_that("get_forecast_date works for multiple key columns", {
+  info <- tribble(
+    ~variable, ~type, ~role, ~source,
+    "time_value", "date", "time_value", "original",
+    "geo_value", "nominal", "geo_value", "original",
+    "source", "nominal", "other_key", "original",
+    "a", "numeric", "raw", "original",
+    "b", "numeric", "raw", "original",
+  )
+  expect_equal(get_forecast_date(toy_df_src, info, c("geo_value", "source"), NULL), attributes(toy_df_src)$metadata$as_of)
+
+})
 
 test_that("pad_to_end works correctly", {
   single_ex <- tribble(

From a5f7d748aa10a128ac55539c6bebc9c575c67caa Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 4 Oct 2024 14:57:46 -0500
Subject: [PATCH 119/387] order disordered quantiles instead of error

---
 R/dist_quantiles.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index 8930bdeaa..bed41a7ba 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -22,11 +22,11 @@ new_quantiles <- function(values = double(1), quantile_levels = double(1)) {
     values <- values[o]
     quantile_levels <- quantile_levels[o]
   }
-  if (is.unsorted(values, na.rm = TRUE)) {
-    cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
-  }
+  # if (is.unsorted(values, na.rm = TRUE)) {
+  #   cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
+  # }
 
-  new_rcrd(list(values = values, quantile_levels = quantile_levels),
+  new_rcrd(list(values = values[order(quantile_levels)], quantile_levels = quantile_levels),
     class = c("dist_quantiles", "dist_default")
   )
 }

From e632f31ac1a142c34d7933b9dbf50eb929c89829 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 4 Oct 2024 14:59:07 -0500
Subject: [PATCH 120/387] new step_adjust_latency parameter: keys_to_ignore

---
 R/step_adjust_latency.R             | 12 ++++++++++--
 R/utils-latency.R                   | 22 +++++++++++++++++++---
 tests/testthat/test-utils_latency.R | 21 +++++++++++++++++++++
 3 files changed, 50 insertions(+), 5 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 433fbb328..b41546675 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -152,6 +152,12 @@
 #'
 #'   Note that this is a separate concern from different latencies across
 #'   different *data columns*, which is only handled by the choice of `method`.
+#' @param keys_to_ignore a list of character vectors. Set this to avoid using
+#'   specific key values in the `epi_keys_checked` to set latency. For example,
+#'   say you have two locations `pr` and `gu` which have useful training data,
+#'   but have stopped providing up-to-date information, and so are no longer
+#'   part of the test set. Setting `keys_to_ignore = list(geo_value = c("pr",
+#'   "gu"))` will exclude them from the latency calculation.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
 #'   vector. Cannot be set at the same time as `fixed_forecast_date`. If
 #'   non-`NULL`, the amount to offset the ahead or lag by. If a single integer,
@@ -203,6 +209,7 @@ step_adjust_latency <-
              "extend_lags"
            ),
            epi_keys_checked = NULL,
+           keys_to_ignore = c(),
            fixed_latency = NULL,
            fixed_forecast_date = NULL,
            check_latency_length = TRUE,
@@ -228,6 +235,7 @@ step_adjust_latency <-
         metadata = NULL,
         method = method,
         epi_keys_checked = epi_keys_checked,
+        keys_to_ignore = keys_to_ignore,
         check_latency_length = check_latency_length,
         columns = NULL,
         skip = FALSE,
@@ -239,7 +247,7 @@ step_adjust_latency <-
 step_adjust_latency_new <-
   function(terms, role, trained, fixed_forecast_date, forecast_date, latency,
            latency_table, latency_sign, metadata, method, epi_keys_checked,
-           check_latency_length, columns, skip, id) {
+           keys_to_ignore, check_latency_length, columns, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
@@ -271,7 +279,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 
   latency_table <- get_latency_table(
     training, NULL, forecast_date, latency,
-    get_sign(x), x$epi_keys_checked, info, x$terms
+    get_sign(x), x$epi_keys_checked, x$keys_to_ignore, info, x$terms
   )
   # get the columns used, even if it's all of them
   terms_used <- x$terms
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 64197d284..3b3cab835 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -290,7 +290,8 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
 #' @keywords internal
 #' @importFrom dplyr rowwise
 get_latency_table <- function(training, columns, forecast_date, latency,
-                              sign_shift, epi_keys_checked, info, terms) {
+                              sign_shift, epi_keys_checked, keys_to_ignore,
+                              info, terms) {
   if (is.null(columns)) {
     columns <- recipes_eval_select(terms, training, info)
   }
@@ -305,7 +306,11 @@ get_latency_table <- function(training, columns, forecast_date, latency,
     latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(
-        training, forecast_date, col_name, sign_shift, epi_keys_checked
+        training %>% drop_ignored_keys(keys_to_ignore),
+        forecast_date,
+        col_name,
+        sign_shift,
+        epi_keys_checked
       ))
   } else if (length(latency) > 1) {
     # if latency has a length, it must also have named elements.
@@ -319,7 +324,7 @@ get_latency_table <- function(training, columns, forecast_date, latency,
     latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(
-        training, forecast_date, col_name, sign_shift, epi_keys_checked
+        training %>% drop_ignored_keys(keys_to_ignore), forecast_date, col_name, sign_shift, epi_keys_checked
       ))
     if (latency) {
       latency_table <- latency_table %>% mutate(latency = latency)
@@ -328,6 +333,17 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   return(latency_table %>% ungroup())
 }
 
+#' given a list named by key columns, remove any matching key values
+drop_ignored_keys <- function(training, keys_to_ignore) {
+  # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
+  # and is needed to bypass an incomplete port of `across` functions to `if_any`
+  training %>%
+    filter((if_all(
+      names(keys_to_ignore),
+      ~ . %nin% keys_to_ignore[[cur_column()]]
+    )))
+}
+
 
 #' checks: the recipe type, whether a previous step is the relevant epi_shift,
 #' that either `fixed_latency` or `fixed_forecast_date` is non-null, and that
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 6e7484dbc..9036ad46b 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -87,6 +87,27 @@ test_that("get_latency works", {
   expect_equal(get_latency(toy_df, as.Date("2015-01-14"), "b", -1, "geo_value"), -3)
 })
 
+test_that("get_latency ignores keys it's supposed to", {
+  keys_to_ignore <- list(geo_value = c("na"), source = c("old", "older"))
+  expected_df <- tribble(
+    ~geo_value, ~source, ~time_value, ~a, ~b,
+    "ma", "old", as.Date("2013-01-01"), 19, 4,
+    "ma", "old", as.Date("2013-01-02"), 20, 2,
+    "ca", "old", as.Date("2013-01-03"), 28, 11,
+    "na", "new", as.Date("2013-01-05"), 28, 11,
+    "ma", "older", as.Date("2010-01-05"), 28, 11,
+  )
+  expect_equal(
+    toy_df_src %>% drop_ignored_keys(keys_to_ignore) %>% as_tibble(),
+    expected_df
+  )
+
+  expect_equal(
+    get_latency_table(toy_df_src, c("a", "b"), as.Date("2015-01-14"), NULL, -1, c("geo_value", "source"), keys_to_ignore),
+    tibble(col_name = c("a", "b"), latency = c(-2, -3))
+  )
+})
+
 test_that("get_latency infers max_time to be the minimum `max time` across grouping the specified keys", {
   # place 2 is already 1 day less latent than place 1, so decreasing it's
   # latency it should have no effect

From bb1911d2ac3357c1858b5efbde5d0a3e3c6036ce Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 4 Oct 2024 14:59:49 -0500
Subject: [PATCH 121/387] step_adj_lat correctly reconstruct as_epi_df

---
 R/step_adjust_latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index b41546675..9fb9eebba 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -313,7 +313,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
-    new_data <- as_epi_df(new_data)
+    new_data <- as_epi_df(new_data, as_of = object$forecast_date, other_keys = object$metadata$other_keys)
     attributes(new_data)$metadata <- object$metadata
     attributes(new_data)$metadata$as_of <- object$forecast_date
   } else {

From 5867bed2a8efea57c58a22523ea519a83b5c4147 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 7 Oct 2024 13:21:23 -0500
Subject: [PATCH 122/387] fixing tests for the above

---
 R/step_adjust_latency.R                       |  5 ++++-
 R/utils-latency.R                             |  8 +++++---
 tests/testthat/_snaps/dist_quantiles.md       |  5 ++---
 tests/testthat/test-dist_quantiles.R          |  4 ++--
 tests/testthat/test-epi_workflow.R            |  2 +-
 tests/testthat/test-grf_quantiles.R           |  2 +-
 tests/testthat/test-layer_add_forecast_date.R |  1 +
 tests/testthat/test-utils_latency.R           | 13 ++++++-------
 8 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 9fb9eebba..866454aee 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -261,6 +261,7 @@ step_adjust_latency_new <-
       metadata = metadata,
       method = method,
       epi_keys_checked = epi_keys_checked,
+      keys_to_ignore = keys_to_ignore,
       check_latency_length = check_latency_length,
       columns = columns,
       skip = skip,
@@ -301,6 +302,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     metadata = attributes(training)$metadata,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
+    keys_to_ignore = x$keys_to_ignore,
     check_latency_length = x$check_latency_length,
     columns = recipes_eval_select(latency_table$col_name, training, info),
     skip = x$skip,
@@ -313,7 +315,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
-    new_data <- as_epi_df(new_data, as_of = object$forecast_date, other_keys = object$metadata$other_keys)
+    new_data <- as_epi_df(new_data, as_of = object$forecast_date, other_keys = object$metadata$other_keys %||% character())
     attributes(new_data)$metadata <- object$metadata
     attributes(new_data)$metadata$as_of <- object$forecast_date
   } else {
@@ -411,3 +413,4 @@ print.step_adjust_latency <-
     cli::cli_end(theme_div_id)
     invisible(x)
   }
+
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 3b3cab835..96f426adf 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -301,12 +301,13 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   if (length(columns) > 0) {
     latency_table <- latency_table %>% filter(col_name %in% columns)
   }
-
+  training_dropped <- training %>%
+    drop_ignored_keys(keys_to_ignore)
   if (is.null(latency)) {
     latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(
-        training %>% drop_ignored_keys(keys_to_ignore),
+        training_dropped,
         forecast_date,
         col_name,
         sign_shift,
@@ -410,7 +411,7 @@ compare_bake_prep_latencies <- function(object, new_data, call = caller_env()) {
     )
   local_latency_table <- get_latency_table(
     new_data, object$columns, current_forecast_date, latency,
-    get_sign(object), object$epi_keys_checked, NULL, NULL
+    get_sign(object), object$epi_keys_checked, object$keys_to_ignore, NULL, NULL
   )
   comparison_table <- local_latency_table %>%
     ungroup() %>%
@@ -478,3 +479,4 @@ create_shift_grid <- function(prefix, amount, target_sign, columns, latency_tabl
     )
   return(list(shift_grid, latency_adjusted))
 }
+
diff --git a/tests/testthat/_snaps/dist_quantiles.md b/tests/testthat/_snaps/dist_quantiles.md
index 1d626e089..7c6676554 100644
--- a/tests/testthat/_snaps/dist_quantiles.md
+++ b/tests/testthat/_snaps/dist_quantiles.md
@@ -26,9 +26,8 @@
 
     Code
       new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.15, 0.2, 0.5, 0.8))
-    Condition
-      Error in `new_quantiles()`:
-      ! `values[order(quantile_levels)]` produces unsorted quantiles.
+    Output
+      quantiles(4)[5]
 
 ---
 
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 9975213c6..62e85d08a 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -1,4 +1,4 @@
-library(distributional)
+suppressPackageStartupMessages(library(distributional))
 
 test_that("constructor returns reasonable quantiles", {
   expect_snapshot(error = TRUE, new_quantiles(rnorm(5), c(-2, -1, 0, 1, 2)))
@@ -6,7 +6,7 @@ test_that("constructor returns reasonable quantiles", {
   expect_snapshot(error = TRUE, new_quantiles(sort(rnorm(5)), sort(runif(2))))
   expect_silent(new_quantiles(1:5, 1:5 / 10))
   expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .1, .2, .5, .8)))
-  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
+  expect_snapshot(error = FALSE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
   expect_snapshot(error = TRUE, new_quantiles(c(1, 2, 3), c(.1, .2, 3)))
 })
 
diff --git a/tests/testthat/test-epi_workflow.R b/tests/testthat/test-epi_workflow.R
index ecd955cc5..af6ef39ca 100644
--- a/tests/testthat/test-epi_workflow.R
+++ b/tests/testthat/test-epi_workflow.R
@@ -103,7 +103,7 @@ test_that("forecast method errors when workflow not fit", {
 test_that("fit method does not silently drop the class", {
   # This is issue #363
 
-  library(recipes)
+  suppressPackageStartupMessages(library(recipes))
   tbl <- tibble::tibble(
     geo_value = 1,
     time_value = 1:100,
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 2570c247d..21573cbd7 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -1,5 +1,5 @@
 set.seed(12345)
-library(grf)
+suppressPackageStartupMessages(library(grf))
 tib <- tibble(
   y = rnorm(100), x = rnorm(100), z = rnorm(100),
   f = factor(sample(letters[1:3], 100, replace = TRUE))
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 491bf5e20..4ae176878 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -137,3 +137,4 @@ test_that("forecast date works for daily", {
   )
   expect_snapshot(error = TRUE, predict(wf3, latest))
 })
+
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 9036ad46b..54920484d 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -90,12 +90,12 @@ test_that("get_latency works", {
 test_that("get_latency ignores keys it's supposed to", {
   keys_to_ignore <- list(geo_value = c("na"), source = c("old", "older"))
   expected_df <- tribble(
-    ~geo_value, ~source, ~time_value, ~a, ~b,
-    "ma", "old", as.Date("2013-01-01"), 19, 4,
-    "ma", "old", as.Date("2013-01-02"), 20, 2,
-    "ca", "old", as.Date("2013-01-03"), 28, 11,
-    "na", "new", as.Date("2013-01-05"), 28, 11,
-    "ma", "older", as.Date("2010-01-05"), 28, 11,
+  ~geo_value, ~source, ~time_value, ~a, ~b,
+  "ma", "new", as.Date("2015-01-11"), 20, 6,
+  "ma", "new", as.Date("2015-01-12"), 23, NA,
+  "ma", "new", as.Date("2015-01-13"), 25, NA,
+  "ca", "new", as.Date("2015-01-11"), 100, 5,
+  "ca", "new", as.Date("2015-01-12"), 103, 10,
   )
   expect_equal(
     toy_df_src %>% drop_ignored_keys(keys_to_ignore) %>% as_tibble(),
@@ -142,7 +142,6 @@ test_that("get_forecast_date works for multiple key columns", {
     "b", "numeric", "raw", "original",
   )
   expect_equal(get_forecast_date(toy_df_src, info, c("geo_value", "source"), NULL), attributes(toy_df_src)$metadata$as_of)
-
 })
 
 test_that("pad_to_end works correctly", {

From ad80549dabf43d6f12f52a3b841ed6530c71445c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 7 Oct 2024 14:01:14 -0500
Subject: [PATCH 123/387] docs and formatting

---
 R/step_adjust_latency.R                       |  1 -
 R/utils-latency.R                             |  1 -
 man/drop_ignored_keys.Rd                      | 11 +++++++++++
 man/get_latency_table.Rd                      |  1 +
 man/step_adjust_latency.Rd                    | 11 +++++++++--
 tests/testthat/test-layer_add_forecast_date.R |  1 -
 tests/testthat/test-utils_latency.R           | 12 ++++++------
 7 files changed, 27 insertions(+), 11 deletions(-)
 create mode 100644 man/drop_ignored_keys.Rd

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 866454aee..0e131564a 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -413,4 +413,3 @@ print.step_adjust_latency <-
     cli::cli_end(theme_div_id)
     invisible(x)
   }
-
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 96f426adf..592240e42 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -479,4 +479,3 @@ create_shift_grid <- function(prefix, amount, target_sign, columns, latency_tabl
     )
   return(list(shift_grid, latency_adjusted))
 }
-
diff --git a/man/drop_ignored_keys.Rd b/man/drop_ignored_keys.Rd
new file mode 100644
index 000000000..41031a49f
--- /dev/null
+++ b/man/drop_ignored_keys.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-latency.R
+\name{drop_ignored_keys}
+\alias{drop_ignored_keys}
+\title{given a list named by key columns, remove any matching key values}
+\usage{
+drop_ignored_keys(training, keys_to_ignore)
+}
+\description{
+given a list named by key columns, remove any matching key values
+}
diff --git a/man/get_latency_table.Rd b/man/get_latency_table.Rd
index ae309c944..853918b23 100644
--- a/man/get_latency_table.Rd
+++ b/man/get_latency_table.Rd
@@ -12,6 +12,7 @@ get_latency_table(
   latency,
   sign_shift,
   epi_keys_checked,
+  keys_to_ignore,
   info,
   terms
 )
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index f0ee41390..baa14581d 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -9,6 +9,7 @@ step_adjust_latency(
   ...,
   method = c("extend_ahead", "locf", "extend_lags"),
   epi_keys_checked = NULL,
+  keys_to_ignore = c(),
   fixed_latency = NULL,
   fixed_forecast_date = NULL,
   check_latency_length = TRUE,
@@ -50,6 +51,12 @@ it will take the maximum across all values, irrespective of any keys.
 Note that this is a separate concern from different latencies across
 different \emph{data columns}, which is only handled by the choice of \code{method}.}
 
+\item{keys_to_ignore}{a list of character vectors. Set this to avoid using
+specific key values in the \code{epi_keys_checked} to set latency. For example,
+say you have two locations \code{pr} and \code{gu} which have useful training data,
+but have stopped providing up-to-date information, and so are no longer
+part of the test set. Setting \code{keys_to_ignore = list(geo_value = c("pr", "gu"))} will exclude them from the latency calculation.}
+
 \item{fixed_latency}{either a positive integer, or a labeled positive integer
 vector. Cannot be set at the same time as \code{fixed_forecast_date}. If
 non-\code{NULL}, the amount to offset the ahead or lag by. If a single integer,
@@ -260,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous
+#> `step_epi_lag`s won't work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 4ae176878..491bf5e20 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -137,4 +137,3 @@ test_that("forecast date works for daily", {
   )
   expect_snapshot(error = TRUE, predict(wf3, latest))
 })
-
diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 54920484d..7bf808835 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -90,12 +90,12 @@ test_that("get_latency works", {
 test_that("get_latency ignores keys it's supposed to", {
   keys_to_ignore <- list(geo_value = c("na"), source = c("old", "older"))
   expected_df <- tribble(
-  ~geo_value, ~source, ~time_value, ~a, ~b,
-  "ma", "new", as.Date("2015-01-11"), 20, 6,
-  "ma", "new", as.Date("2015-01-12"), 23, NA,
-  "ma", "new", as.Date("2015-01-13"), 25, NA,
-  "ca", "new", as.Date("2015-01-11"), 100, 5,
-  "ca", "new", as.Date("2015-01-12"), 103, 10,
+    ~geo_value, ~source, ~time_value, ~a, ~b,
+    "ma", "new", as.Date("2015-01-11"), 20, 6,
+    "ma", "new", as.Date("2015-01-12"), 23, NA,
+    "ma", "new", as.Date("2015-01-13"), 25, NA,
+    "ca", "new", as.Date("2015-01-11"), 100, 5,
+    "ca", "new", as.Date("2015-01-12"), 103, 10,
   )
   expect_equal(
     toy_df_src %>% drop_ignored_keys(keys_to_ignore) %>% as_tibble(),

From aad61799423fcff993c8dcc61ce94093318da0c1 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 7 Oct 2024 14:17:07 -0500
Subject: [PATCH 124/387] missing `::`

---
 R/utils-latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 592240e42..d5107ea1b 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -339,7 +339,7 @@ drop_ignored_keys <- function(training, keys_to_ignore) {
   # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
   # and is needed to bypass an incomplete port of `across` functions to `if_any`
   training %>%
-    filter((if_all(
+    filter((dplyr::if_all(
       names(keys_to_ignore),
       ~ . %nin% keys_to_ignore[[cur_column()]]
     )))

From 2a7429559da1c46fcb028fdf866b550e3786b1e4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 7 Oct 2024 14:52:54 -0500
Subject: [PATCH 125/387] doc warning

---
 R/utils-latency.R        | 2 ++
 man/drop_ignored_keys.Rd | 5 ++++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index d5107ea1b..dec4f8ec6 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -335,6 +335,8 @@ get_latency_table <- function(training, columns, forecast_date, latency,
 }
 
 #' given a list named by key columns, remove any matching key values
+#' keys_to_ignore should have the form list(col_name = c("value_to_ignore", "other_value_to_ignore"))
+#' @keywords internal
 drop_ignored_keys <- function(training, keys_to_ignore) {
   # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
   # and is needed to bypass an incomplete port of `across` functions to `if_any`
diff --git a/man/drop_ignored_keys.Rd b/man/drop_ignored_keys.Rd
index 41031a49f..6adeb9983 100644
--- a/man/drop_ignored_keys.Rd
+++ b/man/drop_ignored_keys.Rd
@@ -2,10 +2,13 @@
 % Please edit documentation in R/utils-latency.R
 \name{drop_ignored_keys}
 \alias{drop_ignored_keys}
-\title{given a list named by key columns, remove any matching key values}
+\title{given a list named by key columns, remove any matching key values
+keys_to_ignore should have the form list(col_name = c("value_to_ignore", "other_value_to_ignore"))}
 \usage{
 drop_ignored_keys(training, keys_to_ignore)
 }
 \description{
 given a list named by key columns, remove any matching key values
+keys_to_ignore should have the form list(col_name = c("value_to_ignore", "other_value_to_ignore"))
 }
+\keyword{internal}

From f43070d0ba6d781f7a8b14868536b5d7ae8360ca Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 8 Oct 2024 17:28:15 -0500
Subject: [PATCH 126/387] quantile sort check, no ahead sign, check shifting

---
 R/dist_quantiles.R |  8 ++++----
 R/step_epi_shift.R | 11 ++++++++++-
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index bed41a7ba..8930bdeaa 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -22,11 +22,11 @@ new_quantiles <- function(values = double(1), quantile_levels = double(1)) {
     values <- values[o]
     quantile_levels <- quantile_levels[o]
   }
-  # if (is.unsorted(values, na.rm = TRUE)) {
-  #   cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
-  # }
+  if (is.unsorted(values, na.rm = TRUE)) {
+    cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
+  }
 
-  new_rcrd(list(values = values[order(quantile_levels)], quantile_levels = quantile_levels),
+  new_rcrd(list(values = values, quantile_levels = quantile_levels),
     class = c("dist_quantiles", "dist_default")
   )
 }
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index a4bcee52e..eeac3b96f 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -111,7 +111,6 @@ step_epi_ahead <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
-    arg_is_nonneg_int(ahead)
     arg_is_chr_scalar(prefix, id)
 
     recipes::add_step(
@@ -193,6 +192,11 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
+  if (nrow(shift_grid)==0) {
+    cli_warn(c("prepping no columns!",
+               "{x$terms} returns no columns for this dataset."),
+             class = "epipredict__step_epi_lag__no_columns_shifted")
+  }
 
   step_epi_lag_new(
     terms = x$terms,
@@ -227,6 +231,11 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
+  if (nrow(shift_grid)==0) {
+    cli_warn(c("prepping no columns!",
+               "{x$terms} returns no columns for this dataset."),
+             class = "epipredict__step_epi_ahead__no_columns_shifted")
+  }
 
   step_epi_ahead_new(
     terms = x$terms,

From c99b9c91778fce4f94bc23371596e5ecd4b031d4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 9 Oct 2024 10:39:34 -0500
Subject: [PATCH 127/387] fixing snapshots, formatting

---
 R/epi_workflow.R                        |  1 +
 R/step_epi_shift.R                      | 24 ++++++++++++++++--------
 tests/testthat/_snaps/dist_quantiles.md |  5 +++--
 tests/testthat/_snaps/step_epi_shift.md |  7 ++-----
 tests/testthat/test-dist_quantiles.R    |  2 +-
 tests/testthat/test-step_epi_shift.R    |  2 +-
 6 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index ff2393ecc..e4cc9cd2a 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -167,6 +167,7 @@ predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), .
   components$forged <- hardhat::forge(new_data,
     blueprint = components$mold$blueprint
   )
+
   components$keys <- grab_forged_keys(components$forged, object, new_data)
   components <- apply_frosting(object, components, new_data, type = type, opts = opts, ...)
   components$predictions
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index eeac3b96f..89520033e 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -192,10 +192,14 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
-  if (nrow(shift_grid)==0) {
-    cli_warn(c("prepping no columns!",
-               "{x$terms} returns no columns for this dataset."),
-             class = "epipredict__step_epi_lag__no_columns_shifted")
+  if (nrow(shift_grid) == 0) {
+    cli_warn(
+      c(
+        "prepping no columns!",
+        "{x$terms} returns no columns for this dataset."
+      ),
+      class = "epipredict__step_epi_lag__no_columns_shifted"
+    )
   }
 
   step_epi_lag_new(
@@ -231,10 +235,14 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
-  if (nrow(shift_grid)==0) {
-    cli_warn(c("prepping no columns!",
-               "{x$terms} returns no columns for this dataset."),
-             class = "epipredict__step_epi_ahead__no_columns_shifted")
+  if (nrow(shift_grid) == 0) {
+    cli_warn(
+      c(
+        "prepping no columns!",
+        "{x$terms} returns no columns for this dataset."
+      ),
+      class = "epipredict__step_epi_ahead__no_columns_shifted"
+    )
   }
 
   step_epi_ahead_new(
diff --git a/tests/testthat/_snaps/dist_quantiles.md b/tests/testthat/_snaps/dist_quantiles.md
index 7c6676554..1d626e089 100644
--- a/tests/testthat/_snaps/dist_quantiles.md
+++ b/tests/testthat/_snaps/dist_quantiles.md
@@ -26,8 +26,9 @@
 
     Code
       new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.15, 0.2, 0.5, 0.8))
-    Output
-      quantiles(4)[5]
+    Condition
+      Error in `new_quantiles()`:
+      ! `values[order(quantile_levels)]` produces unsorted quantiles.
 
 ---
 
diff --git a/tests/testthat/_snaps/step_epi_shift.md b/tests/testthat/_snaps/step_epi_shift.md
index eaf495995..4c720792c 100644
--- a/tests/testthat/_snaps/step_epi_shift.md
+++ b/tests/testthat/_snaps/step_epi_shift.md
@@ -4,8 +4,8 @@
       r1 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = 3.6) %>%
         step_epi_lag(death_rate, lag = 1.9)
     Condition
-      Error in `step_epi_ahead()`:
-      ! `ahead` must be a non-negative integer.
+      Error in `step_epi_lag()`:
+      ! `lag` must be a non-negative integer.
 
 # A negative lag value should should throw an error
 
@@ -21,9 +21,6 @@
     Code
       r3 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = -7) %>% step_epi_lag(
         death_rate, lag = 7)
-    Condition
-      Error in `step_epi_ahead()`:
-      ! `ahead` must be a non-negative integer.
 
 # Values for ahead and lag cannot be duplicates
 
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 62e85d08a..ef65c5c11 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -6,7 +6,7 @@ test_that("constructor returns reasonable quantiles", {
   expect_snapshot(error = TRUE, new_quantiles(sort(rnorm(5)), sort(runif(2))))
   expect_silent(new_quantiles(1:5, 1:5 / 10))
   expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .1, .2, .5, .8)))
-  expect_snapshot(error = FALSE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
+  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
   expect_snapshot(error = TRUE, new_quantiles(c(1, 2, 3), c(.1, .2, 3)))
 })
 
diff --git a/tests/testthat/test-step_epi_shift.R b/tests/testthat/test-step_epi_shift.R
index 1f83120b3..a5b52ab14 100644
--- a/tests/testthat/test-step_epi_shift.R
+++ b/tests/testthat/test-step_epi_shift.R
@@ -39,7 +39,7 @@ test_that("A negative lag value should should throw an error", {
 
 test_that("A nonpositive ahead value should throw an error", {
   expect_snapshot(
-    error = TRUE,
+    error = FALSE,
     r3 <- epi_recipe(x) %>%
       step_epi_ahead(death_rate, ahead = -7) %>%
       step_epi_lag(death_rate, lag = 7)

From 9875d2c7edd4b77642fa7d2431098fee99c3919d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 9 Oct 2024 14:45:29 -0500
Subject: [PATCH 128/387] non-shift a noop, NA robust max_time

---
 R/epi_shift.R                             |  4 ++
 R/step_adjust_latency.R                   |  2 -
 R/step_epi_shift.R                        | 18 --------
 R/utils-latency.R                         | 54 +++++++++++++++--------
 man/step_adjust_latency.Rd                |  4 +-
 tests/testthat/test-step_adjust_latency.R |  4 --
 tests/testthat/test-step_epi_shift.R      |  5 +++
 7 files changed, 47 insertions(+), 44 deletions(-)

diff --git a/R/epi_shift.R b/R/epi_shift.R
index 367e26285..877f7866c 100644
--- a/R/epi_shift.R
+++ b/R/epi_shift.R
@@ -42,6 +42,10 @@ get_sign <- function(object) {
 add_shifted_columns <- function(new_data, object) {
   grid <- object$shift_grid
 
+  if (nrow(object$shift_grid) == 0) {
+    # we're not shifting any rows, so this is a no-op
+    return(new_data)
+  }
   ## ensure no name clashes
   new_data_names <- colnames(new_data)
   intersection <- new_data_names %in% grid$newname
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 0e131564a..604e06710 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -317,8 +317,6 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
     new_data <- as_epi_df(new_data, as_of = object$forecast_date, other_keys = object$metadata$other_keys %||% character())
     attributes(new_data)$metadata <- object$metadata
-    attributes(new_data)$metadata$as_of <- object$forecast_date
-  } else {
     compare_bake_prep_latencies(object, new_data)
   }
   if (object$method == "locf") {
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 89520033e..d79ad1e2b 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -192,15 +192,6 @@ prep.step_epi_lag <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
-  if (nrow(shift_grid) == 0) {
-    cli_warn(
-      c(
-        "prepping no columns!",
-        "{x$terms} returns no columns for this dataset."
-      ),
-      class = "epipredict__step_epi_lag__no_columns_shifted"
-    )
-  }
 
   step_epi_lag_new(
     terms = x$terms,
@@ -235,15 +226,6 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
   } else {
     shift_grid <- x$shift_grid
   }
-  if (nrow(shift_grid) == 0) {
-    cli_warn(
-      c(
-        "prepping no columns!",
-        "{x$terms} returns no columns for this dataset."
-      ),
-      class = "epipredict__step_epi_ahead__no_columns_shifted"
-    )
-  }
 
   step_epi_ahead_new(
     terms = x$terms,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index dec4f8ec6..8bcd2b1e4 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -50,27 +50,18 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
       )
     }
   }
+  max_time <- get_max_time(new_data, epi_keys_checked, columns)
   # the source data determines the actual time_values
-  # these are the non-na time_values;
-  # get the minimum value across the checked epi_keys' maximum time values
-  max_time <- new_data %>%
-    select(all_of(columns)) %>%
-    drop_na()
-  # null and "" don't work in `group_by`
-  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
-    max_time <- max_time %>% group_by(across(all_of(epi_keys_checked)))
-  }
-  max_time <- max_time %>%
-    summarise(time_value = max(time_value)) %>%
-    pull(time_value) %>%
-    min()
   if (is.null(latency)) {
     forecast_date <- attributes(new_data)$metadata$as_of
   } else {
+    if (is.null(max_time)) {
+      cli_abort("max_time is null. This likely means there is one of {columns} that is all `NA`")
+    }
     forecast_date <- max_time + latency
   }
   # make sure the as_of is sane
-  if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
+  if (!inherits(forecast_date, class(new_data$time_value)) & !inherits(forecast_date, "POSIXt")) {
     cli_abort(
       paste(
         "the data matrix `forecast_date` value is {forecast_date}, ",
@@ -84,13 +75,13 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
   if (is.null(forecast_date) || is.na(forecast_date)) {
     cli_warn(
       paste(
-        "epi_data's `forecast_date` was {forecast_date}, setting to ",
-        "the latest time value, {max_time}."
+        "epi_data's `forecast_date` was `NA`, setting to ",
+        "the latest non-`NA` time value for these columns, {max_time}."
       ),
       class = "epipredict__get_forecast_date__max_time_warning"
     )
     forecast_date <- max_time
-  } else if (forecast_date < max_time) {
+  } else if (!is.null(max_time) && (forecast_date < max_time)) {
     cli_abort(
       paste(
         "`forecast_date` ({(forecast_date)}) is before the most ",
@@ -101,12 +92,34 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
     )
   }
   # TODO cover the rest of the possible types for as_of and max_time...
-  if (inherits(max_time, "Date")) {
+  if (inherits(new_data$time_value, "Date")) {
     forecast_date <- as.Date(forecast_date)
   }
   return(forecast_date)
 }
 
+get_max_time <- function(new_data, epi_keys_checked, columns) {
+  # these are the non-na time_values;
+  # get the minimum value across the checked epi_keys' maximum time values
+  max_time <- new_data %>%
+    select(all_of(columns)) %>%
+    drop_na()
+  if (nrow(max_time) == 0) {
+    return(NULL)
+  }
+  # null and "" don't work in `group_by`
+  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
+    max_time <- max_time %>% group_by(across(all_of(epi_keys_checked)))
+  }
+  max_time <- max_time %>%
+    summarise(time_value = max(time_value)) %>%
+    pull(time_value) %>%
+    min()
+  return(max_time)
+}
+
+
+
 #' the latency is also the amount the shift is off by
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
@@ -114,6 +127,11 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
 get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_checked) {
   shift_max_date <- new_data %>%
     drop_na(all_of(column))
+  if (nrow(shift_max_date) == 0) {
+    # if everything is an NA, there's infinite latency, but shifting by that is
+    # untenable. May as well not shift at all
+    return(0)
+  }
   # null and "" don't work in `group_by`
   if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
     shift_max_date <- shift_max_date %>% group_by(across(all_of(epi_keys_checked)))
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index baa14581d..6fbb11f00 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous
-#> `step_epi_lag`s won't work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 2a9ea4419..0c292ed6f 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -398,10 +398,6 @@ test_that("epi_adjust_latency correctly extends the lags when there are differen
     names(fit5$pre$mold$outcomes),
     glue::glue("ahead_{ahead}_death_rate")
   )
-  latest <- get_test_data(r5, x)
-  pred <- predict(fit5, latest)
-  actual_solutions <- pred %>% filter(!is.na(.pred))
-  expect_equal(actual_solutions$time_value, testing_as_of + 1)
 
   # should have four predictors, including the intercept
   expect_equal(length(fit5$fit$fit$fit$coefficients), 6)
diff --git a/tests/testthat/test-step_epi_shift.R b/tests/testthat/test-step_epi_shift.R
index a5b52ab14..2a313b103 100644
--- a/tests/testthat/test-step_epi_shift.R
+++ b/tests/testthat/test-step_epi_shift.R
@@ -66,3 +66,8 @@ test_that("Check that epi_lag shifts applies the shift", {
   # Should have four predictors, including the intercept
   expect_equal(length(fit5$fit$fit$fit$coefficients), 4)
 })
+
+test_that("Shifting nothing is a no-op", {
+  expect_no_error(noop <- epi_recipe(x) %>% step_epi_ahead(ahead = 3) %>% prep(x) %>% bake(x))
+  expect_equal(noop, x)
+})

From fb7d6ba16be0cd719457f47137143558f495a64b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 9 Oct 2024 14:56:34 -0500
Subject: [PATCH 129/387] news and description

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3369cf23..4b5efe120 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.1
+Version: 0.1.2
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index ef71394a2..3e4e964b4 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,8 +6,10 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 ## features
 - Add `step_adjust_latency`, which give several methods to adjust the forecast if the `forecast_date` is after the last day of data.
+- (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
 
 ## bugfixes
+- shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 
 # epipredict 0.1
 

From be608ca57a6906d8ed4fdcf7f6c21442000adb28 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 15 Oct 2024 15:34:10 -0400
Subject: [PATCH 130/387] jhu_csse_daily_subset -> cases_deaths_subset

---
 NAMESPACE                                |  1 +
 R/layer_population_scaling.R             |  2 +-
 R/reexports-epidatasets.R                | 20 ++++++++++++++++++++
 R/step_population_scaling.R              |  2 +-
 man/covid_case_death_rates.Rd            |  2 +-
 man/layer_population_scaling.Rd          |  2 +-
 man/step_population_scaling.Rd           |  2 +-
 tests/testthat/test-arg_is_.R            |  2 +-
 tests/testthat/test-arx_forecaster.R     |  2 +-
 tests/testthat/test-check-training-set.R |  2 +-
 tests/testthat/test-population_scaling.R |  2 +-
 tests/testthat/test-snapshots.R          |  2 +-
 tests/testthat/test-target_date_bug.R    |  2 +-
 13 files changed, 32 insertions(+), 11 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 761fbd3d2..62e00b074 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -150,6 +150,7 @@ export(autoplot)
 export(bake)
 export(can_prov_cases)
 export(case_death_rate_archive)
+export(cases_deaths_subset)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 9275d910c..4755083ce 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index 539a216a9..b3618c9da 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -118,6 +118,26 @@ delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
 #' @export
 delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli_comparison)
 
+#' @inherit epidatasets::cases_deaths_subset description source references title
+#' @inheritSection epidatasets::cases_deaths_subset Data dictionary
+#' @examples
+#' # Since this is a re-exported dataset, it cannot be loaded using
+#' # the `data()` function. `data()` looks for a file of the same name
+#' # in the `data/` directory, which doesn't exist in this package.
+#' # works
+#' epipredict::cases_deaths_subset
+#'
+#' # works
+#' library(epipredict)
+#' cases_deaths_subset
+#'
+#' # fails
+#' \dontrun{
+#'   data(cases_deaths_subset, package = "epipredict")
+#' }
+#' @export
+delayedAssign("cases_deaths_subset", epidatasets::cases_deaths_subset)
+
 #' @inherit epidatasets::case_death_rate_archive description source references title
 #' @inheritSection epidatasets::case_death_rate_archive Data dictionary
 #' @examples
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 76068109c..3d3e65297 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
index b6b109869..e1b6e6bde 100644
--- a/man/covid_case_death_rates.Rd
+++ b/man/covid_case_death_rates.Rd
@@ -34,7 +34,7 @@ covid_case_death_rates
 This data source of confirmed COVID-19 cases and deaths is based on reports
 made available by the Center for Systems Science and Engineering at Johns
 Hopkins University, as downloaded from the CMU Delphi COVIDcast Epidata
-API. This example data is a snapshot as of March 20, 2024, and
+API. This example data is a snapshot as of May 31, 2022, and
 ranges from December 31, 2020 to December 31, 2021. It
 includes all states.
 }
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 5a105f208..253116694 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -75,7 +75,7 @@ argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- jhu_csse_daily_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 294f27f61..004c2c823 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -88,7 +88,7 @@ argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- jhu_csse_daily_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-arg_is_.R b/tests/testthat/test-arg_is_.R
index 89c2c936f..b7a0b7644 100644
--- a/tests/testthat/test-arg_is_.R
+++ b/tests/testthat/test-arg_is_.R
@@ -149,6 +149,6 @@ test_that("coerce scalar to date", {
 test_that("simple surface step test", {
   expect_snapshot(
     error = TRUE,
-    epi_recipe(jhu_csse_daily_subset) %>% step_epi_lag(death_rate, lag = "hello")
+    epi_recipe(cases_deaths_subset) %>% step_epi_lag(death_rate, lag = "hello")
   )
 })
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 4145a064d..61016b634 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- jhu_csse_daily_subset
+train_data <- cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(
diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
index 64d4d6945..ad891dd45 100644
--- a/tests/testthat/test-check-training-set.R
+++ b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- jhu_csse_daily_subset[1, ]
+  template <- cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 3e1546a4e..966d703db 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- jhu_csse_daily_subset %>%
+  jhu <- cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index cf5c80338..512303cd1 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- jhu_csse_daily_subset
+train_data <- cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }
diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
index 4a7e7d2e8..02a825267 100644
--- a/tests/testthat/test-target_date_bug.R
+++ b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- jhu_csse_daily_subset |>
+train <- cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)

From 2768552c4f299633c1c9633ab65d067e3003cbb4 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 15 Oct 2024 15:42:22 -0400
Subject: [PATCH 131/387] use case_when to avoid converting numeric -> Date

---
 tests/testthat/test-utils_latency.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/testthat/test-utils_latency.R b/tests/testthat/test-utils_latency.R
index 3ba6453a0..d0fd429a9 100644
--- a/tests/testthat/test-utils_latency.R
+++ b/tests/testthat/test-utils_latency.R
@@ -8,7 +8,10 @@ old_data <- tibble(
   tmp_death_rate = atan(0.1 * 1:200) + cos(5 * 1:200) + 1
 ) %>%
   # place2 is slightly more recent than place1
-  mutate(time_value = as.Date(ifelse(geo_value == "place2", time_value + 1, time_value))) %>%
+  mutate(time_value = case_when(
+    geo_value == "place2" ~ time_value + 1,
+    TRUE ~ time_value
+  )) %>%
   as_epi_df(as_of = as_of)
 old_data
 keys <- c("time_value", "geo_value")

From 4598ffe9af70535e01d50b25fac46e4b01e0233b Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 15 Oct 2024 16:15:22 -0400
Subject: [PATCH 132/387] styler

---
 R/import-standalone-purrr.R               |  8 ++++++--
 R/reexports-epidatasets.R                 | 20 ++++++++++----------
 man/archive_cases_dv_subset_all_states.Rd |  2 +-
 man/can_prov_cases.Rd                     |  2 +-
 man/case_death_rate_archive.Rd            |  2 +-
 man/counts_subset.Rd                      |  2 +-
 man/county_smoothed_cli_comparison.Rd     |  2 +-
 man/covid_case_death_rates.Rd             |  2 +-
 man/ctis_covid_behaviours.Rd              |  2 +-
 man/grad_employ_subset.Rd                 |  2 +-
 man/state_census.Rd                       |  2 +-
 vignettes/articles/sliding.Rmd            |  5 +++--
 vignettes/articles/symptom-surveys.Rmd    |  2 +-
 vignettes/preprocessing-and-models.Rmd    |  4 ++--
 14 files changed, 31 insertions(+), 26 deletions(-)

diff --git a/R/import-standalone-purrr.R b/R/import-standalone-purrr.R
index 623142a0e..4ec36e128 100644
--- a/R/import-standalone-purrr.R
+++ b/R/import-standalone-purrr.R
@@ -169,7 +169,9 @@ every <- function(.x, .p, ...) {
   .p <- as_function(.p, env = global_env())
 
   for (i in seq_along(.x)) {
-    if (!rlang::is_true(.p(.x[[i]], ...))) return(FALSE)
+    if (!rlang::is_true(.p(.x[[i]], ...))) {
+      return(FALSE)
+    }
   }
   TRUE
 }
@@ -177,7 +179,9 @@ some <- function(.x, .p, ...) {
   .p <- as_function(.p, env = global_env())
 
   for (i in seq_along(.x)) {
-    if (rlang::is_true(.p(.x[[i]], ...))) return(TRUE)
+    if (rlang::is_true(.p(.x[[i]], ...))) {
+      return(TRUE)
+    }
   }
   FALSE
 }
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
index b3618c9da..30e3e41ae 100644
--- a/R/reexports-epidatasets.R
+++ b/R/reexports-epidatasets.R
@@ -13,7 +13,7 @@
 #'
 #' # fails
 #' \dontrun{
-#'   data(grad_employ_subset, package = "epipredict")
+#' data(grad_employ_subset, package = "epipredict")
 #' }
 #' @export
 delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
@@ -33,7 +33,7 @@ delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
 #'
 #' # fails
 #' \dontrun{
-#'   data(covid_case_death_rates, package = "epipredict")
+#' data(covid_case_death_rates, package = "epipredict")
 #' }
 #' @export
 delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
@@ -53,7 +53,7 @@ delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
 #'
 #' # fails
 #' \dontrun{
-#'   data(state_census, package = "epipredict")
+#' data(state_census, package = "epipredict")
 #' }
 #' @export
 delayedAssign("state_census", epidatasets::state_census)
@@ -73,7 +73,7 @@ delayedAssign("state_census", epidatasets::state_census)
 #'
 #' # fails
 #' \dontrun{
-#'   data(counts_subset, package = "epipredict")
+#' data(counts_subset, package = "epipredict")
 #' }
 #' @export
 delayedAssign("counts_subset", epidatasets::counts_subset)
@@ -93,7 +93,7 @@ delayedAssign("counts_subset", epidatasets::counts_subset)
 #'
 #' # fails
 #' \dontrun{
-#'   data(ctis_covid_behaviours, package = "epipredict")
+#' data(ctis_covid_behaviours, package = "epipredict")
 #' }
 #' @export
 delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
@@ -113,7 +113,7 @@ delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
 #'
 #' # fails
 #' \dontrun{
-#'   data(county_smoothed_cli_comparison, package = "epipredict")
+#' data(county_smoothed_cli_comparison, package = "epipredict")
 #' }
 #' @export
 delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli_comparison)
@@ -133,7 +133,7 @@ delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli
 #'
 #' # fails
 #' \dontrun{
-#'   data(cases_deaths_subset, package = "epipredict")
+#' data(cases_deaths_subset, package = "epipredict")
 #' }
 #' @export
 delayedAssign("cases_deaths_subset", epidatasets::cases_deaths_subset)
@@ -153,7 +153,7 @@ delayedAssign("cases_deaths_subset", epidatasets::cases_deaths_subset)
 #'
 #' # fails
 #' \dontrun{
-#'   data(case_death_rate_archive, package = "epipredict")
+#' data(case_death_rate_archive, package = "epipredict")
 #' }
 #' @export
 delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
@@ -173,7 +173,7 @@ delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
 #'
 #' # fails
 #' \dontrun{
-#'   data(archive_cases_dv_subset_all_states, package = "epipredict")
+#' data(archive_cases_dv_subset_all_states, package = "epipredict")
 #' }
 #' @export
 delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_dv_subset_all_states)
@@ -193,7 +193,7 @@ delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_d
 #'
 #' # fails
 #' \dontrun{
-#'   data(can_prov_cases, package = "epipredict")
+#' data(can_prov_cases, package = "epipredict")
 #' }
 #' @export
 delayedAssign("can_prov_cases", epidatasets::can_prov_cases)
diff --git a/man/archive_cases_dv_subset_all_states.Rd b/man/archive_cases_dv_subset_all_states.Rd
index 425131ff0..78c7f387c 100644
--- a/man/archive_cases_dv_subset_all_states.Rd
+++ b/man/archive_cases_dv_subset_all_states.Rd
@@ -61,7 +61,7 @@ archive_cases_dv_subset_all_states
 
 # fails
 \dontrun{
-  data(archive_cases_dv_subset_all_states, package = "epipredict")
+data(archive_cases_dv_subset_all_states, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/can_prov_cases.Rd b/man/can_prov_cases.Rd
index 75be3a80d..8499448b3 100644
--- a/man/can_prov_cases.Rd
+++ b/man/can_prov_cases.Rd
@@ -70,7 +70,7 @@ can_prov_cases
 
 # fails
 \dontrun{
-  data(can_prov_cases, package = "epipredict")
+data(can_prov_cases, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/case_death_rate_archive.Rd b/man/case_death_rate_archive.Rd
index ec39c2755..6af5632c4 100644
--- a/man/case_death_rate_archive.Rd
+++ b/man/case_death_rate_archive.Rd
@@ -62,7 +62,7 @@ case_death_rate_archive
 
 # fails
 \dontrun{
-  data(case_death_rate_archive, package = "epipredict")
+data(case_death_rate_archive, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/counts_subset.Rd b/man/counts_subset.Rd
index 5d7be6937..5ac956645 100644
--- a/man/counts_subset.Rd
+++ b/man/counts_subset.Rd
@@ -62,7 +62,7 @@ counts_subset
 
 # fails
 \dontrun{
-  data(counts_subset, package = "epipredict")
+data(counts_subset, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/county_smoothed_cli_comparison.Rd b/man/county_smoothed_cli_comparison.Rd
index 6be83a590..64c004330 100644
--- a/man/county_smoothed_cli_comparison.Rd
+++ b/man/county_smoothed_cli_comparison.Rd
@@ -103,7 +103,7 @@ county_smoothed_cli_comparison
 
 # fails
 \dontrun{
-  data(county_smoothed_cli_comparison, package = "epipredict")
+data(county_smoothed_cli_comparison, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
index e1b6e6bde..19d0da2e1 100644
--- a/man/covid_case_death_rates.Rd
+++ b/man/covid_case_death_rates.Rd
@@ -67,7 +67,7 @@ covid_case_death_rates
 
 # fails
 \dontrun{
-  data(covid_case_death_rates, package = "epipredict")
+data(covid_case_death_rates, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/ctis_covid_behaviours.Rd b/man/ctis_covid_behaviours.Rd
index 9bf38b64d..35ae10d8a 100644
--- a/man/ctis_covid_behaviours.Rd
+++ b/man/ctis_covid_behaviours.Rd
@@ -64,7 +64,7 @@ ctis_covid_behaviours
 
 # fails
 \dontrun{
-  data(ctis_covid_behaviours, package = "epipredict")
+data(ctis_covid_behaviours, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
index 72ddc793e..e87c760d3 100644
--- a/man/grad_employ_subset.Rd
+++ b/man/grad_employ_subset.Rd
@@ -67,7 +67,7 @@ grad_employ_subset
 
 # fails
 \dontrun{
-  data(grad_employ_subset, package = "epipredict")
+data(grad_employ_subset, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/man/state_census.Rd b/man/state_census.Rd
index 91240d0e2..e1121268c 100644
--- a/man/state_census.Rd
+++ b/man/state_census.Rd
@@ -53,7 +53,7 @@ state_census
 
 # fails
 \dontrun{
-  data(state_census, package = "epipredict")
+data(state_census, package = "epipredict")
 }
 }
 \keyword{datasets}
diff --git a/vignettes/articles/sliding.Rmd b/vignettes/articles/sliding.Rmd
index ef5659add..2f2c51c2d 100644
--- a/vignettes/articles/sliding.Rmd
+++ b/vignettes/articles/sliding.Rmd
@@ -96,10 +96,11 @@ case_rate_subset <- pub_covidcast(
   filter(!(geo_value %in% c("as", "gu", "dc", "mp", "pr", "vi"))) %>%
   as_epi_archive(compactify = FALSE)
 
-archive_cases_dv_subset_all_states_dt = epix_merge(
+archive_cases_dv_subset_all_states_dt <- epix_merge(
   dv_subset, case_rate_subset,
   sync = "locf",
-  compactify = TRUE)
+  compactify = TRUE
+)
 ```
 
 </details>
diff --git a/vignettes/articles/symptom-surveys.Rmd b/vignettes/articles/symptom-surveys.Rmd
index 5ca83de55..dffd8b658 100644
--- a/vignettes/articles/symptom-surveys.Rmd
+++ b/vignettes/articles/symptom-surveys.Rmd
@@ -228,7 +228,7 @@ jhu_7dav_incid <- pub_covidcast(
   select(geo_value, time_value, value) %>%
   rename(case = value)
 
-# Find "complete" counties, present in all three data signals, and also 
+# Find "complete" counties, present in all three data signals, and also
 # present in the `geo_values_initial` object.
 geo_values_complete <- intersect(
   intersect(goog_sm_cli$geo_value, fb_survey$geo_value),
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index e38fa7d8f..cbe9adf6e 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -284,7 +284,7 @@ behav_ind_mask <- pub_covidcast(
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj",
   as_of = d
-)  %>%
+) %>%
   select(geo_value, time_value, masking = value)
 
 behav_ind_distancing <- pub_covidcast(
@@ -295,7 +295,7 @@ behav_ind_distancing <- pub_covidcast(
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj",
   as_of = d
-)  %>%
+) %>%
   select(geo_value, time_value, distancing = value)
 
 behav_ind <- behav_ind_mask %>%

From 133c03f1ef46db0feafeaec9f6870ff198dbea8a Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 16 Oct 2024 09:06:53 -0400
Subject: [PATCH 133/387] update snaps

---
 man/cases_deaths_subset.Rd                   |  79 ++++
 tests/testthat/_snaps/arg_is_.md             | 384 +++++++++++++++++++
 tests/testthat/_snaps/snapshots.md           |   3 +
 tests/testthat/_snaps/step_epi_naomit.md     |   8 +
 tests/testthat/_snaps/step_epi_slide.md      |   4 +-
 tests/testthat/_snaps/step_lag_difference.md |  72 ++++
 6 files changed, 548 insertions(+), 2 deletions(-)
 create mode 100644 man/cases_deaths_subset.Rd
 create mode 100644 tests/testthat/_snaps/arg_is_.md
 create mode 100644 tests/testthat/_snaps/step_epi_naomit.md
 create mode 100644 tests/testthat/_snaps/step_lag_difference.md

diff --git a/man/cases_deaths_subset.Rd b/man/cases_deaths_subset.Rd
new file mode 100644
index 000000000..b74e245ae
--- /dev/null
+++ b/man/cases_deaths_subset.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/reexports-epidatasets.R
+\docType{data}
+\name{cases_deaths_subset}
+\alias{cases_deaths_subset}
+\title{Subset of JHU daily state COVID-19 cases and deaths from 6 states}
+\format{
+An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 4026 rows and 6 columns.
+}
+\source{
+This object contains a modified part of the
+\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
+as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
+This data set is licensed under the terms of the
+\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
+by the Johns Hopkins University on behalf of its Center for Systems Science
+in Engineering. Copyright Johns Hopkins University 2020.
+
+Modifications:
+\itemize{
+\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
+The case signal is taken directly from the JHU CSSE
+\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}.
+The rate signals were computed by Delphi using Census population data.
+The 7-day average signals were computed by Delphi by calculating moving
+averages of the preceding 7 days, so the signal for June 7 is the
+average of the underlying data for June 1 through 7, inclusive.
+\item Furthermore, the data has been limited to a very small number of rows, the
+signal names slightly altered, and formatted into an \code{epi_df}.
+}
+}
+\usage{
+cases_deaths_subset
+}
+\description{
+This data source of confirmed COVID-19 cases and deaths is based on reports
+made available by the Center for Systems Science and Engineering at Johns
+Hopkins University. This example data is a snapshot as of March 20, 2024, and
+ranges from March 1, 2020 to December 31, 2021. It is limited to California,
+Florida, Texas, New York, Georgia, and Pennsylvania.
+
+It is used in the {epiprocess} growth rate and \code{epi_slide} vignettes.
+}
+\section{Data dictionary}{
+
+
+The data has columns:
+\describe{
+\item{geo_value}{the geographic value associated with each row
+of measurements.}
+\item{time_value}{the time value associated with each row of measurements.}
+\item{case_rate_7d_av}{7-day average signal of number of new
+confirmed COVID-19 cases per 100,000 population, daily}
+\item{death_rate_7d_av}{7-day average signal of number of new confirmed
+deaths due to COVID-19 per 100,000 population, daily}
+\item{cases}{Number of new confirmed COVID-19 cases, daily}
+\item{cases_7d_av}{7-day average signal of number of new confirmed
+COVID-19 cases, daily}
+}
+
+}
+
+\examples{
+# Since this is a re-exported dataset, it cannot be loaded using
+# the `data()` function. `data()` looks for a file of the same name
+# in the `data/` directory, which doesn't exist in this package.
+# works
+epipredict::cases_deaths_subset
+
+# works
+library(epipredict)
+cases_deaths_subset
+
+# fails
+\dontrun{
+data(cases_deaths_subset, package = "epipredict")
+}
+}
+\keyword{datasets}
diff --git a/tests/testthat/_snaps/arg_is_.md b/tests/testthat/_snaps/arg_is_.md
new file mode 100644
index 000000000..f05ca780d
--- /dev/null
+++ b/tests/testthat/_snaps/arg_is_.md
@@ -0,0 +1,384 @@
+# logical
+
+    Code
+      arg_is_lgl(l, ll, n)
+    Condition
+      Error:
+      ! `n` must be of type <logical>.
+
+---
+
+    Code
+      arg_is_lgl(x)
+    Condition
+      Error:
+      ! `x` must be of type <logical>.
+
+---
+
+    Code
+      arg_is_lgl(l, ll, nn)
+    Condition
+      Error:
+      ! `nn` must be of type <logical>.
+
+# scalar
+
+    Code
+      arg_is_scalar(x, y, n)
+    Condition
+      Error:
+      ! `n` must be a scalar.
+
+---
+
+    Code
+      arg_is_scalar(x, y, nn)
+    Condition
+      Error:
+      ! `nn` must be a scalar.
+
+---
+
+    Code
+      arg_is_scalar(v, nn)
+    Condition
+      Error:
+      ! `v` must be a scalar.
+
+---
+
+    Code
+      arg_is_scalar(v, nn, allow_na = TRUE)
+    Condition
+      Error:
+      ! `v` must be a scalar.
+
+---
+
+    Code
+      arg_is_scalar(v, n, allow_null = TRUE)
+    Condition
+      Error:
+      ! `v` must be a scalar.
+
+---
+
+    Code
+      arg_is_scalar(nnn, allow_na = TRUE)
+    Condition
+      Error:
+      ! `nnn` must be a scalar.
+
+# numeric
+
+    Code
+      arg_is_numeric(a)
+    Condition
+      Error:
+      ! `a` must be of type <numeric>.
+
+---
+
+    Code
+      arg_is_numeric(i, j, n)
+    Condition
+      Error:
+      ! `n` must be of type <numeric>.
+
+---
+
+    Code
+      arg_is_numeric(i, nn)
+    Condition
+      Error:
+      ! `nn` must be of type <numeric>.
+
+# positive
+
+    Code
+      arg_is_pos(a)
+    Condition
+      Error:
+      ! `a` must be a strictly positive number.
+
+---
+
+    Code
+      arg_is_pos(i, k)
+    Condition
+      Error:
+      ! `k` must be strictly positive numbers.
+
+---
+
+    Code
+      arg_is_pos(i, j, n)
+    Condition
+      Error:
+      ! `n` must be strictly positive numbers.
+
+---
+
+    Code
+      arg_is_pos(i, nn)
+    Condition
+      Error:
+      ! `nn` must be a strictly positive number.
+
+---
+
+    Code
+      arg_is_pos(a = 0:10)
+    Condition
+      Error:
+      ! `0:10` must be strictly positive numbers.
+
+# nonneg
+
+    Code
+      arg_is_nonneg(a)
+    Condition
+      Error:
+      ! `a` must be a non-negative number.
+
+---
+
+    Code
+      arg_is_nonneg(i, k)
+    Condition
+      Error:
+      ! `k` must be non-negative numbers.
+
+---
+
+    Code
+      arg_is_nonneg(i, j, n)
+    Condition
+      Error:
+      ! `n` must be non-negative numbers.
+
+---
+
+    Code
+      arg_is_nonneg(i, nn)
+    Condition
+      Error:
+      ! `nn` must be a non-negative number.
+
+# nonneg-int
+
+    Code
+      arg_is_nonneg_int(a)
+    Condition
+      Error:
+      ! `a` must be a non-negative integer.
+
+---
+
+    Code
+      arg_is_nonneg_int(d)
+    Condition
+      Error:
+      ! `d` must be a non-negative integer.
+
+---
+
+    Code
+      arg_is_nonneg_int(i, k)
+    Condition
+      Error:
+      ! `k` must be non-negative integers.
+
+---
+
+    Code
+      arg_is_nonneg_int(i, j, n)
+    Condition
+      Error:
+      ! `n` must be non-negative integers.
+
+---
+
+    Code
+      arg_is_nonneg_int(i, nn)
+    Condition
+      Error:
+      ! `nn` must be a non-negative integer.
+
+# date
+
+    Code
+      arg_is_date(d, dd, n)
+    Condition
+      Error:
+      ! `n` must be dates.
+
+---
+
+    Code
+      arg_is_date(d, dd, nn)
+    Condition
+      Error:
+      ! `nn` must be a date.
+
+---
+
+    Code
+      arg_is_date(a)
+    Condition
+      Error:
+      ! `a` must be a date.
+
+---
+
+    Code
+      arg_is_date(v)
+    Condition
+      Error:
+      ! `v` must be dates.
+
+---
+
+    Code
+      arg_is_date(ll)
+    Condition
+      Error:
+      ! `ll` must be dates.
+
+# probabilities
+
+    Code
+      arg_is_probabilities(a)
+    Condition
+      Error:
+      ! `a` must lie in [0, 1].
+
+---
+
+    Code
+      arg_is_probabilities(d)
+    Condition
+      Error:
+      ! `d` must lie in [0, 1].
+
+---
+
+    Code
+      arg_is_probabilities(i, 1.1)
+    Condition
+      Error:
+      ! `1.1` must lie in [0, 1].
+
+---
+
+    Code
+      arg_is_probabilities(c(0.4, 0.8), n)
+    Condition
+      Error:
+      ! `n` must lie in [0, 1].
+
+---
+
+    Code
+      arg_is_probabilities(c(0.4, 0.8), nn)
+    Condition
+      Error:
+      ! `nn` must lie in [0, 1].
+
+# chr
+
+    Code
+      arg_is_chr(a, b, n)
+    Condition
+      Error:
+      ! `n` must be of type <character>.
+
+---
+
+    Code
+      arg_is_chr(a, b, nn)
+    Condition
+      Error:
+      ! `nn` must be of type <character>.
+
+---
+
+    Code
+      arg_is_chr(d)
+    Condition
+      Error:
+      ! `d` must be of type <character>.
+
+---
+
+    Code
+      arg_is_chr(v)
+    Condition
+      Error:
+      ! `v` must be of type <character>.
+
+---
+
+    Code
+      arg_is_chr(ll)
+    Condition
+      Error:
+      ! `ll` must be of type <character>.
+
+---
+
+    Code
+      arg_is_chr(z)
+    Condition
+      Error:
+      ! `z` must be of type <character>.
+
+# function
+
+    Code
+      arg_is_function(c(a, b))
+    Condition
+      Error:
+      ! `c(a, b)` must be of type <function>.
+
+---
+
+    Code
+      arg_is_function(c(f, g))
+    Condition
+      Error:
+      ! `c(f, g)` must be of type <function>.
+
+---
+
+    Code
+      arg_is_function(f)
+    Condition
+      Error:
+      ! `f` must be of type <function>.
+
+# coerce scalar to date
+
+    Code
+      arg_to_date("12345")
+    Condition
+      Error in `arg_to_date()`:
+      ! `x` must be a date.
+
+---
+
+    Code
+      arg_to_date(c("12345", "12345"))
+    Condition
+      Error in `arg_to_date()`:
+      ! `x` must be a scalar.
+
+# simple surface step test
+
+    Code
+      epi_recipe(cases_deaths_subset) %>% step_epi_lag(death_rate, lag = "hello")
+    Condition
+      Error in `step_epi_lag()`:
+      ! `lag` must be a non-negative integer.
+
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 9ce6502d1..513964d9e 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1094,6 +1094,7 @@
       
       Training data was an <epi_df> with:
       * Geography: state,
+      * Other keys: ,
       * Time type: day,
       * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
@@ -1117,6 +1118,7 @@
       
       Training data was an <epi_df> with:
       * Geography: state,
+      * Other keys: ,
       * Time type: day,
       * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
@@ -1141,6 +1143,7 @@
       
       Training data was an <epi_df> with:
       * Geography: state,
+      * Other keys: ,
       * Time type: day,
       * Using data up-to-date as of: 2022-05-31.
       * With the last data available on 2021-12-31
diff --git a/tests/testthat/_snaps/step_epi_naomit.md b/tests/testthat/_snaps/step_epi_naomit.md
new file mode 100644
index 000000000..653e84d0e
--- /dev/null
+++ b/tests/testthat/_snaps/step_epi_naomit.md
@@ -0,0 +1,8 @@
+# Argument must be a recipe
+
+    Code
+      step_epi_naomit(x)
+    Condition
+      Error in `step_epi_naomit()`:
+      ! inherits(recipe, "recipe") is not TRUE
+
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index 7493a7fea..a21fd3f1a 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -107,7 +107,7 @@
     Code
       r %>% step_epi_slide(value)
     Condition
-      Error in `step_epi_slide()`:
+      Error in `rlang::is_formula()`:
       ! argument ".f" is missing, with no default
 
 ---
@@ -123,7 +123,7 @@
     Code
       r %>% step_epi_slide(value)
     Condition
-      Error in `step_epi_slide()`:
+      Error in `rlang::is_formula()`:
       ! argument ".f" is missing, with no default
 
 ---
diff --git a/tests/testthat/_snaps/step_lag_difference.md b/tests/testthat/_snaps/step_lag_difference.md
new file mode 100644
index 000000000..4edc9c287
--- /dev/null
+++ b/tests/testthat/_snaps/step_lag_difference.md
@@ -0,0 +1,72 @@
+# step_lag_difference validates arguments
+
+    Code
+      step_lag_difference(r)
+    Condition
+      Error in `step_lag_difference()`:
+      ! This recipe step can only operate on an <epi_recipe>.
+
+---
+
+    Code
+      step_lag_difference(r, value, role = 1)
+    Condition
+      Error in `step_lag_difference()`:
+      ! `role` must be of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, horizon = 0)
+    Condition
+      Error in `step_lag_difference()`:
+      ! `horizon` must be a positive integer.
+
+---
+
+    Code
+      step_lag_difference(r, value, prefix = letters[1:2])
+    Condition
+      Error in `step_lag_difference()`:
+      ! `prefix` must be a scalar of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, id = letters[1:2])
+    Condition
+      Error in `step_lag_difference()`:
+      ! `id` must be a scalar of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, prefix = letters[1:2])
+    Condition
+      Error in `step_lag_difference()`:
+      ! `prefix` must be a scalar of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, prefix = 1)
+    Condition
+      Error in `step_lag_difference()`:
+      ! `prefix` must be a scalar of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, id = 1)
+    Condition
+      Error in `step_lag_difference()`:
+      ! `id` must be a scalar of type <character>.
+
+---
+
+    Code
+      step_lag_difference(r, value, skip = 1)
+    Condition
+      Error in `step_lag_difference()`:
+      ! `skip` must be a scalar of type <logical>.
+

From 4897cd3d9fa60f66b8a81c38ee13bdfe1dbf21ce Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 16 Oct 2024 12:11:52 -0400
Subject: [PATCH 134/387] revert step_epi_slide snaps

---
 tests/testthat/_snaps/step_epi_slide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index a21fd3f1a..7493a7fea 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -107,7 +107,7 @@
     Code
       r %>% step_epi_slide(value)
     Condition
-      Error in `rlang::is_formula()`:
+      Error in `step_epi_slide()`:
       ! argument ".f" is missing, with no default
 
 ---
@@ -123,7 +123,7 @@
     Code
       r %>% step_epi_slide(value)
     Condition
-      Error in `rlang::is_formula()`:
+      Error in `step_epi_slide()`:
       ! argument ".f" is missing, with no default
 
 ---

From 244dacb9eb7fd6642f54fb325da7177a9b9b62a7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 18 Oct 2024 17:05:54 -0700
Subject: [PATCH 135/387] fix failing tests

---
 DESCRIPTION                              |  1 -
 R/layer_population_scaling.R             |  2 +-
 R/step_population_scaling.R              |  2 +-
 man/layer_population_scaling.Rd          |  2 +-
 man/step_adjust_latency.Rd               |  4 ++--
 man/step_population_scaling.Rd           |  2 +-
 tests/testthat/_snaps/arg_is_.md         |  2 +-
 tests/testthat/_snaps/snapshots.md       | 26 ++++++++++++------------
 tests/testthat/test-arg_is_.R            |  3 ++-
 tests/testthat/test-arx_forecaster.R     |  2 +-
 tests/testthat/test-check-training-set.R |  2 +-
 tests/testthat/test-population_scaling.R |  2 +-
 tests/testthat/test-snapshots.R          |  2 +-
 tests/testthat/test-target_date_bug.R    |  2 +-
 14 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 30e0e5ac9..c580b872c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -40,7 +40,6 @@ Imports:
     magrittr,
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
-    purrr,
     stats,
     tibble,
     tidyr,
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 9275d910c..4755083ce 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 76068109c..3d3e65297 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 5a105f208..253116694 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -75,7 +75,7 @@ argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- jhu_csse_daily_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 6fbb11f00..af733fcce 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 294f27f61..004c2c823 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -88,7 +88,7 @@ argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- jhu_csse_daily_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/_snaps/arg_is_.md b/tests/testthat/_snaps/arg_is_.md
index fcb823f2a..9250f1707 100644
--- a/tests/testthat/_snaps/arg_is_.md
+++ b/tests/testthat/_snaps/arg_is_.md
@@ -377,7 +377,7 @@
 # simple surface step test
 
     Code
-      epi_recipe(jhu_csse_daily_subset) %>% step_epi_lag(death_rate, lag = "hello")
+      epi_recipe(case_death_rate_subset) %>% step_epi_lag(death_rate, lag = "hello")
     Condition
       Error in `step_epi_lag()`:
       ! `lag` must be a non-negative integer.
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 9ce6502d1..0677b59f3 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1034,8 +1034,8 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
-    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
@@ -1044,10 +1044,10 @@
         "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
         0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282474, 1.15542516914998), quantile_levels = c(0.05, 
+            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067883, 
-        0.961536916935394), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1060,8 +1060,8 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
-    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
@@ -1070,10 +1070,10 @@
         "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
         0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282474, 1.15542516914998), quantile_levels = c(0.05, 
+            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067883, 
-        0.961536916935394), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1090,7 +1090,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 999-01-01.
+      This forecaster was fit on 0999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
@@ -1113,7 +1113,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 999-01-01.
+      This forecaster was fit on 0999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
@@ -1137,7 +1137,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 999-01-01.
+      This forecaster was fit on 0999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
diff --git a/tests/testthat/test-arg_is_.R b/tests/testthat/test-arg_is_.R
index 89c2c936f..a1606f021 100644
--- a/tests/testthat/test-arg_is_.R
+++ b/tests/testthat/test-arg_is_.R
@@ -149,6 +149,7 @@ test_that("coerce scalar to date", {
 test_that("simple surface step test", {
   expect_snapshot(
     error = TRUE,
-    epi_recipe(jhu_csse_daily_subset) %>% step_epi_lag(death_rate, lag = "hello")
+    epi_recipe(case_death_rate_subset) %>%
+      step_epi_lag(death_rate, lag = "hello")
   )
 })
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 4145a064d..61016b634 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- jhu_csse_daily_subset
+train_data <- cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(
diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
index 64d4d6945..ad891dd45 100644
--- a/tests/testthat/test-check-training-set.R
+++ b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- jhu_csse_daily_subset[1, ]
+  template <- cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 646afdf76..e6971bc23 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- jhu_csse_daily_subset %>%
+  jhu <- cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 5af492378..a3158b050 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- jhu_csse_daily_subset
+train_data <- cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }
diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
index 4a7e7d2e8..02a825267 100644
--- a/tests/testthat/test-target_date_bug.R
+++ b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- jhu_csse_daily_subset |>
+train <- cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)

From fe09e6acc122d71e96dce18c4db34fee33c6dda9 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 18 Oct 2024 17:15:29 -0700
Subject: [PATCH 136/387] snapshot dates starting with 0 are troublesome

---
 tests/testthat/_snaps/snapshots.md | 6 +++---
 tests/testthat/test-snapshots.R    | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 0677b59f3..ae045460b 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1090,7 +1090,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 0999-01-01.
+      This forecaster was fit on 1999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
@@ -1113,7 +1113,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 0999-01-01.
+      This forecaster was fit on 1999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
@@ -1137,7 +1137,7 @@
     Message
       == A basic forecaster of type ARX Forecaster ===================================
       
-      This forecaster was fit on 0999-01-01.
+      This forecaster was fit on 1999-01-01.
       
       Training data was an <epi_df> with:
       * Geography: state,
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index a3158b050..c23631f69 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -118,7 +118,7 @@ test_that("arx_forecaster output format snapshots", {
     c("case_rate", "death_rate")
   )
   expect_equal(as.Date(format(out1$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
-  out1$metadata$forecast_created <- as.Date("0999-01-01")
+  out1$metadata$forecast_created <- as.Date("1999-01-01")
   expect_snapshot(out1)
   out2 <- arx_forecaster(jhu, "case_rate",
     c("case_rate", "death_rate"),
@@ -130,7 +130,7 @@ test_that("arx_forecaster output format snapshots", {
     )
   )
   expect_equal(as.Date(format(out2$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
-  out2$metadata$forecast_created <- as.Date("0999-01-01")
+  out2$metadata$forecast_created <- as.Date("1999-01-01")
   expect_snapshot(out2)
   out3 <- arx_forecaster(jhu, "death_rate",
     c("case_rate", "death_rate"),
@@ -141,7 +141,7 @@ test_that("arx_forecaster output format snapshots", {
     )
   )
   expect_equal(as.Date(format(out3$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
-  out3$metadata$forecast_created <- as.Date("0999-01-01")
+  out3$metadata$forecast_created <- as.Date("1999-01-01")
   expect_snapshot(out3)
 })
 

From cfed37a0b49773b89891f68261208bb65206a225 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 24 Oct 2024 11:34:56 -0500
Subject: [PATCH 137/387] fixing quantile sorting problems, adding epidatasets

---
 DESCRIPTION                              |  1 +
 R/layer_population_scaling.R             |  2 +-
 R/make_grf_quantiles.R                   |  2 +-
 R/step_population_scaling.R              |  2 +-
 man/layer_population_scaling.Rd          |  2 +-
 man/step_population_scaling.Rd           |  2 +-
 tests/testthat/_snaps/snapshots.md       | 59 ++++++++++++------------
 tests/testthat/_snaps/step_epi_slide.md  |  4 +-
 tests/testthat/test-arx_forecaster.R     |  2 +-
 tests/testthat/test-check-training-set.R |  2 +-
 tests/testthat/test-grf_quantiles.R      | 10 ++++
 tests/testthat/test-population_scaling.R |  2 +-
 tests/testthat/test-snapshots.R          |  2 +-
 tests/testthat/test-target_date_bug.R    |  2 +-
 14 files changed, 52 insertions(+), 42 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index c580b872c..d3f7ca3a4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -49,6 +49,7 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
+    epidatasets,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 4755083ce..7ec168822 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 253ea1ac7..2903c93a8 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -165,7 +165,7 @@ make_grf_quantiles <- function() {
 
   # turn the predictions into a tibble with a dist_quantiles column
   process_qrf_preds <- function(x, object) {
-    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig
+    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
     out <- dist_quantiles(out, list(quantile_levels))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 3d3e65297..7bdb00925 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 253116694..3a22dbee5 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -75,7 +75,7 @@ argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- cases_deaths_subset \%>\%
+jhu <- epidatasets::cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 004c2c823..6a0c5dea9 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -88,7 +88,7 @@ argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- cases_deaths_subset \%>\%
+jhu <- epidatasets::cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index ae045460b..f3e7e5737 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -154,7 +154,7 @@
     0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
     0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343, 
+    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343000000001, 
     0.0847941, 0.0966258, 0.103199, 0.1097722, 0.1216039, 0.1314637, 
     0.1419808, 0.15414125, 0.17090286, 0.189932235, 0.22848398, 0.30542311, 
     0.40216399, 0.512353658), quantile_levels = c(0.01, 0.025, 0.05, 
@@ -267,7 +267,7 @@
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
     "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.114729892920429, 
     0.227785958288583, 0.282278878729037, 0.320407599201492, 0.350577823459785, 
-    0.37665230304923, 0.39981364198757, 0.4218461, 0.444009706175862, 
+    0.376652303049231, 0.39981364198757, 0.4218461, 0.444009706175862, 
     0.466962725214852, 0.493098379685547, 0.523708407392674, 0.562100740111401, 
     0.619050517814778, 0.754868363055733, 1.1177263295869, 1.76277018354499, 
     2.37278671910076, 2.9651652434047), quantile_levels = c(0.01, 
@@ -314,7 +314,7 @@
     0.144337973117581, 0.250292371898569, 0.367310419323293, 0.44444044802193, 
     0.506592035751958, 0.558428768125431, 0.602035095628756, 0.64112383905529, 
     0.674354964141041, 0.703707875219752, 0.7319844, 0.760702196782168, 
-    0.78975826405844, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
+    0.789758264058441, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
     0.955736581115011, 1.0165945004053, 1.09529786576616, 1.21614421175967, 
     1.32331604019295, 1.45293812780298), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
@@ -351,7 +351,7 @@
     0.0497573816250162, 0.081255049503995, 0.108502307388674, 0.132961558931189, 
     0.156011650575706, 0.177125892134071, 0.1975426, 0.217737120618906, 
     0.239458499211792, 0.263562581820818, 0.289525383565136, 0.31824420000725, 
-    0.35141305194052, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
+    0.351413051940519, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
     0.657452391363313, 0.767918764883928), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
@@ -412,20 +412,19 @@
         0.0766736159703596, 0.0942284381264812, 0.11050757203172, 
         0.125214601455714, 0.1393442, 0.15359732398729, 0.168500447692877, 
         0.184551468093631, 0.202926420944109, 0.22476606802393, 0.253070223293233, 
-        0.29122995395109, 0.341963643747938, 0.419747975311502, 0.495994046054689, 
-        0.5748791770223), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00603076915889168, 0.0356039073625737, 
-        0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
-        0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
-        0.196866917086671, 0.219796529731897, 0.247137200365254, 
-        0.280371254591746, 0.320842872758278, 0.374783454750148, 
-        0.461368597638526, 0.539683256474915, 0.632562403391324), 
-        quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-        0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 
-        0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
+        0.291229953951089, 0.341963643747938, 0.419747975311502, 
+        0.495994046054689, 0.5748791770223), quantile_levels = c(0.01, 
+        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
+        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
+        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.00603076915889168, 
+    0.0356039073625737, 0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
+    0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
+    0.196866917086671, 0.219796529731897, 0.247137200365254, 0.280371254591746, 
+    0.320842872758278, 0.374783454750148, 0.461368597638526, 0.539683256474915, 
+    0.632562403391324), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
+    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
+    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
         values = c(0, 0, 0, 0, 0, 0, 0.018869505399304, 0.0471517885822858, 
         0.0732707765908659, 0.0969223475714758, 0.118188509171441, 
@@ -650,7 +649,7 @@
     0.0562218087603375, 0.0890356919950198, 0.118731362266373, 0.146216910144001, 
     0.172533896645116, 0.1975426, 0.223021121504065, 0.249412654553045, 
     0.277680444480195, 0.308522683806638, 0.342270845449704, 0.382702709814398, 
-    0.433443929063141, 0.501610622734127, 0.61417580106326, 0.715138862353848, 
+    0.433443929063141, 0.501610622734127, 0.614175801063261, 0.715138862353848, 
     0.833535553075286), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
     0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
     0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
@@ -826,8 +825,8 @@
         0.147940700281253, 0.185518687303273, 0.220197034594646, 
         0.2521005, 0.282477641919719, 0.3121244, 0.3414694, 0.371435390499905, 
         0.402230766363414, 0.436173824348844, 0.474579164424894, 
-        0.519690345185252, 0.57667375206677, 0.655151246845668, 0.78520792902029, 
-        0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
+        0.519690345185252, 0.576673752066771, 0.655151246845668, 
+        0.78520792902029, 0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
         0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
         0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1008,14 +1007,14 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
-    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398634, 0.202494988128882
+    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
+    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398633, 0.202494988128882
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114383, 0.192956249000457), quantile_levels = c(0.05, 
+            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572136, 
+        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
         0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
@@ -1034,7 +1033,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1049,7 +1048,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
@@ -1060,7 +1059,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1075,7 +1074,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index 7493a7fea..a4b9d64c8 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 61016b634..0f2b9bd16 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(
diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
index ad891dd45..071a5ccf3 100644
--- a/tests/testthat/test-check-training-set.R
+++ b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- cases_deaths_subset[1, ]
+  template <- epidatasets::cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 57a9d0f98..ee4b5f911 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -50,3 +50,13 @@ test_that("quantile_rand_forest handles allows setting the trees and mtry", {
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 })
+
+test_that("quantile_rand_forest predicts reasonable quantiles", {
+  spec <- rand_forest(mode = "regression") %>%
+    set_engine("grf_quantiles", quantiles = c(.2, .5, .8))
+  expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
+  # swapping around the probabilities, because somehow this happens in practice,
+  # but I'm not sure how to reproduce
+  out$fit$quantiles.orig <- c(0.5,0.9, 0.1)
+  expect_no_error(predict(out, tib))
+})
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index e6971bc23..a1ccba4a1 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- cases_deaths_subset %>%
+  jhu <- epidatasets::cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index c23631f69..956580e9f 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }
diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
index 02a825267..8be266414 100644
--- a/tests/testthat/test-target_date_bug.R
+++ b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- cases_deaths_subset |>
+train <- epidatasets::cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)

From febc4dcc05792f4912e2accc0c815501e22f565c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 24 Oct 2024 11:38:26 -0500
Subject: [PATCH 138/387] Revert "fixing quantile sorting problems, adding
 epidatasets"

This reverts commit cfed37a0b49773b89891f68261208bb65206a225.
---
 DESCRIPTION                              |  1 -
 R/layer_population_scaling.R             |  2 +-
 R/make_grf_quantiles.R                   |  2 +-
 R/step_population_scaling.R              |  2 +-
 man/layer_population_scaling.Rd          |  2 +-
 man/step_population_scaling.Rd           |  2 +-
 tests/testthat/_snaps/snapshots.md       | 59 ++++++++++++------------
 tests/testthat/_snaps/step_epi_slide.md  |  4 +-
 tests/testthat/test-arx_forecaster.R     |  2 +-
 tests/testthat/test-check-training-set.R |  2 +-
 tests/testthat/test-grf_quantiles.R      | 10 ----
 tests/testthat/test-population_scaling.R |  2 +-
 tests/testthat/test-snapshots.R          |  2 +-
 tests/testthat/test-target_date_bug.R    |  2 +-
 14 files changed, 42 insertions(+), 52 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3f7ca3a4..c580b872c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -49,7 +49,6 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
-    epidatasets,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 7ec168822..4755083ce 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- epidatasets::cases_deaths_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 2903c93a8..253ea1ac7 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -165,7 +165,7 @@ make_grf_quantiles <- function() {
 
   # turn the predictions into a tibble with a dist_quantiles column
   process_qrf_preds <- function(x, object) {
-    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
+    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
     out <- dist_quantiles(out, list(quantile_levels))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 7bdb00925..3d3e65297 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- epidatasets::cases_deaths_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 3a22dbee5..253116694 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -75,7 +75,7 @@ argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- epidatasets::cases_deaths_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 6a0c5dea9..004c2c823 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -88,7 +88,7 @@ argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- epidatasets::cases_deaths_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index f3e7e5737..ae045460b 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -154,7 +154,7 @@
     0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
     0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343000000001, 
+    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343, 
     0.0847941, 0.0966258, 0.103199, 0.1097722, 0.1216039, 0.1314637, 
     0.1419808, 0.15414125, 0.17090286, 0.189932235, 0.22848398, 0.30542311, 
     0.40216399, 0.512353658), quantile_levels = c(0.01, 0.025, 0.05, 
@@ -267,7 +267,7 @@
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
     "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.114729892920429, 
     0.227785958288583, 0.282278878729037, 0.320407599201492, 0.350577823459785, 
-    0.376652303049231, 0.39981364198757, 0.4218461, 0.444009706175862, 
+    0.37665230304923, 0.39981364198757, 0.4218461, 0.444009706175862, 
     0.466962725214852, 0.493098379685547, 0.523708407392674, 0.562100740111401, 
     0.619050517814778, 0.754868363055733, 1.1177263295869, 1.76277018354499, 
     2.37278671910076, 2.9651652434047), quantile_levels = c(0.01, 
@@ -314,7 +314,7 @@
     0.144337973117581, 0.250292371898569, 0.367310419323293, 0.44444044802193, 
     0.506592035751958, 0.558428768125431, 0.602035095628756, 0.64112383905529, 
     0.674354964141041, 0.703707875219752, 0.7319844, 0.760702196782168, 
-    0.789758264058441, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
+    0.78975826405844, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
     0.955736581115011, 1.0165945004053, 1.09529786576616, 1.21614421175967, 
     1.32331604019295, 1.45293812780298), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
@@ -351,7 +351,7 @@
     0.0497573816250162, 0.081255049503995, 0.108502307388674, 0.132961558931189, 
     0.156011650575706, 0.177125892134071, 0.1975426, 0.217737120618906, 
     0.239458499211792, 0.263562581820818, 0.289525383565136, 0.31824420000725, 
-    0.351413051940519, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
+    0.35141305194052, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
     0.657452391363313, 0.767918764883928), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
@@ -412,19 +412,20 @@
         0.0766736159703596, 0.0942284381264812, 0.11050757203172, 
         0.125214601455714, 0.1393442, 0.15359732398729, 0.168500447692877, 
         0.184551468093631, 0.202926420944109, 0.22476606802393, 0.253070223293233, 
-        0.291229953951089, 0.341963643747938, 0.419747975311502, 
-        0.495994046054689, 0.5748791770223), quantile_levels = c(0.01, 
-        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
-        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
-        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.00603076915889168, 
-    0.0356039073625737, 0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
-    0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
-    0.196866917086671, 0.219796529731897, 0.247137200365254, 0.280371254591746, 
-    0.320842872758278, 0.374783454750148, 0.461368597638526, 0.539683256474915, 
-    0.632562403391324), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
-    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
-    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
+        0.29122995395109, 0.341963643747938, 0.419747975311502, 0.495994046054689, 
+        0.5748791770223), quantile_levels = c(0.01, 0.025, 0.05, 
+        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
+        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0, 0, 0, 0, 0, 0.00603076915889168, 0.0356039073625737, 
+        0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
+        0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
+        0.196866917086671, 0.219796529731897, 0.247137200365254, 
+        0.280371254591746, 0.320842872758278, 0.374783454750148, 
+        0.461368597638526, 0.539683256474915, 0.632562403391324), 
+        quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
+        0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 
+        0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
         values = c(0, 0, 0, 0, 0, 0, 0.018869505399304, 0.0471517885822858, 
         0.0732707765908659, 0.0969223475714758, 0.118188509171441, 
@@ -649,7 +650,7 @@
     0.0562218087603375, 0.0890356919950198, 0.118731362266373, 0.146216910144001, 
     0.172533896645116, 0.1975426, 0.223021121504065, 0.249412654553045, 
     0.277680444480195, 0.308522683806638, 0.342270845449704, 0.382702709814398, 
-    0.433443929063141, 0.501610622734127, 0.614175801063261, 0.715138862353848, 
+    0.433443929063141, 0.501610622734127, 0.61417580106326, 0.715138862353848, 
     0.833535553075286), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
     0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
     0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
@@ -825,8 +826,8 @@
         0.147940700281253, 0.185518687303273, 0.220197034594646, 
         0.2521005, 0.282477641919719, 0.3121244, 0.3414694, 0.371435390499905, 
         0.402230766363414, 0.436173824348844, 0.474579164424894, 
-        0.519690345185252, 0.576673752066771, 0.655151246845668, 
-        0.78520792902029, 0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
+        0.519690345185252, 0.57667375206677, 0.655151246845668, 0.78520792902029, 
+        0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
         0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
         0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1007,14 +1008,14 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
-    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398633, 0.202494988128882
+    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
+    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398634, 0.202494988128882
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
+            values = c(0.0865730800114383, 0.192956249000457), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
+        "vctrs_vctr")), structure(list(values = c(0.279994736572136, 
         0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
@@ -1033,7 +1034,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1048,7 +1049,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
@@ -1059,7 +1060,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1074,7 +1075,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index a4b9d64c8..7493a7fea 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 0f2b9bd16..61016b634 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- epidatasets::cases_deaths_subset
+train_data <- cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(
diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
index 071a5ccf3..ad891dd45 100644
--- a/tests/testthat/test-check-training-set.R
+++ b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- epidatasets::cases_deaths_subset[1, ]
+  template <- cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index ee4b5f911..57a9d0f98 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -50,13 +50,3 @@ test_that("quantile_rand_forest handles allows setting the trees and mtry", {
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 })
-
-test_that("quantile_rand_forest predicts reasonable quantiles", {
-  spec <- rand_forest(mode = "regression") %>%
-    set_engine("grf_quantiles", quantiles = c(.2, .5, .8))
-  expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
-  # swapping around the probabilities, because somehow this happens in practice,
-  # but I'm not sure how to reproduce
-  out$fit$quantiles.orig <- c(0.5,0.9, 0.1)
-  expect_no_error(predict(out, tib))
-})
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index a1ccba4a1..e6971bc23 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- epidatasets::cases_deaths_subset %>%
+  jhu <- cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 956580e9f..c23631f69 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- epidatasets::cases_deaths_subset
+train_data <- cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }
diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
index 8be266414..02a825267 100644
--- a/tests/testthat/test-target_date_bug.R
+++ b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- epidatasets::cases_deaths_subset |>
+train <- cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)

From 6d8cd1d1e7cd7d2ce8bef57a50ee084def6983d3 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 24 Oct 2024 11:40:10 -0500
Subject: [PATCH 139/387] fixing quantile sorting problems, adding epidatasets

---
 DESCRIPTION                              |  1 +
 R/layer_population_scaling.R             |  2 +-
 R/make_grf_quantiles.R                   |  2 +-
 R/step_population_scaling.R              |  2 +-
 man/layer_population_scaling.Rd          |  2 +-
 man/step_population_scaling.Rd           |  2 +-
 tests/testthat/_snaps/snapshots.md       | 59 ++++++++++++------------
 tests/testthat/_snaps/step_epi_slide.md  |  4 +-
 tests/testthat/test-arx_forecaster.R     |  2 +-
 tests/testthat/test-check-training-set.R |  2 +-
 tests/testthat/test-grf_quantiles.R      | 10 ++++
 tests/testthat/test-population_scaling.R |  2 +-
 tests/testthat/test-snapshots.R          |  2 +-
 tests/testthat/test-target_date_bug.R    |  2 +-
 14 files changed, 52 insertions(+), 42 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index c580b872c..d3f7ca3a4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -49,6 +49,7 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
+    epidatasets,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 4755083ce..7ec168822 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 253ea1ac7..2903c93a8 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -165,7 +165,7 @@ make_grf_quantiles <- function() {
 
   # turn the predictions into a tibble with a dist_quantiles column
   process_qrf_preds <- function(x, object) {
-    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig
+    quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
     out <- dist_quantiles(out, list(quantile_levels))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 3d3e65297..7bdb00925 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- cases_deaths_subset %>%
+#' jhu <- epidatasets::cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 253116694..3a22dbee5 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -75,7 +75,7 @@ argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- cases_deaths_subset \%>\%
+jhu <- epidatasets::cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 004c2c823..6a0c5dea9 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -88,7 +88,7 @@ argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 library(dplyr)
-jhu <- cases_deaths_subset \%>\%
+jhu <- epidatasets::cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index ae045460b..f3e7e5737 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -154,7 +154,7 @@
     0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
     0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343, 
+    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343000000001, 
     0.0847941, 0.0966258, 0.103199, 0.1097722, 0.1216039, 0.1314637, 
     0.1419808, 0.15414125, 0.17090286, 0.189932235, 0.22848398, 0.30542311, 
     0.40216399, 0.512353658), quantile_levels = c(0.01, 0.025, 0.05, 
@@ -267,7 +267,7 @@
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
     "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.114729892920429, 
     0.227785958288583, 0.282278878729037, 0.320407599201492, 0.350577823459785, 
-    0.37665230304923, 0.39981364198757, 0.4218461, 0.444009706175862, 
+    0.376652303049231, 0.39981364198757, 0.4218461, 0.444009706175862, 
     0.466962725214852, 0.493098379685547, 0.523708407392674, 0.562100740111401, 
     0.619050517814778, 0.754868363055733, 1.1177263295869, 1.76277018354499, 
     2.37278671910076, 2.9651652434047), quantile_levels = c(0.01, 
@@ -314,7 +314,7 @@
     0.144337973117581, 0.250292371898569, 0.367310419323293, 0.44444044802193, 
     0.506592035751958, 0.558428768125431, 0.602035095628756, 0.64112383905529, 
     0.674354964141041, 0.703707875219752, 0.7319844, 0.760702196782168, 
-    0.78975826405844, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
+    0.789758264058441, 0.823427572594726, 0.860294897090771, 0.904032120658957, 
     0.955736581115011, 1.0165945004053, 1.09529786576616, 1.21614421175967, 
     1.32331604019295, 1.45293812780298), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
@@ -351,7 +351,7 @@
     0.0497573816250162, 0.081255049503995, 0.108502307388674, 0.132961558931189, 
     0.156011650575706, 0.177125892134071, 0.1975426, 0.217737120618906, 
     0.239458499211792, 0.263562581820818, 0.289525383565136, 0.31824420000725, 
-    0.35141305194052, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
+    0.351413051940519, 0.393862560773808, 0.453538799225292, 0.558631806850418, 
     0.657452391363313, 0.767918764883928), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
@@ -412,20 +412,19 @@
         0.0766736159703596, 0.0942284381264812, 0.11050757203172, 
         0.125214601455714, 0.1393442, 0.15359732398729, 0.168500447692877, 
         0.184551468093631, 0.202926420944109, 0.22476606802393, 0.253070223293233, 
-        0.29122995395109, 0.341963643747938, 0.419747975311502, 0.495994046054689, 
-        0.5748791770223), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00603076915889168, 0.0356039073625737, 
-        0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
-        0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
-        0.196866917086671, 0.219796529731897, 0.247137200365254, 
-        0.280371254591746, 0.320842872758278, 0.374783454750148, 
-        0.461368597638526, 0.539683256474915, 0.632562403391324), 
-        quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-        0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 
-        0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
+        0.291229953951089, 0.341963643747938, 0.419747975311502, 
+        0.495994046054689, 0.5748791770223), quantile_levels = c(0.01, 
+        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
+        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
+        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.00603076915889168, 
+    0.0356039073625737, 0.0609470811194113, 0.0833232869645198, 0.103265350891109, 
+    0.121507077706427, 0.1393442, 0.157305073932789, 0.176004666813668, 
+    0.196866917086671, 0.219796529731897, 0.247137200365254, 0.280371254591746, 
+    0.320842872758278, 0.374783454750148, 0.461368597638526, 0.539683256474915, 
+    0.632562403391324), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
+    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
+    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
         values = c(0, 0, 0, 0, 0, 0, 0.018869505399304, 0.0471517885822858, 
         0.0732707765908659, 0.0969223475714758, 0.118188509171441, 
@@ -650,7 +649,7 @@
     0.0562218087603375, 0.0890356919950198, 0.118731362266373, 0.146216910144001, 
     0.172533896645116, 0.1975426, 0.223021121504065, 0.249412654553045, 
     0.277680444480195, 0.308522683806638, 0.342270845449704, 0.382702709814398, 
-    0.433443929063141, 0.501610622734127, 0.61417580106326, 0.715138862353848, 
+    0.433443929063141, 0.501610622734127, 0.614175801063261, 0.715138862353848, 
     0.833535553075286), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
     0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
     0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
@@ -826,8 +825,8 @@
         0.147940700281253, 0.185518687303273, 0.220197034594646, 
         0.2521005, 0.282477641919719, 0.3121244, 0.3414694, 0.371435390499905, 
         0.402230766363414, 0.436173824348844, 0.474579164424894, 
-        0.519690345185252, 0.57667375206677, 0.655151246845668, 0.78520792902029, 
-        0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
+        0.519690345185252, 0.576673752066771, 0.655151246845668, 
+        0.78520792902029, 0.90968118047453, 1.05112182091783), quantile_levels = c(0.01, 
         0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
         0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
         0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
@@ -1008,14 +1007,14 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
-    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398634, 0.202494988128882
+    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
+    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398633, 0.202494988128882
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114383, 0.192956249000457), quantile_levels = c(0.05, 
+            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572136, 
+        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
         0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
@@ -1034,7 +1033,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1049,7 +1048,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
@@ -1060,7 +1059,7 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
         structure(list(values = c(0.136509784083987, 0.469979623951498
         ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
@@ -1075,7 +1074,7 @@
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
         0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.473630377159077), quantile_levels = c(0.05, 
+            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index 7493a7fea..a4b9d64c8 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 61016b634..0f2b9bd16 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 test_that("arx_forecaster warns if forecast date beyond the implicit one", {
   bad_date <- max(train_data$time_value) + 300
   expect_warning(
diff --git a/tests/testthat/test-check-training-set.R b/tests/testthat/test-check-training-set.R
index ad891dd45..071a5ccf3 100644
--- a/tests/testthat/test-check-training-set.R
+++ b/tests/testthat/test-check-training-set.R
@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- cases_deaths_subset[1, ]
+  template <- epidatasets::cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 57a9d0f98..ee4b5f911 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -50,3 +50,13 @@ test_that("quantile_rand_forest handles allows setting the trees and mtry", {
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 })
+
+test_that("quantile_rand_forest predicts reasonable quantiles", {
+  spec <- rand_forest(mode = "regression") %>%
+    set_engine("grf_quantiles", quantiles = c(.2, .5, .8))
+  expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
+  # swapping around the probabilities, because somehow this happens in practice,
+  # but I'm not sure how to reproduce
+  out$fit$quantiles.orig <- c(0.5,0.9, 0.1)
+  expect_no_error(predict(out, tib))
+})
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index e6971bc23..a1ccba4a1 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- cases_deaths_subset %>%
+  jhu <- epidatasets::cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index c23631f69..956580e9f 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -1,4 +1,4 @@
-train_data <- cases_deaths_subset
+train_data <- epidatasets::cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }
diff --git a/tests/testthat/test-target_date_bug.R b/tests/testthat/test-target_date_bug.R
index 02a825267..8be266414 100644
--- a/tests/testthat/test-target_date_bug.R
+++ b/tests/testthat/test-target_date_bug.R
@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- cases_deaths_subset |>
+train <- epidatasets::cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)

From 9ee00d4ea66c0b40f6afd9bc257068776ac8d09b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 24 Oct 2024 12:14:20 -0500
Subject: [PATCH 140/387] bump version, local renv needed update for snaps

---
 DESCRIPTION                             | 2 +-
 NEWS.md                                 | 1 +
 tests/testthat/_snaps/step_epi_slide.md | 4 ++--
 tests/testthat/test-grf_quantiles.R     | 2 +-
 4 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3f7ca3a4..0dab118fc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.2
+Version: 0.1.3
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 3e4e964b4..e080d1aa0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -10,6 +10,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 ## bugfixes
 - shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
+- Quantiles produced by `grf` were sometimes out of order.
 
 # epipredict 0.1
 
diff --git a/tests/testthat/_snaps/step_epi_slide.md b/tests/testthat/_snaps/step_epi_slide.md
index a4b9d64c8..7493a7fea 100644
--- a/tests/testthat/_snaps/step_epi_slide.md
+++ b/tests/testthat/_snaps/step_epi_slide.md
@@ -12,7 +12,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = c(3L, 6L))
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a non-null, scalar integer >= 1.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
@@ -60,7 +60,7 @@
       r %>% step_epi_slide(value, .f = mean, .window_size = 1.5)
     Condition
       Error in `epiprocess:::validate_slide_window_arg()`:
-      ! Slide function expected `.window_size` to be a difftime with units in days or non-negative integer or Inf.
+      ! Slide function expected `.window_size` to be a length-1 difftime with units in days or non-negative integer or Inf.
 
 ---
 
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index ee4b5f911..e27a0ac65 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -57,6 +57,6 @@ test_that("quantile_rand_forest predicts reasonable quantiles", {
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   # swapping around the probabilities, because somehow this happens in practice,
   # but I'm not sure how to reproduce
-  out$fit$quantiles.orig <- c(0.5,0.9, 0.1)
+  out$fit$quantiles.orig <- c(0.5, 0.9, 0.1)
   expect_no_error(predict(out, tib))
 })

From f6d01e2eec48b74af079d5091ddadffc61eeac4c Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 17:19:40 -0700
Subject: [PATCH 141/387] arx_forecaster to work with grf_quantiles

---
 R/arx_forecaster.R                  | 32 +++++++++++++++++++++--------
 tests/testthat/test-arx_args_list.R |  5 +++++
 tests/testthat/test-grf_quantiles.R | 21 +++++++++++++++++++
 3 files changed, 50 insertions(+), 8 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index bfd5eaec1..578752626 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -186,14 +186,26 @@ arx_fcast_epi_workflow <- function(
 
   # --- postprocessor
   f <- frosting() %>% layer_predict() # %>% layer_naomit()
-  if (inherits(trainer, "quantile_reg")) {
+  is_quantile_reg <- inherits(trainer, "quantile_reg") |
+    (inherits(trainer, "rand_forest") & trainer$engine == "grf_quantiles")
+  if (is_quantile_reg) {
     # add all quantile_level to the forecaster and update postprocessor
-    quantile_levels <- sort(compare_quantile_args(
-      args_list$quantile_levels,
-      rlang::eval_tidy(trainer$args$quantile_levels)
-    ))
+    if (inherits(trainer, "quantile_reg")) {
+      quantile_levels <- sort(compare_quantile_args(
+        args_list$quantile_levels,
+        rlang::eval_tidy(trainer$args$quantile_levels),
+        train_type
+      ))
+      trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
+    } else {
+      quantile_levels <- sort(compare_quantile_args(
+        args_list$quantile_levels,
+        rlang::eval_tidy(trainer$eng_args$quantiles) %||% c(.1, .5, .9),
+        "grf"
+      ))
+      trainer$eng_args$quantiles <- rlang::enquo(quantile_levels)
+    }
     args_list$quantile_levels <- quantile_levels
-    trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
     f <- f %>%
       layer_quantile_distn(quantile_levels = quantile_levels) %>%
       layer_point_from_distn()
@@ -345,9 +357,13 @@ print.arx_fcast <- function(x, ...) {
   NextMethod(name = name, ...)
 }
 
-compare_quantile_args <- function(alist, tlist) {
+compare_quantile_args <- function(alist, tlist, trainer = "qr") {
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
-  default_tlist <- eval(formals(quantile_reg)$quantile_levels)
+  default_tlist <- switch(
+    trainer,
+    "qr" = eval(formals(quantile_reg)$quantile_levels),
+    "grf" = c(.1, .5, .9)
+  )
   if (setequal(alist, default_alist)) {
     if (setequal(tlist, default_tlist)) {
       return(sort(unique(union(alist, tlist))))
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 9bbff0135..cf392330c 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -43,6 +43,11 @@ test_that("arx forecaster disambiguates quantiles", {
     compare_quantile_args(alist, tlist),
     sort(c(alist, tlist))
   )
+  expect_snapshot(
+    error = TRUE,
+    compare_quantile_args(1:9 / 10, 1:9 / 10, "grf")
+  )
+  expect_identical(compare_quantile_args(alist, 1:9 / 10, "grf"), 1:9 / 10)
   alist <- c(.5, alist)
   expect_identical( # tlist is default, should give alist
     compare_quantile_args(alist, tlist),
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 57a9d0f98..e52a4121d 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -50,3 +50,24 @@ test_that("quantile_rand_forest handles allows setting the trees and mtry", {
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 })
+
+test_that("quantile_rand_forest operates with arx_forecaster", {
+  spec <- rand_forest(mode = "regression") %>%
+    set_engine("grf_quantiles", quantiles = c(.1, .2, .5, .8, .9)) # non-default
+  expect_identical(rlang::eval_tidy(spec$eng_args$quantiles), c(.1, .2, .5, .8, .9))
+  tib <- as_epi_df(tibble(time_value = 1:25, geo_value = "ca", value = rnorm(25)))
+  o <- arx_fcast_epi_workflow(tib, "value", trainer = spec)
+  spec2 <- parsnip::extract_spec_parsnip(o)
+  expect_identical(
+    rlang::eval_tidy(spec2$eng_args$quantiles),
+    rlang::eval_tidy(spec$eng_args$quantiles)
+  )
+  spec <- rand_forest(mode = "regression", "grf_quantiles")
+  expect_null(rlang::eval_tidy(spec$eng_args))
+  o <- arx_fcast_epi_workflow(tib, "value", trainer = spec)
+  spec2 <- parsnip::extract_spec_parsnip(o)
+  expect_identical(
+    rlang::eval_tidy(spec2$eng_args$quantiles),
+    c(.05, .1, .5, .9, .95) # merged with arx_args default
+  )
+})

From d36760b6fa4c1017607cc17a5f4075e13113d0b7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 17:28:29 -0700
Subject: [PATCH 142/387] works in the forecaster

---
 tests/testthat/test-grf_quantiles.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index e52a4121d..662620f26 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -70,4 +70,10 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
     rlang::eval_tidy(spec2$eng_args$quantiles),
     c(.05, .1, .5, .9, .95) # merged with arx_args default
   )
+  df <- epidatasets::counts_subset %>% filter(time_value >= "2021-10-01")
+  z <- arx_forecaster(df, "cases", "cases", spec)
+  expect_identical(
+    nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
+    c(.05, .1, .5, .9, .95)
+  )
 })

From b929e47857089c4264e8166d2b069adb91edcbaa Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 17:50:28 -0700
Subject: [PATCH 143/387] all tests pass

---
 R/arx_forecaster.R                     | 7 ++++---
 R/canned-epipred.R                     | 5 +++--
 tests/testthat/_snaps/arx_args_list.md | 9 +++++++++
 tests/testthat/test-arx_args_list.R    | 2 +-
 tests/testthat/test-snapshots.R        | 2 +-
 5 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 578752626..fec867ece 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -194,7 +194,7 @@ arx_fcast_epi_workflow <- function(
       quantile_levels <- sort(compare_quantile_args(
         args_list$quantile_levels,
         rlang::eval_tidy(trainer$args$quantile_levels),
-        train_type
+        "qr"
       ))
       trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
     } else {
@@ -357,10 +357,11 @@ print.arx_fcast <- function(x, ...) {
   NextMethod(name = name, ...)
 }
 
-compare_quantile_args <- function(alist, tlist, trainer = "qr") {
+compare_quantile_args <- function(alist, tlist, train_method = c("qr", "grf")) {
+  train_method <- rlang::arg_match(train_method)
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
   default_tlist <- switch(
-    trainer,
+    train_method,
     "qr" = eval(formals(quantile_reg)$quantile_levels),
     "grf" = c(.1, .5, .9)
   )
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 1e088426e..8a5baa586 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -77,8 +77,9 @@ print.canned_epipred <- function(x, name, ...) {
   fn_meta <- function() {
     cli::cli_ul()
     cli::cli_li("Geography: {.field {x$metadata$training$geo_type}},")
-    if (!is.null(x$metadata$training$other_keys)) {
-      cli::cli_li("Other keys: {.field {x$metadata$training$other_keys}},")
+    other_keys <- x$metadata$training$other_keys
+    if (!is.null(other_keys) && length(other_keys) > 0L) {
+      cli::cli_li("Other keys: {.field {other_keys}},")
     }
     cli::cli_li("Time type: {.field {x$metadata$training$time_type}},")
     cli::cli_li("Using data up-to-date as of: {.field {format(x$metadata$training$as_of)}}.")
diff --git a/tests/testthat/_snaps/arx_args_list.md b/tests/testthat/_snaps/arx_args_list.md
index 959a5e25b..2579c5f0c 100644
--- a/tests/testthat/_snaps/arx_args_list.md
+++ b/tests/testthat/_snaps/arx_args_list.md
@@ -124,6 +124,15 @@
 
 # arx forecaster disambiguates quantiles
 
+    Code
+      compare_quantile_args(alist / 10, 1:9 / 10, "grf")
+    Condition
+      Error in `compare_quantile_args()`:
+      ! You have specified different, non-default, quantiles in the trainier and `arx_args` options.
+      i Please only specify quantiles in one location.
+
+---
+
     Code
       compare_quantile_args(alist, tlist)
     Condition
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index cf392330c..226444e37 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -45,7 +45,7 @@ test_that("arx forecaster disambiguates quantiles", {
   )
   expect_snapshot(
     error = TRUE,
-    compare_quantile_args(1:9 / 10, 1:9 / 10, "grf")
+    compare_quantile_args(alist / 10, 1:9 / 10, "grf")
   )
   expect_identical(compare_quantile_args(alist, 1:9 / 10, "grf"), 1:9 / 10)
   alist <- c(.5, alist)
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index c23631f69..6ded0d896 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -110,7 +110,7 @@ test_that("arx_forecaster snapshots", {
 })
 
 test_that("arx_forecaster output format snapshots", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- epidatasets::covid_case_death_rates %>%
     dplyr::filter(time_value >= as.Date("2021-12-01"))
   attributes(jhu)$metadata$as_of <- as.Date(attributes(jhu)$metadata$as_of)
   out1 <- arx_forecaster(

From c3fb1c33e4c675fe20eaa411de4f80dfd15dafd2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 22:53:10 -0700
Subject: [PATCH 144/387] remove epidatasets

---
 tests/testthat/test-grf_quantiles.R | 6 ++++--
 tests/testthat/test-snapshots.R     | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 662620f26..c45dfe962 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -70,8 +70,10 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
     rlang::eval_tidy(spec2$eng_args$quantiles),
     c(.05, .1, .5, .9, .95) # merged with arx_args default
   )
-  df <- epidatasets::counts_subset %>% filter(time_value >= "2021-10-01")
-  z <- arx_forecaster(df, "cases", "cases", spec)
+  df <- case_death_rate_subset %>%
+    filter(time_value >= "2021-10-01", geo_value %in% c("ca", "ny"))
+
+  z <- arx_forecaster(df, "case_rate", "case_rate", spec2)
   expect_identical(
     nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
     c(.05, .1, .5, .9, .95)
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 6ded0d896..c23631f69 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -110,7 +110,7 @@ test_that("arx_forecaster snapshots", {
 })
 
 test_that("arx_forecaster output format snapshots", {
-  jhu <- epidatasets::covid_case_death_rates %>%
+  jhu <- case_death_rate_subset %>%
     dplyr::filter(time_value >= as.Date("2021-12-01"))
   attributes(jhu)$metadata$as_of <- as.Date(attributes(jhu)$metadata$as_of)
   out1 <- arx_forecaster(

From ea6ebc449c88c60d7d19405bc47a11340a47482e Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 23:02:08 -0700
Subject: [PATCH 145/387] add remote for epidatasets so it will install

---
 DESCRIPTION | 1 +
 1 file changed, 1 insertion(+)

diff --git a/DESCRIPTION b/DESCRIPTION
index 0dab118fc..ceb2de7fc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -71,6 +71,7 @@ VignetteBuilder:
 Remotes:
     cmu-delphi/epidatr,
     cmu-delphi/epiprocess,
+    cmu-delphi/epidatasets,
     dajmcdon/smoothqr
 Config/testthat/edition: 3
 Encoding: UTF-8

From a3d1b119411f0bde1c4bac58691f5b7016a99cef Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 25 Oct 2024 23:08:35 -0700
Subject: [PATCH 146/387] styler

---
 R/arx_forecaster.R                  | 3 +--
 tests/testthat/test-grf_quantiles.R | 5 ++---
 tests/testthat/test-snapshots.R     | 2 +-
 3 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index fec867ece..c2f044d17 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -360,8 +360,7 @@ print.arx_fcast <- function(x, ...) {
 compare_quantile_args <- function(alist, tlist, train_method = c("qr", "grf")) {
   train_method <- rlang::arg_match(train_method)
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
-  default_tlist <- switch(
-    train_method,
+  default_tlist <- switch(train_method,
     "qr" = eval(formals(quantile_reg)$quantile_levels),
     "grf" = c(.1, .5, .9)
   )
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index c45dfe962..e2cf90cf7 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -70,10 +70,9 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
     rlang::eval_tidy(spec2$eng_args$quantiles),
     c(.05, .1, .5, .9, .95) # merged with arx_args default
   )
-  df <- case_death_rate_subset %>%
-    filter(time_value >= "2021-10-01", geo_value %in% c("ca", "ny"))
+  df <- epidatasets::counts_subset %>% filter(time_value >= "2021-10-01")
 
-  z <- arx_forecaster(df, "case_rate", "case_rate", spec2)
+  z <- arx_forecaster(df, "cases", "cases", spec2)
   expect_identical(
     nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
     c(.05, .1, .5, .9, .95)
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 956580e9f..9766618bc 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -110,7 +110,7 @@ test_that("arx_forecaster snapshots", {
 })
 
 test_that("arx_forecaster output format snapshots", {
-  jhu <- case_death_rate_subset %>%
+  jhu <- epidatasets::covid_case_death_rates %>%
     dplyr::filter(time_value >= as.Date("2021-12-01"))
   attributes(jhu)$metadata$as_of <- as.Date(attributes(jhu)$metadata$as_of)
   out1 <- arx_forecaster(

From 58b61eed90e91c2958bd8b06262a9f47e5944920 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 09:23:51 -0700
Subject: [PATCH 147/387] Revert "attribution"

This reverts commit 00d0dd72b133a84bfe73f805f5e7e7b15b65799f.
---
 DESCRIPTION | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 822d05d60..f97363098 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,19 +6,13 @@ Authors@R: c(
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
     person("Dmitry", "Shemetov", email = "dshemeto@andrew.cmu.edu", role = "aut"),
     person("David", "Weber", email = "davidweb@andrew.cmu.edu", role = "aut"),
-    person("CMU's Delphi Research Group", role = c("cph", "fnd", "dtc"),
-        comment = "Owner of masking, social-distancing, and CLI data from the COVID-19 Trends and Impacts Survey. Owner of claims-based CLI data from the Delphi Epidata API"),
+    person("CMU's Delphi Research Group", role = c("cph", "fnd")),
     person("Logan", "Brooks", role = "aut"),
     person("Rachel", "Lobay", role = "aut"),
     person("Maggie", "Liu", role = "ctb"),
     person("Ken", "Mawer", role = "ctb"),
     person("Chloe", "You", role = "ctb"),
-    person("Jacob", "Bien", role = "ctb"),
-    person("Johns Hopkins University Center for Systems Science and Engineering", role = "dtc", comment = "Owner of COVID-19 cases and deaths data from the COVID-19 Data Repository"),
-    person("Johns Hopkins University", role = "cph", comment = "Copyright holder of COVID-19 cases and deaths data from the COVID-19 Data Repository"),
-    person("The COVID-19 Canada Open Data Working Group", role = "dtc", comment = "Owner of Canadian COVID-19 cases rates from the Covid19Canada data repository"),
-    person("Statistics Canada", role = "dtc", comment = "Owner of Canadian graduate employment income data from the Statistics Canada website"),
-    person("Google", role = "dtc", comment = "Collaborator on CLI data from the Google symptom surveys")
+    person("Jacob", "Bien", role = "ctb")
   )
 Description: A forecasting "framework" for creating epidemiological
     forecasts from versioned data. The framework is designed to be modular

From 48866ff4aca4fd51f3b1c4b47753bc733439b137 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 09:26:22 -0700
Subject: [PATCH 148/387] bake updates

Co-authored-by: Daniel McDonald <dajmcdon@gmail.com>
---
 R/step_growth_rate.R    | 2 +-
 R/step_lag_difference.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 8a3862e48..b3a712313 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -38,7 +38,7 @@
 #'
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
-#'   bake(covid_case_death_rates)
+#'   bake(new_data = NULL)
 step_growth_rate <-
   function(recipe,
            ...,
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index 2483b8b73..2b0af00f2 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -22,7 +22,7 @@
 #'
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
-#'   bake(covid_case_death_rates)
+#'   bake(new_data = NULL)
 step_lag_difference <-
   function(recipe,
            ...,

From 5267a710f792962c64be4a2020477b2f4a5c0438 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 09:36:11 -0700
Subject: [PATCH 149/387] undo styling on purr import

---
 R/import-standalone-purrr.R | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/R/import-standalone-purrr.R b/R/import-standalone-purrr.R
index 4ec36e128..623142a0e 100644
--- a/R/import-standalone-purrr.R
+++ b/R/import-standalone-purrr.R
@@ -169,9 +169,7 @@ every <- function(.x, .p, ...) {
   .p <- as_function(.p, env = global_env())
 
   for (i in seq_along(.x)) {
-    if (!rlang::is_true(.p(.x[[i]], ...))) {
-      return(FALSE)
-    }
+    if (!rlang::is_true(.p(.x[[i]], ...))) return(FALSE)
   }
   TRUE
 }
@@ -179,9 +177,7 @@ some <- function(.x, .p, ...) {
   .p <- as_function(.p, env = global_env())
 
   for (i in seq_along(.x)) {
-    if (rlang::is_true(.p(.x[[i]], ...))) {
-      return(TRUE)
-    }
+    if (rlang::is_true(.p(.x[[i]], ...))) return(TRUE)
   }
   FALSE
 }

From 1305af7bdd36f80248b57e4a063c8e530babbe08 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 09:57:48 -0700
Subject: [PATCH 150/387] depend on epidatasets, but don't reexport

---
 DESCRIPTION                               |   1 +
 NAMESPACE                                 |  10 --
 R/reexports-epidatasets.R                 | 199 ----------------------
 _pkgdown.yml                              |   5 -
 man/archive_cases_dv_subset_all_states.Rd |  67 --------
 man/can_prov_cases.Rd                     |  76 ---------
 man/case_death_rate_archive.Rd            |  68 --------
 man/cases_deaths_subset.Rd                |  79 ---------
 man/counts_subset.Rd                      |  68 --------
 man/county_smoothed_cli_comparison.Rd     | 109 ------------
 man/covid_case_death_rates.Rd             |  73 --------
 man/ctis_covid_behaviours.Rd              |  70 --------
 man/grad_employ_subset.Rd                 |  73 --------
 man/state_census.Rd                       |  59 -------
 man/step_growth_rate.Rd                   |   2 +-
 man/step_lag_difference.Rd                |   2 +-
 16 files changed, 3 insertions(+), 958 deletions(-)
 delete mode 100644 R/reexports-epidatasets.R
 delete mode 100644 man/archive_cases_dv_subset_all_states.Rd
 delete mode 100644 man/can_prov_cases.Rd
 delete mode 100644 man/case_death_rate_archive.Rd
 delete mode 100644 man/cases_deaths_subset.Rd
 delete mode 100644 man/counts_subset.Rd
 delete mode 100644 man/county_smoothed_cli_comparison.Rd
 delete mode 100644 man/covid_case_death_rates.Rd
 delete mode 100644 man/ctis_covid_behaviours.Rd
 delete mode 100644 man/grad_employ_subset.Rd
 delete mode 100644 man/state_census.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index f97363098..19b4480ac 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -25,6 +25,7 @@ URL: https://github.com/cmu-delphi/epipredict/,
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epiprocess (>= 0.9.0),
+    epidatasets,
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:
diff --git a/NAMESPACE b/NAMESPACE
index 62e00b074..5ee13f730 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -139,7 +139,6 @@ export(add_model)
 export(adjust_epi_recipe)
 export(adjust_frosting)
 export(apply_frosting)
-export(archive_cases_dv_subset_all_states)
 export(arx_args_list)
 export(arx_class_args_list)
 export(arx_class_epi_workflow)
@@ -148,17 +147,10 @@ export(arx_fcast_epi_workflow)
 export(arx_forecaster)
 export(autoplot)
 export(bake)
-export(can_prov_cases)
-export(case_death_rate_archive)
-export(cases_deaths_subset)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
 export(clean_f_name)
-export(counts_subset)
-export(county_smoothed_cli_comparison)
-export(covid_case_death_rates)
-export(ctis_covid_behaviours)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
@@ -177,7 +169,6 @@ export(flusight_hub_formatter)
 export(forecast)
 export(frosting)
 export(get_test_data)
-export(grad_employ_subset)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
@@ -207,7 +198,6 @@ export(remove_frosting)
 export(remove_model)
 export(slather)
 export(smooth_quantile_reg)
-export(state_census)
 export(step_adjust_latency)
 export(step_epi_ahead)
 export(step_epi_lag)
diff --git a/R/reexports-epidatasets.R b/R/reexports-epidatasets.R
deleted file mode 100644
index 30e3e41ae..000000000
--- a/R/reexports-epidatasets.R
+++ /dev/null
@@ -1,199 +0,0 @@
-#' @inherit epidatasets::grad_employ_subset description source references title
-#' @inheritSection epidatasets::grad_employ_subset Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::grad_employ_subset
-#'
-#' # works
-#' library(epipredict)
-#' grad_employ_subset
-#'
-#' # fails
-#' \dontrun{
-#' data(grad_employ_subset, package = "epipredict")
-#' }
-#' @export
-delayedAssign("grad_employ_subset", epidatasets::grad_employ_subset)
-
-#' @inherit epidatasets::covid_case_death_rates description source references title
-#' @inheritSection epidatasets::covid_case_death_rates Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::covid_case_death_rates
-#'
-#' # works
-#' library(epipredict)
-#' covid_case_death_rates
-#'
-#' # fails
-#' \dontrun{
-#' data(covid_case_death_rates, package = "epipredict")
-#' }
-#' @export
-delayedAssign("covid_case_death_rates", epidatasets::covid_case_death_rates)
-
-#' @inherit epidatasets::state_census description source references title
-#' @inheritSection epidatasets::state_census Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::state_census
-#'
-#' # works
-#' library(epipredict)
-#' state_census
-#'
-#' # fails
-#' \dontrun{
-#' data(state_census, package = "epipredict")
-#' }
-#' @export
-delayedAssign("state_census", epidatasets::state_census)
-
-#' @inherit epidatasets::counts_subset description source references title
-#' @inheritSection epidatasets::counts_subset Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::counts_subset
-#'
-#' # works
-#' library(epipredict)
-#' counts_subset
-#'
-#' # fails
-#' \dontrun{
-#' data(counts_subset, package = "epipredict")
-#' }
-#' @export
-delayedAssign("counts_subset", epidatasets::counts_subset)
-
-#' @inherit epidatasets::ctis_covid_behaviours description source references title
-#' @inheritSection epidatasets::ctis_covid_behaviours Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::ctis_covid_behaviours
-#'
-#' # works
-#' library(epipredict)
-#' ctis_covid_behaviours
-#'
-#' # fails
-#' \dontrun{
-#' data(ctis_covid_behaviours, package = "epipredict")
-#' }
-#' @export
-delayedAssign("ctis_covid_behaviours", epidatasets::ctis_covid_behaviours)
-
-#' @inherit epidatasets::county_smoothed_cli_comparison description source references title
-#' @inheritSection epidatasets::county_smoothed_cli_comparison Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::county_smoothed_cli_comparison
-#'
-#' # works
-#' library(epipredict)
-#' county_smoothed_cli_comparison
-#'
-#' # fails
-#' \dontrun{
-#' data(county_smoothed_cli_comparison, package = "epipredict")
-#' }
-#' @export
-delayedAssign("county_smoothed_cli_comparison", epidatasets::county_smoothed_cli_comparison)
-
-#' @inherit epidatasets::cases_deaths_subset description source references title
-#' @inheritSection epidatasets::cases_deaths_subset Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::cases_deaths_subset
-#'
-#' # works
-#' library(epipredict)
-#' cases_deaths_subset
-#'
-#' # fails
-#' \dontrun{
-#' data(cases_deaths_subset, package = "epipredict")
-#' }
-#' @export
-delayedAssign("cases_deaths_subset", epidatasets::cases_deaths_subset)
-
-#' @inherit epidatasets::case_death_rate_archive description source references title
-#' @inheritSection epidatasets::case_death_rate_archive Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::case_death_rate_archive
-#'
-#' # works
-#' library(epipredict)
-#' case_death_rate_archive
-#'
-#' # fails
-#' \dontrun{
-#' data(case_death_rate_archive, package = "epipredict")
-#' }
-#' @export
-delayedAssign("case_death_rate_archive", epidatasets::case_death_rate_archive)
-
-#' @inherit epidatasets::archive_cases_dv_subset_all_states description source references title
-#' @inheritSection epidatasets::archive_cases_dv_subset_all_states Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::archive_cases_dv_subset_all_states
-#'
-#' # works
-#' library(epipredict)
-#' archive_cases_dv_subset_all_states
-#'
-#' # fails
-#' \dontrun{
-#' data(archive_cases_dv_subset_all_states, package = "epipredict")
-#' }
-#' @export
-delayedAssign("archive_cases_dv_subset_all_states", epidatasets::archive_cases_dv_subset_all_states)
-
-#' @inherit epidatasets::can_prov_cases description source references title
-#' @inheritSection epidatasets::can_prov_cases Data dictionary
-#' @examples
-#' # Since this is a re-exported dataset, it cannot be loaded using
-#' # the `data()` function. `data()` looks for a file of the same name
-#' # in the `data/` directory, which doesn't exist in this package.
-#' # works
-#' epipredict::can_prov_cases
-#'
-#' # works
-#' library(epipredict)
-#' can_prov_cases
-#'
-#' # fails
-#' \dontrun{
-#' data(can_prov_cases, package = "epipredict")
-#' }
-#' @export
-delayedAssign("can_prov_cases", epidatasets::can_prov_cases)
diff --git a/_pkgdown.yml b/_pkgdown.yml
index ecc312bf1..9711baef3 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -121,8 +121,3 @@ reference:
   - title: Other utilities
     contents:
       - clean_f_name
-  - title: Included datasets
-    contents:
-      - covid_case_death_rates
-      - state_census
-      - grad_employ_subset
diff --git a/man/archive_cases_dv_subset_all_states.Rd b/man/archive_cases_dv_subset_all_states.Rd
deleted file mode 100644
index 78c7f387c..000000000
--- a/man/archive_cases_dv_subset_all_states.Rd
+++ /dev/null
@@ -1,67 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{archive_cases_dv_subset_all_states}
-\alias{archive_cases_dv_subset_all_states}
-\title{Daily COVID-19 doctor visits and cases from all states in archive format}
-\format{
-An object of class \code{epi_archive} of length 6.
-}
-\source{
-This object contains a modified part of the \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University} as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}. This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
-Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/doctor-visits.html}{From the COVIDcast Doctor Visits API}: The signal \code{percent_cli} is taken directly from the API without changes.
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}: \code{case_rate} signal was computed by Delphi from the original JHU-CSSE data by calculating moving averages of the preceding 7 days, so the signal for June 7 is the average of the underlying data for June 1 through 7, inclusive.
-\item Furthermore, the data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_archive}.
-}
-}
-\usage{
-archive_cases_dv_subset_all_states
-}
-\description{
-This data source is based on information about outpatient visits, provided
-to us by health system partners, and also contains confirmed COVID-19
-cases based on reports made available by the Center for Systems Science
-and Engineering at Johns Hopkins University. This example data ranges from
-June 1, 2020 to December 1, 2021, issued on dates from June 1, 2020 to December 1,
-2021. It includes all US states.
-
-It is used in the {epipredict} \code{sliding} article.
-}
-\section{Data dictionary}{
-
-
-The data in the \code{epi_archive$DT} attribute has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{version}{the time value specifying the version for each row of measurements. }
-\item{percent_cli}{percentage of doctor’s visits with CLI (COVID-like illness) computed from medical insurance claims}
-\item{case_rate}{7-day average signal of number of new confirmed cases due to COVID-19 per 100,000 population, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::archive_cases_dv_subset_all_states
-
-# works
-library(epipredict)
-archive_cases_dv_subset_all_states
-
-# fails
-\dontrun{
-data(archive_cases_dv_subset_all_states, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/can_prov_cases.Rd b/man/can_prov_cases.Rd
deleted file mode 100644
index 8499448b3..000000000
--- a/man/can_prov_cases.Rd
+++ /dev/null
@@ -1,76 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{can_prov_cases}
-\alias{can_prov_cases}
-\title{Canadian COVID-19 case rates}
-\format{
-An object of class \code{epi_archive} of length 6.
-}
-\source{
-This object contains a modified part of the COVID-19 Canada Open
-Data Working Group's
-\href{https://github.com/ccodwg/Covid19Canada}{Covid19Canada data repository} (archived).
-This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by the COVID-19 Canada Open Data Working Group. The COVID-19 Canada Open
-Data Working Group collected the data from publicly available sources such
-as government datasets and news releases.
-
-Modifications:
-\itemize{
-\item The case rate signal are calculated using the case count taken directly from the CCODWG
-\href{https://github.com/ccodwg/Covid19Canada}{ccodwg/Covid19Canada GitHub repository}
-and population data.
-\item Furthermore, the data has been limited to a very small number of rows, the
-signal names slightly altered, some province names replaced with abbreviations, and
-formatted into an \code{epi_archive}.
-}
-
-The population data used (but not included in the dataset itself) is from the
-\href{https://github.com/mountainMath/BCCovidSnippets/}{mountainMath/BCCovidSnippets GitHub repository}.
-}
-\usage{
-can_prov_cases
-}
-\description{
-Data set for all Canadian provinces and territories containing COVID-19
-case rates (COVID-19 cases per 100,000 people) derived from COVID-19 case
-counts as reported by the
-\href{https://opencovid.ca/}{COVID-19 Canada Open Data Working Group (CCODWG)}.
-Data is available both through the archived
-\href{https://github.com/ccodwg/Covid19Canada}{ccodwg/Covid19Canada GitHub repository}
-and the newer
-\href{https://github.com/ccodwg/CovidTimelineCanada}{ccodwg/CovidTimelineCanada GitHub repository},
-which also reports vaccine-related signals.
-}
-\section{Data dictionary}{
-
-
-The data in the \code{epi_archive$DT} attribute has columns:
-\describe{
-\item{version}{the time value specifying the version for each row of measurements.}
-\item{geo_value}{the province or territory associated with each row of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{case_rate}{number of new confirmed cases due to COVID-19 per 100,000 population, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::can_prov_cases
-
-# works
-library(epipredict)
-can_prov_cases
-
-# fails
-\dontrun{
-data(can_prov_cases, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/case_death_rate_archive.Rd b/man/case_death_rate_archive.Rd
deleted file mode 100644
index 6af5632c4..000000000
--- a/man/case_death_rate_archive.Rd
+++ /dev/null
@@ -1,68 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{case_death_rate_archive}
-\alias{case_death_rate_archive}
-\title{Daily COVID-19 case and death rates from all states in archive format}
-\format{
-An object of class \code{epi_archive} of length 6.
-}
-\source{
-This object contains a modified part of the \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University} as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}. This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
-Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:  The signals \code{case_rate} and \code{death_rate} are taken directly from the JHU CSSE GitHub repo without changes, served through the Delphi API.
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}: Averaged signals were computed from the original JHU-CSSE data by calculating moving averages of the preceding 7 days, so the signal for June 7 is the average of the underlying data for June 1 through 7, inclusive.
-\item Furthermore, the data has been limited to a specific time range, the
-signal names slightly altered, and formatted into an \code{epi_archive}.
-}
-}
-\usage{
-case_death_rate_archive
-}
-\description{
-Data set containing COVID-19 case and death rates (counts per 100000
-population) as reported by the Delphi API, based on reports made available
-by the Center for Systems Science and Engineering at Johns Hopkins
-University. This example data ranges from March 1, 2020 to November 30,
-2021, issued monthly on the first day of each month from September 1, 2020
-to December 1, 2021. It includes all US states, Washington DC, Guam, Puerto
-Rico, and the Virgin Islands.
-}
-\section{Data dictionary}{
-
-
-The data in the \code{epi_archive$DT} attribute has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{version}{the time value specifying the version for each row of measurements. }
-\item{case_rate}{Number of new confirmed cases due to COVID-19 per 100,000 population, daily}
-\item{case_rate_7d_av}{7-day average signal of number of new confirmed cases due to COVID-19 per 100,000 population, daily}
-\item{death_rate}{Number of new confirmed deaths due to COVID-19 per 100,000 population, daily}
-\item{death_rate_7d_av}{7-day average signal of number of new confirmed deaths due to COVID-19 per 100,000 population, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::case_death_rate_archive
-
-# works
-library(epipredict)
-case_death_rate_archive
-
-# fails
-\dontrun{
-data(case_death_rate_archive, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/cases_deaths_subset.Rd b/man/cases_deaths_subset.Rd
deleted file mode 100644
index b74e245ae..000000000
--- a/man/cases_deaths_subset.Rd
+++ /dev/null
@@ -1,79 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{cases_deaths_subset}
-\alias{cases_deaths_subset}
-\title{Subset of JHU daily state COVID-19 cases and deaths from 6 states}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 4026 rows and 6 columns.
-}
-\source{
-This object contains a modified part of the
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
-This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by the Johns Hopkins University on behalf of its Center for Systems Science
-in Engineering. Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
-The case signal is taken directly from the JHU CSSE
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}.
-The rate signals were computed by Delphi using Census population data.
-The 7-day average signals were computed by Delphi by calculating moving
-averages of the preceding 7 days, so the signal for June 7 is the
-average of the underlying data for June 1 through 7, inclusive.
-\item Furthermore, the data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-}
-\usage{
-cases_deaths_subset
-}
-\description{
-This data source of confirmed COVID-19 cases and deaths is based on reports
-made available by the Center for Systems Science and Engineering at Johns
-Hopkins University. This example data is a snapshot as of March 20, 2024, and
-ranges from March 1, 2020 to December 31, 2021. It is limited to California,
-Florida, Texas, New York, Georgia, and Pennsylvania.
-
-It is used in the {epiprocess} growth rate and \code{epi_slide} vignettes.
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row
-of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{case_rate_7d_av}{7-day average signal of number of new
-confirmed COVID-19 cases per 100,000 population, daily}
-\item{death_rate_7d_av}{7-day average signal of number of new confirmed
-deaths due to COVID-19 per 100,000 population, daily}
-\item{cases}{Number of new confirmed COVID-19 cases, daily}
-\item{cases_7d_av}{7-day average signal of number of new confirmed
-COVID-19 cases, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::cases_deaths_subset
-
-# works
-library(epipredict)
-cases_deaths_subset
-
-# fails
-\dontrun{
-data(cases_deaths_subset, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/counts_subset.Rd b/man/counts_subset.Rd
deleted file mode 100644
index 5ac956645..000000000
--- a/man/counts_subset.Rd
+++ /dev/null
@@ -1,68 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{counts_subset}
-\alias{counts_subset}
-\title{Subset of COVID-19 incident cases and deaths from 5 states}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
-}
-\source{
-This object contains a modified part of the \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
-This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
-Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
-The signals are taken directly from the JHU CSSE
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
-without changes.
-\item Furthermore, the data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-}
-\usage{
-counts_subset
-}
-\description{
-Data set for 5 states containing COVID-19 Incident Cases and Deaths as
-reported by JHU-CSSE and downloaded from the CMU Delphi COVIDcast Epidata
-API. This example data is a snapshot as of March 20, 2024, and
-ranges from June 4, 2021 to December 31, 2021. It
-is limited to California, Florida, Texas, New Jersey, and New York.
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row
-of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{cases}{Number of new confirmed COVID-19 cases, daily}
-\item{deaths}{Number of new confirmed COVID-19 deaths, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::counts_subset
-
-# works
-library(epipredict)
-counts_subset
-
-# fails
-\dontrun{
-data(counts_subset, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/county_smoothed_cli_comparison.Rd b/man/county_smoothed_cli_comparison.Rd
deleted file mode 100644
index 64c004330..000000000
--- a/man/county_smoothed_cli_comparison.Rd
+++ /dev/null
@@ -1,109 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{county_smoothed_cli_comparison}
-\alias{county_smoothed_cli_comparison}
-\title{Percent CLI from different surveys, compared to ground truth COVID incidence in a subset of counties}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 63840 rows and 5 columns.
-}
-\source{
-This object contains a modified part of the \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University} as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}. This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
-Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:  The signal \code{confirmed_cumulative_num} was used to determine eligibility for inclusion. The signal \code{confirmed_7dav_incidence_prop} was computed by Delphi from the original JHU-CSSE data by calculating moving averages of the preceding 7 days, so the signal for June 7 is the average of the underlying data for June 1 through 7, inclusive.
-\item Furthermore, the data has been limited to a specific time range, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-
-This object contains a modified part of the
-\href{https://cmu-delphi.github.io/delphi-epidata/symptom-survey/#covid-19-trends-and-impact-survey}{data
-aggregations in the API} that are prepared from the
-\href{https://www.pnas.org/doi/full/10.1073/pnas.2111454118}{COVID-19
-Trends and Impact Survey}; see the first link for more information on
-citing in publications.
-The data is made available via the
-\href{https://cmu-delphi.github.io/delphi-epidata/}{Delphi Epidata API}.
-
-These aggregations are licensed under the terms of
-the \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons
-Attribution license}.
-
-Modifications:
-\itemize{
-\item The data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-
-This object contains a modified part of the
-\href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/google-survey.html}{Google symptom surveys}.
-Aggregations based on the survey are licensed under the terms of
-the \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons
-Attribution license}.
-
-Modifications:
-\itemize{
-\item The data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-}
-\usage{
-county_smoothed_cli_comparison
-}
-\description{
-Data set for more than 400 US counties containing CLI
-(COVID-19-like-illness) incidence derived from two surveys, and a reference signal as
-reported by JHU CSSE. This example data is a snapshot as of September 21,
-2020, and ranges from April 11, 2020 to September 01, 2020.
-
-The reference signal \code{case} is based on reports made available
-by the Center for Systems Science and Engineering at Johns Hopkins
-University.
-
-One survey was
-\href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/google-survey.html}{run by Google},
-in partnership with Delphi.
-
-The other survey, the
-\href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html}{COVID-19 Trends and Impact Survey},
-was run by Delphi in collaboration with Facebook.
-
-Data is reported for counties that had at least 200 cumulative COVID-19 cases
-on May 14, 2020, according to JHU CSSE.
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{The 5-digit county FIPS code associated with each
-row of measurements.}
-\item{time_value}{The time value, a date in YYYY-MM-DD format}
-\item{goog}{Seven-day average of CLI (covid-like-illness) cases from the Google survey}
-\item{fb}{Seven-day average of CLI (covid-like-illness) cases from CTIS}
-\item{case}{Reference signal. Seven-day average of CLI (covid-like-illness) cases}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::county_smoothed_cli_comparison
-
-# works
-library(epipredict)
-county_smoothed_cli_comparison
-
-# fails
-\dontrun{
-data(county_smoothed_cli_comparison, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/covid_case_death_rates.Rd b/man/covid_case_death_rates.Rd
deleted file mode 100644
index 19d0da2e1..000000000
--- a/man/covid_case_death_rates.Rd
+++ /dev/null
@@ -1,73 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{covid_case_death_rates}
-\alias{covid_case_death_rates}
-\title{JHU daily COVID-19 cases and deaths rates from all states}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 20496 rows and 4 columns.
-}
-\source{
-This object contains a modified part of the
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
-This data set is licensed under the terms of the
-\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
-by the Johns Hopkins University on behalf of its Center for Systems Science
-in Engineering. Copyright Johns Hopkins University 2020.
-
-Modifications:
-\itemize{
-\item \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
-These signals are taken directly from the JHU CSSE
-\href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
-without changes. The 7-day average signals are computed by Delphi by
-calculating moving averages of the preceding 7 days, so the signal for
-June 7 is the average of the underlying data for June 1 through 7,
-inclusive.
-}
-}
-\usage{
-covid_case_death_rates
-}
-\description{
-This data source of confirmed COVID-19 cases and deaths is based on reports
-made available by the Center for Systems Science and Engineering at Johns
-Hopkins University, as downloaded from the CMU Delphi COVIDcast Epidata
-API. This example data is a snapshot as of May 31, 2022, and
-ranges from December 31, 2020 to December 31, 2021. It
-includes all states.
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row
-of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{case_rate}{7-day average signal of number of new
-confirmed COVID-19 cases per 100,000 population, daily}
-\item{death_rate}{7-day average signal of number of new confirmed
-deaths due to COVID-19 per 100,000 population, daily}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::covid_case_death_rates
-
-# works
-library(epipredict)
-covid_case_death_rates
-
-# fails
-\dontrun{
-data(covid_case_death_rates, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/ctis_covid_behaviours.Rd b/man/ctis_covid_behaviours.Rd
deleted file mode 100644
index 35ae10d8a..000000000
--- a/man/ctis_covid_behaviours.Rd
+++ /dev/null
@@ -1,70 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{ctis_covid_behaviours}
-\alias{ctis_covid_behaviours}
-\title{Subset of CTIS COVID-19-related behaviours from 5 states}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1055 rows and 4 columns.
-}
-\source{
-This object contains a modified part of the
-\href{https://cmu-delphi.github.io/delphi-epidata/symptom-survey/#covid-19-trends-and-impact-survey}{data
-aggregations in the API} that are prepared from the
-\href{https://www.pnas.org/doi/full/10.1073/pnas.2111454118}{COVID-19
-Trends and Impact Survey}; see the first link for more information on
-citing in publications.
-The data is made available via the
-\href{https://cmu-delphi.github.io/delphi-epidata/}{Delphi Epidata API}.
-
-These aggregations are licensed under the terms of
-the \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons
-Attribution license}.
-
-Modifications:
-\itemize{
-\item The data has been limited to a very small number of rows, the
-signal names slightly altered, and formatted into an \code{epi_df}.
-}
-}
-\usage{
-ctis_covid_behaviours
-}
-\description{
-Data set for a handful of states on masking and distancing behaviours
-during the COVID-19 Pandemic, and downloaded from the CMU Delphi COVIDcast
-Epidata API. This example data is a snapshot as of March 20, 2024, and
-ranges from June 4, 2021 to December 31, 2021.
-It is limited to California, Florida, Texas, New Jersey, and New York.
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{the geographic value associated with each row
-of measurements.}
-\item{time_value}{the time value associated with each row of measurements.}
-\item{masking}{Estimated percentage of people who wore a mask for most or all of the time while in public in the past 7 days; those not in public in the past 7 days are not counted.}
-\item{distancing}{Estimated percentage of respondents who reported that all or most people they encountered in public in the past 7 days maintained a distance of at least 6 feet. Respondents who said that they have not been in public for the past 7 days are excluded.}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::ctis_covid_behaviours
-
-# works
-library(epipredict)
-ctis_covid_behaviours
-
-# fails
-\dontrun{
-data(ctis_covid_behaviours, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
deleted file mode 100644
index e87c760d3..000000000
--- a/man/grad_employ_subset.Rd
+++ /dev/null
@@ -1,73 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{grad_employ_subset}
-\alias{grad_employ_subset}
-\title{Subset of Statistics Canada median employment income for postsecondary graduates}
-\format{
-An object of class \code{epi_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 1445 rows and 7 columns.
-}
-\source{
-This object contains modified data adapted from
-Statistics Canada, \href{https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501}{
-Table 37-10-0115-01 Characteristics and median employment income of
-longitudinal cohorts of postsecondary graduates two and five years after
-graduation, by educational qualification and field of study
-(primary groupings)}. This does not constitute an endorsement by Statistics Canada of this product.
-
-The data is licensed under the terms of the
-\href{https://www.statcan.gc.ca/en/reference/licence}{Statistics Canada Open License}.
-
-Modifications:
-\itemize{
-\item Only provincial and territorial regions are kept.
-\item Only age group, field of study, and educational qualification are kept as
-covariates. For the remaining covariates, we keep aggregated values and
-drop the level-specific rows.
-\item No modifications were made to the time range of the data.
-}
-}
-\usage{
-grad_employ_subset
-}
-\description{
-Data set for all territories (aggregated) and all 10 provinces containing
-yearly income data for postsecondary graduates as reported by Statistics
-Canada, downloaded from the Statistics Canada website at
-www.statcan.gc.ca. This example data is a snapshot as of September 18,
-2024, and ranges from 2010 to 2017 (yearly).
-}
-\section{Data dictionary}{
-
-
-The data has columns:
-\describe{
-\item{geo_value}{The province in Canada associated with each
-row of measurements.}
-\item{time_value}{The time value, a year integer in YYYY format}
-\item{edu_qual}{The education qualification}
-\item{age_group}{The age group; either 15 to 34 or 35 to 64}
-\item{num_graduates}{The number of graduates for the given row of characteristics}
-\item{med_income_2y}{The median employment income two years after graduation}
-\item{med_income_5y}{The median employment income five years after graduation}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::grad_employ_subset
-
-# works
-library(epipredict)
-grad_employ_subset
-
-# fails
-\dontrun{
-data(grad_employ_subset, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/state_census.Rd b/man/state_census.Rd
deleted file mode 100644
index e1121268c..000000000
--- a/man/state_census.Rd
+++ /dev/null
@@ -1,59 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/reexports-epidatasets.R
-\docType{data}
-\name{state_census}
-\alias{state_census}
-\title{State population data}
-\format{
-An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 57 rows and 4 columns.
-}
-\source{
-This object is derived from several datasets from the United States
-Census Bureau, Population Division, at
-\url{https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.pdf},
-\url{https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-puerto-rico-municipios.html},
-and \url{https://www.census.gov/data/tables/2010/dec/2010-island-areas.html}.
-It is made available through the \code{covidcast} package. This data is
-public domain.
-}
-\usage{
-state_census
-}
-\description{
-Data set on state populations, from the 2019 US Census.
-}
-\section{Data dictionary}{
-
-
-The data includes 57 regions (all US states, the United
-States as a whole, the District of Columbia, Puerto Rico Commonwealth,
-American Samoa, Guam, the U.S. Virgin Islands, and the Northern Mariana
-Islands) with columns:
-
-\describe{
-\item{fips}{2-digit FIPS code}
-\item{name}{Full name of the state or territory}
-\item{pop}{Estimate of the location's resident population in
-2019.}
-\item{abbr}{Postal abbreviation for the location}
-}
-
-}
-
-\examples{
-# Since this is a re-exported dataset, it cannot be loaded using
-# the `data()` function. `data()` looks for a file of the same name
-# in the `data/` directory, which doesn't exist in this package.
-# works
-epipredict::state_census
-
-# works
-library(epipredict)
-state_census
-
-# fails
-\dontrun{
-data(state_census, package = "epipredict")
-}
-}
-\keyword{datasets}
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index 7773c6062..12963f8da 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -79,7 +79,7 @@ r
 
 r \%>\%
   prep(covid_case_death_rates) \%>\%
-  bake(covid_case_death_rates)
+  bake(new_data = NULL)
 }
 \seealso{
 Other row operation steps: 
diff --git a/man/step_lag_difference.Rd b/man/step_lag_difference.Rd
index a156ae42d..6151bee84 100644
--- a/man/step_lag_difference.Rd
+++ b/man/step_lag_difference.Rd
@@ -54,7 +54,7 @@ r
 
 r \%>\%
   prep(covid_case_death_rates) \%>\%
-  bake(covid_case_death_rates)
+  bake(new_data = NULL)
 }
 \seealso{
 Other row operation steps: 

From ac145f3043a9e11e62a745821a94314220bdf9b8 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Tue, 1 Oct 2024 21:55:24 -0700
Subject: [PATCH 151/387] doc: update sliding vignette * rename to backtesting,
 simplify * move out of articles and into dropdown

---
 DESCRIPTION                            |   2 +-
 DEVELOPMENT.md                         |  70 +++-
 _pkgdown.yml                           |  44 +--
 vignettes/.gitignore                   |   1 +
 vignettes/_common.R                    |  23 ++
 vignettes/articles/sliding.Rmd         | 460 ------------------------
 vignettes/arx-classifier.Rmd           |  11 +-
 vignettes/backtesting.Rmd              | 467 +++++++++++++++++++++++++
 vignettes/epipredict.Rmd               |   7 +-
 vignettes/panel-data.Rmd               | 264 +++++++-------
 vignettes/preprocessing-and-models.Rmd |  25 +-
 vignettes/update.Rmd                   |   7 +-
 12 files changed, 709 insertions(+), 672 deletions(-)
 create mode 100644 vignettes/_common.R
 delete mode 100644 vignettes/articles/sliding.Rmd
 create mode 100644 vignettes/backtesting.Rmd

diff --git a/DESCRIPTION b/DESCRIPTION
index c580b872c..274fef04c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.2
+Version: 0.1.3
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 8bc251e77..f710c2842 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -1,10 +1,8 @@
 ## Setting up the development environment
 
 ```r
-install.packages(c('devtools', 'pkgdown', 'styler', 'lintr')) # install dev dependencies
-devtools::install_deps(dependencies = TRUE) # install package dependencies
-devtools::document() # generate package meta data and man files
-devtools::build() # build package
+install.packages(c('devtools', 'pkgdown', 'styler', 'lintr', 'pak')) # install dev dependencies
+pak::pkg_install(".") # install package and dependencies
 ```
 
 ## Validating the package
@@ -13,35 +11,71 @@ devtools::build() # build package
 styler::style_pkg() # format code
 lintr::lint_package() # lint code
 
+devtools::check() # run R CMD check, which runs everything below
+devtools::document() # generate package meta data and man files
 devtools::test() # test package
-devtools::check() # check package for errors
+devtools::build_vignettes() # build vignettes only
+devtools::run_examples() # run doc examples
+devtools::check(vignettes = FALSE) # check package without vignettes
 ```
 
 ## Developing the documentation site
 
-The [documentation site](https://cmu-delphi.github.io/epipredict/) is built off of the `main` branch. The `dev` version of the site is available at https://cmu-delphi.github.io/epipredict/dev.
-
-The documentation site can be previewed locally by running in R
-
-```r
-pkgdown::build_site(preview=TRUE)
-```
+Our CI builds two versions of the documentation:
 
-The `main` version is available at `file:///<local path>/epidatr/epipredict/index.html` and `dev` at `file:///<local path>/epipredict/docs/dev/index.html`.
+- https://cmu-delphi.github.io/epipredict/ from the `main` branch and
+- https://cmu-delphi.github.io/epipredict/dev from the `dev` branch.
 
-You can also build the docs manually and launch the site with python. From the terminal, this looks like
+Commands for developing the documentation site:
 
-```bash
+```sh
+# Basic build and preview
 R -e 'pkgdown::clean_site()'
 R -e 'devtools::document()'
 R -e 'pkgdown::build_site()'
-python -m http.server -d docs
 ```
 
+If you work without R Studio and want to iterate on documentation, you might
+find [this
+script](https://gist.github.com/gadenbuie/d22e149e65591b91419e41ea5b2e0621)
+helpful.
+
 ## Versioning
 
 Please follow the guidelines in the [PR template document](.github/pull_request_template.md).
 
-## Release process
+## Planned CRAN release process
+
+Open a release issue and then copy and follow this checklist in the issue (modified from the checklist generated by `usethis::use_release_issue(version = "1.0.2")`):
+
+- [ ] `git pull` on `dev` branch.
+- [ ] Make sure all changes are committed and pushed.
+- [ ] Check [current CRAN check results](https://cran.rstudio.org/web/checks/check_results_epipredict.html).
+- [ ] `devtools::check(".", manual = TRUE, env_vars = c(NOT_CRAN = "false"))`.
+  - Aim for 10/10, no notes.
+- [ ] If check works well enough, merge to main. Otherwise open a PR to fix up.
+- [ ] [Polish NEWS](https://github.com/cmu-delphi/epipredict/blob/dev/NEWS.md).
+  - Some [guidelines](https://style.tidyverse.org/news.html#news-release).
+- [ ] `git checkout main`
+- [ ] `git pull`
+- [ ] `urlchecker::url_check()`.
+  - This may choke on the MIT license url, and that's ok.
+- [ ] `devtools::build_readme()`
+- [ ] `devtools::check_win_devel()`
+- [ ] Have maintainer ("cre" in description) check email for problems.
+- [ ] `revdepcheck::revdep_check(num_workers = 4)`.
+  - This may choke, it is very sensitive to the binary versions of packages on a given system. Either bypass or ask someone else to run it if you're concerned.
+- [ ] Update `cran-comments.md`
+- [ ] PR with any changes (and go through the list again) into `dev` and run through the list again.
+
+Submit to CRAN:
+
+- [ ] `devtools::submit_cran()`.
+- [ ] Maintainer approves email.
+
+Wait for CRAN...
 
-TBD
+- [ ] If accepted :tada:, move to next steps. If rejected, fix and resubmit.
+- [ ] Open and merge a PR containing any updates made to `main` back to `dev`.
+- [ ] `usethis::use_github_release(publish = FALSE)` (publish off, otherwise it won't push) will create a draft release based on the commit hash in CRAN-SUBMISSION and push a tag to the GitHub repo.
+- [ ] Go to the repo, verify the release notes, and publish when ready.
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 468da62ac..5222999b6 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -22,20 +22,20 @@ navbar:
   type: light
 
 articles:
-- title: Get started
-  navbar: ~
-  contents:
-  - epipredict
-  - preprocessing-and-models
-  - arx-classifier
-  - update
+  - title: Get started
+    navbar: ~
+    contents:
+      - epipredict
+      - preprocessing-and-models
+      - backtesting
+      - arx-classifier
+      - update
 
-- title: Advanced methods
-  contents:
-  - articles/sliding
-  - articles/smooth-qr
-  - articles/symptom-surveys
-  - panel-data
+  - title: Advanced methods
+    contents:
+      - articles/smooth-qr
+      - articles/symptom-surveys
+      - panel-data
 
 repo:
   url:
@@ -79,15 +79,15 @@ reference:
       - grf_quantiles
   - title: Custom panel data forecasting workflows
     contents:
-    - epi_recipe
-    - epi_workflow
-    - add_epi_recipe
-    - adjust_epi_recipe
-    - Add_model
-    - predict.epi_workflow
-    - fit.epi_workflow
-    - augment.epi_workflow
-    - forecast.epi_workflow
+      - epi_recipe
+      - epi_workflow
+      - add_epi_recipe
+      - adjust_epi_recipe
+      - Add_model
+      - predict.epi_workflow
+      - fit.epi_workflow
+      - augment.epi_workflow
+      - forecast.epi_workflow
 
   - title: Epi recipe preprocessing steps
     contents:
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
index 324ceaf7e..d255e9687 100644
--- a/vignettes/.gitignore
+++ b/vignettes/.gitignore
@@ -1,3 +1,4 @@
 *.html
 *_cache/
 *.R
+!_common.R
diff --git a/vignettes/_common.R b/vignettes/_common.R
new file mode 100644
index 000000000..3bd7c929d
--- /dev/null
+++ b/vignettes/_common.R
@@ -0,0 +1,23 @@
+knitr::opts_chunk$set(
+  digits = 3,
+  comment = "#>",
+  collapse = TRUE,
+  cache = TRUE,
+  dev.args = list(bg = "transparent"),
+  dpi = 300,
+  cache.lazy = FALSE,
+  out.width = "90%",
+  fig.align = "center",
+  fig.width = 9,
+  fig.height = 6
+)
+ggplot2::theme_set(ggplot2::theme_bw())
+options(
+  dplyr.print_min = 6,
+  dplyr.print_max = 6,
+  pillar.max_footer_lines = 2,
+  pillar.min_chars = 15,
+  stringr.view_n = 6,
+  pillar.bold = TRUE,
+  width = 77
+)
diff --git a/vignettes/articles/sliding.Rmd b/vignettes/articles/sliding.Rmd
deleted file mode 100644
index 908caf307..000000000
--- a/vignettes/articles/sliding.Rmd
+++ /dev/null
@@ -1,460 +0,0 @@
----
-title: "Demonstrations of sliding AR and ARX forecasters"
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  warning = FALSE,
-  message = FALSE,
-  cache = TRUE
-)
-```
-
-```{r pkgs}
-library(epipredict)
-library(epidatr)
-library(data.table)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-library(magrittr)
-library(purrr)
-```
-
-# Demonstrations of sliding AR and ARX forecasters
-
-A key function from the epiprocess package is `epix_slide()` (refer to the
-following vignette for the basics of the function: ["Work with archive objects
-and data
-revisions"](https://cmu-delphi.github.io/epiprocess/articles/archive.html))
-which allows performing version-aware computations. That is, the function only
-uses data that would have been available as of time t for that reference time.
-
-In this vignette, we use `epix_slide()` for backtesting our `arx_forecaster` on
-historical COVID-19 case data from the US and from Canada. We first examine the
-results from a version-unaware forecaster, comparing two different fitting
-engines and then we contrast this with version-aware forecasting. The former
-will proceed by constructing an `epi_archive` that erases its version
-information and then use `epix_slide()` to forecast the future. The latter will
-keep the versioned data and proceed similarly by using `epix_slide()` to
-forecast the future.
-
-## Comparing different forecasting engines
-
-### Example using CLI and case data from US states
-
-First, we download the version history (ie. archive) of the percentage of
-doctor's visits with CLI (COVID-like illness) computed from medical insurance
-claims and the number of new confirmed COVID-19 cases per 100,000 population
-(daily) for all 50 states from the COVIDcast API.
-
-<details>
-
-<summary>Load a data archive</summary>
-
-We process as before, with the modification that we use `sync = locf` in
-`epix_merge()` so that the last version of each observation can be carried
-forward to extrapolate unavailable versions for the less up-to-date input
-archive.
-
-```{r grab-epi-data}
-theme_set(theme_bw())
-
-y <- readRDS("all_states_covidcast_signals.rds") %>%
-  purrr::map(~ select(.x, geo_value, time_value, version = issue, value))
-
-x <- epix_merge(
-  y[[1]] %>% rename(percent_cli = value) %>% as_epi_archive(compactify = FALSE),
-  y[[2]] %>% rename(case_rate = value) %>% as_epi_archive(compactify = FALSE),
-  sync = "locf",
-  compactify = TRUE
-)
-rm(y)
-```
-
-</details>
-
-We then obtaining the latest snapshot of the data and proceed to fake the
-version information by setting `version = time_value`. This has the effect of
-obtaining data that arrives exactly at the day of the time_value.
-
-```{r arx-kweek-preliminaries, warning = FALSE}
-# Latest snapshot of data, and forecast dates
-x_latest <- epix_as_of(x, version = max(x$versions_end)) %>%
-  mutate(version = time_value) %>%
-  as_epi_archive()
-fc_time_values <- seq(
-  from = as.Date("2020-08-01"),
-  to = as.Date("2021-11-01"),
-  by = "1 month"
-)
-aheads <- c(7, 14, 21, 28)
-
-forecast_k_week_ahead <- function(epi_archive, outcome, predictors, ahead = 7, engine) {
-  epi_archive %>%
-    epix_slide(
-      .f = function(x, gk, rtv) {
-        arx_forecaster(
-          x, outcome, predictors, engine,
-          args_list = arx_args_list(ahead = ahead)
-        )$predictions %>%
-          mutate(engine_type = engine$engine) %>%
-          pivot_quantiles_wider(.pred_distn)
-      },
-      .before = 120,
-      .versions = fc_time_values
-    )
-}
-```
-
-```{r make-arx-kweek}
-# Generate the forecasts and bind them together
-fc <- bind_rows(
-  map(aheads, ~ forecast_k_week_ahead(
-    x_latest,
-    outcome = "case_rate",
-    predictors = c("case_rate", "percent_cli"),
-    ahead = .x,
-    engine = linear_reg()
-  )),
-  map(aheads, ~ forecast_k_week_ahead(
-    x_latest,
-    outcome = "case_rate",
-    predictors = c("case_rate", "percent_cli"),
-    ahead = .x,
-    engine = rand_forest(mode = "regression")
-  ))
-)
-```
-
-Here, `arx_forecaster()` does all the heavy lifting. It creates leads of the
-target (respecting time stamps and locations) along with lags of the features
-(here, the response and doctors visits), estimates a forecasting model using the
-specified engine, creates predictions, and non-parametric confidence bands.
-
-To see how the predictions compare, we plot them on top of the latest case
-rates. Note that even though we've fitted the model on all states, we'll just
-display the results for two states, California (CA) and Florida (FL), to get a
-sense of the model performance while keeping the graphic simple.
-
-<details>
-
-<summary>Code for plotting</summary>
-```{r plot-arx, message = FALSE, warning = FALSE}
-fc_cafl <- fc %>%
-  tibble() %>%
-  filter(geo_value %in% c("ca", "fl"))
-x_latest_cafl <- x_latest$DT %>%
-  tibble() %>%
-  filter(geo_value %in% c("ca", "fl"))
-
-p1 <- ggplot(fc_cafl, aes(target_date, group = forecast_date, fill = engine_type)) +
-  geom_line(
-    data = x_latest_cafl, aes(x = time_value, y = case_rate),
-    inherit.aes = FALSE, color = "gray50"
-  ) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`), alpha = 0.4) +
-  geom_line(aes(y = .pred)) +
-  geom_point(aes(y = .pred), size = 0.5) +
-  geom_vline(aes(xintercept = forecast_date), linetype = 2, alpha = 0.5) +
-  facet_grid(vars(geo_value), vars(engine_type), scales = "free") +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
-  scale_fill_brewer(palette = "Set1") +
-  labs(x = "Date", y = "Reported COVID-19 case rates") +
-  theme(legend.position = "none")
-```
-</details>
-
-```{r show-plot1, fig.width = 9, fig.height = 6, echo=FALSE}
-p1
-```
-
-For the two states of interest, simple linear regression clearly performs better
-than random forest in terms of accuracy of the predictions and does not result
-in such in overconfident predictions (overly narrow confidence bands). Though,
-in general, neither approach produces amazingly accurate forecasts. This could
-be because the behaviour is rather different across states and the effects of
-other notable factors such as age and public health measures may be important to
-account for in such forecasting. Including such factors as well as making
-enhancements such as correcting for outliers are some improvements one could
-make to this simple model.[^1]
-
-[^1]: Note that, despite the above caveats, simple models like this tend to
-out-perform many far more complicated models in the online Covid forecasting due
-to those models high variance predictions.
-
-
-### Example using case data from Canada
-
-<details>
-
-<summary>Data and forecasts. Similar to the above.</summary>
-
-By leveraging the flexibility of `epiprocess`, we can apply the same techniques
-to data from other sources. Since some collaborators are in British Columbia,
-Canada, we'll do essentially the same thing for Canada as we did above.
-
-The [COVID-19 Canada Open Data Working Group](https://opencovid.ca/) collects
-daily time series data on COVID-19 cases, deaths, recoveries, testing and
-vaccinations at the health region and province levels. Data are collected from
-publicly available sources such as government datasets and news releases.
-Unfortunately, there is no simple versioned source, so we have created our own
-from the Github commit history.
-
-First, we load versioned case rates at the provincial level. After converting
-these to 7-day averages (due to highly variable provincial reporting
-mismatches), we then convert the data to an `epi_archive` object, and extract
-the latest version from it. Finally, we run the same forcasting exercise as for
-the American data, but here we compare the forecasts produced from using simple
-linear regression with those from using boosted regression trees.
-
-```{r get-can-fc, warning = FALSE}
-# source("drafts/canada-case-rates.R)
-can <- readRDS(system.file(
-  "extdata", "can_prov_cases.rds",
-  package = "epipredict", mustWork = TRUE
-)) %>%
-  group_by(version, geo_value) %>%
-  arrange(time_value) %>%
-  mutate(cr_7dav = RcppRoll::roll_meanr(case_rate, n = 7L)) %>%
-  as_epi_archive(compactify = TRUE)
-
-can_latest <- epix_as_of(can, version = max(can$DT$version)) %>%
-  mutate(version = time_value) %>%
-  as_epi_archive()
-
-# Generate the forecasts, and bind them together
-can_fc <- bind_rows(
-  map(
-    aheads,
-    ~ forecast_k_week_ahead(can_latest, "cr_7dav", "cr_7dav", .x, linear_reg())
-  ),
-  map(
-    aheads,
-    ~ forecast_k_week_ahead(
-      can_latest, "cr_7dav", "cr_7dav", .x,
-      boost_tree(mode = "regression", trees = 20)
-    )
-  )
-)
-```
-
-The figures below shows the results for all of the provinces.
-
-```{r plot-can-fc-lr, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
-ggplot(
-  can_fc %>% filter(engine_type == "lm"),
-  aes(x = target_date, group = forecast_date)
-) +
-  coord_cartesian(xlim = lubridate::ymd(c("2020-12-01", NA))) +
-  geom_line(
-    data = can_latest$DT %>% tibble(), aes(x = time_value, y = cr_7dav),
-    inherit.aes = FALSE, color = "gray50"
-  ) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = geo_value),
-    alpha = 0.4
-  ) +
-  geom_line(aes(y = .pred)) +
-  geom_point(aes(y = .pred), size = 0.5) +
-  geom_vline(aes(xintercept = forecast_date), linetype = 2, alpha = 0.5) +
-  facet_wrap(~geo_value, scales = "free_y", ncol = 3) +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
-  labs(
-    title = "Using simple linear regression", x = "Date",
-    y = "Reported COVID-19 case rates"
-  ) +
-  theme(legend.position = "none")
-```
-
-```{r plot-can-fc-boost, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
-ggplot(
-  can_fc %>% filter(engine_type == "xgboost"),
-  aes(x = target_date, group = forecast_date)
-) +
-  coord_cartesian(xlim = lubridate::ymd(c("2020-12-01", NA))) +
-  geom_line(
-    data = can_latest$DT %>% tibble(), aes(x = time_value, y = cr_7dav),
-    inherit.aes = FALSE, color = "gray50"
-  ) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = geo_value),
-    alpha = 0.4
-  ) +
-  geom_line(aes(y = .pred)) +
-  geom_point(aes(y = .pred), size = 0.5) +
-  geom_vline(aes(xintercept = forecast_date), linetype = 2, alpha = 0.5) +
-  facet_wrap(~geo_value, scales = "free_y", ncol = 3) +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
-  labs(
-    title = "Using boosted regression trees", x = "Date",
-    y = "Reported COVID-19 case rates"
-  ) +
-  theme(legend.position = "none")
-```
-
-Both approaches tend to produce quite volatile forecasts (point predictions)
-and/or are overly confident (very narrow bands), particularly when boosted
-regression trees are used. But as this is meant to be a simple demonstration of
-sliding with different engines in `arx_forecaster`, we may devote another
-vignette to work on improving the predictive modelling using the suite of tools
-available in `{epipredict}`.
-
-</details>
-
-## Version-aware forecasting
-
-### Example using case data from US states
-
-We will now employ a forecaster that uses properly-versioned data (that would
-have been available in real-time) to forecast the 7 day average of future
-COVID-19 case rates from current and past COVID-19 case rates and death rates
-for all states. That is, we can make forecasts on the archive, `x`, and compare
-those to forecasts on the latest data, `x_latest` using the same general set-up
-as above. Note that in this example, we use a geo-pooled approach (using
-combined data from all US states and territories) to train our model.
-
-<details>
-
-<summary>Download data using `{epidatr}`</summary>
-```{r load-data, eval=FALSE}
-# loading in the data
-states <- "*"
-
-confirmed_incidence_prop <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "confirmed_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20200301, 20211231),
-  geo_values = states,
-  issues = epirange(20000101, 20221231)
-) %>%
-  select(geo_value, time_value, version = issue, case_rate = value) %>%
-  arrange(geo_value, time_value) %>%
-  as_epi_archive(compactify = FALSE)
-
-deaths_incidence_prop <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "deaths_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20200301, 20211231),
-  geo_values = states,
-  issues = epirange(20000101, 20221231)
-) %>%
-  select(geo_value, time_value, version = issue, death_rate = value) %>%
-  arrange(geo_value, time_value) %>%
-  as_epi_archive(compactify = FALSE)
-
-
-x <- epix_merge(confirmed_incidence_prop, deaths_incidence_prop, sync = "locf")
-
-x <- x %>%
-  epix_slide(
-    .versions = fc_time_values,
-    function(x, gk, rtv) {
-      x %>%
-        group_by(geo_value) %>%
-        epi_slide_mean(case_rate, .window_size = 7L) %>%
-        rename(case_rate_7d_av = slide_value_case_rate) %>%
-        epi_slide_mean(death_rate, ..window_size = 7L) %>%
-        rename(death_rate_7d_av = slide_value_death_rate) %>%
-        ungroup()
-    }
-  ) %>%
-  rename(version = time_value) %>%
-  rename(
-    time_value = slide_value_time_value,
-    geo_value = slide_value_geo_value,
-    case_rate = slide_value_case_rate,
-    death_rate = slide_value_death_rate,
-    case_rate_7d_av = slide_value_case_rate_7d_av,
-    death_rate_7d_av = slide_value_death_rate_7d_av
-  ) %>%
-  as_epi_archive(compactify = TRUE)
-
-saveRDS(x$DT, file = "case_death_rate_archive.rds")
-```
-
-```{r load-stored-data}
-x <- readRDS("case_death_rate_archive.rds")
-x <- as_epi_archive(x)
-```
-</details>
-
-Here we specify the ARX model.
-
-```{r make-arx-model}
-aheads <- c(7, 14, 21)
-fc_time_values <- seq(
-  from = as.Date("2020-09-01"),
-  to = as.Date("2021-12-31"),
-  by = "1 month"
-)
-forecaster <- function(x) {
-  map(aheads, function(ahead) {
-    arx_forecaster(
-      epi_data = x,
-      outcome = "death_rate_7d_av",
-      predictors = c("death_rate_7d_av", "case_rate_7d_av"),
-      trainer = quantile_reg(),
-      args_list = arx_args_list(lags = c(0, 7, 14, 21), ahead = ahead)
-    )$predictions
-  }) %>%
-    bind_rows()
-}
-```
-
-We can now use our forecaster function that we've created and use it in the
-pipeline for forecasting the predictions. We store the predictions into the
-`arx_preds` variable and calculate the most up to date version of the data in the
-epi archive and store it as `x_latest`.
-
-```{r running-arx-forecaster}
-arx_preds <- x %>%
-  epix_slide(
-    ~ forecaster(.x),
-    .before = 120, .versions = fc_time_values
-  ) %>%
-  mutate(engine_type = quantile_reg()$engine) %>%
-  mutate(ahead_val = target_date - forecast_date)
-
-x_latest <- epix_as_of(x, version = max(x$versions_end))
-```
-
-Now we plot both the actual and predicted 7 day average of the death rate for
-the chosen states
-
-<details>
-
-<summary>Code for the plot</summary>
-```{r plot-arx-asof, message = FALSE, warning = FALSE}
-states_to_show <- c("ca", "ny", "mi", "az")
-fc_states <- arx_preds %>%
-  filter(geo_value %in% states_to_show) %>%
-  pivot_quantiles_wider(.pred_distn)
-
-x_latest_states <- x_latest %>% filter(geo_value %in% states_to_show)
-
-p2 <- ggplot(fc_states, aes(target_date, group = forecast_date)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = geo_value), alpha = 0.4) +
-  geom_line(
-    data = x_latest_states, aes(x = time_value, y = death_rate_7d_av),
-    inherit.aes = FALSE, color = "gray50"
-  ) +
-  geom_line(aes(y = .pred, color = geo_value)) +
-  geom_point(aes(y = .pred, color = geo_value), size = 0.5) +
-  geom_vline(aes(xintercept = forecast_date), linetype = 2, alpha = 0.5) +
-  facet_wrap(~geo_value, scales = "free_y", ncol = 1L) +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
-  scale_fill_brewer(palette = "Set1") +
-  scale_color_brewer(palette = "Set1") +
-  labs(x = "Date", y = "7 day average COVID-19 death rates") +
-  theme(legend.position = "none")
-```
-</details>
-
-```{r show-plot2, fig.width = 9, fig.height = 6, echo = FALSE}
-p2
-```
diff --git a/vignettes/arx-classifier.Rmd b/vignettes/arx-classifier.Rmd
index b2a2bbf8e..3813e7d13 100644
--- a/vignettes/arx-classifier.Rmd
+++ b/vignettes/arx-classifier.Rmd
@@ -7,15 +7,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
-  echo = TRUE,
-  collapse = FALSE,
-  comment = "#>",
-  warning = FALSE,
-  message = FALSE,
-  out.width = "100%"
-)
+```{r, include = FALSE}
+source("_common.R")
 ```
 
 ## Load required packages
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
new file mode 100644
index 000000000..152b2460d
--- /dev/null
+++ b/vignettes/backtesting.Rmd
@@ -0,0 +1,467 @@
+---
+title: "Accurately backtesting forecasters"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Accurately backtesting forecasters}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+source("_common.R")
+```
+
+```{r pkgs, include=FALSE, echo=TRUE}
+library(epipredict)
+library(epidatr)
+library(data.table)
+library(dplyr)
+library(tidyr)
+library(ggplot2)
+library(magrittr)
+library(purrr)
+```
+
+# Accurately backtesting forecasters
+
+Backtesting is a crucial step in the development of forecasting models. It
+involves testing the model on historical data to see how well it performs. This
+is important because it allows us to see how well the model generalizes to new
+data and to identify any potential issues with the model. In the context of
+epidemiological forecasting, to do backtesting accurately, we need to account
+for the fact that the data available at the time of the forecast would have been
+different from the data available at the time of the backtest. This is because
+new data is constantly being collected and added to the dataset, which can
+affect the accuracy of the forecast.
+
+For this reason, it is important to use version-aware forecasting, where the
+model is trained on data that would have been available at the time of the
+forecast. This ensures that the model is tested on data that is as close as
+possible to what would have been available in real-time; training and making
+predictions on finalized data can lead to an overly optimistic sense of accuracy
+(see, for example, [McDonald et al.
+(2021)](https://www.pnas.org/content/118/51/e2111453118/) and the references
+therein).
+
+In the `{epiprocess}` package, we provide `epix_slide()`, a function that allows
+a convenient way to perform version-aware forecasting by only using the data as
+it would have been available at forecast reference time. In
+`vignette("epi_archive", package = "epiprocess")`, we introduced the concept of
+an `epi_archive` and we demonstrated how to use `epix_slide()` to forecast the
+future using a simple quantile regression model. In this vignette, we will
+demonstrate how to use `epix_slide()` to backtest an auto-regressive forecaster
+on historical COVID-19 case data from the US and Canada. Instead of building a
+forecaster from scratch as we did in the previous vignette, we will use the
+`arx_forecaster()` function from the `{epipredict}` package.
+
+## Getting case data from US states into an `epi_archive`
+
+First, we download the version history (ie. archive) of the percentage of
+doctor's visits with CLI (COVID-like illness) computed from medical insurance
+claims and the number of new confirmed COVID-19 cases per 100,000 population
+(daily) for 6 states from the COVIDcast API (as used in the `epiprocess`
+vignette mentioned above).
+
+```{r grab-epi-data}
+# Select the `percent_cli` column from the data archive
+doctor_visits <- archive_cases_dv_subset$DT %>%
+  select(geo_value, time_value, version, percent_cli) %>%
+  tidyr::drop_na(percent_cli) %>%
+  as_epi_archive(compactify = TRUE)
+```
+
+The data can also be fetched from the Delphi Epidata API with the following
+query:
+
+```{r, message = FALSE, warning = FALSE, eval = FALSE}
+library(epidatr)
+doctor_visits <- pub_covidcast(
+  source = "doctor-visits",
+  signals = "smoothed_adj_cli",
+  geo_type = "state",
+  time_type = "day",
+  geo_values = "ca,fl,ny,tx",
+  time_values = epirange(20200601, 20211201),
+  issues = epirange(20200601, 20211201)
+) %>%
+  rename(version = issue, percent_cli = value) %>%
+  as_epi_archive(compactify = TRUE)
+```
+
+## Backtesting a simple autoregressive forecaster
+
+One of the most common use cases of `epiprocess::epi_archive()` object
+is for accurate model backtesting.
+
+In this section we will:
+
+- develop a simple autoregressive forecaster that predicts the next value of the
+signal based on the current and past values of the signal itself, and
+- demonstrate how to slide this forecaster over the `epi_archive` object to
+produce forecasts at a few dates date, using version-unaware and -aware
+computations,
+- compare the two approaches.
+
+To start, let's use a simple autoregressive forecaster to predict the percentage
+of doctor's hospital visits with CLI (COVID-like illness) (`percent_cli`) in the
+future (we choose this target because of the dataset's pattern of substantial
+revisions; forecasting doctor's visits is an unusual forecasting target
+otherwise). While some AR models output single point forecasts, we will use
+quantile regression to produce a point prediction along with an 90\% uncertainty
+band, represented by a predictive quantiles at the 5\% and 95\% levels (lower
+and upper endpoints of the uncertainty band).
+
+The `arx_forecaster()` function wraps the autoregressive forecaster we need and
+comes with sensible defaults:
+
+- we specify the predicted outcome to be the percentage of doctor's visits with
+  CLI (`percent_cli`),
+- we use a linear regression model as the engine,
+- the autoregressive features assume lags of 0, 7, and 14 days,
+- we forecast 7 days ahead.
+
+All these default settings and more can be seen by calling `arx_args_list()`:
+
+```{r}
+arx_args_list()
+```
+
+These can be modified as needed, by sending your desired arguments into
+`arx_forecaster(args_list = arx_args_list())`. For now we will use the defaults.
+
+__Note__: We will use a __geo-pooled approach__, where we train the model on
+data from all states and territories combined. This is because the data is quite
+similar across states, and pooling the data can help improve the accuracy of the
+forecasts, while also reducing the susceptibility of the model to noise. In the
+interest of computational speed, we only use the 6 state dataset here, but the
+full archive can be used in the same way and has performed well in the past.
+Implementation-wise, geo-pooling is achieved by not using `group_by(geo_value)`
+prior to `epix_slide()`. In other cases, grouping may be preferrable, so we
+leave it to the user to decide, but flag this modeling decision here.
+
+Let's use the `epix_as_of()` method to generate a snapshot of the archive at the
+last date, and then run the forecaster.
+
+```{r}
+# Let's forecast 14 days prior to the last date in the archive, to compare.
+forecast_date <- doctor_visits$versions_end - 14
+# The .versions argument selects only the last version in the archive and
+# produces a forecast only on that date.
+forecasts <- doctor_visits %>%
+  epix_slide(
+    ~ arx_forecaster(
+      .x,
+      outcome = "percent_cli",
+      predictors = "percent_cli",
+      args_list = arx_args_list()
+    )$predictions %>%
+      pivot_quantiles_wider(.pred_distn),
+    .versions = forecast_date
+  )
+# Join the forecasts with with the latest data at the time of the forecast to
+# compare. Since `percent_cli` data has a few days of lag, we use `tidyr::fill` to
+# fill the missing values with the last observed value.
+forecasts %>%
+  inner_join(
+    doctor_visits %>%
+      epix_as_of(doctor_visits$versions_end) %>%
+      group_by(geo_value) %>%
+      tidyr::fill(percent_cli),
+    by = c("geo_value", "target_date" = "time_value")
+  ) %>%
+  select(geo_value, forecast_date, .pred, `0.05`, `0.95`, percent_cli)
+```
+
+The resulting epi_df now contains two new columns: `.pred` and `.pred_distn`,
+corresponding to the point forecast (median) and the quantile distribution
+containing our requested quantile forecasts (in this case, 0.05 and 0.95)
+respectively. The forecasts fall within the prediction interval, so our
+implementation passes a simple validation.
+
+Now let's go ahead and slide this forecaster in a version unaware way and a
+version aware way. For the version unaware way, we need to snapshot the latest
+version of the data, and then make a faux archive by setting `version =
+time_value`. This has the effect of simulating a data set that receives the
+final version updates every day. For the version aware way, we will simply use
+the true `epi_archive` object.
+
+```{r}
+archive_cases_dv_subset_faux <- doctor_visits %>%
+  epix_as_of(doctor_visits$versions_end) %>%
+  mutate(version = time_value) %>%
+  as_epi_archive()
+```
+
+To reduce typing, we create the wrapper function `forecast_k_week_ahead()`.
+
+```{r arx-kweek-preliminaries, warning = FALSE}
+# Latest snapshot of data, and forecast dates
+forecast_dates <- seq(from = as.Date("2020-08-01"), to = as.Date("2021-11-01"), by = "1 month")
+aheads <- c(7, 14, 21, 28)
+
+# @param epi_archive The epi_archive object to forecast from
+# @param ahead The number of days ahead to forecast
+# @param outcome The outcome variable to forecast
+# @param predictors The predictors to use in the model
+# @param forecast_dates The dates to forecast on
+# @param process_data A function to process the data before forecasting
+forecast_k_week_ahead <- function(
+    epi_archive,
+    ahead = 7,
+    outcome = NULL, predictors = NULL, forecast_dates = NULL, process_data = identity) {
+  if (is.null(forecast_dates)) {
+    forecast_dates <- epi_archive$versions_end
+  }
+  if (is.null(outcome) || is.null(predictors)) {
+    stop("Please specify the outcome and predictors.")
+  }
+  epi_archive %>%
+    epix_slide(
+      ~ arx_forecaster(
+        process_data(.x), outcome, predictors,
+        args_list = arx_args_list(ahead = ahead)
+      )$predictions %>%
+        pivot_quantiles_wider(.pred_distn),
+      .before = 120,
+      .versions = forecast_dates
+    )
+}
+```
+
+```{r}
+# Generate the forecasts and bind them together
+forecasts <- bind_rows(
+  map(aheads, ~ forecast_k_week_ahead(
+    archive_cases_dv_subset_faux,
+    ahead = .x,
+    outcome = "percent_cli",
+    predictors = "percent_cli",
+    forecast_dates = forecast_dates
+  ) %>% mutate(version_aware = FALSE)),
+  map(aheads, ~ forecast_k_week_ahead(
+    doctor_visits,
+    ahead = .x,
+    outcome = "percent_cli",
+    predictors = "percent_cli",
+    forecast_dates = forecast_dates
+  ) %>% mutate(version_aware = TRUE))
+)
+```
+
+Here, `arx_forecaster()` does all the heavy lifting. It creates leads of the
+target (respecting time stamps and locations) along with lags of the features
+(here, the response and doctors visits), estimates a forecasting model using the
+specified engine, creates predictions, and non-parametric confidence bands.
+
+To see how the predictions compare, we plot them on top of the latest case
+rates. Note that even though we've fitted the model on all states, we'll just
+display the results for two states, California (CA) and Florida (FL), to get a
+sense of the model performance while keeping the graphic simple.
+
+<details>
+<summary>Code for plotting</summary>
+
+```{r}
+geo_choose <- "ca"
+forecasts_filtered <- forecasts %>%
+  filter(geo_value == geo_choose) %>%
+  mutate(time_value = version)
+percent_cli_data <- bind_rows(
+  # Snapshotted data for the version-aware forecasts
+  map(
+    forecast_dates,
+    ~ doctor_visits %>%
+      epix_as_of(.x) %>%
+      mutate(version = .x)
+  ) %>%
+    bind_rows() %>%
+    mutate(version_aware = TRUE),
+  # Latest data for the version-unaware forecasts
+  doctor_visits %>%
+    epix_as_of(doctor_visits$versions_end) %>%
+    mutate(version_aware = FALSE)
+) %>%
+  filter(geo_value == geo_choose)
+
+p1 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
+  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
+  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
+  geom_vline(data = percent_cli_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_line(
+    data = percent_cli_data,
+    aes(x = time_value, y = percent_cli, color = factor(version)),
+    inherit.aes = FALSE, na.rm = TRUE
+  ) +
+  facet_grid(version_aware ~ geo_value, scales = "free") +
+  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  scale_y_continuous(expand = expansion(c(0, 0.05))) +
+  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  theme(legend.position = "none")
+```
+
+```{r}
+geo_choose <- "fl"
+forecasts_filtered <- forecasts %>%
+  filter(geo_value == geo_choose) %>%
+  mutate(time_value = version)
+percent_cli_data <- bind_rows(
+  # Snapshotted data for the version-aware forecasts
+  map(
+    forecast_dates,
+    ~ doctor_visits %>%
+      epix_as_of(.x) %>%
+      mutate(version = .x)
+  ) %>%
+    bind_rows() %>%
+    mutate(version_aware = TRUE),
+  # Latest data for the version-unaware forecasts
+  doctor_visits %>%
+    epix_as_of(doctor_visits$versions_end) %>%
+    mutate(version_aware = FALSE)
+) %>%
+  filter(geo_value == geo_choose)
+
+p2 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
+  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
+  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
+  geom_vline(data = percent_cli_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_line(
+    data = percent_cli_data,
+    aes(x = time_value, y = percent_cli, color = factor(version)),
+    inherit.aes = FALSE, na.rm = TRUE
+  ) +
+  facet_grid(version_aware ~ geo_value, scales = "free") +
+  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  scale_y_continuous(expand = expansion(c(0, 0.05))) +
+  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  theme(legend.position = "none")
+```
+</details>
+
+```{r show-plot1, echo=FALSE}
+p1
+p2
+```
+
+For the two states of interest, neither approach produces amazingly accurate
+forecasts. However, the extent to which using versioned data can affect
+backtesting, scoring, and therefore model choice for production can be inferred
+from these plots.
+
+### Example using case data from Canada
+
+<details>
+
+<summary>Data and forecasts. Similar to the above.</summary>
+
+By leveraging the flexibility of `epiprocess`, we can apply the same techniques
+to data from other sources. Since some collaborators are in British Columbia,
+Canada, we'll do essentially the same thing for Canada as we did above.
+
+The [COVID-19 Canada Open Data Working Group](https://opencovid.ca/) collects
+daily time series data on COVID-19 cases, deaths, recoveries, testing and
+vaccinations at the health region and province levels. Data are collected from
+publicly available sources such as government datasets and news releases.
+Unfortunately, there is no simple versioned source, so we have created our own
+from the Github commit history.
+
+First, we load versioned case rates at the provincial level. After converting
+these to 7-day averages (due to highly variable provincial reporting
+mismatches), we then convert the data to an `epi_archive` object, and extract
+the latest version from it. Finally, we run the same forcasting exercise as for
+the American data, but here we compare the forecasts produced from using simple
+linear regression with those from using boosted regression trees.
+
+```{r get-can-fc, warning = FALSE}
+aheads <- c(7, 14, 21, 28)
+canada_archive <- epidatasets::can_prov_cases
+canada_archive_faux <- epix_as_of(canada_archive, canada_archive$versions_end) %>%
+  mutate(version = time_value) %>%
+  as_epi_archive()
+# This function will add the 7-day average of the case rate to the data
+# before forecasting.
+smooth_cases <- function(epi_df) {
+  epi_df %>%
+    group_by(geo_value) %>%
+    epi_slide_mean("case_rate", .window_size = 7, na.rm = TRUE) %>%
+    rename(cr_7dav = slide_value_case_rate)
+}
+forecast_dates <- seq.Date(
+  from = min(canada_archive$DT$version),
+  to = max(canada_archive$DT$version),
+  by = "1 month"
+)
+
+# Generate the forecasts, and bind them together
+canada_forecasts <- bind_rows(
+  map(
+    aheads,
+    ~ forecast_k_week_ahead(
+      canada_archive_faux,
+      ahead = .x,
+      outcome = "cr_7dav",
+      predictors = "cr_7dav",
+      forecast_dates = forecast_dates,
+      process_data = smooth_cases
+    ) %>% mutate(version_aware = FALSE)
+  ),
+  map(
+    aheads,
+    ~ forecast_k_week_ahead(
+      canada_archive,
+      ahead = .x,
+      outcome = "cr_7dav",
+      predictors = "cr_7dav",
+      forecast_dates = forecast_dates,
+      process_data = smooth_cases
+    ) %>% mutate(version_aware = TRUE)
+  )
+)
+```
+
+The figures below shows the results for a single province.
+
+```{r plot-can-fc-lr, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
+geo_choose <- "Alberta"
+forecasts_filtered <- canada_forecasts %>%
+  filter(geo_value == geo_choose) %>%
+  mutate(time_value = version)
+case_rate_data <- bind_rows(
+  # Snapshotted data for the version-aware forecasts
+  map(
+    forecast_dates,
+    ~ canada_archive %>%
+      epix_as_of(.x) %>%
+      smooth_cases() %>%
+      mutate(case_rate = cr_7dav, version = .x)
+  ) %>%
+    bind_rows() %>%
+    mutate(version_aware = TRUE),
+  # Latest data for the version-unaware forecasts
+  canada_archive %>%
+    epix_as_of(doctor_visits$versions_end) %>%
+    smooth_cases() %>%
+    mutate(case_rate = cr_7dav, version_aware = FALSE)
+) %>%
+  filter(geo_value == geo_choose)
+
+ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
+  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
+  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
+  geom_vline(data = case_rate_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_line(
+    data = case_rate_data,
+    aes(x = time_value, y = case_rate, color = factor(version)),
+    inherit.aes = FALSE, na.rm = TRUE
+  ) +
+  facet_grid(version_aware ~ geo_value, scales = "free") +
+  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  scale_y_continuous(expand = expansion(c(0, 0.05))) +
+  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  theme(legend.position = "none")
+```
+
+</details>
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 1925de2fb..32e0d7d16 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -8,12 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-knitr::opts_chunk$set(
-  echo = TRUE,
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "100%"
-)
+source("_common.R")
 ```
 
 ```{r setup, message=FALSE}
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 0dea322f2..79186a6c2 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -7,14 +7,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r setup, include=F}
-knitr::opts_chunk$set(
-  echo = TRUE,
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "90%",
-  fig.align = "center"
-)
+```{r, include = FALSE}
+source("_common.R")
 ```
 
 ```{r libraries, warning=FALSE, message=FALSE}
@@ -28,22 +22,22 @@ library(ggplot2)
 theme_set(theme_bw())
 ```
 
-[Panel data](https://en.wikipedia.org/wiki/Panel_data), or longitudinal data, 
-contain cross-sectional measurements of subjects over time. The `epipredict` 
-package is most suitable for running forecasters on epidemiological panel data. 
+[Panel data](https://en.wikipedia.org/wiki/Panel_data), or longitudinal data,
+contain cross-sectional measurements of subjects over time. The `epipredict`
+package is most suitable for running forecasters on epidemiological panel data.
 A built-in example of this is the [`case_death_rate_subset`](
-  https://cmu-delphi.github.io/epipredict/reference/case_death_rate_subset.html) 
-dataset, which contains daily state-wise measures of `case_rate` and 
+  https://cmu-delphi.github.io/epipredict/reference/case_death_rate_subset.html)
+dataset, which contains daily state-wise measures of `case_rate` and
 `death_rate` for COVID-19 in 2021:
 
 ```{r epi-panel-ex, include=T}
 head(case_death_rate_subset, 3)
 ```
 
-`epipredict` functions work with data in 
-[`epi_df`](https://cmu-delphi.github.io/epiprocess/reference/epi_df.html) 
-format. Despite the stated goal and name of the package, other panel datasets 
-are also valid candidates for `epipredict` functionality, as long as they are 
+`epipredict` functions work with data in
+[`epi_df`](https://cmu-delphi.github.io/epiprocess/reference/epi_df.html)
+format. Despite the stated goal and name of the package, other panel datasets
+are also valid candidates for `epipredict` functionality, as long as they are
 in `epi_df` format.
 
 ```{r employ-stats, include=F}
@@ -55,36 +49,36 @@ year_end <- max(grad_employ_subset$time_value)
 # Example panel data overview
 
 In this vignette, we will demonstrate using `epipredict` with employment panel
-data from Statistics Canada. We will be using 
+data from Statistics Canada. We will be using
 [
-  Table 37-10-0115-01: Characteristics and median employment income of 
-  longitudinal cohorts of postsecondary graduates two and five years after 
-  graduation, by educational qualification and field of study (primary 
+  Table 37-10-0115-01: Characteristics and median employment income of
+  longitudinal cohorts of postsecondary graduates two and five years after
+  graduation, by educational qualification and field of study (primary
   groupings)
-](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501). 
-
-The full dataset contains yearly median employment income two and five years 
-after graduation, and number of graduates. The data is stratified by 
-variables such as geographic region (Canadian province), education, and 
-age group. The year range of the dataset is `r year_start` to `r year_end`, 
-inclusive. The full dataset also contains metadata that describes the 
-quality of data collected. For demonstration purposes, we make the following 
+](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710011501).
+
+The full dataset contains yearly median employment income two and five years
+after graduation, and number of graduates. The data is stratified by
+variables such as geographic region (Canadian province), education, and
+age group. The year range of the dataset is `r year_start` to `r year_end`,
+inclusive. The full dataset also contains metadata that describes the
+quality of data collected. For demonstration purposes, we make the following
 modifications to get a subset of the full dataset:
 
-* Only keep provincial-level geographic region (the full data also has 
+* Only keep provincial-level geographic region (the full data also has
 "Canada" as a region)
 * Only keep "good" or better quality data rows, as indicated by the [`STATUS`](
   https://www.statcan.gc.ca/en/concepts/definitions/guide-symbol) column
-* Choose a subset of covariates and aggregate across the remaining ones. The 
+* Choose a subset of covariates and aggregate across the remaining ones. The
 chosen covariates are age group, and educational qualification.
 
-To use this data with `epipredict`, we need to convert it into `epi_df` format 
+To use this data with `epipredict`, we need to convert it into `epi_df` format
 using `epiprocess::as_epi_df()`
 with additional keys. In our case, the additional keys are `age_group`,
-and `edu_qual`. Note that in the above modifications, we encoded `time_value` 
+and `edu_qual`. Note that in the above modifications, we encoded `time_value`
 as type `integer`. This lets us set `time_type = "year"`, and ensures that
 lag and ahead modifications later on are using the correct time units. See the
-`epiprocess::epi_df` for 
+`epiprocess::epi_df` for
 a list of all the `time_type`s available.
 
 ```{r data-dim, include=F}
@@ -92,20 +86,20 @@ employ_rowcount <- format(nrow(grad_employ_subset), big.mark = ",")
 employ_colcount <- length(names(grad_employ_subset))
 ```
 
-Now, we are ready to use `grad_employ_subset` with `epipredict`. 
+Now, we are ready to use `grad_employ_subset` with `epipredict`.
 Our `epi_df` contains `r employ_rowcount` rows and `r employ_colcount` columns.
 Here is a quick summary of the columns in our `epi_df`:
 
-* `time_value` (time value): year in `date` format 
+* `time_value` (time value): year in `date` format
 * `geo_value` (geo value): province in Canada
-* `num_graduates` (raw, time series value): number of graduates 
-* `med_income_2y` (raw, time series value): median employment income 2 years 
-after graduation 
-* `med_income_5y` (raw, time series value): median employment income 5 years 
-after graduation  
-* `age_group` (key): one of two age groups, either 15 to 34 years, or 35 to 64 
-years 
-* `edu_qual` (key): one of 32 unique educational qualifications, e.g., 
+* `num_graduates` (raw, time series value): number of graduates
+* `med_income_2y` (raw, time series value): median employment income 2 years
+after graduation
+* `med_income_5y` (raw, time series value): median employment income 5 years
+after graduation
+* `age_group` (key): one of two age groups, either 15 to 34 years, or 35 to 64
+years
+* `edu_qual` (key): one of 32 unique educational qualifications, e.g.,
 "Master's diploma"
 
 ```{r preview-data, include=T}
@@ -114,18 +108,18 @@ employ <- grad_employ_subset
 sample_n(employ, 6)
 ```
 
-In the following sections, we will go over pre-processing the data in the 
-`epi_recipe` framework, and fitting a model and making predictions within the 
+In the following sections, we will go over pre-processing the data in the
+`epi_recipe` framework, and fitting a model and making predictions within the
 `epipredict` framework and using the package's canned forecasters.
 
-# Autoregressive (AR) model to predict number of graduates in a year 
+# Autoregressive (AR) model to predict number of graduates in a year
 
-## Pre-processing 
+## Pre-processing
 
-As a simple example, let's work with the `num_graduates` column for now. We will 
-first pre-process by standardizing each numeric column by the total within 
+As a simple example, let's work with the `num_graduates` column for now. We will
+first pre-process by standardizing each numeric column by the total within
 each group of keys. We do this since those raw numeric values will vary greatly
-from province to province since there are large differences in population. 
+from province to province since there are large differences in population.
 
 ```{r employ-small, include=T}
 employ_small <- employ %>%
@@ -141,9 +135,9 @@ employ_small <- employ %>%
 head(employ_small)
 ```
 
-Below is a visualization for a sample of the small data for British Columbia and Ontario. 
-Note that some groups 
-do not have any time series information since we filtered out all time series 
+Below is a visualization for a sample of the small data for British Columbia and Ontario.
+Note that some groups
+do not have any time series information since we filtered out all time series
 with incomplete dates.
 
 ```{r employ-small-graph, include=T, eval=T, fig.width=9, fig.height=6}
@@ -161,32 +155,32 @@ employ_small %>%
   theme(legend.position = "bottom")
 ```
 
-We will predict the standardized number of graduates (a proportion) in the 
-next year (time $t+1$) using an autoregressive model with three lags (i.e., an 
-AR(3) model). Such a model is represented algebraically like this: 
+We will predict the standardized number of graduates (a proportion) in the
+next year (time $t+1$) using an autoregressive model with three lags (i.e., an
+AR(3) model). Such a model is represented algebraically like this:
 
 \[
-  y_{t+1,ijk} = 
+  y_{t+1,ijk} =
   \alpha_0 + \alpha_1 y_{tijk} + \alpha_2 y_{t-1,ijk} + \alpha_3 y_{t-2,ijk} + \epsilon_{tijk}
 \]
 
 where $y_{tij}$ is the proportion of graduates at time $t$ in location $i$ and
 age group $j$ with education quality $k$.
 
-In the pre-processing step, we need to create additional columns in `employ` for 
-each of $y_{t+1,ijk}$, $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$. 
-We do this via an 
-`epi_recipe`. Note that creating an `epi_recipe` alone doesn't add these 
-outcome and predictor columns; the recipe just stores the instructions for 
-adding them. 
+In the pre-processing step, we need to create additional columns in `employ` for
+each of $y_{t+1,ijk}$, $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$.
+We do this via an
+`epi_recipe`. Note that creating an `epi_recipe` alone doesn't add these
+outcome and predictor columns; the recipe just stores the instructions for
+adding them.
 
-Our `epi_recipe` should add one `ahead` column representing $y_{t+1,ijk}$ and 
+Our `epi_recipe` should add one `ahead` column representing $y_{t+1,ijk}$ and
 3 `lag` columns representing $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$
 (it's more accurate to think of the  0th "lag" as the "current" value with 2 lags,
-but that's not quite how the processing works). 
-Also note that 
+but that's not quite how the processing works).
+Also note that
 since we specified our `time_type` to be `year`, our `lag` and `lead`
-values are both in years. 
+values are both in years.
 
 ```{r make-recipe, include=T, eval=T}
 r <- epi_recipe(employ_small) %>%
@@ -196,7 +190,7 @@ r <- epi_recipe(employ_small) %>%
 r
 ```
 
-Let's apply this recipe using `prep` and `bake` to generate and view the `lag` 
+Let's apply this recipe using `prep` and `bake` to generate and view the `lag`
 and `ahead` columns.
 
 ```{r view-preprocessed, include=T}
@@ -211,25 +205,25 @@ bake_and_show_sample <- function(recipe, data, n = 5) {
 r %>% bake_and_show_sample(employ_small)
 ```
 
-We can see that the `prep` and `bake` steps created new columns according to 
+We can see that the `prep` and `bake` steps created new columns according to
 our `epi_recipe`:
 
 - `ahead_1_num_graduates_prop` corresponds to $y_{t+1,ijk}$
-- `lag_0_num_graduates_prop`, `lag_1_num_graduates_prop`, and 
-`lag_2_num_graduates_prop` correspond to $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$ 
+- `lag_0_num_graduates_prop`, `lag_1_num_graduates_prop`, and
+`lag_2_num_graduates_prop` correspond to $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$
 respectively.
 
 ## Model fitting and prediction
 
 Since our goal for now is to fit a simple autoregressive model, we can use
 [`parsnip::linear_reg()`](
-  https://parsnip.tidymodels.org/reference/linear_reg.html) with the default 
-engine `lm`, which fits a linear regression using ordinary least squares. 
+  https://parsnip.tidymodels.org/reference/linear_reg.html) with the default
+engine `lm`, which fits a linear regression using ordinary least squares.
 
-We will use `epi_workflow` with the `epi_recipe` we defined in the 
-pre-processing section along with the `parsnip::linear_reg()` model. Note 
-that `epi_workflow` is a container and doesn't actually do the fitting. We have 
-to pass the workflow into `fit()` to get our estimated model coefficients 
+We will use `epi_workflow` with the `epi_recipe` we defined in the
+pre-processing section along with the `parsnip::linear_reg()` model. Note
+that `epi_workflow` is a container and doesn't actually do the fitting. We have
+to pass the workflow into `fit()` to get our estimated model coefficients
 $\widehat{\alpha}_i,\ i=0,...,3$.
 
 ```{r linearreg-wf, include=T}
@@ -238,23 +232,23 @@ wf_linreg <- epi_workflow(r, linear_reg()) %>%
 summary(extract_fit_engine(wf_linreg))
 ```
 
-This output tells us the coefficients of the fitted model; for instance, 
-the estimated intercept is $\widehat{\alpha}_0 =$ 
-`r round(coef(extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for 
-$y_{tijk}$ is 
+This output tells us the coefficients of the fitted model; for instance,
+the estimated intercept is $\widehat{\alpha}_0 =$
+`r round(coef(extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for
+$y_{tijk}$ is
 $\widehat\alpha_1 =$ `r round(coef(extract_fit_engine(wf_linreg))[2], 3)`.
-The summary also tells us that all estimated coefficients are significantly 
-different from zero. Extracting the 95% confidence intervals for the 
+The summary also tells us that all estimated coefficients are significantly
+different from zero. Extracting the 95% confidence intervals for the
 coefficients also leads us to
 the same conclusion: all the coefficient estimates are significantly different
-from 0. 
+from 0.
 
 ```{r}
 confint(extract_fit_engine(wf_linreg))
 ```
 
-Now that we have our workflow, we can generate predictions from a subset of our 
-data. For this demo, we will predict the number of graduates using the last 2 
+Now that we have our workflow, we can generate predictions from a subset of our
+data. For this demo, we will predict the number of graduates using the last 2
 years of our dataset.
 
 ```{r linearreg-predict, include=T}
@@ -264,17 +258,17 @@ preds <- stats::predict(wf_linreg, latest) %>% filter(!is.na(.pred))
 preds %>% sample_n(5)
 ```
 
-We can do this using the `augment` function too. Note that `predict` and 
-`augment` both still return an `epiprocess::epi_df` with all of the keys that 
+We can do this using the `augment` function too. Note that `predict` and
+`augment` both still return an `epiprocess::epi_df` with all of the keys that
 were present in the original dataset.
 
 ```{r linearreg-augment}
 augment(wf_linreg, latest) %>% sample_n(5)
 ```
 
-## Model diagnostics 
+## Model diagnostics
 
-First, we'll plot the residuals (that is, $y_{tijk} - \widehat{y}_{tijk}$) 
+First, we'll plot the residuals (that is, $y_{tijk} - \widehat{y}_{tijk}$)
 against the fitted values ($\widehat{y}_{tijk}$).
 
 ```{r lienarreg-resid-plot, include=T, fig.height = 5, fig.width = 5}
@@ -282,35 +276,35 @@ par(mfrow = c(2, 2), mar = c(5, 3, 1.2, 0))
 plot(extract_fit_engine(wf_linreg))
 ```
 
-The fitted values vs. residuals plot shows us that the residuals are mostly 
-clustered around zero, but do not form an even band around the zero line, 
+The fitted values vs. residuals plot shows us that the residuals are mostly
+clustered around zero, but do not form an even band around the zero line,
 indicating that the variance of the residuals is not constant. Additionally,
-the fitted values vs. square root of standardized residuals makes this more 
-obvious - the spread of the square root of standardized residuals varies with 
-the fitted values. 
+the fitted values vs. square root of standardized residuals makes this more
+obvious - the spread of the square root of standardized residuals varies with
+the fitted values.
 
-The Q-Q plot shows us that the residuals have heavier tails than a Normal 
+The Q-Q plot shows us that the residuals have heavier tails than a Normal
 distribution. So the normality of residuals assumption doesn't hold either.
 
 Finally, the residuals vs. leverage plot shows us that we have a few influential
-points based on the Cook's distance (those outside the red dotted line). 
+points based on the Cook's distance (those outside the red dotted line).
 
-Since we appear to be violating the linear model assumptions, we might consider 
-transforming our data differently, or considering a non-linear model, or 
+Since we appear to be violating the linear model assumptions, we might consider
+transforming our data differently, or considering a non-linear model, or
 something else.
 
 # AR model with exogenous inputs
 
-Now suppose we want to model the 1-step-ahead 5-year employment income using 
-current and two previous values, while 
-also incorporating information from the other two time-series in our dataset: 
-the 2-year employment income and the number of graduates in the previous 2 
+Now suppose we want to model the 1-step-ahead 5-year employment income using
+current and two previous values, while
+also incorporating information from the other two time-series in our dataset:
+the 2-year employment income and the number of graduates in the previous 2
 years. We would do this using an autoregressive model with exogenous inputs,
 defined as follows:
 
 \[
 \begin{aligned}
-  y_{t+1,ijk} &= 
+  y_{t+1,ijk} &=
   \alpha_0 + \alpha_1 y_{tijk} + \alpha_2 y_{t-1,ijk} + \alpha_3 y_{t-2,ijk}\\
   &\quad + \beta_1 x_{tijk} + \beta_2 x_{t-1,ijk}\\
   &\quad + \gamma_2 z_{tijk} + \gamma_2 z_{t-1,ijk} + \epsilon_{tijk}
@@ -318,11 +312,11 @@ defined as follows:
 \]
 
 where $y_{tijk}$ is the 5-year median income (proportion) at time $t$ (in
-location $i$, age group $j$ with education quality $k$), 
-$x_{tijk}$ is the 2-year median income (proportion) at time $t$, and 
-$z_{tijk}$ is the number of graduates (proportion) at time $t$. 
+location $i$, age group $j$ with education quality $k$),
+$x_{tijk}$ is the 2-year median income (proportion) at time $t$, and
+$z_{tijk}$ is the number of graduates (proportion) at time $t$.
 
-## Pre-processing 
+## Pre-processing
 
 Again, we construct an `epi_recipe` detailing the pre-processing steps.
 
@@ -343,17 +337,17 @@ bake_and_show_sample(rx, employ_small)
 
 Before fitting our model and making predictions, let's add some post-processing
 steps using a few [`frosting`](
-  https://cmu-delphi.github.io/epipredict/reference/frosting.html) layers to do 
+  https://cmu-delphi.github.io/epipredict/reference/frosting.html) layers to do
 a few things:
 
-1. Threshold our predictions to 0. We are predicting proportions, which can't 
-be negative. And the transformed values back to dollars and people can't be 
+1. Threshold our predictions to 0. We are predicting proportions, which can't
+be negative. And the transformed values back to dollars and people can't be
 negative either.
-1. Generate prediction intervals based on residual quantiles, allowing us to 
+1. Generate prediction intervals based on residual quantiles, allowing us to
 quantify the uncertainty associated with future predicted values.
-1. Convert our predictions back to income values and number of graduates, 
-rather than standardized proportions. We do this via the frosting layer 
-`layer_population_scaling()`. 
+1. Convert our predictions back to income values and number of graduates,
+rather than standardized proportions. We do this via the frosting layer
+`layer_population_scaling()`.
 
 
 ```{r custom-arx-post, include=T}
@@ -406,28 +400,28 @@ predsx %>%
 
 # Using canned forecasters
 
-We've seen what we can do with non-epidemiological panel data using the 
-recipes frame, with `epi_recipe` for pre-processing, `epi_workflow` for model 
-fitting, and `frosting` for post-processing. 
+We've seen what we can do with non-epidemiological panel data using the
+recipes frame, with `epi_recipe` for pre-processing, `epi_workflow` for model
+fitting, and `frosting` for post-processing.
 
-`epipredict` also comes with canned forecasters that do all of those steps 
-behind the scenes for some simple models. Even though we aren't working with 
+`epipredict` also comes with canned forecasters that do all of those steps
+behind the scenes for some simple models. Even though we aren't working with
 epidemiological data, canned forecasters still work as expected, out of the box.
 We will demonstrate this with the simple
 [`flatline_forecaster`](
-  https://cmu-delphi.github.io/epipredict/reference/flatline_forecaster.html) 
-and the direct autoregressive (AR) forecaster 
+  https://cmu-delphi.github.io/epipredict/reference/flatline_forecaster.html)
+and the direct autoregressive (AR) forecaster
 [`arx_forecaster`](
   https://cmu-delphi.github.io/epipredict/reference/arx_forecaster.html).
 
-For both illustrations, we will continue to use the `employ_small` dataset 
-with the transformed numeric columns that are proportions within each group 
+For both illustrations, we will continue to use the `employ_small` dataset
+with the transformed numeric columns that are proportions within each group
 by the keys in our `epi_df`.
 
-## Flatline forecaster 
+## Flatline forecaster
 
 In this first example, we'll use `flatline_forecaster` to make a simple
-prediction of the 2-year median income for the next year, based on one previous 
+prediction of the 2-year median income for the next year, based on one previous
 time point. This model is representated algebraically as:
 \[y_{t+1,ijk} = y_{tijk} + \epsilon_{tijk}\]
 where $y_{tijk}$ is the 2-year median income (proportion) at time $t$.
@@ -442,14 +436,14 @@ out_fl
 
 ## Autoregressive forecaster with exogenous inputs
 
-In this second example, we'll use `arx_forecaster` to make a prediction of the 
+In this second example, we'll use `arx_forecaster` to make a prediction of the
 5-year median income based using two lags, _and_ using two lags on two exogenous
-variables: 2-year median income and number of graduates. 
+variables: 2-year median income and number of graduates.
 
-The canned forecaster gives us a simple way of making this forecast since it 
-defines the recipe, workflow, and post-processing steps behind the scenes. This 
-is very similar to the model we introduced in the "Autoregressive Linear Model 
-with Exogenous Inputs" section of this article, but where all inputs have the 
+The canned forecaster gives us a simple way of making this forecast since it
+defines the recipe, workflow, and post-processing steps behind the scenes. This
+is very similar to the model we introduced in the "Autoregressive Linear Model
+with Exogenous Inputs" section of this article, but where all inputs have the
 same number of lags.
 
 ```{r arx-lr, include=T, warning=F}
@@ -463,7 +457,7 @@ out_arx_lr <- arx_forecaster(employ_small, "med_income_5y_prop",
 out_arx_lr
 ```
 
-Other changes to the direct AR forecaster, like changing the engine, also work 
+Other changes to the direct AR forecaster, like changing the engine, also work
 as expected. Below we use a boosted tree model instead of a linear regression.
 
 ```{r arx-rf, include=T, warning=F}
@@ -479,7 +473,7 @@ out_arx_rf
 
 # Conclusion
 
-While the purpose of `{epipredict}` is to allow `{tidymodels}` to operate on 
+While the purpose of `{epipredict}` is to allow `{tidymodels}` to operate on
 epidemiology data, it can be easily adapted (both the workflows and the canned
 forecasters) to work for generic panel data modelling.
 
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 63a27bd55..987ecdef4 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -7,13 +7,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(
-  echo = TRUE,
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "100%"
-)
+```{r, include = FALSE}
+source("_common.R")
 ```
 
 ## Introduction
@@ -58,7 +53,7 @@ and forecasts to characterize the state of an outbreak and its course. They use
 it to inform public health decision makers on potential consequences of
 deploying control measures.
 
-One of the outcomes that the CDC forecasts is 
+One of the outcomes that the CDC forecasts is
 [death counts from COVID-19](https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html).
 Although there are many state-of-the-art models, we choose to use Poisson
 regression, the textbook example for modeling count data, as an illustration
@@ -110,7 +105,7 @@ Poisson distribution with mean $\mu_{t+7}$; $s_{\text{state}}$ are dummy
 variables for each state and take values of either 0 or 1.
 
 Preprocessing steps will be performed to prepare the
-data for model fitting. But before diving into them, it will be helpful to 
+data for model fitting. But before diving into them, it will be helpful to
 understand what `roles` are in the `recipes` framework.
 
 ---
@@ -239,7 +234,7 @@ the transformation will make the results uninterpretable.
 
 We wish, now, to predict the 7-day ahead death counts with lagged case rates and
 death rates, along with some extra behaviourial predictors. Namely, we will use
-survey data from 
+survey data from
 [COVID-19 Trends and Impact Survey](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html#behavior-indicators).
 
 The survey data provides the estimated percentage of people who wore a mask for
@@ -416,7 +411,7 @@ We say location $\ell$ is a hotspot at time $t$ when $Z_{\ell,t}$ is
 `up`, meaning the number of newly reported cases over the past 7 days has
 increased by at least 25% compared to the preceding week. When $Z_{\ell,t}$
 is categorized as `down`, it suggests that there has been at least a 20%
-decrease in newly reported cases over the past 7 days (a 20% decrease is the 
+decrease in newly reported cases over the past 7 days (a 20% decrease is the
 inverse of a 25% increase). Otherwise, we will
 consider the trend to be `flat`.
 
@@ -590,12 +585,12 @@ Sciences 118.51 (2021): e2111453118. [doi:10.1073/pnas.2111453118](https://doi.o
 
 ## Attribution
 
-This object contains a modified part of the 
-[COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19) 
+This object contains a modified part of the
+[COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19)
 as [republished in the COVIDcast Epidata API.](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
 
-This data set is licensed under the terms of the 
-[Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) 
+This data set is licensed under the terms of the
+[Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/)
 by the Johns Hopkins
 University on behalf of its Center for Systems Science in Engineering. Copyright
 Johns Hopkins University 2020.
diff --git a/vignettes/update.Rmd b/vignettes/update.Rmd
index fcd3653ca..6e9e8745c 100644
--- a/vignettes/update.Rmd
+++ b/vignettes/update.Rmd
@@ -8,12 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-knitr::opts_chunk$set(
-  echo = TRUE,
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "100%"
-)
+source("_common.R")
 ```
 
 ```{r setup, message=FALSE}

From 4a89419a965fd9374f3c2f743b7e3882b94d4648 Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 13:21:38 -0700
Subject: [PATCH 152/387] test snap

---
 tests/testthat/_snaps/arg_is_.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/tests/testthat/_snaps/arg_is_.md b/tests/testthat/_snaps/arg_is_.md
index c37ed6661..f05ca780d 100644
--- a/tests/testthat/_snaps/arg_is_.md
+++ b/tests/testthat/_snaps/arg_is_.md
@@ -377,11 +377,7 @@
 # simple surface step test
 
     Code
-<<<<<<< HEAD
       epi_recipe(cases_deaths_subset) %>% step_epi_lag(death_rate, lag = "hello")
-=======
-      epi_recipe(case_death_rate_subset) %>% step_epi_lag(death_rate, lag = "hello")
->>>>>>> dev
     Condition
       Error in `step_epi_lag()`:
       ! `lag` must be a non-negative integer.

From ceaeeecd1b39724deca9ada81e275be8707c422a Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 13:21:43 -0700
Subject: [PATCH 153/387] news

---
 NEWS.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 8df41423a..3fcf4dc0c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,8 +8,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 - Moved example datasets from being hosted in the package to being loaded
   from the `epidatasets` package. The datasets can no longer be loaded with
-  `data()`, but can be accessed with `epidatasets::` or, after loading the package,
-  the name of the dataset alone (#382).
+  `data(<dataset name>)`, but can be accessed with 
+  `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>` 
+  or, after loading the package, the name of the dataset alone (#382).
 
 ## Improvements
 

From 4001e1985770a301183d990e940a2176b4f60f0e Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 28 Oct 2024 13:58:50 -0700
Subject: [PATCH 154/387] change wording to specify datasets are in epidatasets
 only

---
 DESCRIPTION                            |  2 --
 vignettes/articles/symptom-surveys.Rmd |  5 ++---
 vignettes/panel-data.Rmd               |  1 +
 vignettes/preprocessing-and-models.Rmd | 12 +++++-------
 4 files changed, 8 insertions(+), 12 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 5ac302914..2ae9d3337 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -33,7 +33,6 @@ Imports:
     cli,
     distributional,
     dplyr,
-    epidatasets,
     generics,
     ggplot2,
     glue,
@@ -51,7 +50,6 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
-    epidatasets,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/vignettes/articles/symptom-surveys.Rmd b/vignettes/articles/symptom-surveys.Rmd
index dffd8b658..af692726e 100644
--- a/vignettes/articles/symptom-surveys.Rmd
+++ b/vignettes/articles/symptom-surveys.Rmd
@@ -146,8 +146,7 @@ re-scale and transform the signals to our `epi_recipe`. This would make the code
 more succinct and self-contained.
 
 We will compare two CLI-in-community indicators from
-different sources. The data are included in this package (via the
-[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+different sources. The data are available in the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
 and can be loaded with:
 
 ```{r, message = FALSE, warning = FALSE}
@@ -156,7 +155,7 @@ library(purrr)
 library(epipredict)
 library(recipes)
 
-z <- county_smoothed_cli_comparison
+z <- epidatasets::county_smoothed_cli_comparison
 ```
 
 The data can also be constructed from data the Delphi API with the following code:
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 02bd589dd..1faf5b56f 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -18,6 +18,7 @@ library(parsnip)
 library(recipes)
 library(epiprocess)
 library(epipredict)
+library(epidatasets)
 library(ggplot2)
 theme_set(theme_bw())
 ```
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 912b096c2..8d1d2f19f 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -58,13 +58,12 @@ Although there are many state-of-the-art models, we choose to use Poisson
 regression, the textbook example for modeling count data, as an illustration
 for using the `epipredict` package with other existing tidymodels packages.
 
-The `counts_subset` dataset is included in this package (via the
-[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+The `counts_subset` dataset is available in the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
 and contains the number of confirmed cases and deaths from June 4, 2021 to Dec
 31, 2021 in some U.S. states. It can be loaded with:
 
 ```{r poisson-reg-data}
-x <- counts_subset
+x <- epidatasets::counts_subset
 ```
 
 The data can also be fetched from the Delphi API with the following query:
@@ -256,13 +255,12 @@ in public in the past 7 days maintained a distance of at least 6 feet.
 State-wise population data from the 2019 U.S. Census will be used in 
 `layer_population_scaling()`.
 
-Both datasets are included in this package (via the
-[`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
+Both datasets are available in the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
 and can be loaded with:
 
 ```{r}
-behav_ind <- ctis_covid_behaviours
-pop_dat <- state_census %>% select(abbr, pop)
+behav_ind <- epidatasets::ctis_covid_behaviours
+pop_dat <- epidatasets::state_census %>% select(abbr, pop)
 ```
 
 The data can also be fetched from the Delphi API with the following query:

From 3ab495600da832c4958a25548e26c1b48b18dcd2 Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 22 Oct 2024 10:32:14 -0700
Subject: [PATCH 155/387] Change `key_colnames(extra_keys =)` to supported
 `other_keys =`

Recent/current epiprocess versions silently ignore `extra_keys =`. Pending
epiprocess changes will soft-deprecate and route it to `other_keys =` instead,
plus add some stricter checks on `other_keys =`.
---
 R/utils-misc.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils-misc.R b/R/utils-misc.R
index a1e0f025f..bb6c09e76 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -40,7 +40,7 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   # 2. these are the keys in the training data
   old_keys <- key_colnames(workflow)
   # 3. these are the keys in the test data as input
-  new_df_keys <- key_colnames(new_data, extra_keys = setdiff(new_keys, c("geo_value", "time_value")))
+  new_df_keys <- key_colnames(new_data, other_keys = setdiff(new_keys, c("geo_value", "time_value")))
   if (!(setequal(old_keys, new_df_keys) && setequal(new_keys, new_df_keys))) {
     cli_warn(paste(
       "Not all epi keys that were present in the training data are available",

From 06f54588457fd3d717e45d15646ea14e0b3b6d6c Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 22 Oct 2024 10:41:48 -0700
Subject: [PATCH 156/387] Add `key_colnames(exclude =)` support for epipredict
 objects

---
 R/key_colnames.R                   | 10 ++++++----
 tests/testthat/test-key_colnames.R |  6 ++++++
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/R/key_colnames.R b/R/key_colnames.R
index b9ebde5dc..b8d07ce82 100644
--- a/R/key_colnames.R
+++ b/R/key_colnames.R
@@ -1,20 +1,22 @@
 #' @export
-key_colnames.recipe <- function(x, ...) {
+key_colnames.recipe <- function(x, ..., exclude = character()) {
   geo_key <- x$var_info$variable[x$var_info$role %in% "geo_value"]
   time_key <- x$var_info$variable[x$var_info$role %in% "time_value"]
   keys <- x$var_info$variable[x$var_info$role %in% "key"]
-  c(geo_key, keys, time_key) %||% character(0L)
+  full_key <- c(geo_key, keys, time_key) %||% character(0L)
+  full_key[!full_key %in% exclude]
 }
 
 #' @export
-key_colnames.epi_workflow <- function(x, ...) {
+key_colnames.epi_workflow <- function(x, ..., exclude = character()) {
   # safer to look at the mold than the preprocessor
   mold <- hardhat::extract_mold(x)
   molded_names <- names(mold$extras$roles)
   geo_key <- names(mold$extras$roles[molded_names %in% "geo_value"]$geo_value)
   time_key <- names(mold$extras$roles[molded_names %in% "time_value"]$time_value)
   keys <- names(mold$extras$roles[molded_names %in% "key"]$key)
-  c(geo_key, keys, time_key) %||% character(0L)
+  full_key <- c(geo_key, keys, time_key) %||% character(0L)
+  full_key[!full_key %in% exclude]
 }
 
 kill_time_value <- function(v) {
diff --git a/tests/testthat/test-key_colnames.R b/tests/testthat/test-key_colnames.R
index d94daaec4..021bbb50c 100644
--- a/tests/testthat/test-key_colnames.R
+++ b/tests/testthat/test-key_colnames.R
@@ -17,6 +17,9 @@ test_that("key_colnames extracts time_value and geo_value, but not raw", {
     fit(data = covid_case_death_rates)
 
   expect_identical(key_colnames(my_workflow), c("geo_value", "time_value"))
+
+  # `exclude =` works:
+  expect_identical(key_colnames(my_workflow, exclude = "geo_value"), c("time_value"))
 })
 
 test_that("key_colnames extracts additional keys when they are present", {
@@ -49,4 +52,7 @@ test_that("key_colnames extracts additional keys when they are present", {
 
   # order of the additional keys may be different
   expect_equal(key_colnames(my_workflow), c("geo_value", "state", "pol", "time_value"))
+
+  # `exclude =` works:
+  expect_equal(key_colnames(my_workflow, exclude = c("time_value", "pol")), c("geo_value", "state"))
 })

From f406a0f9c5aac23af80fe0da86419c7f44012755 Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 22 Oct 2024 11:12:53 -0700
Subject: [PATCH 157/387] Refactor: rename some `extra_keys`, remove some NULL
 hedges

- Rename `extra_keys` -> `other_keys` when we are going to feed it into
  `other_keys =`.
- Remove some `<edf metadata>$other_keys %||% character()` hedges now that
  current epiprocess standardizes to character() not NULL and example `epi_df`
  objects have been updated to that standard.
---
 R/autoplot.R   | 5 ++---
 R/utils-misc.R | 6 +++---
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 4f4222979..870dcb8d8 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -127,11 +127,10 @@ autoplot.epi_workflow <- function(
   if (!is.null(shift)) {
     edf <- mutate(edf, time_value = time_value + shift)
   }
-  extra_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
-  if (length(extra_keys) == 0L) extra_keys <- NULL
+  other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
   edf <- as_epi_df(edf,
     as_of = object$fit$meta$as_of,
-    other_keys = extra_keys %||% character()
+    other_keys = other_keys
   )
   if (is.null(predictions)) {
     return(autoplot(
diff --git a/R/utils-misc.R b/R/utils-misc.R
index bb6c09e76..7f1eaf843 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -49,10 +49,10 @@ grab_forged_keys <- function(forged, workflow, new_data) {
   }
   if (is_epi_df(new_data)) {
     meta <- attr(new_data, "metadata")
-    extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys %||% character())
+    extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys)
   } else if (all(c("geo_value", "time_value") %in% new_keys)) {
-    if (length(new_keys) > 2) other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
-    extras <- as_epi_df(extras, other_keys = other_keys %||% character())
+    other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
+    extras <- as_epi_df(extras, other_keys = other_keys)
   }
   extras
 }

From e2cb1a1147de1bcc3e4c965ef3a821631b604efd Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Thu, 24 Oct 2024 08:40:05 -0700
Subject: [PATCH 158/387] Fix `process_rq_preds` on single-level predictions

---
 R/make_quantile_reg.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 9e653184c..1388dd859 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -112,7 +112,7 @@ make_quantile_reg <- function() {
 
     # can't make a method because object is second
     out <- switch(type,
-      rq = dist_quantiles(unname(as.list(x)), object$quantile_levels), # one quantile
+      rq = dist_quantiles(unname(as.list(x)), object$tau), # one quantile
       rqs = {
         x <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
         dist_quantiles(x, list(object$tau))

From f0ace50ff6d74c4175f177af20b46fb041081d5d Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 22 Oct 2024 17:29:06 -0700
Subject: [PATCH 159/387] population_scaling: fix w/ other_keys, upstream
 updates

---
 R/layer_population_scaling.R             |  23 ++-
 R/step_population_scaling.R              |  27 +++-
 tests/testthat/test-population_scaling.R | 193 +++++++++++++++++++++++
 3 files changed, 237 insertions(+), 6 deletions(-)

diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 7ec168822..741a5adf8 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -135,6 +135,25 @@ slather.layer_population_scaling <-
     )
     rlang::check_dots_empty()
 
+    if (is.null(object$by)) {
+      # Assume `layer_predict` has calculated the prediction keys and other layers
+      # don't change the prediction key colnames:
+      prediction_key_colnames <- names(components$keys)
+      lhs_potential_keys <- prediction_key_colnames
+      rhs_potential_keys <- colnames(select(object$df, !object$df_pop_col))
+      object$by <- intersect(lhs_potential_keys, rhs_potential_keys)
+      suggested_min_keys <- kill_time_value(lhs_potential_keys)
+      if (!all(suggested_min_keys %in% object$by)) {
+        cli_warn(c(
+          "Couldn't find {setdiff(suggested_min_keys, object$by)} in population `df`",
+          "i" = "Defaulting to join by {object$by}",
+          ">" = "Double-check whether column names on the population `df` match those expected in your predictions",
+          ">" = "Consider using population data with breakdowns by {suggested_min_keys}",
+          ">" = "Manually specify `by =` to silence"
+        ), class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys")
+      }
+    }
+
     object$by <- object$by %||% intersect(
       epi_keys_only(components$predictions),
       colnames(select(object$df, !object$df_pop_col))
@@ -152,10 +171,12 @@ slather.layer_population_scaling <-
     suffix <- ifelse(object$create_new, object$suffix, "")
     col_to_remove <- setdiff(colnames(object$df), colnames(components$predictions))
 
-    components$predictions <- left_join(
+    components$predictions <- inner_join(
       components$predictions,
       object$df,
       by = object$by,
+      relationship = "many-to-one",
+      unmatched = c("error", "drop"),
       suffix = c("", ".df")
     ) %>%
       mutate(across(
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 7bdb00925..901d80867 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -156,10 +156,25 @@ prep.step_population_scaling <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_population_scaling <- function(object, new_data, ...) {
-  object$by <- object$by %||% intersect(
-    epi_keys_only(new_data),
-    colnames(select(object$df, !object$df_pop_col))
-  )
+  if (is.null(object$by)) {
+    rhs_potential_keys <- colnames(select(object$df, !object$df_pop_col))
+    if (is_epi_df(new_data)) {
+      lhs_potential_keys <- key_colnames(new_data)
+      object$by <- intersect(lhs_potential_keys, rhs_potential_keys)
+      suggested_min_keys <- kill_time_value(lhs_potential_keys)
+      if (!all(suggested_min_keys %in% object$by)) {
+        cli_warn(c(
+          "Couldn't find {setdiff(suggested_min_keys, object$by)} in population `df`",
+          "i" = "Defaulting to join by {object$by}",
+          ">" = "Double-check whether column names on the population `df` match those for your time series",
+          ">" = "Consider using population data with breakdowns by {suggested_min_keys}",
+          ">" = "Manually specify `by =` to silence"
+        ), class = "epipredict__step_population_scaling__default_by_missing_suggested_keys")
+      }
+    } else {
+      object$by <- intersect(names(new_data), rhs_potential_keys)
+    }
+  }
   joinby <- list(x = names(object$by) %||% object$by, y = object$by)
   hardhat::validate_column_names(new_data, joinby$x)
   hardhat::validate_column_names(object$df, joinby$y)
@@ -177,7 +192,9 @@ bake.step_population_scaling <- function(object, new_data, ...) {
   suffix <- ifelse(object$create_new, object$suffix, "")
   col_to_remove <- setdiff(colnames(object$df), colnames(new_data))
 
-  left_join(new_data, object$df, by = object$by, suffix = c("", ".df")) %>%
+  inner_join(new_data, object$df,
+             by = object$by, relationship = "many-to-one", unmatched = c("error", "drop"),
+             suffix = c("", ".df")) %>%
     mutate(
       across(
         all_of(object$columns),
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 1f356c0cc..9de0c7d6d 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -304,6 +304,199 @@ test_that("test joining by default columns", {
 })
 
 
+test_that("test joining by default columns with less common keys/classes", {
+  # Make a model spec that expects no predictor columns and outputs a fixed
+  # (rate) prediction. Based on combining two linear inequalities.
+  fixed_rate_prediction <- 2e-6
+  model_spec <- quantile_reg(quantile_levels = 0.5, method = "fnc") %>%
+    set_engine(
+      "rq",
+      R = matrix(c(1, -1), 2, 1), r = c(1, -1) * fixed_rate_prediction,
+      eps = fixed_rate_prediction * 1e-6 # prevent early stop
+    )
+
+  # Here's the typical setup
+  dat1 <- tibble::tibble(geo_value = 1:2, time_value = 1, y = c(3 * 5, 7 * 11)) %>%
+    as_epi_df()
+  pop1 <- tibble::tibble(geo_value = 1:2, population = c(5e6, 11e6))
+  ewf1 <- epi_workflow(
+    epi_recipe(dat1) %>%
+      step_population_scaling(y, df = pop1, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf1, estimated = FALSE) %>%
+      prep(dat1) %>%
+      bake(new_data = NULL),
+    dat1 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf1, dat1)) %>%
+      pivot_quantiles_wider(.pred),
+    dat1 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      mutate(`0.5` = c(2 * 5, 2 * 11))
+  )
+
+  # With geo x age in time series but only geo in population data:
+  dat1b <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1) %>%
+    as_epi_df(other_keys = "age_group")
+  pop1b <- pop1
+  ewf1b <- epi_workflow(
+    epi_recipe(dat1b) %>%
+      step_population_scaling(y, df = pop1b, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1b, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_warning(
+    expect_equal(
+      extract_recipe(ewf1b, estimated = FALSE) %>%
+        prep(dat1b) %>%
+        bake(new_data = NULL),
+      dat1b %>%
+        # geo 1 scaling used for both:
+        mutate(y_scaled = c(3e-6, 7 * 11 / 5e6), ahead_0_y_scaled = y_scaled)
+    ),
+    class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+  )
+  expect_warning(
+    expect_warning(
+      expect_equal(
+        forecast(fit(ewf1b, dat1b)) %>%
+          pivot_quantiles_wider(.pred),
+        dat1b %>%
+          select(!"y") %>%
+          as_tibble() %>%
+          # geo 1 scaling used for both:
+          mutate(`0.5` = c(2 * 5, 2 * 5))
+      ),
+      class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+    ),
+    class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
+  )
+
+  # With geo x age_group breakdown on both:
+  dat2 <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1) %>%
+    as_epi_df(other_keys = "age_group")
+  pop2 <- pop1 %>%
+    mutate(age_group = geo_value, geo_value = 1)
+  ewf2 <- epi_workflow(
+    epi_recipe(dat2) %>%
+      step_population_scaling(y, df = pop2, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop2, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf2, estimated = FALSE) %>%
+      prep(dat2) %>%
+      bake(new_data = NULL),
+    dat2 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf2, dat2)) %>%
+      pivot_quantiles_wider(.pred),
+    dat2 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      mutate(`0.5` = c(2 * 5, 2 * 11))
+  )
+
+  # With only an age column in population data:
+  dat2b <- dat2
+  pop2b <- pop1 %>%
+    mutate(age_group = geo_value, geo_value = NULL)
+  ewf2b <- epi_workflow(
+    epi_recipe(dat2b) %>%
+      step_population_scaling(y, df = pop2b, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop2b, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_warning(
+    expect_equal(
+      extract_recipe(ewf2b, estimated = FALSE) %>%
+        prep(dat2b) %>%
+        bake(new_data = NULL),
+      dat2b %>%
+        mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+    ),
+    class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+  )
+  expect_warning(
+    expect_warning(
+      expect_equal(
+        forecast(fit(ewf2b, dat2b)) %>%
+          pivot_quantiles_wider(.pred),
+        dat2b %>%
+          select(!"y") %>%
+          as_tibble() %>%
+          mutate(`0.5` = c(2 * 5, 2 * 11))
+      ),
+      class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+    ),
+    class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
+  )
+
+  # with geo x time_value breakdown instead:
+  dat3 <- dat1 %>%
+    as_tibble() %>%
+    mutate(time_value = geo_value, geo_value = 1) %>%
+    as_epi_df()
+  pop3 <- pop1 %>%
+    mutate(time_value = geo_value, geo_value = 1)
+  ewf3 <- epi_workflow(
+    epi_recipe(dat3) %>%
+      step_population_scaling(y, df = pop3, df_pop_col = "population") %>%
+      step_epi_ahead(y_scaled, ahead = 0),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop3, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf3, estimated = FALSE) %>%
+      prep(dat3) %>%
+      bake(new_data = NULL),
+    dat3 %>%
+      mutate(y_scaled = c(3e-6, 7e-6), ahead_0_y_scaled = y_scaled)
+  )
+  expect_equal(
+    forecast(fit(ewf3, dat3)) %>%
+      pivot_quantiles_wider(.pred),
+    # slightly edited copy-pasta due to test time selection:
+    dat3 %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      slice_max(by = geo_value, time_value) %>%
+      mutate(`0.5` = 2 * 11)
+  )
+
+  # TODO non-`epi_df` scaling?
+
+  # TODO multikey scaling?
+
+})
+
+
 test_that("expect error if `by` selector does not match", {
   jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%

From 29a320458d7025fcbac7499f6b9703c281c6c854 Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 29 Oct 2024 11:04:16 -0700
Subject: [PATCH 160/387] Move validation to prep and make it rely on roles

- Roles should already factor in key_colnames.
---
 NAMESPACE                                |  2 +
 R/epipredict-package.R                   |  2 +
 R/layer_population_scaling.R             |  4 +-
 R/step_population_scaling.R              | 59 +++++++++++++++++-------
 tests/testthat/test-population_scaling.R | 39 ++++++++++++++++
 5 files changed, 87 insertions(+), 19 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 5ee13f730..e7e720f11 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -242,6 +242,7 @@ importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,group_by_at)
+importFrom(dplyr,inner_join)
 importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
@@ -273,6 +274,7 @@ importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(magrittr,"%>%")
+importFrom(magrittr,extract2)
 importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index 3dee263e2..b4b9973bf 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -7,7 +7,9 @@
 #' @importFrom cli cli_abort cli_warn
 #' @importFrom dplyr arrange across all_of any_of bind_cols bind_rows group_by
 #' @importFrom dplyr full_join relocate summarise everything
+#' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
+#' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
 #' @importFrom stats poly predict lm residuals quantile
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 741a5adf8..5a982d344 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -136,8 +136,8 @@ slather.layer_population_scaling <-
     rlang::check_dots_empty()
 
     if (is.null(object$by)) {
-      # Assume `layer_predict` has calculated the prediction keys and other layers
-      # don't change the prediction key colnames:
+      # Assume `layer_predict` has calculated the prediction keys and other
+      # layers don't change the prediction key colnames:
       prediction_key_colnames <- names(components$keys)
       lhs_potential_keys <- prediction_key_colnames
       rhs_potential_keys <- colnames(select(object$df, !object$df_pop_col))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 901d80867..fe07c2808 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -138,6 +138,42 @@ step_population_scaling_new <-
 
 #' @export
 prep.step_population_scaling <- function(x, training, info = NULL, ...) {
+  hardhat::validate_column_names(x$df, x$df_pop_col)
+  if (is.null(x$by)) {
+    rhs_potential_keys <- setdiff(colnames(x$df), x$df_pop_col)
+    lhs_potential_keys <- info %>%
+      filter(role %in% c("geo_value", "key", "time_value")) %>%
+      extract2("variable") %>%
+      unique() # in case of weird var with multiple of above roles
+    if (length(lhs_potential_keys) == 0L) {
+      # We're working with a recipe and tibble, and *_role hasn't set up any of
+      # the above roles. Let's say any column could actually act as a key, and
+      # lean on `intersect` below to make this something reasonable.
+      lhs_potential_keys <- names(training)
+    }
+    suggested_min_keys <- info %>%
+      filter(role %in% c("geo_value", "key")) %>%
+      extract2("variable") %>%
+      unique()
+    # (0 suggested keys if we weren't given any epikeytime var info.)
+    x$by <- intersect(lhs_potential_keys, rhs_potential_keys)
+    if (length(x$by) == 0L) {
+      cli_stop(c(
+        "Couldn't guess a default for `by`",
+        ">" = "Please rename columns in your population data to match those in your training data,
+               or manually specify `by =` in `step_population_scaling()`."
+      ), class = "epipredict__step_population_scaling__default_by_no_intersection")
+    }
+    if (!all(suggested_min_keys %in% x$by)) {
+      cli_warn(c(
+        "Couldn't find {setdiff(suggested_min_keys, x$by)} in population `df`.",
+        "i" = "Defaulting to join by {x$by}.",
+        ">" = "Double-check whether column names on the population `df` match those for your time series.",
+        ">" = "Consider using population data with breakdowns by {suggested_min_keys}.",
+        ">" = "Manually specify `by =` to silence."
+      ), class = "epipredict__step_population_scaling__default_by_missing_suggested_keys")
+    }
+  }
   step_population_scaling_new(
     terms = x$terms,
     role = x$role,
@@ -157,23 +193,12 @@ prep.step_population_scaling <- function(x, training, info = NULL, ...) {
 #' @export
 bake.step_population_scaling <- function(object, new_data, ...) {
   if (is.null(object$by)) {
-    rhs_potential_keys <- colnames(select(object$df, !object$df_pop_col))
-    if (is_epi_df(new_data)) {
-      lhs_potential_keys <- key_colnames(new_data)
-      object$by <- intersect(lhs_potential_keys, rhs_potential_keys)
-      suggested_min_keys <- kill_time_value(lhs_potential_keys)
-      if (!all(suggested_min_keys %in% object$by)) {
-        cli_warn(c(
-          "Couldn't find {setdiff(suggested_min_keys, object$by)} in population `df`",
-          "i" = "Defaulting to join by {object$by}",
-          ">" = "Double-check whether column names on the population `df` match those for your time series",
-          ">" = "Consider using population data with breakdowns by {suggested_min_keys}",
-          ">" = "Manually specify `by =` to silence"
-        ), class = "epipredict__step_population_scaling__default_by_missing_suggested_keys")
-      }
-    } else {
-      object$by <- intersect(names(new_data), rhs_potential_keys)
-    }
+    cli::cli_abort(c(
+      "`by` was not set and no default was filled in",
+      ">" = "If this was a fit recipe generated from an older version
+             of epipredict that you loaded in from a file,
+             please regenerate with the current version of epipredict."
+    ))
   }
   joinby <- list(x = names(object$by) %||% object$by, y = object$by)
   hardhat::validate_column_names(new_data, joinby$x)
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 9de0c7d6d..0a16dff6f 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -386,6 +386,45 @@ test_that("test joining by default columns with less common keys/classes", {
     class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
   )
 
+  # Same thing but with time series in tibble:
+  dat1bb <- dat1 %>%
+    as_tibble() %>%
+    mutate(age_group = geo_value, geo_value = 1)
+  pop1bb <- pop1b
+  ewf1bb <- epi_workflow(
+    # Can't use epi_recipe or step_epi_ahead; adjust.
+    recipe(dat1bb) %>%
+    update_role("geo_value", new_role = "geo_value") %>%
+    update_role("age_group", new_role = "key") %>%
+    update_role("time_value", new_role = "time_value") %>%
+    step_population_scaling(y, df = pop1bb, df_pop_col = "population", role = "outcome") %>%
+      # XXX key_colnames inference differs at fit vs. predict time, so we also
+      # need to manually provide some key role settings to not have trouble at
+      # predict time.
+   {.},
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1bb, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf1bb, estimated = FALSE) %>%
+      prep(dat1bb) %>%
+      bake(new_data = NULL),
+    dat1bb %>%
+      # geo 1 scaling used for both:
+      mutate(y_scaled = c(3e-6, 7 * 11 / 5e6))
+  )
+  expect_equal(
+    predict(fit(ewf1bb, dat1bb), dat1bb) %>%
+      pivot_quantiles_wider(.pred),
+    dat1bb %>%
+      select(!"y") %>%
+      as_tibble() %>%
+      # geo 1 scaling used for both:
+      mutate(`0.5` = c(2 * 5, 2 * 5))
+  )
+
   # With geo x age_group breakdown on both:
   dat2 <- dat1 %>%
     as_tibble() %>%

From 194735fd992b7ad0b55d27b5ed384537c16dab9b Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Mon, 28 Oct 2024 05:06:37 -0700
Subject: [PATCH 161/387] population_scaling: do more validation on creation

---
 R/step_population_scaling.R | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index fe07c2808..d7b7893e3 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -89,13 +89,25 @@ step_population_scaling <-
            suffix = "_scaled",
            skip = FALSE,
            id = rand_id("population_scaling")) {
-    arg_is_scalar(role, df_pop_col, rate_rescaling, create_new, suffix, id)
-    arg_is_lgl(create_new, skip)
-    arg_is_chr(df_pop_col, suffix, id)
+    if (rlang::dots_n(...) == 0L) {
+      cli_abort(c(
+        "`...` must not be empty.",
+        ">" = "Please provide one or more tidyselect expressions in `...`
+               specifying the columns to which scaling should be applied.",
+        ">" = "If you really want to list `step_population_scaling` in your
+               recipe but not have it do anything, you can use a tidyselection
+               that selects zero variables, such as `c()`."
+      ))
+    }
+    arg_is_scalar(role, df_pop_col, rate_rescaling, create_new, suffix, skip, id)
+    arg_is_chr(role, df_pop_col, suffix, id)
+    hardhat::validate_column_names(df, df_pop_col)
     arg_is_chr(by, allow_null = TRUE)
+    arg_is_numeric(rate_rescaling)
     if (rate_rescaling <= 0) {
       cli_abort("`rate_rescaling` must be a positive number.")
     }
+    arg_is_lgl(create_new, skip)
 
     recipes::add_step(
       recipe,
@@ -138,7 +150,6 @@ step_population_scaling_new <-
 
 #' @export
 prep.step_population_scaling <- function(x, training, info = NULL, ...) {
-  hardhat::validate_column_names(x$df, x$df_pop_col)
   if (is.null(x$by)) {
     rhs_potential_keys <- setdiff(colnames(x$df), x$df_pop_col)
     lhs_potential_keys <- info %>%

From 7090cf0580a37a57b676943409dd3c9df5ea5bae Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Mon, 28 Oct 2024 15:21:01 -0700
Subject: [PATCH 162/387] Adjust message on `grab_forged_keys` mismatches to
 apply to testing situation

---
 R/utils-misc.R                           | 49 ++++++++++++---
 tests/testthat/test-population_scaling.R | 79 ++++++++++++++++++------
 2 files changed, 100 insertions(+), 28 deletions(-)

diff --git a/R/utils-misc.R b/R/utils-misc.R
index 7f1eaf843..7ab4ad953 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -31,26 +31,59 @@ check_pname <- function(res, preds, object, newname = NULL) {
   res
 }
 
+# Copied from `epiprocess`:
+
+#' "Format" a character vector of column/variable names for cli interpolation
+#'
+#' Designed to give good output if interpolated with cli. Main purpose is to add
+#' backticks around variable names when necessary, and something other than an
+#' empty string if length 0.
+#'
+#' @param x `chr`; e.g., `colnames` of some data frame
+#' @param empty string; what should be output if `x` is of length 0?
+#' @return `chr`
+#' @keywords internal
+format_varnames <- function(x, empty = "*none*") {
+  if (length(x) == 0L) {
+    empty
+  } else {
+    as.character(syms(x))
+  }
+}
 
 grab_forged_keys <- function(forged, workflow, new_data) {
+  # 1. keys in the training data post-prep, based on roles:
+  old_keys <- key_colnames(workflow)
+  # 3. keys in the test data post-bake, based on roles:
   forged_roles <- names(forged$extras$roles)
   extras <- bind_cols(forged$extras$roles[forged_roles %in% c("geo_value", "time_value", "key")])
-  # 1. these are the keys in the test data after prep/bake
   new_keys <- names(extras)
-  # 2. these are the keys in the training data
-  old_keys <- key_colnames(workflow)
-  # 3. these are the keys in the test data as input
+  if (length(new_keys) == 0L) {
+    # No epikeytime role assignment; infer from all columns:
+    potential_keys <- c("geo_value", "time_value")
+    new_keys <- potential_keys[potential_keys %in% names(bind_cols(forged$extras$roles))]
+  }
+  # 2. keys in the test data pre-bake based on data structure + post-bake roles:
   new_df_keys <- key_colnames(new_data, other_keys = setdiff(new_keys, c("geo_value", "time_value")))
-  if (!(setequal(old_keys, new_df_keys) && setequal(new_keys, new_df_keys))) {
-    cli_warn(paste(
-      "Not all epi keys that were present in the training data are available",
-      "in `new_data`. Predictions will have only the available keys."
+  # Softly validate, assuming that no steps change epikeytime role assignments:
+  if (!(setequal(old_keys, new_df_keys) && setequal(new_df_keys, new_keys))) {
+    cli_warn(c(
+      "Inconsistent epikeytime identifier columns specified/inferred.",
+      "i" = "training epikeytime columns, based on roles post-mold/prep: {format_varnames(old_keys)}",
+      "i" = " testing epikeytime columns, based on data structure pre-bake and roles post-forge/bake: {format_varnames(new_df_keys)}",
+      "i" = " testing epikeytime columns, based on roles post-forge/bake: {format_varnames(new_keys)}",
+      "*" = "Keys will be set giving preference to test-time `epi_df` metadata followed by test-time
+             post-bake role settings.",
+      ">" = 'Some mismatches can be addressed by using `epi_df`s instead of tibbles, or by using `update_role`
+             to assign pre-`prep` columns the "geo_value", "key", and "time_value" roles.'
     ))
   }
   if (is_epi_df(new_data)) {
+    # Inference based on test data pre-bake data structure "wins":
     meta <- attr(new_data, "metadata")
     extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys)
   } else if (all(c("geo_value", "time_value") %in% new_keys)) {
+    # Inference based on test data post-bake roles "wins":
     other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
     extras <- as_epi_df(extras, other_keys = other_keys)
   }
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 0a16dff6f..eabca9f7e 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -386,43 +386,82 @@ test_that("test joining by default columns with less common keys/classes", {
     class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
   )
 
-  # Same thing but with time series in tibble:
-  dat1bb <- dat1 %>%
+  # Same thing but with time series in tibble, but with role hints:
+  dat1b2 <- dat1 %>%
     as_tibble() %>%
     mutate(age_group = geo_value, geo_value = 1)
-  pop1bb <- pop1b
-  ewf1bb <- epi_workflow(
+  pop1b2 <- pop1b
+  ewf1b2 <- epi_workflow(
     # Can't use epi_recipe or step_epi_ahead; adjust.
-    recipe(dat1bb) %>%
-    update_role("geo_value", new_role = "geo_value") %>%
-    update_role("age_group", new_role = "key") %>%
-    update_role("time_value", new_role = "time_value") %>%
-    step_population_scaling(y, df = pop1bb, df_pop_col = "population", role = "outcome") %>%
-      # XXX key_colnames inference differs at fit vs. predict time, so we also
-      # need to manually provide some key role settings to not have trouble at
-      # predict time.
-   {.},
+    recipe(dat1b2) %>%
+      update_role("geo_value", new_role = "geo_value") %>%
+      update_role("age_group", new_role = "key") %>%
+      update_role("time_value", new_role = "time_value") %>%
+      step_population_scaling(y, df = pop1b2, df_pop_col = "population", role = "outcome") %>%
+      {.},
     model_spec,
     frosting() %>%
       layer_predict() %>%
-      layer_population_scaling(.pred, df = pop1bb, df_pop_col = "population", create_new = FALSE)
+      layer_population_scaling(.pred, df = pop1b2, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_warning(
+    expect_equal(
+      extract_recipe(ewf1b2, estimated = FALSE) %>%
+        prep(dat1b2) %>%
+        bake(new_data = NULL),
+      dat1b2 %>%
+        # geo 1 scaling used for both:
+        mutate(y_scaled = c(3e-6, 7 * 11 / 5e6))
+    ),
+    class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+  )
+  expect_warning(
+    expect_warning(
+      expect_equal(
+        predict(fit(ewf1b2, dat1b2), dat1b2) %>%
+          pivot_quantiles_wider(.pred),
+        dat1b2 %>%
+          select(!"y") %>%
+          as_tibble() %>%
+          # geo 1 scaling used for both:
+          mutate(`0.5` = c(2 * 5, 2 * 5)) %>%
+          select(geo_value, age_group, time_value, `0.5`)
+      ),
+      class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
+    ),
+    class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
+  )
+
+  # Same thing but with time series in tibble, but no role hints -> different inference&messaging:
+  dat1b3 <- dat1b2
+  pop1b3 <- pop1b2
+  ewf1b3 <- epi_workflow(
+    # Can't use epi_recipe or step_epi_ahead; adjust.
+    recipe(dat1b3) %>%
+      step_population_scaling(y, df = pop1b3, df_pop_col = "population", role = "outcome") %>%
+      {.},
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1b3, df_pop_col = "population", create_new = FALSE)
   )
   expect_equal(
-    extract_recipe(ewf1bb, estimated = FALSE) %>%
-      prep(dat1bb) %>%
+    extract_recipe(ewf1b3, estimated = FALSE) %>%
+      prep(dat1b3) %>%
       bake(new_data = NULL),
-    dat1bb %>%
+    dat1b3 %>%
       # geo 1 scaling used for both:
       mutate(y_scaled = c(3e-6, 7 * 11 / 5e6))
   )
   expect_equal(
-    predict(fit(ewf1bb, dat1bb), dat1bb) %>%
+    predict(fit(ewf1b3, dat1b3), dat1b3) %>%
       pivot_quantiles_wider(.pred),
-    dat1bb %>%
+    dat1b3 %>%
       select(!"y") %>%
       as_tibble() %>%
       # geo 1 scaling used for both:
-      mutate(`0.5` = c(2 * 5, 2 * 5))
+      mutate(`0.5` = c(2 * 5, 2 * 5)) %>%
+      select(geo_value, age_group, time_value, `0.5`)
   )
 
   # With geo x age_group breakdown on both:

From 2e307577fb0c611a22d11806898483fd2795b194 Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Mon, 28 Oct 2024 17:08:51 -0700
Subject: [PATCH 163/387] Make key inference more consistent; allow
 non-`epi_df` forged data

---
 R/key_colnames.R                         | 13 +++--
 R/layer_population_scaling.R             |  3 +-
 R/step_population_scaling.R              | 10 ++--
 R/utils-misc.R                           | 61 ++++++++++++++----------
 tests/testthat/test-population_scaling.R | 20 ++++----
 5 files changed, 62 insertions(+), 45 deletions(-)

diff --git a/R/key_colnames.R b/R/key_colnames.R
index b8d07ce82..9e0d44dcf 100644
--- a/R/key_colnames.R
+++ b/R/key_colnames.R
@@ -11,11 +11,14 @@ key_colnames.recipe <- function(x, ..., exclude = character()) {
 key_colnames.epi_workflow <- function(x, ..., exclude = character()) {
   # safer to look at the mold than the preprocessor
   mold <- hardhat::extract_mold(x)
-  molded_names <- names(mold$extras$roles)
-  geo_key <- names(mold$extras$roles[molded_names %in% "geo_value"]$geo_value)
-  time_key <- names(mold$extras$roles[molded_names %in% "time_value"]$time_value)
-  keys <- names(mold$extras$roles[molded_names %in% "key"]$key)
-  full_key <- c(geo_key, keys, time_key) %||% character(0L)
+  molded_roles <- mold$extras$roles
+  extras <- bind_cols(molded_roles$geo_value, molded_roles$key, molded_roles$time_value)
+  full_key <- names(extras)
+  if (length(full_key) == 0L) {
+    # No epikeytime role assignment; infer from all columns:
+    potential_keys <- c("geo_value", "time_value")
+    full_key <- potential_keys[potential_keys %in% names(bind_cols(molded_roles))]
+  }
   full_key[!full_key %in% exclude]
 }
 
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 5a982d344..f47e3f292 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -145,7 +145,8 @@ slather.layer_population_scaling <-
       suggested_min_keys <- kill_time_value(lhs_potential_keys)
       if (!all(suggested_min_keys %in% object$by)) {
         cli_warn(c(
-          "Couldn't find {setdiff(suggested_min_keys, object$by)} in population `df`",
+          "{setdiff(suggested_min_keys, object$by)} {?was an/were} epikey column{?s} in the predictions,
+           but {?wasn't/weren't} found in the population `df`.",
           "i" = "Defaulting to join by {object$by}",
           ">" = "Double-check whether column names on the population `df` match those expected in your predictions",
           ">" = "Consider using population data with breakdowns by {suggested_min_keys}",
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index d7b7893e3..297c30727 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -177,9 +177,10 @@ prep.step_population_scaling <- function(x, training, info = NULL, ...) {
     }
     if (!all(suggested_min_keys %in% x$by)) {
       cli_warn(c(
-        "Couldn't find {setdiff(suggested_min_keys, x$by)} in population `df`.",
+        "{setdiff(suggested_min_keys, x$by)} {?was an/were} epikey column{?s} in the training data,
+         but {?wasn't/weren't} found in the population `df`.",
         "i" = "Defaulting to join by {x$by}.",
-        ">" = "Double-check whether column names on the population `df` match those for your time series.",
+        ">" = "Double-check whether column names on the population `df` match those for your training data.",
         ">" = "Consider using population data with breakdowns by {suggested_min_keys}.",
         ">" = "Manually specify `by =` to silence."
       ), class = "epipredict__step_population_scaling__default_by_missing_suggested_keys")
@@ -229,8 +230,9 @@ bake.step_population_scaling <- function(object, new_data, ...) {
   col_to_remove <- setdiff(colnames(object$df), colnames(new_data))
 
   inner_join(new_data, object$df,
-             by = object$by, relationship = "many-to-one", unmatched = c("error", "drop"),
-             suffix = c("", ".df")) %>%
+    by = object$by, relationship = "many-to-one", unmatched = c("error", "drop"),
+    suffix = c("", ".df")
+  ) %>%
     mutate(
       across(
         all_of(object$columns),
diff --git a/R/utils-misc.R b/R/utils-misc.R
index 7ab4ad953..fec707913 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -54,40 +54,53 @@ format_varnames <- function(x, empty = "*none*") {
 grab_forged_keys <- function(forged, workflow, new_data) {
   # 1. keys in the training data post-prep, based on roles:
   old_keys <- key_colnames(workflow)
-  # 3. keys in the test data post-bake, based on roles:
-  forged_roles <- names(forged$extras$roles)
-  extras <- bind_cols(forged$extras$roles[forged_roles %in% c("geo_value", "time_value", "key")])
-  new_keys <- names(extras)
+  # 2. keys in the test data post-bake, based on roles & structure:
+  forged_roles <- forged$extras$roles
+  new_key_tbl <- bind_cols(forged_roles$geo_value, forged_roles$key, forged_roles$time_value)
+  new_keys <- names(new_key_tbl)
   if (length(new_keys) == 0L) {
     # No epikeytime role assignment; infer from all columns:
-    potential_keys <- c("geo_value", "time_value")
-    new_keys <- potential_keys[potential_keys %in% names(bind_cols(forged$extras$roles))]
+    potential_new_keys <- c("geo_value", "time_value")
+    forged_tbl <- bind_cols(forged$extras$roles)
+    new_keys <- potential_new_keys[potential_new_keys %in% names(forged_tbl)]
+    new_key_tbl <- forged_tbl[new_keys]
   }
-  # 2. keys in the test data pre-bake based on data structure + post-bake roles:
-  new_df_keys <- key_colnames(new_data, other_keys = setdiff(new_keys, c("geo_value", "time_value")))
-  # Softly validate, assuming that no steps change epikeytime role assignments:
-  if (!(setequal(old_keys, new_df_keys) && setequal(new_df_keys, new_keys))) {
+  # Softly validate:
+  if (!(setequal(old_keys, new_keys))) {
     cli_warn(c(
-      "Inconsistent epikeytime identifier columns specified/inferred.",
+      "Inconsistent epikeytime identifier columns specified/inferred in training vs. in testing data.",
       "i" = "training epikeytime columns, based on roles post-mold/prep: {format_varnames(old_keys)}",
-      "i" = " testing epikeytime columns, based on data structure pre-bake and roles post-forge/bake: {format_varnames(new_df_keys)}",
-      "i" = " testing epikeytime columns, based on roles post-forge/bake: {format_varnames(new_keys)}",
-      "*" = "Keys will be set giving preference to test-time `epi_df` metadata followed by test-time
-             post-bake role settings.",
+      "i" = "testing epikeytime columns, based on roles post-forge/bake: {format_varnames(new_keys)}",
+      "*" = "",
       ">" = 'Some mismatches can be addressed by using `epi_df`s instead of tibbles, or by using `update_role`
              to assign pre-`prep` columns the "geo_value", "key", and "time_value" roles.'
     ))
   }
-  if (is_epi_df(new_data)) {
-    # Inference based on test data pre-bake data structure "wins":
-    meta <- attr(new_data, "metadata")
-    extras <- as_epi_df(extras, as_of = meta$as_of, other_keys = meta$other_keys)
-  } else if (all(c("geo_value", "time_value") %in% new_keys)) {
-    # Inference based on test data post-bake roles "wins":
-    other_keys <- new_keys[!new_keys %in% c("geo_value", "time_value")]
-    extras <- as_epi_df(extras, other_keys = other_keys)
+  # Convert `new_key_tbl` to `epi_df` if not renaming columns nor violating
+  # `epi_df` invariants.  Require that our key is a unique key in any case.
+  if (all(c("geo_value", "time_value") %in% new_keys)) {
+    maybe_as_of <- attr(new_data, "metadata")$as_of # NULL if wasn't epi_df
+    try(return(as_epi_df(new_key_tbl, other_keys = new_keys, as_of = maybe_as_of)),
+      silent = TRUE
+    )
+  }
+  if (anyDuplicated(new_key_tbl)) {
+    duplicate_key_tbl <- new_key_tbl %>% filter(.by = everything(), dplyr::n() > 1L)
+    error_part1 <- cli::format_error(
+      c(
+        "Specified/inferred key columns had repeated combinations in the forged/baked test data.",
+        "i" = "Key columns: {format_varnames(new_keys)}",
+        "Duplicated keys:"
+      )
+    )
+    error_part2 <- capture.output(print(duplicate_key_tbl))
+    rlang::abort(
+      paste(collapse = "\n", c(error_part1, error_part2)),
+      class = "epipredict__grab_forged_keys__nonunique_key"
+    )
+  } else {
+    return(new_key_tbl)
   }
-  extras
 }
 
 get_parsnip_mode <- function(trainer) {
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index eabca9f7e..63c23a388 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -398,7 +398,9 @@ test_that("test joining by default columns with less common keys/classes", {
       update_role("age_group", new_role = "key") %>%
       update_role("time_value", new_role = "time_value") %>%
       step_population_scaling(y, df = pop1b2, df_pop_col = "population", role = "outcome") %>%
-      {.},
+      {
+        .
+      },
     model_spec,
     frosting() %>%
       layer_predict() %>%
@@ -432,14 +434,16 @@ test_that("test joining by default columns with less common keys/classes", {
     class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
   )
 
-  # Same thing but with time series in tibble, but no role hints -> different inference&messaging:
+  # Same thing but with time series in tibble, but no role hints -> different behavior:
   dat1b3 <- dat1b2
   pop1b3 <- pop1b2
   ewf1b3 <- epi_workflow(
     # Can't use epi_recipe or step_epi_ahead; adjust.
     recipe(dat1b3) %>%
       step_population_scaling(y, df = pop1b3, df_pop_col = "population", role = "outcome") %>%
-      {.},
+      {
+        .
+      },
     model_spec,
     frosting() %>%
       layer_predict() %>%
@@ -453,15 +457,10 @@ test_that("test joining by default columns with less common keys/classes", {
       # geo 1 scaling used for both:
       mutate(y_scaled = c(3e-6, 7 * 11 / 5e6))
   )
-  expect_equal(
+  expect_error(
     predict(fit(ewf1b3, dat1b3), dat1b3) %>%
       pivot_quantiles_wider(.pred),
-    dat1b3 %>%
-      select(!"y") %>%
-      as_tibble() %>%
-      # geo 1 scaling used for both:
-      mutate(`0.5` = c(2 * 5, 2 * 5)) %>%
-      select(geo_value, age_group, time_value, `0.5`)
+    class = "epipredict__grab_forged_keys__nonunique_key"
   )
 
   # With geo x age_group breakdown on both:
@@ -571,7 +570,6 @@ test_that("test joining by default columns with less common keys/classes", {
   # TODO non-`epi_df` scaling?
 
   # TODO multikey scaling?
-
 })
 
 

From 8dae68b464425fbd46edb345463ed9c9c06d326b Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 29 Oct 2024 10:34:58 -0700
Subject: [PATCH 164/387] Add a renamed-geo_value pop scaling test, minor
 cleanup

---
 tests/testthat/test-population_scaling.R | 45 +++++++++++++++++-------
 1 file changed, 33 insertions(+), 12 deletions(-)

diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 63c23a388..6e4cd5df2 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -397,10 +397,7 @@ test_that("test joining by default columns with less common keys/classes", {
       update_role("geo_value", new_role = "geo_value") %>%
       update_role("age_group", new_role = "key") %>%
       update_role("time_value", new_role = "time_value") %>%
-      step_population_scaling(y, df = pop1b2, df_pop_col = "population", role = "outcome") %>%
-      {
-        .
-      },
+      step_population_scaling(y, df = pop1b2, df_pop_col = "population", role = "outcome"),
     model_spec,
     frosting() %>%
       layer_predict() %>%
@@ -420,6 +417,7 @@ test_that("test joining by default columns with less common keys/classes", {
   expect_warning(
     expect_warning(
       expect_equal(
+        # get_test_data doesn't work with non-`epi_df`s, so provide test data manually:
         predict(fit(ewf1b2, dat1b2), dat1b2) %>%
           pivot_quantiles_wider(.pred),
         dat1b2 %>%
@@ -434,16 +432,15 @@ test_that("test joining by default columns with less common keys/classes", {
     class = "epipredict__layer_population_scaling__default_by_missing_suggested_keys"
   )
 
-  # Same thing but with time series in tibble, but no role hints -> different behavior:
+  # Same thing but with time series in tibble, but no role hints -> incorrect
+  # key guess -> we prep without warning, but error when we realize we don't
+  # actually have a unique key for our predictions:
   dat1b3 <- dat1b2
   pop1b3 <- pop1b2
   ewf1b3 <- epi_workflow(
     # Can't use epi_recipe or step_epi_ahead; adjust.
     recipe(dat1b3) %>%
-      step_population_scaling(y, df = pop1b3, df_pop_col = "population", role = "outcome") %>%
-      {
-        .
-      },
+      step_population_scaling(y, df = pop1b3, df_pop_col = "population", role = "outcome"),
     model_spec,
     frosting() %>%
       layer_predict() %>%
@@ -567,9 +564,33 @@ test_that("test joining by default columns with less common keys/classes", {
       mutate(`0.5` = 2 * 11)
   )
 
-  # TODO non-`epi_df` scaling?
-
-  # TODO multikey scaling?
+  # With alternative geo naming... we're able to successfully prep (and we're
+  # not missing a warning as in 1b3 since we're actually in a "correct" setup),
+  # but still like 1b3, we fail at prediction time as key roles have not been
+  # set up and inference fails:
+  dat4 <- dat1 %>% rename(geo = geo_value)
+  pop4 <- pop1 %>% rename(geo = geo_value)
+  ewf4 <- epi_workflow(
+    recipe(dat4) %>%
+      step_population_scaling(y, df = pop4, df_pop_col = "population", role = "outcome"),
+    model_spec,
+    frosting() %>%
+      layer_predict() %>%
+      layer_population_scaling(.pred, df = pop1, df_pop_col = "population", create_new = FALSE)
+  )
+  expect_equal(
+    extract_recipe(ewf4, estimated = FALSE) %>%
+      prep(dat4) %>%
+      bake(new_data = NULL),
+    dat4 %>%
+      mutate(y_scaled = c(3e-6, 7e-6))
+  )
+  expect_error(
+    # get_test_data doesn't work with non-`epi_df`s, so provide test data manually:
+    predict(fit(ewf4, dat4), dat4) %>%
+      pivot_quantiles_wider(.pred),
+    class = "epipredict__grab_forged_keys__nonunique_key"
+  )
 })
 
 

From 3685e67e0dd686e3445590d3285ea515858c4f5c Mon Sep 17 00:00:00 2001
From: "Logan C. Brooks" <lcbrooks+github@andrew.cmu.edu>
Date: Tue, 29 Oct 2024 15:22:47 -0700
Subject: [PATCH 165/387] Update docs, news for `*_population_scaling(by =)`
 and keys updates

---
 DESCRIPTION                     |  2 +-
 NEWS.md                         |  3 +++
 R/layer_population_scaling.R    | 17 +++++++++++------
 R/step_population_scaling.R     | 19 ++++++++++++-------
 man/format_varnames.Rd          | 22 ++++++++++++++++++++++
 man/layer_population_scaling.Rd | 17 +++++++++++------
 man/step_population_scaling.Rd  | 19 ++++++++++++-------
 7 files changed, 72 insertions(+), 27 deletions(-)
 create mode 100644 man/format_varnames.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 2ae9d3337..456cdb54f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.3
+Version: 0.1.4
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 3fcf4dc0c..8be7d7c0d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -15,6 +15,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 ## Improvements
 
 - Add `step_adjust_latency`, which give several methods to adjust the forecast if the `forecast_date` is after the last day of data.
+- Fix `layer_population_scaling` default `by` with `other_keys`.
+- Make key column inference more consistent within the package and with current `epiprocess`.
+- Fix `quantile_reg()` producing error when asked to output just median-level predictions.
 - (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
 
 ## Bug fixes
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index f47e3f292..badd073f8 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -19,12 +19,17 @@
 #'   inverting the existing scaling.
 #' @param by A (possibly named) character vector of variables to join by.
 #'
-#' If `NULL`, the default, the function will perform a natural join, using all
-#' variables in common across the `epi_df` produced by the `predict()` call
-#' and the user-provided dataset.
-#' If columns in that `epi_df` and `df` have the same name (and aren't
-#' included in `by`), `.df` is added to the one from the user-provided data
-#' to disambiguate.
+#' If `NULL`, the default, the function will try to infer a reasonable set of
+#' columns. First, it will try to join by all variables in the test data with
+#' roles `"geo_value"`, `"key"`, or `"time_value"` that also appear in `df`;
+#' these roles are automatically set if you are using an `epi_df`, or you can
+#' use, e.g., `update_role`. If no such roles are set, it will try to perform a
+#' natural join, using variables in common between the training/test data and
+#' population data.
+#'
+#' If columns in the training/testing data and `df` have the same name (and
+#' aren't included in `by`), a `.df` suffix is added to the one from the
+#' user-provided data to disambiguate.
 #'
 #' To join by different variables on the `epi_df` and `df`, use a named vector.
 #' For example, `by = c("geo_value" = "states")` will match `epi_df$geo_value`
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 297c30727..6047f5c4c 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -16,12 +16,17 @@
 #'   inverting the existing scaling.
 #' @param by A (possibly named) character vector of variables to join by.
 #'
-#' If `NULL`, the default, the function will perform a natural join, using all
-#' variables in common across the `epi_df` produced by the `predict()` call
-#' and the user-provided dataset.
-#' If columns in that `epi_df` and `df` have the same name (and aren't
-#' included in `by`), `.df` is added to the one from the user-provided data
-#' to disambiguate.
+#' If `NULL`, the default, the function will try to infer a reasonable set of
+#' columns. First, it will try to join by all variables in the training/test
+#' data with roles `"geo_value"`, `"key"`, or `"time_value"` that also appear in
+#' `df`; these roles are automatically set if you are using an `epi_df`, or you
+#' can use, e.g., `update_role`. If no such roles are set, it will try to
+#' perform a natural join, using variables in common between the training/test
+#' data and population data.
+#'
+#' If columns in the training/testing data and `df` have the same name (and
+#' aren't included in `by`), a `.df` suffix is added to the one from the
+#' user-provided data to disambiguate.
 #'
 #' To join by different variables on the `epi_df` and `df`, use a named vector.
 #' For example, `by = c("geo_value" = "states")` will match `epi_df$geo_value`
@@ -29,7 +34,7 @@
 #' For example, `by = c("geo_value" = "states", "county" = "county")` will match
 #' `epi_df$geo_value` to `df$states` and `epi_df$county` to `df$county`.
 #'
-#' See [dplyr::left_join()] for more details.
+#' See [dplyr::inner_join()] for more details.
 #' @param df_pop_col the name of the column in the data frame `df` that
 #' contains the population data and will be used for scaling.
 #' This should be one column.
diff --git a/man/format_varnames.Rd b/man/format_varnames.Rd
new file mode 100644
index 000000000..59fe218af
--- /dev/null
+++ b/man/format_varnames.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-misc.R
+\name{format_varnames}
+\alias{format_varnames}
+\title{"Format" a character vector of column/variable names for cli interpolation}
+\usage{
+format_varnames(x, empty = "*none*")
+}
+\arguments{
+\item{x}{\code{chr}; e.g., \code{colnames} of some data frame}
+
+\item{empty}{string; what should be output if \code{x} is of length 0?}
+}
+\value{
+\code{chr}
+}
+\description{
+Designed to give good output if interpolated with cli. Main purpose is to add
+backticks around variable names when necessary, and something other than an
+empty string if length 0.
+}
+\keyword{internal}
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 3a22dbee5..083dd6b48 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -28,12 +28,17 @@ inverting the existing scaling.}
 
 \item{by}{A (possibly named) character vector of variables to join by.
 
-If \code{NULL}, the default, the function will perform a natural join, using all
-variables in common across the \code{epi_df} produced by the \code{predict()} call
-and the user-provided dataset.
-If columns in that \code{epi_df} and \code{df} have the same name (and aren't
-included in \code{by}), \code{.df} is added to the one from the user-provided data
-to disambiguate.
+If \code{NULL}, the default, the function will try to infer a reasonable set of
+columns. First, it will try to join by all variables in the test data with
+roles \code{"geo_value"}, \code{"key"}, or \code{"time_value"} that also appear in \code{df};
+these roles are automatically set if you are using an \code{epi_df}, or you can
+use, e.g., \code{update_role}. If no such roles are set, it will try to perform a
+natural join, using variables in common between the training/test data and
+population data.
+
+If columns in the training/testing data and \code{df} have the same name (and
+aren't included in \code{by}), a \code{.df} suffix is added to the one from the
+user-provided data to disambiguate.
 
 To join by different variables on the \code{epi_df} and \code{df}, use a named vector.
 For example, \code{by = c("geo_value" = "states")} will match \code{epi_df$geo_value}
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 6a0c5dea9..c41ec7fcc 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -33,12 +33,17 @@ inverting the existing scaling.}
 
 \item{by}{A (possibly named) character vector of variables to join by.
 
-If \code{NULL}, the default, the function will perform a natural join, using all
-variables in common across the \code{epi_df} produced by the \code{predict()} call
-and the user-provided dataset.
-If columns in that \code{epi_df} and \code{df} have the same name (and aren't
-included in \code{by}), \code{.df} is added to the one from the user-provided data
-to disambiguate.
+If \code{NULL}, the default, the function will try to infer a reasonable set of
+columns. First, it will try to join by all variables in the training/test
+data with roles \code{"geo_value"}, \code{"key"}, or \code{"time_value"} that also appear in
+\code{df}; these roles are automatically set if you are using an \code{epi_df}, or you
+can use, e.g., \code{update_role}. If no such roles are set, it will try to
+perform a natural join, using variables in common between the training/test
+data and population data.
+
+If columns in the training/testing data and \code{df} have the same name (and
+aren't included in \code{by}), a \code{.df} suffix is added to the one from the
+user-provided data to disambiguate.
 
 To join by different variables on the \code{epi_df} and \code{df}, use a named vector.
 For example, \code{by = c("geo_value" = "states")} will match \code{epi_df$geo_value}
@@ -46,7 +51,7 @@ to \code{df$states}. To join by multiple variables, use a vector with length > 1
 For example, \code{by = c("geo_value" = "states", "county" = "county")} will match
 \code{epi_df$geo_value} to \code{df$states} and \code{epi_df$county} to \code{df$county}.
 
-See \code{\link[dplyr:mutate-joins]{dplyr::left_join()}} for more details.}
+See \code{\link[dplyr:mutate-joins]{dplyr::inner_join()}} for more details.}
 
 \item{df_pop_col}{the name of the column in the data frame \code{df} that
 contains the population data and will be used for scaling.

From 202a226cf388b6dd52d4ac87023260e3764eec6c Mon Sep 17 00:00:00 2001
From: Nat DeFries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 30 Oct 2024 15:51:10 -0700
Subject: [PATCH 166/387] define case_num var even when pulling data from
 epidatasets

---
 vignettes/articles/smooth-qr.Rmd       | 2 +-
 vignettes/articles/symptom-surveys.Rmd | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/vignettes/articles/smooth-qr.Rmd b/vignettes/articles/smooth-qr.Rmd
index 801934e8f..a73ffd221 100644
--- a/vignettes/articles/smooth-qr.Rmd
+++ b/vignettes/articles/smooth-qr.Rmd
@@ -97,7 +97,7 @@ state cases and deaths. This sample data ranges from Dec. 31, 2020 to
 Dec. 31, 2021.
 
 ```{r}
-edf <- covid_case_death_rates
+edf <- epidatasets::covid_case_death_rates
 ```
 
 We will set the forecast date to be November 30, 2021 so that we can produce
diff --git a/vignettes/articles/symptom-surveys.Rmd b/vignettes/articles/symptom-surveys.Rmd
index af692726e..cf8013572 100644
--- a/vignettes/articles/symptom-surveys.Rmd
+++ b/vignettes/articles/symptom-surveys.Rmd
@@ -155,6 +155,7 @@ library(purrr)
 library(epipredict)
 library(recipes)
 
+case_num <- 200
 z <- epidatasets::county_smoothed_cli_comparison
 ```
 

From 156c7282fe8004e9fdbd05b233747050c7f5b8df Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 7 Nov 2024 17:23:17 -0600
Subject: [PATCH 167/387] patch drop_ignored_keys broken grouped behavior

---
 R/utils-latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 8bcd2b1e4..25799f224 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -358,7 +358,7 @@ get_latency_table <- function(training, columns, forecast_date, latency,
 drop_ignored_keys <- function(training, keys_to_ignore) {
   # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
   # and is needed to bypass an incomplete port of `across` functions to `if_any`
-  training %>%
+  training %>% ungroup %>%
     filter((dplyr::if_all(
       names(keys_to_ignore),
       ~ . %nin% keys_to_ignore[[cur_column()]]

From a92f0767917539a7b68242a293df5303eaf083ed Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 7 Nov 2024 17:25:40 -0600
Subject: [PATCH 168/387] bump version

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2ae9d3337..456cdb54f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.3
+Version: 0.1.4
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),

From 265468789ac607bb9716e51d5285ed9087b4685c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 7 Nov 2024 17:28:58 -0600
Subject: [PATCH 169/387] formatting

---
 R/utils-latency.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index 25799f224..d854cb9cf 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -358,7 +358,8 @@ get_latency_table <- function(training, columns, forecast_date, latency,
 drop_ignored_keys <- function(training, keys_to_ignore) {
   # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
   # and is needed to bypass an incomplete port of `across` functions to `if_any`
-  training %>% ungroup %>%
+  training %>%
+    ungroup() %>%
     filter((dplyr::if_all(
       names(keys_to_ignore),
       ~ . %nin% keys_to_ignore[[cur_column()]]

From 1c1c79b7aab7ba927fe19490972f0cfcaf46c4c8 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 11 Nov 2024 13:46:05 -0800
Subject: [PATCH 170/387] rm silly panel_normalize

---
 NAMESPACE            |  5 -----
 man/tidy.frosting.Rd | 11 ++++-------
 2 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index f198ac3c0..5ee13f730 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -25,7 +25,6 @@ S3method(bake,step_epi_lag)
 S3method(bake,step_epi_slide)
 S3method(bake,step_growth_rate)
 S3method(bake,step_lag_difference)
-S3method(bake,step_panel_normalize)
 S3method(bake,step_population_scaling)
 S3method(bake,step_training_window)
 S3method(detect_layer,frosting)
@@ -66,7 +65,6 @@ S3method(prep,step_epi_lag)
 S3method(prep,step_epi_slide)
 S3method(prep,step_growth_rate)
 S3method(prep,step_lag_difference)
-S3method(prep,step_panel_normalize)
 S3method(prep,step_population_scaling)
 S3method(prep,step_training_window)
 S3method(print,alist)
@@ -98,7 +96,6 @@ S3method(print,step_epi_slide)
 S3method(print,step_growth_rate)
 S3method(print,step_lag_difference)
 S3method(print,step_naomit)
-S3method(print,step_panel_normalize)
 S3method(print,step_population_scaling)
 S3method(print,step_training_window)
 S3method(quantile,dist_quantiles)
@@ -124,7 +121,6 @@ S3method(snap,distribution)
 S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
-S3method(tidy,step_panel_normalize)
 S3method(update,layer)
 S3method(vec_ptype_abbr,dist_quantiles)
 S3method(vec_ptype_full,dist_quantiles)
@@ -209,7 +205,6 @@ export(step_epi_naomit)
 export(step_epi_slide)
 export(step_growth_rate)
 export(step_lag_difference)
-export(step_panel_normalize)
 export(step_population_scaling)
 export(step_training_window)
 export(tibble)
diff --git a/man/tidy.frosting.Rd b/man/tidy.frosting.Rd
index 7bb56c8c3..8152b1440 100644
--- a/man/tidy.frosting.Rd
+++ b/man/tidy.frosting.Rd
@@ -1,19 +1,14 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/step_panel_normalize.R, R/tidy.R
-\name{tidy.step_panel_normalize}
-\alias{tidy.step_panel_normalize}
+% Please edit documentation in R/tidy.R
+\name{tidy.frosting}
 \alias{tidy.frosting}
 \title{Tidy the result of a frosting object}
 \usage{
-\method{tidy}{step_panel_normalize}(x, ...)
-
 \method{tidy}{frosting}(x, number = NA, id = NA, ...)
 }
 \arguments{
 \item{x}{A \code{frosting} or \code{layer} object}
 
-\item{...}{Not currently used.}
-
 \item{number}{An integer or \code{NA}. If missing, and \code{id} is not provided,
 the return value is a list of the operations in the frosting.
 If a number is given, a \code{tidy} method is executed for that operation
@@ -25,6 +20,8 @@ the return value is a list of the operations in the frosting.
 If a character string is given, a \code{tidy} method is executed for that
 operation in the frosting (if it exists). \code{id} must not be provided
 if \code{number} is.}
+
+\item{...}{Not currently used.}
 }
 \value{
 A tibble with columns that vary depending on what

From ddc80839b7b552a07fd0a29736bb26b68e2bb59c Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 12 Nov 2024 10:34:02 -0800
Subject: [PATCH 171/387] stray file

---
 _pkgdown.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 5be6bc4e9..d0bc5bf87 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -17,7 +17,6 @@ articles:
       - update
   - title: Advanced methods
     contents:
-      - articles/sliding
       - articles/smooth-qr
       - panel-data
 

From 437e2c731cc8d8a3ed942159550c092306438f12 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 12 Nov 2024 11:12:15 -0800
Subject: [PATCH 172/387] annoying workaround needs to come back

---
 .github/workflows/pkgdown.yaml | 17 ++++++++++++++++-
 _pkgdown.yml                   |  2 +-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index df24f20da..2aae89fa9 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -40,7 +40,22 @@ jobs:
           needs: website
 
       - name: Build site
-        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        # our versioning system+dev branch doesn't match the requirements for
+        # develop mode = auto
+        run: |
+           target_ref <- "${{ github.event_name == 'pull_request' && github.base_ref || github.ref }}"
+           override <- if (target_ref == "main" || target_ref == "refs/heads/main") {
+             list(development = list(mode = "release"))
+           } else if (target_ref == "dev" || target_ref == "refs/heads/dev") {
+             list(development = list(mode = "devel"))
+           } else {
+             stop("Unexpected target_ref: ", target_ref)
+           }
+           pkg <- pkgdown::as_pkgdown(".", override = override)
+           cli::cli_rule("Cleaning files from old site...")
+           pkgdown::clean_site(pkg)
+           pkgdown::build_site(pkg, preview = FALSE, install = FALSE, new_process = FALSE)
+           pkgdown:::build_github_pages(pkg)
         shell: Rscript {0}
 
       - name: Deploy to GitHub pages 🚀
diff --git a/_pkgdown.yml b/_pkgdown.yml
index d0bc5bf87..a115871d4 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,7 +1,7 @@
 url: https://cmu-delphi.github.io/epipredict/
 
 development:
-  mode: auto
+  mode: devel
 
 template:
   package: delphidocs

From 2811d2ff2f07753489ef50995d337108f67f5f1c Mon Sep 17 00:00:00 2001
From: Daniel McDonald <dajmcdon@gmail.com>
Date: Thu, 28 Nov 2024 11:32:43 -0800
Subject: [PATCH 173/387] Update _pkgdown.yml

---
 _pkgdown.yml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index a115871d4..86b4d16ad 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -20,12 +20,6 @@ articles:
       - articles/smooth-qr
       - panel-data
 
-repo:
-  url:
-    home: https://github.com/cmu-delphi/epipredict/
-    source: https://github.com/cmu-delphi/epipredict/
-    issue: https://github.com/cmu-delphi/epipredict/issues
-
 home:
   links:
     - text: The epiprocess package

From 463734cc5f83a6597b9bd89bb4090d4b2fc06f77 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 10:49:42 -0800
Subject: [PATCH 174/387] add lubridate

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3527ddfd..2493ac41f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -38,6 +38,7 @@ Imports:
     glue,
     hardhat (>= 1.3.0),
     lifecycle,
+    lubridate,
     magrittr,
     recipes (>= 1.0.4),
     rlang (>= 1.1.0),
@@ -54,7 +55,6 @@ Suggests:
     fs,
     grf,
     knitr,
-    lubridate,
     poissonreg,
     purrr,
     quantreg,

From a64b6db68408028b0da13ac707251c4de56566d0 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 10:50:20 -0800
Subject: [PATCH 175/387] style on short line

---
 R/utils-latency.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index d854cb9cf..af76bab16 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -479,8 +479,7 @@ compare_bake_prep_latencies <- function(object, new_data, call = caller_env()) {
 
 #' @keywords internal
 create_shift_grid <- function(prefix, amount, target_sign, columns, latency_table, latency_sign) {
-  if (!is.null(latency_table) &&
-    latency_sign == target_sign) {
+  if (!is.null(latency_table) && latency_sign == target_sign) {
     # get the actually used latencies
     rel_latency <- latency_table %>% filter(col_name %in% columns)
     latency_adjusted <- TRUE

From 3f2c1c1ec8bf24a5d18baa844331c9a5ebabe712 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 10:50:51 -0800
Subject: [PATCH 176/387] add a hidden column to track the time_value of the
 outcome

---
 R/epi_recipe.R     |  3 +++
 R/step_epi_shift.R | 27 +++++++++++++++++++++++----
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 646cb1b6d..e004c9806 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -568,8 +568,11 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
     }
     composition <- "tibble"
   }
+  new_data$.target_time_value <- new_data$time_value
 
   new_data <- NextMethod("bake")
+
+  new_data$.target_time_value <- NULL
   if (!is.null(meta)) {
     # Baking should have dropped epi_df-ness and metadata. Re-infer some
     # metadata and assume others remain the same as the object/template:
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index beda182e6..f12464f6e 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -66,7 +66,10 @@ step_epi_lag <-
       ))
     }
     arg_is_nonneg_int(lag)
-    arg_is_chr_scalar(prefix, id)
+    arg_is_chr_scalar(prefix, id, role)
+    if (role == "outcome" && length(lag) > 1L) {
+      cli_abort("Only one {.val outcome} may be created with this step.")
+    }
 
     recipes::add_step(
       recipe,
@@ -111,7 +114,11 @@ step_epi_ahead <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
-    arg_is_chr_scalar(prefix, id)
+    arg_is_chr_scalar(prefix, id, role)
+    arg_is_nonneg_int(ahead)
+    if (role == "outcome" && length(ahead) > 1L) {
+      cli_abort("Only one {.val outcome} may be created with this step.")
+    }
 
     recipes::add_step(
       recipe,
@@ -247,12 +254,24 @@ prep.step_epi_ahead <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_epi_lag <- function(object, new_data, ...) {
-  add_shifted_columns(new_data, object)
+  new_data <- add_shifted_columns(new_data, object)
+  if (object$role == "outcome") {
+    shift_val <- object$shift_grid$shift_val[1]
+    new_data <- new_data %>%
+      mutate(.target_time_value = .target_time_value + shift_val)
+  }
+  new_data
 }
 
 #' @export
 bake.step_epi_ahead <- function(object, new_data, ...) {
-  add_shifted_columns(new_data, object)
+  new_data <- add_shifted_columns(new_data, object)
+  if (object$role == "outcome") {
+    shift_val <- object$shift_grid$shift_val[1]
+    new_data <- new_data %>%
+      mutate(.target_time_value = .target_time_value + shift_val)
+  }
+  new_data
 }
 
 #' @export

From 4af6a8a3b33b9f4e09c7719178938cb31891bf2e Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 10:51:58 -0800
Subject: [PATCH 177/387] typo in documentation and error msg

---
 R/flatline_forecaster.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 7efda3efd..bf3d3e867 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -16,7 +16,7 @@
 #'
 #' @param epi_data An [epiprocess::epi_df][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
-#' @param args_list A list of dditional arguments as created by the
+#' @param args_list A list of additional arguments as created by the
 #'   [flatline_args_list()] constructor function.
 #'
 #' @return A data frame of point (and optionally interval) forecasts at a single
@@ -34,7 +34,7 @@ flatline_forecaster <- function(
     args_list = flatline_args_list()) {
   validate_forecaster_inputs(epi_data, outcome, "time_value")
   if (!inherits(args_list, c("flat_fcast", "alist"))) {
-    cli_abort("`args_list` was not created using `flatline_args_list().")
+    cli_abort("`args_list` was not created using `flatline_args_list()`.")
   }
   keys <- key_colnames(epi_data)
   ek <- kill_time_value(keys)

From 301e65130e1acc6434e2faa0e6a300195c365690 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 10:52:25 -0800
Subject: [PATCH 178/387] adding predictor drafted

---
 R/step_climate_predictor.R              | 212 ++++++++++++++++++++++++
 tests/testthat/test-climate_predictor.R |  62 +++++++
 2 files changed, 274 insertions(+)
 create mode 100644 R/step_climate_predictor.R
 create mode 100644 tests/testthat/test-climate_predictor.R

diff --git a/R/step_climate_predictor.R b/R/step_climate_predictor.R
new file mode 100644
index 000000000..003eee488
--- /dev/null
+++ b/R/step_climate_predictor.R
@@ -0,0 +1,212 @@
+#' Calculate a growth rate
+#'
+#' `step_growth_rate()` creates a *specification* of a recipe step
+#'   that will generate one or more new columns of derived data.
+#'
+#'
+#' @inheritParams step_epi_lag
+#' @param horizon Bandwidth for the sliding window, when `method` is
+#'   "rel_change" or "linear_reg". See [epiprocess::growth_rate()] for more
+#'   details.
+#' @param method Either "rel_change" or "linear_reg",
+#'   indicating the method to use for the growth rate
+#'   calculation. These are local methods: they are run in a sliding
+#'   fashion over the sequence (in order to estimate derivatives and hence
+#'   growth rates). See [epiprocess::growth_rate()] for more details.
+#' @param log_scale Should growth rates be estimated using the parameterization
+#'   on the log scale? See details for an explanation. Default is `FALSE`.
+#' @param replace_Inf Sometimes, the growth rate calculation can result in
+#'   infinite values (if the denominator is zero, for example). In this case,
+#'   most prediction methods will fail. This argument specifies potential
+#'   replacement values. The default (`NA`) will likely result in these rows
+#'   being removed from the data. Alternatively, you could specify arbitrary
+#'   large values, or perhaps zero. Setting this argument to `NULL` will result
+#'   in no replacement.
+#' @param additional_gr_args_list A list of additional arguments used by
+#'   [epiprocess::growth_rate()]. All `...` arguments may be passed here along
+#'   with `dup_rm` and `na_rm`.
+#' @template step-return
+#'
+#'
+#' @family row operation steps
+#' @importFrom epiprocess growth_rate
+#' @export
+#' @examples
+#' r <- epi_recipe(covid_case_death_rates) %>%
+#'   step_growth_rate(case_rate, death_rate)
+#' r
+#'
+#' r %>%
+#'   prep(covid_case_death_rates) %>%
+#'   bake(new_data = NULL)
+step_climate_predictor <-
+  function(recipe,
+           ...,
+           role = "predictor",
+           time_type = c("epiweek", "week", "month", "day"),
+           center_method = c("median", "mean"),
+           window_size = 3L,
+           epi_keys = NULL,
+           prefix = "climate_",
+           skip = FALSE,
+           id = rand_id("climate_predictor")) {
+    if (!is_epi_recipe(recipe)) {
+      cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
+    }
+    time_type <- rlang::arg_match(time_type)
+    center_method <- rlang::arg_match(center_method)
+    arg_is_chr(role)
+    arg_is_chr(epi_keys, allow_null = TRUE)
+    arg_is_nonneg_int(window_size)
+    arg_is_scalar(window_size)
+    arg_is_chr_scalar(prefix, id)
+    arg_is_lgl_scalar(skip)
+
+    aggr <- switch(time_type,
+                   epiweek = lubridate::epiweek, week = lubridate::week,
+                   month = lubridate::month, day = lubridate::yday)
+
+    recipes::add_step(
+      recipe,
+      step_climate_predictor_new(
+        terms = enquos(...),
+        role = role,
+        trained = FALSE,
+        time_type = time_type,
+        aggr = aggr,
+        center_method = center_method,
+        window_size = window_size,
+        epi_keys = epi_keys,
+        result = NULL,
+        prefix = prefix,
+        columns = NULL,
+        skip = skip,
+        id = id,
+        case_weights = NULL
+      )
+    )
+  }
+
+
+step_climate_predictor_new <-
+  function(terms,
+           role,
+           trained,
+           time_type,
+           aggr,
+           center_method,
+           window_size,
+           epi_keys,
+           result,
+           prefix,
+           columns,
+           skip,
+           id,
+           case_weights) {
+    recipes::step(
+      subclass = "climate_predictor",
+      terms = terms,
+      role = role,
+      trained = trained,
+      time_type = time_type,
+      aggr = aggr,
+      center_method = center_method,
+      window_size = window_size,
+      epi_keys = epi_keys,
+      result = result,
+      prefix = prefix,
+      columns = columns,
+      skip = skip,
+      id = id,
+      case_weights = case_weights
+    )
+  }
+
+
+
+#' @export
+prep.step_climate_predictor <- function(x, training, info = NULL, ...) {
+  col_names <- recipes_eval_select(x$terms, training, info)
+  recipes::check_type(training[, col_names], types = c("double", "integer"))
+  wts <- recipes::get_case_weights(info, training)
+  were_weights_used <- recipes::are_weights_used(wts, unsupervised = TRUE)
+  if (isFALSE(were_weights_used)) {
+    wts <- rep(1, nrow(training))
+  }
+
+  modulus <- switch(x$time_type, epiweek = 7L, week = 7L, month = 12L, day = 365L)
+
+  result <- training %>%
+    mutate(.idx = x$aggr(time_value), .weights = wts) %>%
+    select(.idx, .weights, c(col_names, x$epi_keys)) %>%
+    summarize(across(
+      all_of(col_names),
+      ~ roll_modular_multivec(.x, .idx, .weights, x$center_method,
+                              x$window_size, modulus)),
+      .by = x$epi_keys
+    ) %>%
+    unnest(col_names)
+
+  step_climate_predictor_new(
+    terms = x$terms,
+    role = x$role,
+    trained = TRUE,
+    time_type = x$time_type,
+    aggr = x$aggr,
+    center_method = x$center_method,
+    window_size = x$window_size,
+    epi_keys = x$epi_keys,
+    result = result,
+    prefix = x$prefix,
+    columns = col_names,
+    skip = x$skip,
+    id = x$id,
+    case_weights = were_weights_used
+  )
+}
+
+
+
+#' @export
+bake.step_climate_predictor <- function(object, new_data, ...) {
+  new_data %>%
+    mutate(.idx = object$aggr(.target_time_value)) %>%
+    left_join(object$result, by = c(".idx", object$epi_keys)) %>%
+    select(-.idx)
+}
+
+
+#' @export
+print.step_climate_predictor <- function(x, width = max(20, options()$width - 30), ...) {
+  print_epi_step(
+    x$columns, x$terms, x$trained,
+    title = "Calculating climate_predictor for ",
+    conjunction = "by",
+    extra_text = paste(x$time_type, "using the", x$center_method)
+  )
+  invisible(x)
+}
+
+roll_modular_multivec <- function(col, index, weights, center_method,
+                                  window_size, modulus) {
+  if (center_method == "mean") {
+    aggr <- function(x, w) weighted.mean(x, w, na.rm = TRUE)
+  } else {
+    aggr <- function(x, w) median(x, na.rm = TRUE)
+  }
+  tib <- tibble(col = col, weights = weights, index = index) |>
+    arrange(index) |>
+    tidyr::nest(data = c(col, weights), .by = index)
+  out <- double(nrow(tib))
+  for (iter in seq_along(out)) {
+    entries <- (iter - window_size):(iter + window_size) %% modulus
+    entries[entries == 0] <- modulus
+    out[iter] <- with(
+      purrr::list_rbind(tib$data[entries]),
+      aggr(col, weights)
+    )
+  }
+  tibble(index = unique(tib$index), climate_pred = out)
+}
+
+
diff --git a/tests/testthat/test-climate_predictor.R b/tests/testthat/test-climate_predictor.R
new file mode 100644
index 000000000..0662e90d2
--- /dev/null
+++ b/tests/testthat/test-climate_predictor.R
@@ -0,0 +1,62 @@
+test_that("weighted_median works", {
+  col <- c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0)
+  w <- rep(1, 10)
+  expect_equal(weighted_median(col, w), median(col))
+
+  w <- c(1, 1, 2, 2, 1, 1, 1, 6, 2, 2)
+  expect_equal(weighted_median(col, w), median(rep(col, times = w)))
+})
+
+test_that("roll_modular_multivec works", {
+  tib <- tibble(
+    col = c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0),
+    indx = c(1, 1, 1, 2, 2, 3, 3, 3, 1, 1),
+    w = rep(1, 10)
+  )
+  modulus <- 3L
+  # unweighted mean
+  expected_res <- tib |>
+    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
+    summarise(climate_pred = weighted.mean(col, w = w), .by = index)
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 0, modulus),
+    expected_res
+  )
+  w_size <- 1L
+  expected_res <- tibble(index = as.double(1:3), climate_pred = mean(tib$col))
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 1L, modulus),
+    expected_res
+  )
+  # weighted mean
+  tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
+  expected_res <- tib |>
+    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
+    summarise(climate_pred = weighted.mean(col, w = w), .by = index)
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 0, modulus),
+    expected_res
+  )
+  tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
+  expected_res <- tibble(
+    index = as.double(1:3),
+    climate_pred = weighted.mean(tib$col, tib$w)
+  )
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 1L, modulus),
+    expected_res
+  )
+  # median
+  expected_res <- tib |>
+    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
+    summarise(climate_pred = median(col), .by = index)
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "median", 0, modulus),
+    expected_res
+  )
+  expected_res <- tibble(index = as.double(1:3), climate_pred = median(tib$col))
+  expect_equal(
+    roll_modular_multivec(tib$col, tib$indx, tib$w, "median", 1L, modulus),
+    expected_res
+  )
+})

From 6cb789750f31d2e3139a58a4e8a1aa87b6c736eb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 14:49:33 -0800
Subject: [PATCH 179/387] remove ability for `...` to be empty in
 adjust_latency

---
 R/step_adjust_latency.R | 78 ++++++++---------------------------------
 R/utils-latency.R       |  2 +-
 2 files changed, 15 insertions(+), 65 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 3d9f19891..688f17d1b 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -31,7 +31,7 @@
 #' 1. `"ca"` is latent by 2 days, whereas `"ma"` is latent by 1
 #' 2. if we want to use `b` as an exogenous variable, for `"ma"` it is latent by 3 days instead of just 1.
 #'
-#' Regardless of `method`, `epi_keys_checked="geo_value"` guarantees tha the
+#' Regardless of `method`, `epi_keys_checked="geo_value"` guarantees that the
 #'   difference between `"ma"` and `"ca"` is accounted for by making  the
 #'   latency adjustment at least 2. For some comparison, here's what the various
 #'   methods will do:
@@ -47,7 +47,7 @@
 #'   forward to the `forecast_date`:
 #'   ```{r toy_df}
 #'   toy_recipe <- epi_recipe(toy_df) %>%
-#'     step_adjust_latency(method="locf")
+#'     step_adjust_latency(has_role("raw"), method="locf")
 #'
 #'   toy_recipe %>%
 #'     prep(toy_df) %>%
@@ -62,9 +62,9 @@
 #'   latencies, the lags for each are adjusted separately. In the toy example:
 #'   ```{r toy_df}
 #'   toy_recipe <- epi_recipe(toy_df) %>%
-#'     step_adjust_latency(method="extend_lags") %>%
-#'     step_epi_lag(a,lag=1) %>%
-#'     step_epi_lag(b,lag=1) %>%
+#'     step_adjust_latency(has_role("raw"), method = "extend_lags") %>%
+#'     step_epi_lag(a, lag=1) %>%
+#'     step_epi_lag(b, lag=1) %>%
 #'     step_epi_ahead(a, ahead=1)
 #'
 #'   toy_recipe %>%
@@ -89,8 +89,8 @@
 #'   the most latent data to insure there is data available. In the toy example:
 #'   ```{r toy_df}
 #'   toy_recipe <- epi_recipe(toy_df) %>%
-#'     step_adjust_latency(method="extend_ahead") %>%
-#'     step_epi_lag(a,lag=0) %>%
+#'     step_adjust_latency(has_role("raw"), method="extend_ahead") %>%
+#'     step_epi_lag(a, lag=0) %>%
 #'     step_epi_ahead(a, ahead=1)
 #'
 #'   toy_recipe %>%
@@ -186,7 +186,7 @@
 #' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 #'
 #' r <- epi_recipe(covid_case_death_rates) %>%
-#'   step_adjust_latency(method = "extend_ahead") %>%
+#'   step_adjust_latency(has_role("raw"), method = "extend_ahead") %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
@@ -275,20 +275,15 @@ step_adjust_latency_new <-
 #' @importFrom dplyr rowwise
 prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
   latency <- x$latency
+  col_names <- recipes::recipes_eval_select(x$terms, training, info)
+
   forecast_date <- x$fixed_forecast_date %||%
     get_forecast_date(training, info, x$epi_keys_checked, latency)
 
   latency_table <- get_latency_table(
-    training, NULL, forecast_date, latency,
+    training, col_names, forecast_date, latency,
     get_sign(x), x$epi_keys_checked, x$keys_to_ignore, info, x$terms
   )
-  # get the columns used, even if it's all of them
-  terms_used <- x$terms
-  if (is_empty(terms_used)) {
-    terms_used <- info %>%
-      filter(role == "raw") %>%
-      pull(variable)
-  }
 
   step_adjust_latency_new(
     terms = x$terms,
@@ -304,7 +299,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     epi_keys_checked = x$epi_keys_checked,
     keys_to_ignore = x$keys_to_ignore,
     check_latency_length = x$check_latency_length,
-    columns = recipes_eval_select(latency_table$col_name, training, info),
+    columns = col_names,
     skip = x$skip,
     id = x$id
   )
@@ -363,51 +358,6 @@ print.step_adjust_latency <-
       conj <- "with latency"
       extra_text <- "set at train time"
     }
-    # what follows is a somewhat modified version of print_epi_step, since the case of no arguments for adjust_latency means apply to all relevant columns, and not none of them
-    theme_div_id <- cli::cli_div(
-      theme = list(.pkg = list(`vec-trunc` = Inf, `vec-last` = ", "))
-    )
-    # this is a slightly modified copy of
-    title <- trimws(x$method)
-    trained_text <- dplyr::if_else(x$trained, "Trained", "")
-    vline_seperator <- dplyr::if_else(trained_text == "", "", "|")
-    comma_seperator <- dplyr::if_else(
-      trained_text != "", true = ",", false = ""
-    )
-    extra_text <- recipes::format_ch_vec(extra_text)
-    width_title <- nchar(paste0(
-      "* ", title, ":", " ", conj, " ", extra_text, " ", vline_seperator,
-      " ", trained_text, " "
-    ))
-    width_diff <- cli::console_width() * 1 - width_title
-    if (x$trained) {
-      elements <- x$columns
-    } else {
-      if (is_empty(x$terms)) {
-        elements <- "all future predictors"
-      } else {
-        elements <- lapply(x$terms, function(x) {
-          rlang::expr_deparse(rlang::quo_get_expr(x), width = Inf)
-        })
-        elements <- vctrs::list_unchop(elements, ptype = character())
-      }
-    }
-
-    element_print_lengths <- cumsum(nchar(elements)) +
-      c(0L, cumsum(rep(2L, length(elements) - 1))) +
-      c(rep(5L, length(elements) - 1), 0L)
-    first_line <- which(width_diff >= element_print_lengths)
-    first_line <- unname(first_line)
-    first_line <- ifelse(
-      test = identical(first_line, integer(0)),
-      yes = length(element_print_lengths),
-      no = max(first_line)
-    )
-    more_dots <- ifelse(first_line == length(elements), "", ", ...")
-    cli::cli_bullets(
-      c("\n    {title}: \\\n    {.pkg {cli::cli_vec(elements[seq_len(first_line)])}}\\\n    {more_dots} \\\n    {conj} \\\n    {.pkg {extra_text}} \\\n    {vline_seperator} \\\n    {.emph {trained_text}}")
-    )
-
-    cli::cli_end(theme_div_id)
-    invisible(x)
+    print_epi_step(x$columns, x$terms, x$trained, "Adjusting",
+                   conjunction = conj, extra_text = extra_text)
   }
diff --git a/R/utils-latency.R b/R/utils-latency.R
index af76bab16..2a700cd93 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -315,7 +315,7 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   }
   # construct the latency table
   latency_table <- tibble(col_name = names(training)) %>%
-    filter(col_name %nin% key_colnames(training))
+    filter(col_name %nin% c(key_colnames(training), ".target_time_value"))
   if (length(columns) > 0) {
     latency_table <- latency_table %>% filter(col_name %in% columns)
   }

From 426f0c7250bc2af8dff02ba9dffe6526468369cb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 14:50:10 -0800
Subject: [PATCH 180/387] add/remove hidden target_time_value column

---
 R/epi_recipe.R | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index e004c9806..f9cbcc814 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -123,7 +123,11 @@ epi_recipe.epi_df <-
           unique(role)
         ) # anything else
       ))
-
+    tv_info <- var_info %>%
+      filter(role == "time_value") %>%
+      mutate(variable = ".target_time_value", role = ".target_time_value",
+             source = "derived")
+    var_info <- tibble::add_row(var_info, tv_info)
     ## Return final object of class `recipe`
     out <- list(
       var_info = var_info,
@@ -429,7 +433,7 @@ prep.epi_recipe <- function(
     x, training = NULL, fresh = FALSE, verbose = FALSE,
     retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ...) {
   if (is.null(training)) {
-    cli_warn(c(
+    cli_abort(c(
       "!" = "No training data was supplied to {.fn prep}.",
       "!" = "Unlike a {.cls recipe}, an {.cls epi_recipe} does not ",
       "!" = "store the full template data in the object.",
@@ -437,6 +441,12 @@ prep.epi_recipe <- function(
       "!" = "to avoid addtional warning messages."
     ))
   }
+  tr_names <- names(training)
+  if ("time_value" %in% tr_names && !(".target_time_value" %in% tr_names)) {
+    training$.target_time_value <- training$time_value
+  } else {
+    cli_abort("The training data does not contain a `time_value` column.")
+  }
   training <- recipes:::check_training_set(training, x, fresh)
   training <- epi_check_training_set(training, x)
   training <- relocate(training, all_of(key_colnames(training)))
@@ -568,11 +578,16 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
     }
     composition <- "tibble"
   }
-  new_data$.target_time_value <- new_data$time_value
+  tr_names <- names(new_data)
+  if ("time_value" %in% tr_names && !(".target_time_value" %in% tr_names)) {
+    new_data$.target_time_value <- new_data$time_value
+  } else {
+    cli_abort("The {.val new_data} does not contain a `time_value` column.")
+  }
 
   new_data <- NextMethod("bake")
 
-  new_data$.target_time_value <- NULL
+  new_data <- dplyr::select(new_data, -.target_time_value)
   if (!is.null(meta)) {
     # Baking should have dropped epi_df-ness and metadata. Re-infer some
     # metadata and assume others remain the same as the object/template:
@@ -599,6 +614,7 @@ print.epi_recipe <- function(x, form_width = 30, ...) {
   cli::cli_h3("Inputs")
 
   tab <- table(x$var_info$role, useNA = "ifany")
+  tab <- tab[names(tab) != ".target_time_value"]
   tab <- stats::setNames(tab, names(tab))
   names(tab)[is.na(names(tab))] <- "undeclared role"
 

From fef636cc1653bef8719451c6324254bb96b0185f Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 14:50:46 -0800
Subject: [PATCH 181/387] finish climate predictor draft

---
 R/step_climate_predictor.R | 82 ++++++++++++++++++--------------------
 1 file changed, 38 insertions(+), 44 deletions(-)

diff --git a/R/step_climate_predictor.R b/R/step_climate_predictor.R
index 003eee488..896eb9e2d 100644
--- a/R/step_climate_predictor.R
+++ b/R/step_climate_predictor.R
@@ -1,45 +1,39 @@
-#' Calculate a growth rate
+#' Calculate a climatological variable based on the history
 #'
-#' `step_growth_rate()` creates a *specification* of a recipe step
-#'   that will generate one or more new columns of derived data.
+#' `step_climate()` creates a *specification* of a recipe step
+#'   that will generate one or more new columns of derived data. Using this as
+#'   a predictor is most useful when predicting strongly seasonal data. The
+#'   climate timing will automatically be aligned to the outcome at bake time.
 #'
 #'
-#' @inheritParams step_epi_lag
-#' @param horizon Bandwidth for the sliding window, when `method` is
-#'   "rel_change" or "linear_reg". See [epiprocess::growth_rate()] for more
-#'   details.
-#' @param method Either "rel_change" or "linear_reg",
-#'   indicating the method to use for the growth rate
-#'   calculation. These are local methods: they are run in a sliding
-#'   fashion over the sequence (in order to estimate derivatives and hence
-#'   growth rates). See [epiprocess::growth_rate()] for more details.
-#' @param log_scale Should growth rates be estimated using the parameterization
-#'   on the log scale? See details for an explanation. Default is `FALSE`.
-#' @param replace_Inf Sometimes, the growth rate calculation can result in
-#'   infinite values (if the denominator is zero, for example). In this case,
-#'   most prediction methods will fail. This argument specifies potential
-#'   replacement values. The default (`NA`) will likely result in these rows
-#'   being removed from the data. Alternatively, you could specify arbitrary
-#'   large values, or perhaps zero. Setting this argument to `NULL` will result
-#'   in no replacement.
-#' @param additional_gr_args_list A list of additional arguments used by
-#'   [epiprocess::growth_rate()]. All `...` arguments may be passed here along
-#'   with `dup_rm` and `na_rm`.
+#' @inheritParams step_growth_rate
+#' @param time_type The duration over which time aggregation should be performed.
+#' @param center_method The measure of center to be calculated over the time
+#'   window.
+#' @param window_size Scalar integer. How many time units on each side should
+#'   be included. For example, if `window_size = 3` and `time_type = "day"`,
+#'   then on each day in the data, the center will be calculated using 3 days
+#'   before and three days after. So, in this case, it operates like a weekly
+#'   rolling average, centered at each day.
+#' @param epi_keys Character vector or `NULL`. Any columns mentioned will be
+#'   grouped before performing any center calculation. So for example, given
+#'   state-level data, a national climate would be calculated if `NULL`, but
+#'   passing `epi_keys = "geo_value"` would calculate the climate separately
+#'   by state.
 #' @template step-return
 #'
 #'
-#' @family row operation steps
-#' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
 #' r <- epi_recipe(covid_case_death_rates) %>%
-#'   step_growth_rate(case_rate, death_rate)
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   step_climate(death_rate, time_type = "day")
 #' r
 #'
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
 #'   bake(new_data = NULL)
-step_climate_predictor <-
+step_climate <-
   function(recipe,
            ...,
            role = "predictor",
@@ -62,18 +56,18 @@ step_climate_predictor <-
     arg_is_chr_scalar(prefix, id)
     arg_is_lgl_scalar(skip)
 
-    aggr <- switch(time_type,
-                   epiweek = lubridate::epiweek, week = lubridate::week,
-                   month = lubridate::month, day = lubridate::yday)
+    time_aggr <- switch(time_type,
+                        epiweek = lubridate::epiweek, week = lubridate::week,
+                        month = lubridate::month, day = lubridate::yday)
 
     recipes::add_step(
       recipe,
-      step_climate_predictor_new(
+      step_climate_new(
         terms = enquos(...),
         role = role,
         trained = FALSE,
         time_type = time_type,
-        aggr = aggr,
+        time_aggr = time_aggr,
         center_method = center_method,
         window_size = window_size,
         epi_keys = epi_keys,
@@ -88,12 +82,12 @@ step_climate_predictor <-
   }
 
 
-step_climate_predictor_new <-
+step_climate_new <-
   function(terms,
            role,
            trained,
            time_type,
-           aggr,
+           time_aggr,
            center_method,
            window_size,
            epi_keys,
@@ -104,12 +98,12 @@ step_climate_predictor_new <-
            id,
            case_weights) {
     recipes::step(
-      subclass = "climate_predictor",
+      subclass = "climate",
       terms = terms,
       role = role,
       trained = trained,
       time_type = time_type,
-      aggr = aggr,
+      time_aggr = time_aggr,
       center_method = center_method,
       window_size = window_size,
       epi_keys = epi_keys,
@@ -125,7 +119,7 @@ step_climate_predictor_new <-
 
 
 #' @export
-prep.step_climate_predictor <- function(x, training, info = NULL, ...) {
+prep.step_climate <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   recipes::check_type(training[, col_names], types = c("double", "integer"))
   wts <- recipes::get_case_weights(info, training)
@@ -137,7 +131,7 @@ prep.step_climate_predictor <- function(x, training, info = NULL, ...) {
   modulus <- switch(x$time_type, epiweek = 7L, week = 7L, month = 12L, day = 365L)
 
   result <- training %>%
-    mutate(.idx = x$aggr(time_value), .weights = wts) %>%
+    mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
     select(.idx, .weights, c(col_names, x$epi_keys)) %>%
     summarize(across(
       all_of(col_names),
@@ -147,12 +141,12 @@ prep.step_climate_predictor <- function(x, training, info = NULL, ...) {
     ) %>%
     unnest(col_names)
 
-  step_climate_predictor_new(
+  step_climate_new(
     terms = x$terms,
     role = x$role,
     trained = TRUE,
     time_type = x$time_type,
-    aggr = x$aggr,
+    time_aggr = x$time_aggr,
     center_method = x$center_method,
     window_size = x$window_size,
     epi_keys = x$epi_keys,
@@ -168,16 +162,16 @@ prep.step_climate_predictor <- function(x, training, info = NULL, ...) {
 
 
 #' @export
-bake.step_climate_predictor <- function(object, new_data, ...) {
+bake.step_climate <- function(object, new_data, ...) {
   new_data %>%
-    mutate(.idx = object$aggr(.target_time_value)) %>%
+    mutate(.idx = object$time_aggr(.target_time_value)) %>%
     left_join(object$result, by = c(".idx", object$epi_keys)) %>%
     select(-.idx)
 }
 
 
 #' @export
-print.step_climate_predictor <- function(x, width = max(20, options()$width - 30), ...) {
+print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
   print_epi_step(
     x$columns, x$terms, x$trained,
     title = "Calculating climate_predictor for ",

From 3c2c9b7c033954c279ac5d130fab4d9c154e785a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 3 Dec 2024 14:51:11 -0800
Subject: [PATCH 182/387] redocument

---
 NAMESPACE                  |  9 +++++
 man/flatline_forecaster.Rd |  2 +-
 man/step_adjust_latency.Rd | 16 ++++----
 man/step_climate.Rd        | 77 ++++++++++++++++++++++++++++++++++++++
 4 files changed, 95 insertions(+), 9 deletions(-)
 create mode 100644 man/step_climate.Rd

diff --git a/NAMESPACE b/NAMESPACE
index e7e720f11..89e5683d0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -20,6 +20,7 @@ S3method(autoplot,epi_workflow)
 S3method(bake,check_enough_train_data)
 S3method(bake,epi_recipe)
 S3method(bake,step_adjust_latency)
+S3method(bake,step_climate)
 S3method(bake,step_epi_ahead)
 S3method(bake,step_epi_lag)
 S3method(bake,step_epi_slide)
@@ -60,6 +61,7 @@ S3method(predict,flatline)
 S3method(prep,check_enough_train_data)
 S3method(prep,epi_recipe)
 S3method(prep,step_adjust_latency)
+S3method(prep,step_climate)
 S3method(prep,step_epi_ahead)
 S3method(prep,step_epi_lag)
 S3method(prep,step_epi_slide)
@@ -90,6 +92,7 @@ S3method(print,layer_residual_quantiles)
 S3method(print,layer_threshold)
 S3method(print,layer_unnest)
 S3method(print,step_adjust_latency)
+S3method(print,step_climate)
 S3method(print,step_epi_ahead)
 S3method(print,step_epi_lag)
 S3method(print,step_epi_slide)
@@ -151,6 +154,8 @@ export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
 export(check_enough_train_data)
 export(clean_f_name)
+export(climate_args_list)
+export(climatological_forecaster)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
@@ -199,6 +204,7 @@ export(remove_model)
 export(slather)
 export(smooth_quantile_reg)
 export(step_adjust_latency)
+export(step_climate)
 export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
@@ -273,6 +279,9 @@ importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lubridate,epiweek)
+importFrom(lubridate,epiyear)
+importFrom(lubridate,ymd)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
diff --git a/man/flatline_forecaster.Rd b/man/flatline_forecaster.Rd
index f70c05e0f..f78e2f931 100644
--- a/man/flatline_forecaster.Rd
+++ b/man/flatline_forecaster.Rd
@@ -11,7 +11,7 @@ flatline_forecaster(epi_data, outcome, args_list = flatline_args_list())
 
 \item{outcome}{A scalar character for the column name we wish to predict.}
 
-\item{args_list}{A list of dditional arguments as created by the
+\item{args_list}{A list of additional arguments as created by the
 \code{\link[=flatline_args_list]{flatline_args_list()}} constructor function.}
 }
 \value{
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 0078de100..1493898b3 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -114,7 +114,7 @@ there are two issues we will need to address:
 \item if we want to use \code{b} as an exogenous variable, for \code{"ma"} it is latent by 3 days instead of just 1.
 }
 
-Regardless of \code{method}, \code{epi_keys_checked="geo_value"} guarantees tha the
+Regardless of \code{method}, \code{epi_keys_checked="geo_value"} guarantees that the
 difference between \code{"ma"} and \code{"ca"} is accounted for by making  the
 latency adjustment at least 2. For some comparison, here's what the various
 methods will do:
@@ -130,7 +130,7 @@ doesn't matter which day we're trying to predict, since it just fills
 forward to the \code{forecast_date}:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
-  step_adjust_latency(method="locf")
+  step_adjust_latency(has_role("raw"), method="locf")
 
 toy_recipe \%>\%
   prep(toy_df) \%>\%
@@ -163,9 +163,9 @@ a per-column basis; if cases and deaths are reported at different
 latencies, the lags for each are adjusted separately. In the toy example:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
-  step_adjust_latency(method="extend_lags") \%>\%
-  step_epi_lag(a,lag=1) \%>\%
-  step_epi_lag(b,lag=1) \%>\%
+  step_adjust_latency(has_role("raw"), method = "extend_lags") \%>\%
+  step_epi_lag(a, lag=1) \%>\%
+  step_epi_lag(b, lag=1) \%>\%
   step_epi_ahead(a, ahead=1)
 
 toy_recipe \%>\%
@@ -212,8 +212,8 @@ different data sources have different latencies; it must use the latency of
 the most latent data to insure there is data available. In the toy example:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
-  step_adjust_latency(method="extend_ahead") \%>\%
-  step_epi_lag(a,lag=0) \%>\%
+  step_adjust_latency(has_role("raw"), method="extend_ahead") \%>\%
+  step_epi_lag(a, lag=0) \%>\%
   step_epi_ahead(a, ahead=1)
 
 toy_recipe \%>\%
@@ -283,7 +283,7 @@ jhu <- covid_case_death_rates \%>\%
 attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
 
 r <- epi_recipe(covid_case_death_rates) \%>\%
-  step_adjust_latency(method = "extend_ahead") \%>\%
+  step_adjust_latency(has_role("raw"), method = "extend_ahead") \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
 r
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
new file mode 100644
index 000000000..db4f027f0
--- /dev/null
+++ b/man/step_climate.Rd
@@ -0,0 +1,77 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate_predictor.R
+\name{step_climate}
+\alias{step_climate}
+\title{Calculate a climatological variable based on the history}
+\usage{
+step_climate(
+  recipe,
+  ...,
+  role = "predictor",
+  time_type = c("epiweek", "week", "month", "day"),
+  center_method = c("median", "mean"),
+  window_size = 3L,
+  epi_keys = NULL,
+  prefix = "climate_",
+  skip = FALSE,
+  id = rand_id("climate_predictor")
+)
+}
+\arguments{
+\item{recipe}{A recipe object. The step will be added to the
+sequence of operations for this recipe.}
+
+\item{...}{One or more selector functions to choose variables
+for this step. See \code{\link[recipes:selections]{recipes::selections()}} for more details.}
+
+\item{role}{For model terms created by this step, what analysis role should
+they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.}
+
+\item{time_type}{The duration over which time aggregation should be performed.}
+
+\item{center_method}{The measure of center to be calculated over the time
+window.}
+
+\item{window_size}{Scalar integer. How many time units on each side should
+be included. For example, if \code{window_size = 3} and \code{time_type = "day"},
+then on each day in the data, the center will be calculated using 3 days
+before and three days after. So, in this case, it operates like a weekly
+rolling average, centered at each day.}
+
+\item{epi_keys}{Character vector or \code{NULL}. Any columns mentioned will be
+grouped before performing any center calculation. So for example, given
+state-level data, a national climate would be calculated if \code{NULL}, but
+passing \code{epi_keys = "geo_value"} would calculate the climate separately
+by state.}
+
+\item{prefix}{A character string that will be prefixed to the new column.}
+
+\item{skip}{A logical. Should the step be skipped when the
+recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
+when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
+conducted on new data (e.g. processing the outcome variable(s)).
+Care should be taken when using \code{skip = TRUE} as it may affect
+the computations for subsequent operations.}
+
+\item{id}{A unique identifier for the step}
+}
+\value{
+An updated version of \code{recipe} with the new step added to the
+sequence of any existing operations.
+}
+\description{
+\code{step_climate()} creates a \emph{specification} of a recipe step
+that will generate one or more new columns of derived data. Using this as
+a predictor is most useful when predicting strongly seasonal data. The
+climate timing will automatically be aligned to the outcome at bake time.
+}
+\examples{
+r <- epi_recipe(covid_case_death_rates) \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
+  step_climate(death_rate, time_type = "day")
+r
+
+r \%>\%
+  prep(covid_case_death_rates) \%>\%
+  bake(new_data = NULL)
+}

From 6f059f474ab184f38edcec2ba000e6e412b84d6a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 12 Dec 2024 16:35:05 -0600
Subject: [PATCH 183/387] fix for #414

---
 R/dist_quantiles.R                   |  7 ++++++-
 tests/testthat/test-dist_quantiles.R | 11 +++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index 8930bdeaa..d40a1d481 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -128,10 +128,12 @@ is_dist_quantiles <- function(x) {
 median.dist_quantiles <- function(x, na.rm = FALSE, ..., middle = c("cubic", "linear")) {
   quantile_levels <- field(x, "quantile_levels")
   values <- field(x, "values")
+  # we have exactly that quantile
   if (0.5 %in% quantile_levels) {
     return(values[match(0.5, quantile_levels)])
   }
-  if (length(quantile_levels) < 2 || min(quantile_levels) > 0.5 || max(quantile_levels) < 0.5) {
+  # if there's only 1 quantile_level (and it isn't 0.5), or the smallest quantile is larger than 0.5 or the largest smaller than 0.5, or if every value is NA, return NA
+  if (length(quantile_levels) < 2 || min(quantile_levels) > 0.5 || max(quantile_levels) < 0.5 || all(is.na(values))) {
     return(NA)
   }
   if (length(quantile_levels) < 3 || min(quantile_levels) > .25 || max(quantile_levels) < .75) {
@@ -161,6 +163,9 @@ quantile_extrapolate <- function(x, tau_out, middle) {
   tau <- field(x, "quantile_levels")
   qvals <- field(x, "values")
   nas <- is.na(qvals)
+  if (all(nas)) {
+    return(rep(NA, times = length(tau_out)))
+  }
   qvals_out <- rep(NA, length(tau_out))
   qvals <- qvals[!nas]
   tau <- tau[!nas]
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index ef65c5c11..0face873b 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -110,3 +110,14 @@ test_that("arithmetic works on quantiles", {
   expect_snapshot(error = TRUE, sum(dstn))
   expect_snapshot(error = TRUE, suppressWarnings(dstn + distributional::dist_normal()))
 })
+
+test_that("quantile.dist_quantile works for NA vectors", {
+  distn <- dist_quantiles(
+    list(c(NA, NA)),
+    list(1:2/3)
+  )
+  expect_true(is.na(quantile(distn, p = 0.5)))
+  expect_true(is.na(median(distn)))
+  expect_true(is.na(mean(distn)))
+  expect_equal(format(distn), "quantiles(NA)[2]")
+})

From c576b4bdce2e428d1413092d423eadc524a9a5ad Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 12 Dec 2024 16:36:49 -0600
Subject: [PATCH 184/387] news & description

---
 DESCRIPTION | 2 +-
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3527ddfd..017084322 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.5
+Version: 0.1.6
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 8be7d7c0d..e5e1bb819 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -23,6 +23,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 ## Bug fixes
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 - Quantiles produced by `grf` were sometimes out of order.
+- dist_quantiles can have all `NA` values without causing unrelated errors
 
 # epipredict 0.1
 

From 0f3a28d2ae4535b8bd935a8267110c3fcf1d309a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 12 Dec 2024 16:54:26 -0600
Subject: [PATCH 185/387] formatting

---
 tests/testthat/test-dist_quantiles.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 0face873b..78d74c812 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -114,7 +114,7 @@ test_that("arithmetic works on quantiles", {
 test_that("quantile.dist_quantile works for NA vectors", {
   distn <- dist_quantiles(
     list(c(NA, NA)),
-    list(1:2/3)
+    list(1:2 / 3)
   )
   expect_true(is.na(quantile(distn, p = 0.5)))
   expect_true(is.na(median(distn)))

From 964d800fc9b501d9c05831f9c2076804047cd641 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 16 Dec 2024 12:58:33 -0600
Subject: [PATCH 186/387] epiprocess epi_slide_opt update

---
 man/step_adjust_latency.Rd |  4 ++--
 vignettes/backtesting.Rmd  | 15 +++++++--------
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 0078de100..53098504e 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 152b2460d..765a684f4 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -385,8 +385,7 @@ canada_archive_faux <- epix_as_of(canada_archive, canada_archive$versions_end) %
 smooth_cases <- function(epi_df) {
   epi_df %>%
     group_by(geo_value) %>%
-    epi_slide_mean("case_rate", .window_size = 7, na.rm = TRUE) %>%
-    rename(cr_7dav = slide_value_case_rate)
+    epi_slide_mean("case_rate", .window_size = 7, na.rm = TRUE, .suffix = "_{.n}dav")
 }
 forecast_dates <- seq.Date(
   from = min(canada_archive$DT$version),
@@ -401,8 +400,8 @@ canada_forecasts <- bind_rows(
     ~ forecast_k_week_ahead(
       canada_archive_faux,
       ahead = .x,
-      outcome = "cr_7dav",
-      predictors = "cr_7dav",
+      outcome = "case_rate_7dav",
+      predictors = "case_rate_7dav",
       forecast_dates = forecast_dates,
       process_data = smooth_cases
     ) %>% mutate(version_aware = FALSE)
@@ -412,8 +411,8 @@ canada_forecasts <- bind_rows(
     ~ forecast_k_week_ahead(
       canada_archive,
       ahead = .x,
-      outcome = "cr_7dav",
-      predictors = "cr_7dav",
+      outcome = "case_rate_7dav",
+      predictors = "case_rate_7dav",
       forecast_dates = forecast_dates,
       process_data = smooth_cases
     ) %>% mutate(version_aware = TRUE)
@@ -435,7 +434,7 @@ case_rate_data <- bind_rows(
     ~ canada_archive %>%
       epix_as_of(.x) %>%
       smooth_cases() %>%
-      mutate(case_rate = cr_7dav, version = .x)
+      mutate(case_rate = case_rate_7dav, version = .x)
   ) %>%
     bind_rows() %>%
     mutate(version_aware = TRUE),
@@ -443,7 +442,7 @@ case_rate_data <- bind_rows(
   canada_archive %>%
     epix_as_of(doctor_visits$versions_end) %>%
     smooth_cases() %>%
-    mutate(case_rate = cr_7dav, version_aware = FALSE)
+    mutate(case_rate = case_rate_7dav, version_aware = FALSE)
 ) %>%
   filter(geo_value == geo_choose)
 

From 031b6055863be9e5fb189eeaef890087772e4eaf Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 17 Jan 2025 10:31:26 -0800
Subject: [PATCH 187/387] update rproj

---
 epipredict.Rproj | 1 +
 1 file changed, 1 insertion(+)

diff --git a/epipredict.Rproj b/epipredict.Rproj
index 69fafd4b6..0c2659d89 100644
--- a/epipredict.Rproj
+++ b/epipredict.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: a71c2044-10c8-46a9-9702-f4bfc95042c8
 
 RestoreWorkspace: No
 SaveWorkspace: No

From c598840d1ac57eceabd66fb2fb0da728a3e6cb05 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 17 Jan 2025 10:36:23 -0800
Subject: [PATCH 188/387] drafted, needs refactor

---
 R/climatological_forecaster.R                 | 252 ++++++++++++++++++
 R/epi_recipe.R                                |  15 +-
 R/step_adjust_latency.R                       |  13 +-
 ...tep_climate_predictor.R => step_climate.R} |   6 +-
 R/step_epi_shift.R                            |   3 -
 man/climate_args_list.Rd                      |  97 +++++++
 man/climatological_forecaster.Rd              |  37 +++
 man/step_climate.Rd                           |   2 +-
 tests/testthat/_snaps/bake-method.md          |   2 +-
 .../_snaps/climatological_baseline.md         |  32 +++
 tests/testthat/_snaps/step_adjust_latency.md  |  15 +-
 tests/testthat/_snaps/step_epi_shift.new.md   |  27 ++
 ...est-climate_predictor.R => test-climate.R} |   9 -
 tests/testthat/test-climatological_baseline.R |  35 +++
 tests/testthat/test-epi_recipe.R              |  15 +-
 tests/testthat/test-layer_add_forecast_date.R |   2 +-
 tests/testthat/test-step_adjust_latency.R     |  30 +--
 17 files changed, 531 insertions(+), 61 deletions(-)
 create mode 100644 R/climatological_forecaster.R
 rename R/{step_climate_predictor.R => step_climate.R} (96%)
 create mode 100644 man/climate_args_list.Rd
 create mode 100644 man/climatological_forecaster.Rd
 create mode 100644 tests/testthat/_snaps/climatological_baseline.md
 create mode 100644 tests/testthat/_snaps/step_epi_shift.new.md
 rename tests/testthat/{test-climate_predictor.R => test-climate.R} (87%)
 create mode 100644 tests/testthat/test-climatological_baseline.R

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
new file mode 100644
index 000000000..92f5b7413
--- /dev/null
+++ b/R/climatological_forecaster.R
@@ -0,0 +1,252 @@
+#' Climatological forecaster
+#'
+#' This is another "baseline" type forecaster, but it is especially appropriate
+#' for strongly seasonal diseases (e.g., influenza). The idea is to forecast
+#' the "typical season" by summarizing over all available history in the
+#' `epi_data`. This is analogous to a "climate" forecast rather than a "weather"
+#' forecast, essentially predicting "typical January" behavior by relying on a
+#' long history of such periods rather than heavily using recent data.
+#'
+#' The forecast is taken as the quantiles of the `outcome` in a small window
+#' around the target week, computed over the entire available history.
+#'
+#' @inheritParams flatline_forecaster
+#' @param args_list A list of additional arguments as created by the
+#'   [climate_args_list()] constructor function.
+#'
+#' @return A data frame of point and interval) forecasts at a all horizons
+#'  for each unique combination of `key_vars`.
+#' @export
+#'
+#' @examples
+#'
+#' 1 + 1
+#' 2 + 2
+climatological_forecaster <- function(
+    epi_data,
+    outcome,
+    args_list = climate_args_list()
+) {
+  if (!is_epi_df(epi_data)) {
+    cli_abort(c(
+      "`epi_data` must be an {.cls epi_df}.",
+      "!" = "This one is a {.cls {class(epi_data)}}."
+    ))
+  }
+  if (!inherits(args_list, c("climate_fcast", "alist"))) {
+    cli_abort("`args_list` was not created using `climate_args_list()`.")
+  }
+  arg_is_chr_scalar(outcome)
+  hardhat::check_column_names(epi_data, c(outcome, args_list$quantile_by_key))
+  ahead <- args_list$ahead
+  window_size <- args_list$window_size
+  names(ahead) <- paste0(".ahead_", ahead)
+  ahead_tib <- tibble(.ahead_set = names(ahead), ahead = ahead)
+  forecast_date <- lubridate::ymd(args_list$forecast_date) %||%
+    max(epi_data$time_value) %||%
+    attributes(epi_data)$metadata$as_of
+  probs <- switch(args_list$quantile_method,
+                  epipredict = c(.25, .5, .75),
+                  base8 = args_list$quantile_values)
+  outcome <- sym(outcome)
+  epi_data <- filter(epi_data, !is.na(!!outcome))
+  target_dates <- forecast_date + lubridate::weeks(ahead)
+  in_window <- map2(
+    target_dates, names(ahead),
+    ~ tibble(!!.y := date_window_mod(.x, epi_data$time_value, window_size, window_size))
+  )
+
+  epi_data <- bind_cols(epi_data, purrr::list_cbind(in_window)) %>%
+    pivot_longer(
+      dplyr::starts_with(".ahead_"), names_to = ".ahead_set",
+      values_to = ".in_window"
+    ) %>%
+    filter(.in_window) %>%
+    select(-.in_window) %>%
+    left_join(ahead_tib, by = ".ahead_set")
+
+  Quantile <- function(x) {
+    x <- x - median(x, na.rm = TRUE)
+    if (args_list$symmetrize) x <- c(x, -x)
+    list(unname(quantile(x, probs = probs, na.rm = TRUE, type = 8)))
+  }
+  quantiles <- group_by(epi_data, .ahead_set, ahead) %>%
+    summarise(
+      .pred_distn = dist_quantiles(Quantile(!!outcome), probs),
+      .by = ,
+      .groups = "drop"
+    )
+
+  epi_data <- epi_data %>%
+    group_by(.ahead_set, ahead, args_list$quantile_by_key) %>%
+    summarise(.pred = median(!!outcome, na.rm = TRUE), .groups = "drop") %>%
+    left_join(quantiles, by = c(".ahead_set", "ahead")) %>%
+    mutate(
+      .pred_distn = .pred_distn + .pred,
+      forecast_date = ymd(forecast_date),
+      target_date = epiweek(forecast_date + weeks(ahead)),
+      .ahead_set = NULL
+    )
+
+  if (args_list$quantile_method == "epipredict") {
+    epi_data <- mutate(
+      epi_data,
+      .pred_distn = extrapolate_quantiles(.pred_distn, args_list$quantile_values)
+    )
+  }
+  if (args_list$nonneg) {
+    epi_data <- mutate(
+      epi_data,
+      .pred = snap(.pred, 0, Inf),
+      .pred_distn = snap(.pred_distn, 0, Inf)
+    )
+  }
+  structure(list(
+    predictions = epi_data,
+    metadata = list(
+      training = attr(epi_data, "metadata"),
+      forecast_created = Sys.time()
+    )),
+    class = c("climate_fcast", "canned_epipred")
+  )
+}
+
+#' Climatological forecaster argument constructor
+#'
+#' @inheritParams flatline_args_list
+#' @param ahead Vector of integers giving the number of time steps ahead
+#'   (in weeks) of the forecast date for which forecasts should be produced.
+#' @param time_type Character. The duration to which the forecasts correspond.
+#' @param window_size Integer. The number of time points on each side of the
+#'   target to include in the calculation.
+#' @param quantile_method One of either `"base8"` or `"epipredict"`. The first
+#'   case uses the quantiles of the observed history within the window, calculated
+#'   using `type = 8`. See `?stats::quantile` for additional information.
+#'   Alternatively, `"epipredict"` computes only the quartiles of the observed
+#'   data and interpolates (or extrapolates the remainder). Those quantiles
+#'   between .25 and .75 are interpolated with a cubic spline, while those
+#'   outside this range are extrapolated on the logistic-linear scale. This
+#'   produces a "parametric" quantile estimate with tails that are heavier than
+#'   a normal distribution. See the Examples for this comparison.
+#'
+#' @return A list containing updated parameter choices with class `climate_alist`.
+#' @export
+#'
+#' @examples
+#'
+#' climate_args_list()
+#' climate_args_list(
+#'   ahead = 0:10,
+#'   quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
+#' )
+#'
+#' # To visualize the quantiles produced with the `epipredict` method
+#' tau <- c(.01, .025, 1:19 / 20, .975, .99)
+#' sm_tau <- 5:15 / 20 # values between .25 and .75
+#' distn <- dist_quantiles(qnorm(sm_tau), sm_tau)
+#' epipredict_quantiles <- extrapolate_quantiles(distn, tau) %>%
+#'   nested_quantiles %>%
+#'   purrr::pluck(1, "values")
+#'
+#' plot(
+#'   qnorm(tau),
+#'   epipredict_quantiles,
+#'   pch = 16, col = 4, ylab = "epipredict", xlab = "Normal quantiles"
+#' )
+#' abline(0, 1, col = 2)
+climate_args_list <- function(
+    ahead = 1:4,
+    forecast_date = NULL,
+    time_type = c("epiweek", "week", "month", "year", "day"),
+    window_size = 3L,
+    quantile_method = c("base8", "epipredict"),
+    quantile_levels = c(.1, .25, .5, .75, .9),
+    symmetrize = FALSE,
+    nonneg = TRUE,
+    quantile_by_key = character(0L),
+    ...) {
+
+  rlang::check_dots_empty()
+  time_type <- arg_match(time_type)
+  quantile_method <- arg_match(quantile_method)
+  if (time_type != "epiweek") {
+    cli_abort("Only epiweek forecasts are currently supported.")
+  }
+  arg_is_scalar(ahead, window_size, symmetrize, nonneg)
+  arg_is_chr(quantile_by_key, allow_empty = TRUE)
+  arg_is_scalar(forecast_date, allow_null = TRUE)
+  arg_is_date(forecast_date, allow_null = TRUE)
+  arg_is_nonneg_int(ahead, window_size)
+  arg_is_lgl(symmetrize, nonneg)
+  arg_is_probabilities(quantile_levels)
+
+  structure(enlist(
+    ahead, forecast_date, time_type, window_size, quantile_method,
+    quantile_levels, symmetrize, nonneg, quantile_by_key),
+    class = c("climate_fcast", "alist")
+  )
+}
+
+
+#' @importFrom lubridate epiyear epiweek ymd
+units_in_year <- function(years, units = c("epiweek", "week", "month", "day")) {
+  units <- arg_match(units)
+  if (units == "month") return(rep(12L, length(years)))
+  if (units == "day") return(c(365, 366)[lubridate::leap_year(years) + 1])
+  unitf <- switch(units, epiweek = lubridate::epiweek, week = lubridate::isoweek)
+  mat <- outer(years, 25:31, function(x, y) paste0(x, "12", y))
+  apply(mat, 1, function(x) max(unitf(lubridate::ymd(x))))
+}
+
+date_window_mod <- function(t0, time_value, left = 1L, right = 1L,
+                            type = c("epiweek", "isoweek", "month", "day")) {
+  checkmate::assert_integerish(left, lower = 0, len = 1)
+  checkmate::assert_integerish(right, lower = 0, len = 1)
+  type <- arg_match(type)
+  unitf <- switch(
+    type,
+    epiweek = lubridate::epiweek,
+    iso = lubridate::isoweek,
+    month = lubridate::month,
+    day = lubridate::day
+  )
+  yearf <- switch(
+    type,
+    epiweek = lubridate::epiyear,
+    isoweek = lubridate::isoyear,
+    month = lubridate::year,
+    day = lubridate::year
+  )
+  cur_years <- yearf(unique(c(t0, time_value)))
+  ll <- vctrs::vec_recycle_common(t0 = t0, time_value = time_value)
+  t0 <- ll$t0
+  time_value <- ll$time_value
+
+  back_years <- cur_years - 1
+  all_years <- sort(unique(c(cur_years, back_years)))
+  year_nunits <- units_in_year(all_years, type)
+  back_nunits <- year_nunits[match(yearf(time_value) - 1, all_years)]
+  forward_nunits <- year_nunits[match(yearf(time_value), all_years)]
+
+  unit_diff <- unitf(t0) - unitf(time_value)
+  in_window <- ((unit_diff >= 0) & (unit_diff %% back_nunits <= left)) |
+    ((unit_diff <= 0) & (unit_diff %% forward_nunits <= right))
+
+  in_window
+}
+
+
+episeason <- function(time_value) {
+  time_value <- time_value - lubridate::dmonths(6)
+  paste0(
+    strftime(time_value, "%Y"),
+    "/",
+    strftime(time_value + lubridate::years(1), "%y")
+  )
+}
+
+season_week <- function(time_value, season_start_epiweek = 39) {
+  stopifnot(season_start_epiweek >= 0L, season_start_epiweek <= 53L)
+  time_value <- time_value - lubridate::weeks(season_start_epiweek)
+  lubridate::epiweek(time_value)
+}
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index f9cbcc814..c390d2cca 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -123,11 +123,7 @@ epi_recipe.epi_df <-
           unique(role)
         ) # anything else
       ))
-    tv_info <- var_info %>%
-      filter(role == "time_value") %>%
-      mutate(variable = ".target_time_value", role = ".target_time_value",
-             source = "derived")
-    var_info <- tibble::add_row(var_info, tv_info)
+
     ## Return final object of class `recipe`
     out <- list(
       var_info = var_info,
@@ -433,7 +429,7 @@ prep.epi_recipe <- function(
     x, training = NULL, fresh = FALSE, verbose = FALSE,
     retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ...) {
   if (is.null(training)) {
-    cli_abort(c(
+    cli_warn(c(
       "!" = "No training data was supplied to {.fn prep}.",
       "!" = "Unlike a {.cls recipe}, an {.cls epi_recipe} does not ",
       "!" = "store the full template data in the object.",
@@ -441,12 +437,7 @@ prep.epi_recipe <- function(
       "!" = "to avoid addtional warning messages."
     ))
   }
-  tr_names <- names(training)
-  if ("time_value" %in% tr_names && !(".target_time_value" %in% tr_names)) {
-    training$.target_time_value <- training$time_value
-  } else {
-    cli_abort("The training data does not contain a `time_value` column.")
-  }
+
   training <- recipes:::check_training_set(training, x, fresh)
   training <- epi_check_training_set(training, x)
   training <- relocate(training, all_of(key_colnames(training)))
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 688f17d1b..7ce64b302 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -345,19 +345,20 @@ bake.step_adjust_latency <- function(object, new_data, ...) {
 print.step_adjust_latency <-
   function(x, width = max(20, options$width - 35), ...) {
     if (!is.null(x$forecast_date)) {
-      conj <- "with forecast date"
+      conj <- "w/ forecast date"
       extra_text <- x$forecast_date
     } else if (!is.null(x$latency_table)) {
       conj <- if (nrow(x$latency) == 1) {
-        "with latency"
+        "w/ latency"
       } else {
-        "with latencies"
+        "w/ latencies"
       }
       extra_text <- unique(x$latency_table$latency)
     } else {
-      conj <- "with latency"
-      extra_text <- "set at train time"
+      conj <- "latency"
+      extra_text <- "TBD at train time"
     }
-    print_epi_step(x$columns, x$terms, x$trained, "Adjusting",
+    title <- trimws(paste("Adj.", x$method))
+    print_epi_step(x$columns, x$terms, x$trained, title,
                    conjunction = conj, extra_text = extra_text)
   }
diff --git a/R/step_climate_predictor.R b/R/step_climate.R
similarity index 96%
rename from R/step_climate_predictor.R
rename to R/step_climate.R
index 896eb9e2d..4b4f8ed01 100644
--- a/R/step_climate_predictor.R
+++ b/R/step_climate.R
@@ -43,10 +43,14 @@ step_climate <-
            epi_keys = NULL,
            prefix = "climate_",
            skip = FALSE,
-           id = rand_id("climate_predictor")) {
+           id = rand_id("climate")) {
     if (!is_epi_recipe(recipe)) {
       cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
+    n_outcomes <- sum(recipe$var_info$role == "outcome")
+    if (n_outcomes > 1L) {
+      cli_abort("Only one {.var outcome} role can be used with this step.")
+    }
     time_type <- rlang::arg_match(time_type)
     center_method <- rlang::arg_match(center_method)
     arg_is_chr(role)
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index f12464f6e..30c5f7564 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -116,9 +116,6 @@ step_epi_ahead <-
     }
     arg_is_chr_scalar(prefix, id, role)
     arg_is_nonneg_int(ahead)
-    if (role == "outcome" && length(ahead) > 1L) {
-      cli_abort("Only one {.val outcome} may be created with this step.")
-    }
 
     recipes::add_step(
       recipe,
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
new file mode 100644
index 000000000..325b2c348
--- /dev/null
+++ b/man/climate_args_list.Rd
@@ -0,0 +1,97 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/climatological_forecaster.R
+\name{climate_args_list}
+\alias{climate_args_list}
+\title{Climatological forecaster argument constructor}
+\usage{
+climate_args_list(
+  ahead = 1:4,
+  forecast_date = NULL,
+  time_type = c("epiweek", "week", "month", "year", "day"),
+  window_size = 3L,
+  quantile_method = c("base8", "epipredict"),
+  quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9),
+  symmetrize = FALSE,
+  nonneg = TRUE,
+  quantile_by_key = character(0L),
+  ...
+)
+}
+\arguments{
+\item{ahead}{Vector of integers giving the number of time steps ahead
+(in weeks) of the forecast date for which forecasts should be produced.}
+
+\item{forecast_date}{Date. The date from which the forecast is occurring.
+The default \code{NULL} will determine this automatically from either
+\enumerate{
+\item the maximum time value for which there's data if there is no latency
+adjustment (the default case), or
+\item the \code{as_of} date of \code{epi_data} if \code{adjust_latency} is
+non-\code{NULL}.
+}}
+
+\item{time_type}{Character. The duration to which the forecasts correspond.}
+
+\item{window_size}{Integer. The number of time points on each side of the
+target to include in the calculation.}
+
+\item{quantile_method}{One of either \code{"base8"} or \code{"epipredict"}. The first
+case uses the quantiles of the observed history within the window, calculated
+using \code{type = 8}. See \code{?stats::quantile} for additional information.
+Alternatively, \code{"epipredict"} computes only the quartiles of the observed
+data and interpolates (or extrapolates the remainder). Those quantiles
+between .25 and .75 are interpolated with a cubic spline, while those
+outside this range are extrapolated on the logistic-linear scale. This
+produces a "parametric" quantile estimate with tails that are heavier than
+a normal distribution. See the Examples for this comparison.}
+
+\item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
+prediction intervals. These are created by computing the quantiles of
+training residuals. A \code{NULL} value will result in point forecasts only.}
+
+\item{symmetrize}{Logical. The default \code{TRUE} calculates
+symmetric prediction intervals. This argument only applies when
+residual quantiles are used. It is not applicable with
+\code{trainer = quantile_reg()}, for example.}
+
+\item{nonneg}{Logical. The default \code{TRUE} enforces nonnegative predictions
+by hard-thresholding at 0.}
+
+\item{quantile_by_key}{Character vector. Groups residuals by listed keys
+before calculating residual quantiles. See the \code{by_key} argument to
+\code{\link[=layer_residual_quantiles]{layer_residual_quantiles()}} for more information. The default,
+\code{character(0)} performs no grouping. This argument only applies when
+residual quantiles are used. It is not applicable with
+\code{trainer = quantile_reg()}, for example.}
+
+\item{...}{Space to handle future expansions (unused).}
+}
+\value{
+A list containing updated parameter choices with class \code{climate_alist}.
+}
+\description{
+Climatological forecaster argument constructor
+}
+\examples{
+
+climate_args_list()
+climate_args_list(
+  ahead = 0:10,
+  quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
+)
+
+# To visualize the quantiles produced with the `epipredict` method
+tau <- c(.01, .025, 1:19 / 20, .975, .99)
+sm_tau <- 5:15 / 20 # values between .25 and .75
+distn <- dist_quantiles(qnorm(sm_tau), sm_tau)
+epipredict_quantiles <- extrapolate_quantiles(distn, tau) \%>\%
+  nested_quantiles \%>\%
+  purrr::pluck(1, "values")
+
+plot(
+  qnorm(tau),
+  epipredict_quantiles,
+  pch = 16, col = 4, ylab = "epipredict", xlab = "Normal quantiles"
+)
+abline(0, 1, col = 2)
+}
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
new file mode 100644
index 000000000..249c37d8b
--- /dev/null
+++ b/man/climatological_forecaster.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/climatological_forecaster.R
+\name{climatological_forecaster}
+\alias{climatological_forecaster}
+\title{Climatological forecaster}
+\usage{
+climatological_forecaster(epi_data, outcome, args_list = climate_args_list())
+}
+\arguments{
+\item{epi_data}{An \link[epiprocess:epi_df]{epiprocess::epi_df}}
+
+\item{outcome}{A scalar character for the column name we wish to predict.}
+
+\item{args_list}{A list of additional arguments as created by the
+\code{\link[=climate_args_list]{climate_args_list()}} constructor function.}
+}
+\value{
+A data frame of point and interval) forecasts at a all horizons
+for each unique combination of \code{key_vars}.
+}
+\description{
+This is another "baseline" type forecaster, but it is especially appropriate
+for strongly seasonal diseases (e.g., influenza). The idea is to forecast
+the "typical season" by summarizing over all available history in the
+\code{epi_data}. This is analogous to a "climate" forecast rather than a "weather"
+forecast, essentially predicting "typical January" behavior by relying on a
+long history of such periods rather than heavily using recent data.
+}
+\details{
+The forecast is taken as the quantiles of the \code{outcome} in a small window
+around the target week, computed over the entire available history.
+}
+\examples{
+
+1 + 1
+2 + 2
+}
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index db4f027f0..f6a5bb795 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/step_climate_predictor.R
+% Please edit documentation in R/step_climate.R
 \name{step_climate}
 \alias{step_climate}
 \title{Calculate a climatological variable based on the history}
diff --git a/tests/testthat/_snaps/bake-method.md b/tests/testthat/_snaps/bake-method.md
index eee28cc4b..252bc23ee 100644
--- a/tests/testthat/_snaps/bake-method.md
+++ b/tests/testthat/_snaps/bake-method.md
@@ -5,5 +5,5 @@
     Condition
       Error in `hardhat::recompose()`:
       ! `data` must only contain numeric columns.
-      i These columns aren't numeric: "geo_value" and "time_value".
+      i These columns aren't numeric: "geo_value", "time_value", and ".target_time_value".
 
diff --git a/tests/testthat/_snaps/climatological_baseline.md b/tests/testthat/_snaps/climatological_baseline.md
new file mode 100644
index 000000000..6270e2822
--- /dev/null
+++ b/tests/testthat/_snaps/climatological_baseline.md
@@ -0,0 +1,32 @@
+# epiwindow function works
+
+    Code
+      epiwindow(t0, time_value, 1.5)
+    Condition
+      Error in `epiwindow()`:
+      ! Assertion on 'left' failed: Must be of type 'integerish', but element 1 is not close to an integer.
+
+---
+
+    Code
+      epiwindow(t0, time_value, 1, 1.5)
+    Condition
+      Error in `epiwindow()`:
+      ! Assertion on 'right' failed: Must be of type 'integerish', but element 1 is not close to an integer.
+
+---
+
+    Code
+      epiwindow(t0, time_value, seasons = "other")
+    Condition
+      Error in `epiwindow()`:
+      ! `seasons` must be one of "all" or "current", not "other".
+
+---
+
+    Code
+      epiwindow(c(t0, t0), time_value)
+    Condition
+      Error in `epiwindow()`:
+      ! Can't recycle `t0` (size 2) to match `time_value` (size 4).
+
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 8d09248f3..320c6ebb7 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -13,7 +13,7 @@
       time_value: 1
       
       -- Operations 
-      1. extend_lags: case_rate with latency set at train time
+      1. Adj. extend_lags: has_role("raw") latency TBD at train time
       2. Lagging: death_rate by 0, 6, 11
       3. Lagging: case_rate by 1, 5
       4. Leading: death_rate by 7
@@ -36,10 +36,11 @@
       Training data contained 200 data points and no incomplete rows.
       
       -- Operations 
-      1. extend_lags: case_rate with forecast date 2021-07-24 | Trained
-      2. Lagging: death_rate by 0, 6, 11, (lat adj) | Trained
-      3. Lagging: case_rate by 6, 10, (lat adj) | Trained
-      4. Leading: death_rate by 7 | Trained
+      1. Adj. extend_lags: case_rate and death_rate w/ forecast date 2021-07-24 |
+      2. Trained
+      3. Lagging: death_rate by 5, 11, 16, (lat adj) | Trained
+      4. Lagging: case_rate by 6, 10, (lat adj) | Trained
+      5. Leading: death_rate by 7 | Trained
 
 ---
 
@@ -57,7 +58,7 @@
       
       -- Operations 
       1. Lagging: death_rate by 0, 7, 14
-      2. extend_ahead: all future predictors with latency set at train time
+      2. Adj. extend_ahead: has_role("raw") latency TBD at train time
       3. Leading: death_rate by 7
 
 ---
@@ -79,6 +80,6 @@
       
       -- Operations 
       1. Lagging: death_rate by 0, 7, 14 | Trained
-      2. extend_ahead: case_rate, ... with forecast date 2022-05-31 | Trained
+      2. Adj. extend_ahead: case_rate, ... w/ forecast date 2022-05-31 | Trained
       3. Leading: death_rate by -158, (lat adj) | Trained
 
diff --git a/tests/testthat/_snaps/step_epi_shift.new.md b/tests/testthat/_snaps/step_epi_shift.new.md
new file mode 100644
index 000000000..f18e82e9c
--- /dev/null
+++ b/tests/testthat/_snaps/step_epi_shift.new.md
@@ -0,0 +1,27 @@
+# Values for ahead and lag must be integer values
+
+    Code
+      r1 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = 3.6) %>%
+        step_epi_lag(death_rate, lag = 1.9)
+    Condition
+      Error in `step_epi_ahead()`:
+      ! `ahead` must be a non-negative integer.
+
+# A negative lag value should should throw an error
+
+    Code
+      r2 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = 7) %>% step_epi_lag(
+        death_rate, lag = -7)
+    Condition
+      Error in `step_epi_lag()`:
+      ! `lag` must be a non-negative integer.
+
+# Values for ahead and lag cannot be duplicates
+
+    Code
+      slm_fit(r4)
+    Condition
+      Error in `add_shifted_columns()`:
+      ! Name collision occured in <step_epi_lag>
+      The following variable name already exists: "lag_7_death_rate".
+
diff --git a/tests/testthat/test-climate_predictor.R b/tests/testthat/test-climate.R
similarity index 87%
rename from tests/testthat/test-climate_predictor.R
rename to tests/testthat/test-climate.R
index 0662e90d2..75e6d33d0 100644
--- a/tests/testthat/test-climate_predictor.R
+++ b/tests/testthat/test-climate.R
@@ -1,12 +1,3 @@
-test_that("weighted_median works", {
-  col <- c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0)
-  w <- rep(1, 10)
-  expect_equal(weighted_median(col, w), median(col))
-
-  w <- c(1, 1, 2, 2, 1, 1, 1, 6, 2, 2)
-  expect_equal(weighted_median(col, w), median(rep(col, times = w)))
-})
-
 test_that("roll_modular_multivec works", {
   tib <- tibble(
     col = c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0),
diff --git a/tests/testthat/test-climatological_baseline.R b/tests/testthat/test-climatological_baseline.R
new file mode 100644
index 000000000..e4fa52077
--- /dev/null
+++ b/tests/testthat/test-climatological_baseline.R
@@ -0,0 +1,35 @@
+test_that("epiwindow function works", {
+  skip()
+
+  t0 <- ymd("2024-01-08")
+  time_value <- ymd(c("2024-01-01", "2023-12-30", "2023-12-01", "2022-01-08"))
+  expect_snapshot(error = TRUE, epiwindow(t0, time_value, 1.5))
+  expect_snapshot(error = TRUE, epiwindow(t0, time_value, 1, 1.5))
+  expect_snapshot(error = TRUE, epiwindow(t0, time_value, seasons = "other"))
+  expect_snapshot(error = TRUE, epiwindow(c(t0, t0), time_value))
+
+  expect_identical(epiwindow(t0, time_value), c(TRUE, FALSE, FALSE, TRUE))
+  time_value <- ymd(c("2024-01-01", "2023-12-31", "2023-12-01", "2022-01-08"))
+  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
+  t0 <- ymd("20231231")
+  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
+  expect_identical(
+    epiwindow(t0, time_value, seasons = "current"),
+    c(TRUE, TRUE, FALSE, FALSE)
+  )
+
+  # 2025 has 53 epiweeks in the year
+  t0 <- ymd("2026-01-08") # week 1
+  # second is week 53
+  time_value <- ymd(c("2026-01-01", "2025-12-27", "2025-12-01", "2022-01-08"))
+  expect_identical(epiwindow(t0, time_value), c(TRUE, FALSE, FALSE, TRUE))
+  # second is week 52
+  time_value <- ymd(c("2025-01-01", "2024-12-30", "2024-12-01", "2022-01-08"))
+  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
+  t0 <- ymd("20251231")
+  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
+  expect_identical(
+    epiwindow(t0, time_value, seasons = "current"),
+    c(TRUE, TRUE, FALSE, FALSE)
+  )
+})
diff --git a/tests/testthat/test-epi_recipe.R b/tests/testthat/test-epi_recipe.R
index b4c59c0e5..9f432b7f0 100644
--- a/tests/testthat/test-epi_recipe.R
+++ b/tests/testthat/test-epi_recipe.R
@@ -23,7 +23,8 @@ test_that("epi_recipe formula works", {
     "x", c("integer", "numeric"), "predictor", "original",
     "y", c("integer", "numeric"), "outcome", "original",
     "time_value", "date", "time_value", "original",
-    "geo_value", c("string", "unordered", "nominal"), "geo_value", "original"
+    "geo_value", c("string", "unordered", "nominal"), "geo_value", "original",
+    ".target_time_value", "date", ".target_time_value", "derived"
   )
   expect_identical(r$var_info, ref_var_info)
   expect_equal(nrow(r$template), 1L)
@@ -52,7 +53,8 @@ test_that("epi_recipe formula works", {
     tibble::add_row(
       variable = "z", type = list(c("string", "unordered", "nominal")),
       role = "key",
-      source = "original"
+      source = "original",
+      .before = 6L
     )
 
   expect_identical(r$var_info, ref_var_info)
@@ -71,7 +73,8 @@ test_that("epi_recipe epi_df works", {
     "time_value", "date", "time_value", "original",
     "geo_value", c("string", "unordered", "nominal"), "geo_value", "original",
     "x", c("integer", "numeric"), "raw", "original",
-    "y", c("integer", "numeric"), "raw", "original"
+    "y", c("integer", "numeric"), "raw", "original",
+    ".target_time_value", "date", ".target_time_value", "derived"
   )
   expect_identical(r$var_info, ref_var_info)
   expect_equal(nrow(r$template), 1L)
@@ -82,7 +85,8 @@ test_that("epi_recipe epi_df works", {
     "x", c("integer", "numeric"), "predictor", "original",
     "y", c("integer", "numeric"), "outcome", "original",
     "time_value", "date", "time_value", "original",
-    "geo_value", c("string", "unordered", "nominal"), "geo_value", "original"
+    "geo_value", c("string", "unordered", "nominal"), "geo_value", "original",
+    ".target_time_value", "date", ".target_time_value", "derived"
   )
   expect_identical(r$var_info, ref_var_info)
   expect_equal(nrow(r$template), 1L)
@@ -95,7 +99,8 @@ test_that("epi_recipe epi_df works", {
   ref_var_info <- ref_var_info %>%
     tibble::add_row(
       variable = "time_value", type = list("date"), role = "funny_business",
-      source = "original"
+      source = "original",
+      .before = 5
     )
   expect_identical(r$var_info, ref_var_info)
   expect_equal(nrow(r$template), 1L)
diff --git a/tests/testthat/test-layer_add_forecast_date.R b/tests/testthat/test-layer_add_forecast_date.R
index 8bf452a81..3beb87dc9 100644
--- a/tests/testthat/test-layer_add_forecast_date.R
+++ b/tests/testthat/test-layer_add_forecast_date.R
@@ -77,7 +77,7 @@ test_that("`layer_add_forecast_date()` infers correct date when using `adjust_la
   attributes(jhu_reasonable_date)$metadata$as_of <- as.Date("2022-01-03")
   r_latent <- epi_recipe(jhu_reasonable_date) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
+    step_adjust_latency(death_rate, method = "extend_ahead") %>%
     step_epi_ahead(death_rate, ahead = 7) %>%
     step_naomit(all_predictors()) %>%
     step_naomit(all_outcomes(), skip = TRUE)
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 7b1f320e4..1e9127bbe 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -42,10 +42,10 @@ attributes(x_lagged)$metadata$as_of <- testing_as_of
 test_that("epi_adjust_latency correctly extends the lags", {
   expect_warning(epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-    step_adjust_latency(method = "extend_lags"))
+    step_adjust_latency(death_rate, method = "extend_lags"))
 
   r1 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "extend_lags") %>%
+    step_adjust_latency(death_rate, case_rate, method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -130,7 +130,7 @@ test_that("epi_adjust_latency correctly extends the lags", {
 
 test_that("epi_adjust_latency correctly extends the ahead", {
   r2 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
+    step_adjust_latency(death_rate, method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -175,7 +175,7 @@ test_that("epi_adjust_latency correctly extends the ahead", {
 
 test_that("epi_adjust_latency correctly locfs", {
   r1 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "locf") %>%
+    step_adjust_latency(has_role("raw"), method = "locf") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -228,7 +228,7 @@ test_that("epi_adjust_latency correctly locfs", {
 test_that("epi_adjust_latency extends multiple aheads", {
   aheads <- 1:3
   r3 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
+    step_adjust_latency(death_rate, method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = aheads)
@@ -285,7 +285,7 @@ test_that("epi_adjust_latency extends multiple aheads", {
 
 test_that("epi_adjust_latency fixed_forecast_date works", {
   r4 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "extend_lags", fixed_forecast_date = max_time + 14) %>%
+    step_adjust_latency(has_role("raw"), method = "extend_lags", fixed_forecast_date = max_time + 14) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -316,7 +316,7 @@ test_that("epi_adjust_latency fixed_forecast_date works", {
 
 test_that("epi_adjust_latency fixed_latency works", {
   r4.1 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "extend_lags", fixed_latency = 2) %>%
+    step_adjust_latency(has_role("raw"), method = "extend_lags", fixed_latency = 2) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -357,13 +357,13 @@ test_that("epi_adjust_latency warns there's steps before it", {
   expect_warning(
     r5 <- epi_recipe(x) %>%
       step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-      step_adjust_latency(method = "extend_lags"),
+      step_adjust_latency(death_rate, method = "extend_lags"),
     regexp = "extend_lags"
   )
   expect_warning(
     r5 <- epi_recipe(x) %>%
       step_epi_ahead(death_rate, ahead = ahead) %>%
-      step_adjust_latency(method = "extend_ahead"),
+      step_adjust_latency(death_rate, method = "extend_ahead"),
     regexp = "extend_ahead"
   )
 })
@@ -375,7 +375,7 @@ test_that("epi_adjust_latency warns there's steps before it", {
 
 test_that("epi_adjust_latency correctly extends the lags when there are different delays per-geo", {
   r5 <- epi_recipe(x_lagged) %>%
-    step_adjust_latency(method = "extend_lags") %>%
+    step_adjust_latency(has_role("raw"), method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -417,7 +417,7 @@ test_that("epi_adjust_latency correctly extends the lags when there are differen
 
 test_that("epi_adjust_latency correctly extends the ahead when there are different delays per-geo", {
   r5 <- epi_recipe(x_lagged) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
+    step_adjust_latency(death_rate, method = "extend_ahead") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -502,7 +502,7 @@ test_that("`step_adjust_latency` only uses the columns specified in the `...`",
 
 test_that("printing step_adjust_latency results in expected output", {
   r5 <- epi_recipe(x) %>%
-    step_adjust_latency(case_rate, method = "extend_lags") %>%
+    step_adjust_latency(has_role("raw"), method = "extend_lags") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)
@@ -510,7 +510,7 @@ test_that("printing step_adjust_latency results in expected output", {
   expect_snapshot(prep(r5, real_x))
   r6 <- epi_recipe(covid_case_death_rates) %>%
     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-    step_adjust_latency(method = "extend_ahead") %>%
+    step_adjust_latency(has_role("raw"), method = "extend_ahead") %>%
     step_epi_ahead(death_rate, ahead = 7)
   expect_snapshot(r6)
   expect_snapshot(prep(r6, covid_case_death_rates))
@@ -519,10 +519,10 @@ test_that("printing step_adjust_latency results in expected output", {
 test_that("locf works as intended", {
   expect_warning(epi_recipe(x) %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
-    step_adjust_latency(method = "locf"))
+    step_adjust_latency(death_rate, method = "locf"))
 
   r6 <- epi_recipe(x) %>%
-    step_adjust_latency(method = "locf") %>%
+    step_adjust_latency(has_role("raw"), method = "locf") %>%
     step_epi_lag(death_rate, lag = c(0, 6, 11)) %>%
     step_epi_lag(case_rate, lag = c(1, 5)) %>%
     step_epi_ahead(death_rate, ahead = ahead)

From e99a768033e09c2e6f4830de253e2d0182bfb987 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 17 Jan 2025 14:26:35 -0800
Subject: [PATCH 189/387] default is not required

---
 R/epi_recipe.R | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 646cb1b6d..c0a83b54f 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -13,14 +13,6 @@ epi_recipe <- function(x, ...) {
 }
 
 
-#' @rdname epi_recipe
-#' @export
-epi_recipe.default <- function(x, ...) {
-  cli_abort(paste(
-    "`x` must be an {.cls epi_df} or a {.cls formula},",
-    "not a {.cls {class(x)[[1]]}}."
-  ))
-}
 
 #' @rdname epi_recipe
 #' @inheritParams recipes::recipe

From f62d77896c3500e8ee185f1f070443f9f1da97c2 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 22 Jan 2025 16:35:59 -0800
Subject: [PATCH 190/387] ci: add doc preview

---
 .github/workflows/doc-preview.yaml | 55 ++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 .github/workflows/doc-preview.yaml

diff --git a/.github/workflows/doc-preview.yaml b/.github/workflows/doc-preview.yaml
new file mode 100644
index 000000000..61ae64a17
--- /dev/null
+++ b/.github/workflows/doc-preview.yaml
@@ -0,0 +1,55 @@
+on:
+  issue_comment:
+    types: [created]
+
+name: doc-preview.yaml
+
+permissions: read-all
+
+jobs:
+  preview:
+    if: ${{ github.event.issue.pull_request && (github.event.comment.author_association == 'COLLABORATOR' || github.event.comment.author_association == 'CONTRIBUTOR' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') && startsWith(github.event.comment.body, '/preview-docs') }}
+
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+      contents: write
+    steps:
+      - name: Get PR number
+        id: pr
+        run: |
+          PR_NUM=$(echo "${{ github.event.issue.number }}")
+          echo "number=$PR_NUM" >> $GITHUB_OUTPUT
+
+      - uses: actions/checkout@v4
+        with:
+          ref: refs/pull/${{ steps.pr.outputs.number }}/head
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to Netlify
+        uses: nwtgck/actions-netlify@v3.0
+        with:
+          publish-dir: './docs'
+          production-deploy: false
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          deploy-message: "Deploy from GitHub Actions"
+          enable-github-deployment: false
+          enable-commit-status: false
+        env:
+          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
+        timeout-minutes: 1
\ No newline at end of file

From acb953374fa734a79fc5fb42c52ad130fd3b9515 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Thu, 23 Jan 2025 16:22:06 -0800
Subject: [PATCH 191/387] rebuild docs, rm epi_recipe default

---
 NAMESPACE                  |  1 -
 R/epi_recipe.R             | 13 ++++++++++---
 README.md                  | 21 +++++++++++----------
 man/epi_recipe.Rd          | 16 +++++++++++-----
 man/step_adjust_latency.Rd |  4 ++--
 5 files changed, 34 insertions(+), 21 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index e7e720f11..3a1fed508 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -29,7 +29,6 @@ S3method(bake,step_population_scaling)
 S3method(bake,step_training_window)
 S3method(detect_layer,frosting)
 S3method(detect_layer,workflow)
-S3method(epi_recipe,default)
 S3method(epi_recipe,epi_df)
 S3method(epi_recipe,formula)
 S3method(extract_argument,epi_workflow)
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index c0a83b54f..7104a9901 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -8,7 +8,7 @@
 #' @aliases epi_recipe epi_recipe.default epi_recipe.formula
 #' @import recipes
 #' @export
-epi_recipe <- function(x, ...) {
+epi_recipe <- function(x, forecast_date = NULL, ...) {
   UseMethod("epi_recipe")
 }
 
@@ -20,6 +20,14 @@ epi_recipe <- function(x, ...) {
 #'   describes a single role that the variable will take. This value could be
 #'   anything but common roles are `"outcome"`, `"predictor"`,
 #'   `"time_value"`, and `"geo_value"`
+#' @param forecast_date Either a date of the same class as the `time_value`
+#'   column in the `epi_df` or `NULL`. If a date, it gives the date on which the
+#'   forecast is created. This is often the same as the most recent date of
+#'   data availability, but when data is "latent" (reported after the date to
+#'   which it corresponds), the `forecast_date` may come later. Setting this
+#'   to a value BEFORE the most recent data is no longer a true "forecast"
+#'   because future data is being used to create the prediction. If `NULL`,
+#'   the `forecast_date` is
 #' @param ... Further arguments passed to or from other methods (not currently
 #'   used).
 #' @param formula A model formula. No in-line functions should be used here
@@ -27,8 +35,7 @@ epi_recipe <- function(x, ...) {
 #'  transformations should be enacted using `step` functions in this package.
 #'  Dots are allowed as are simple multivariate outcome terms (i.e. no need for
 #'  `cbind`; see Examples).
-#' @param x,data A data frame, tibble, or epi_df of the *template* data set
-#'   (see below). This is always coerced to the first row to avoid memory issues
+#' @param x,data An epi_df of the *template* data set (see below).
 #' @inherit recipes::recipe return
 #'
 #' @export
diff --git a/README.md b/README.md
index 5293a0e9c..174196343 100644
--- a/README.md
+++ b/README.md
@@ -78,7 +78,7 @@ covid_case_death_rates
 #> An `epi_df` object, 20,496 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
-#> * as_of     = 2022-05-31 12:08:25.791826
+#> * as_of     = 2022-05-31
 #> 
 #> # A tibble: 20,496 × 4
 #>    geo_value time_value case_rate death_rate
@@ -92,7 +92,7 @@ covid_case_death_rates
 #>  7 co        2020-12-31      35.8      0.649
 #>  8 ct        2020-12-31      52.1      0.819
 #>  9 dc        2020-12-31      31.0      0.601
-#> 10 de        2020-12-31      65.2      0.807
+#> 10 de        2020-12-31      64.3      0.912
 #> # ℹ 20,486 more rows
 ```
 
@@ -113,19 +113,20 @@ two_week_ahead <- arx_forecaster(
 two_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
 #> 
-#> This forecaster was fit on 2024-11-11 11:38:31.
+#> This forecaster was fit on 2025-01-23 14:01:04.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
-#> • Using data up-to-date as of: 2022-05-31 12:08:25.
+#> • Using data up-to-date as of: 2022-05-31.
+#> • With the last data available on 2021-12-31
 #> 
 #> ── Predictions ─────────────────────────────────────────────────────────────
 #> 
 #> A total of 56 predictions are available for
 #> • 56 unique geographic regions,
 #> • At forecast date: 2021-12-31,
-#> • For target date: 2022-01-14.
+#> • For target date: 2022-01-14,
 #> 
 ```
 
@@ -161,11 +162,11 @@ two_week_ahead$epi_workflow
 #> 
 #> Coefficients:
 #>       (Intercept)    lag_0_case_rate    lag_1_case_rate    lag_2_case_rate  
-#>        -0.0073358          0.0030365          0.0012467          0.0009536  
+#>        -0.0072151          0.0030311          0.0012525          0.0009551  
 #>   lag_3_case_rate    lag_7_case_rate   lag_14_case_rate   lag_0_death_rate  
-#>         0.0011425          0.0012481          0.0003041          0.1351769  
+#>         0.0011488          0.0012238          0.0003301          0.1348459  
 #>  lag_7_death_rate  lag_14_death_rate  
-#>         0.1471127          0.1062473
+#>         0.1468325          0.1056316
 #> 
 #> ── Postprocessor ───────────────────────────────────────────────────────────
 #> 
@@ -188,11 +189,11 @@ two_week_ahead$predictions
 #> # A tibble: 56 × 5
 #>    geo_value .pred        .pred_distn forecast_date target_date
 #>    <chr>     <dbl>             <dist> <date>        <date>     
-#>  1 ak        0.449 quantiles(0.45)[2] 2021-12-31    2022-01-14 
+#>  1 ak        0.448 quantiles(0.45)[2] 2021-12-31    2022-01-14 
 #>  2 al        0.574 quantiles(0.57)[2] 2021-12-31    2022-01-14 
 #>  3 ar        0.673 quantiles(0.67)[2] 2021-12-31    2022-01-14 
 #>  4 as        0     quantiles(0.12)[2] 2021-12-31    2022-01-14 
-#>  5 az        0.679 quantiles(0.68)[2] 2021-12-31    2022-01-14 
+#>  5 az        0.678 quantiles(0.68)[2] 2021-12-31    2022-01-14 
 #>  6 ca        0.575 quantiles(0.57)[2] 2021-12-31    2022-01-14 
 #>  7 co        0.862 quantiles(0.86)[2] 2021-12-31    2022-01-14 
 #>  8 ct        1.07  quantiles(1.07)[2] 2021-12-31    2022-01-14 
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 9ef5eb288..9c9558093 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -7,17 +7,23 @@
 \alias{epi_recipe.epi_df}
 \title{Create a epi_recipe for preprocessing data}
 \usage{
-epi_recipe(x, ...)
-
-\method{epi_recipe}{default}(x, ...)
+epi_recipe(x, forecast_date = NULL, ...)
 
 \method{epi_recipe}{epi_df}(x, formula = NULL, ..., vars = NULL, roles = NULL)
 
 \method{epi_recipe}{formula}(formula, data, ...)
 }
 \arguments{
-\item{x, data}{A data frame, tibble, or epi_df of the \emph{template} data set
-(see below). This is always coerced to the first row to avoid memory issues}
+\item{x, data}{An epi_df of the \emph{template} data set (see below).}
+
+\item{forecast_date}{Either a date of the same class as the \code{time_value}
+column in the \code{epi_df} or \code{NULL}. If a date, it gives the date on which the
+forecast is created. This is often the same as the most recent date of
+data availability, but when data is "latent" (reported after the date to
+which it corresponds), the \code{forecast_date} may come later. Setting this
+to a value BEFORE the most recent data is no longer a true "forecast"
+because future data is being used to create the prediction. If \code{NULL},
+the \code{forecast_date} is}
 
 \item{...}{Further arguments passed to or from other methods (not currently
 used).}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 53098504e..0078de100 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as

From 5570cf481d358af2ebda33e164eaf7d0cee11a43 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Fri, 24 Jan 2025 12:52:37 -0800
Subject: [PATCH 192/387] ci(netlify): add comments, simplify, fix

---
 .github/workflows/doc-preview.yaml | 32 ++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/doc-preview.yaml b/.github/workflows/doc-preview.yaml
index 61ae64a17..068184225 100644
--- a/.github/workflows/doc-preview.yaml
+++ b/.github/workflows/doc-preview.yaml
@@ -12,18 +12,15 @@ jobs:
 
     runs-on: ubuntu-latest
     permissions:
+      # Needed to write a comment on the PR
       pull-requests: write
-      contents: write
+      # Needed to read the PR branch
+      contents: read
     steps:
-      - name: Get PR number
-        id: pr
-        run: |
-          PR_NUM=$(echo "${{ github.event.issue.number }}")
-          echo "number=$PR_NUM" >> $GITHUB_OUTPUT
-
       - uses: actions/checkout@v4
         with:
-          ref: refs/pull/${{ steps.pr.outputs.number }}/head
+          # Checkout the PR branch
+          ref: refs/pull/${{ github.event.issue.number }}/head
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -43,13 +40,26 @@ jobs:
       - name: Deploy to Netlify
         uses: nwtgck/actions-netlify@v3.0
         with:
-          publish-dir: './docs'
-          production-deploy: false
+          # Standard config
           github-token: ${{ secrets.GITHUB_TOKEN }}
           deploy-message: "Deploy from GitHub Actions"
+          # 'docs/' is the default directory for pkgdown::build_site()
+          # we add 'dev' because _pkgdown.yml has 'development: mode: devel'
+          publish-dir: './docs/dev'
+          # Development deploys only
+          production-deploy: false
+          # Enable pull request comment (default)
+          enable-pull-request-comment: true
+          # Overwrite the pull request comment with updated link (default)
+          overwrites-pull-request-comment: true
+          # Don't deploy to GitHub
           enable-github-deployment: false
+          # Don't update the status of the commit
           enable-commit-status: false
+          # Don't comment on the commit
+          enable-commit-comment: false
         env:
+          # Netlify credentials (currently from Dmitry's account)
           NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
           NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
-        timeout-minutes: 1
\ No newline at end of file
+        timeout-minutes: 1

From 792fec54a7ea795832615556c2ff83eb469d0bca Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 27 Jan 2025 15:29:54 -0800
Subject: [PATCH 193/387] rm unused

---
 man/figures/lifecycle-archived.svg        | 21 ----------------
 man/figures/lifecycle-defunct.svg         | 21 ----------------
 man/figures/lifecycle-deprecated.svg      | 21 ----------------
 man/figures/lifecycle-experimental.svg    | 21 ----------------
 man/figures/lifecycle-maturing.svg        | 21 ----------------
 man/figures/lifecycle-questioning.svg     | 21 ----------------
 man/figures/lifecycle-soft-deprecated.svg | 21 ----------------
 man/figures/lifecycle-stable.svg          | 29 -----------------------
 man/figures/lifecycle-superseded.svg      | 21 ----------------
 9 files changed, 197 deletions(-)
 delete mode 100644 man/figures/lifecycle-archived.svg
 delete mode 100644 man/figures/lifecycle-defunct.svg
 delete mode 100644 man/figures/lifecycle-deprecated.svg
 delete mode 100644 man/figures/lifecycle-experimental.svg
 delete mode 100644 man/figures/lifecycle-maturing.svg
 delete mode 100644 man/figures/lifecycle-questioning.svg
 delete mode 100644 man/figures/lifecycle-soft-deprecated.svg
 delete mode 100644 man/figures/lifecycle-stable.svg
 delete mode 100644 man/figures/lifecycle-superseded.svg

diff --git a/man/figures/lifecycle-archived.svg b/man/figures/lifecycle-archived.svg
deleted file mode 100644
index 745ab0c78..000000000
--- a/man/figures/lifecycle-archived.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="112" height="20" role="img" aria-label="lifecycle: archived">
-    <title>lifecycle: archived</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="112" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="57" height="20" fill="#e05d44" />
-        <rect width="112" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="825" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="470">archived</text>
-        <text x="825" y="140" transform="scale(.1)" fill="#fff" textLength="470">archived</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-defunct.svg b/man/figures/lifecycle-defunct.svg
deleted file mode 100644
index d5c9559ed..000000000
--- a/man/figures/lifecycle-defunct.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="106" height="20" role="img" aria-label="lifecycle: defunct">
-    <title>lifecycle: defunct</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="106" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="51" height="20" fill="#fe7d37" />
-        <rect width="106" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="795" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="410">defunct</text>
-        <text x="795" y="140" transform="scale(.1)" fill="#fff" textLength="410">defunct</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-deprecated.svg b/man/figures/lifecycle-deprecated.svg
deleted file mode 100644
index b61c57c3f..000000000
--- a/man/figures/lifecycle-deprecated.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="126" height="20" role="img" aria-label="lifecycle: deprecated">
-    <title>lifecycle: deprecated</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="126" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="71" height="20" fill="#fe7d37" />
-        <rect width="126" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="895" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="610">deprecated</text>
-        <text x="895" y="140" transform="scale(.1)" fill="#fff" textLength="610">deprecated</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-experimental.svg b/man/figures/lifecycle-experimental.svg
deleted file mode 100644
index 5d88fc2c6..000000000
--- a/man/figures/lifecycle-experimental.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="138" height="20" role="img" aria-label="lifecycle: experimental">
-    <title>lifecycle: experimental</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="138" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="83" height="20" fill="#fe7d37" />
-        <rect width="138" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="955" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="730">experimental</text>
-        <text x="955" y="140" transform="scale(.1)" fill="#fff" textLength="730">experimental</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-maturing.svg b/man/figures/lifecycle-maturing.svg
deleted file mode 100644
index 897370ecf..000000000
--- a/man/figures/lifecycle-maturing.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="116" height="20" role="img" aria-label="lifecycle: maturing">
-    <title>lifecycle: maturing</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="116" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="61" height="20" fill="#007ec6" />
-        <rect width="116" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="845" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">maturing</text>
-        <text x="845" y="140" transform="scale(.1)" fill="#fff" textLength="510">maturing</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-questioning.svg b/man/figures/lifecycle-questioning.svg
deleted file mode 100644
index 7c1721d05..000000000
--- a/man/figures/lifecycle-questioning.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="128" height="20" role="img" aria-label="lifecycle: questioning">
-    <title>lifecycle: questioning</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="128" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="73" height="20" fill="#007ec6" />
-        <rect width="128" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="905" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="630">questioning</text>
-        <text x="905" y="140" transform="scale(.1)" fill="#fff" textLength="630">questioning</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-soft-deprecated.svg b/man/figures/lifecycle-soft-deprecated.svg
deleted file mode 100644
index 9c166ff30..000000000
--- a/man/figures/lifecycle-soft-deprecated.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="152" height="20" role="img" aria-label="lifecycle: soft-deprecated">
-    <title>lifecycle: soft-deprecated</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="152" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="97" height="20" fill="#007ec6" />
-        <rect width="152" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="1025" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="870">soft-deprecated</text>
-        <text x="1025" y="140" transform="scale(.1)" fill="#fff" textLength="870">soft-deprecated</text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-stable.svg b/man/figures/lifecycle-stable.svg
deleted file mode 100644
index 9bf21e76b..000000000
--- a/man/figures/lifecycle-stable.svg
+++ /dev/null
@@ -1,29 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="98" height="20" role="img" aria-label="lifecycle: stable">
-    <title>lifecycle: stable</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="98" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="43" height="20" fill="#4c1" />
-        <rect width="98" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">
-      lifecycle
-    </text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">
-      lifecycle
-    </text>
-        <text aria-hidden="true" x="755" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="330">
-      stable
-    </text>
-        <text x="755" y="140" transform="scale(.1)" fill="#fff" textLength="330">
-      stable
-    </text>
-    </g>
-</svg>
diff --git a/man/figures/lifecycle-superseded.svg b/man/figures/lifecycle-superseded.svg
deleted file mode 100644
index db8d757f7..000000000
--- a/man/figures/lifecycle-superseded.svg
+++ /dev/null
@@ -1,21 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="128" height="20" role="img" aria-label="lifecycle: superseded">
-    <title>lifecycle: superseded</title>
-    <linearGradient id="s" x2="0" y2="100%">
-        <stop offset="0" stop-color="#bbb" stop-opacity=".1" />
-        <stop offset="1" stop-opacity=".1" />
-    </linearGradient>
-    <clipPath id="r">
-        <rect width="128" height="20" rx="3" fill="#fff" />
-    </clipPath>
-    <g clip-path="url(#r)">
-        <rect width="55" height="20" fill="#555" />
-        <rect x="55" width="73" height="20" fill="#007ec6" />
-        <rect width="128" height="20" fill="url(#s)" />
-    </g>
-    <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110">
-        <text aria-hidden="true" x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">lifecycle</text>
-        <text x="285" y="140" transform="scale(.1)" fill="#fff" textLength="450">lifecycle</text>
-        <text aria-hidden="true" x="905" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="630">superseded</text>
-        <text x="905" y="140" transform="scale(.1)" fill="#fff" textLength="630">superseded</text>
-    </g>
-</svg>

From 009218f30d3650ff19fa5d4a7c5e30ae5d905eca Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 10:40:33 -0800
Subject: [PATCH 194/387] pass tests

---
 R/epi_recipe.R                      | 44 ++++++++++++++++++-----------
 R/utils-latency.R                   |  1 +
 man/epi_recipe.Rd                   | 36 +++++++++++++++--------
 tests/testthat/_snaps/epi_recipe.md |  8 +++---
 4 files changed, 57 insertions(+), 32 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 7104a9901..c3d384099 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -8,7 +8,7 @@
 #' @aliases epi_recipe epi_recipe.default epi_recipe.formula
 #' @import recipes
 #' @export
-epi_recipe <- function(x, forecast_date = NULL, ...) {
+epi_recipe <- function(x, ...) {
   UseMethod("epi_recipe")
 }
 
@@ -20,14 +20,19 @@ epi_recipe <- function(x, forecast_date = NULL, ...) {
 #'   describes a single role that the variable will take. This value could be
 #'   anything but common roles are `"outcome"`, `"predictor"`,
 #'   `"time_value"`, and `"geo_value"`
-#' @param forecast_date Either a date of the same class as the `time_value`
-#'   column in the `epi_df` or `NULL`. If a date, it gives the date on which the
-#'   forecast is created. This is often the same as the most recent date of
+#' @param reference_date Either a date of the same class as the `time_value`
+#'   column in the `epi_df` or `NULL`. If a date, it gives the date to which all
+#'   operations are relative. Typically, in real-time tasks this is the date that
+#'   the model is created (and presumably trained). In forecasting, this is
+#'   often the same the most recent date of
 #'   data availability, but when data is "latent" (reported after the date to
-#'   which it corresponds), the `forecast_date` may come later. Setting this
-#'   to a value BEFORE the most recent data is no longer a true "forecast"
-#'   because future data is being used to create the prediction. If `NULL`,
-#'   the `forecast_date` is
+#'   which it corresponds), or if performing a nowcast, the `reference_date` may
+#'   be later than this. Setting `reference_date`
+#'   to a value BEFORE the most recent data is not a true "forecast",
+#'   because future data is being used to create the model But this may be
+#'   reasonable in model building, nowcasting (predicting finalized values from
+#'   preliminary data), or if producing a backcast. If `NULL`, it will be set
+#'   to the `as_of` date of the `epi_df`.
 #' @param ... Further arguments passed to or from other methods (not currently
 #'   used).
 #' @param formula A model formula. No in-line functions should be used here
@@ -55,8 +60,12 @@ epi_recipe <- function(x, forecast_date = NULL, ...) {
 #'   step_naomit(all_outcomes(), skip = TRUE)
 #'
 #' r
-epi_recipe.epi_df <-
-  function(x, formula = NULL, ..., vars = NULL, roles = NULL) {
+epi_recipe.epi_df <- function(x,
+                              reference_date = NULL,
+                              formula = NULL,
+                              ...,
+                              vars = NULL,
+                              roles = NULL) {
     attr(x, "decay_to_tibble") <- FALSE
     if (!is.null(formula)) {
       if (!is.null(vars)) {
@@ -124,12 +133,15 @@ epi_recipe.epi_df <-
       ))
 
     ## Return final object of class `recipe`
+    max_time_value = max(x$time_value)
+    reference_date <- reference_date %||% attr(x, "metadata")$as_of
     out <- list(
       var_info = var_info,
       term_info = var_info,
       steps = NULL,
       template = x[1, ],
-      max_time_value = max(x$time_value),
+      max_time_value = max_time_value,
+      reference_date = reference_date,
       levels = NULL,
       retained = NA
     )
@@ -140,15 +152,15 @@ epi_recipe.epi_df <-
 
 #' @rdname epi_recipe
 #' @export
-epi_recipe.formula <- function(formula, data, ...) {
+epi_recipe.formula <- function(formula, data, reference_date = NULL, ...) {
   # we ensure that there's only 1 row in the template
   data <- data[1, ]
   # check for minus:
   if (!epiprocess::is_epi_df(data)) {
-    cli_abort(paste(
-      "`epi_recipe()` has been called with a non-{.cls epi_df} object.",
-      "Use `recipe()` instead."
-    ))
+    cli_abort(
+      "`epi_recipe()` has been called with a non-{.cls epi_df} object.
+      Use `recipe()` instead."
+    )
   }
 
   attr(data, "decay_to_tibble") <- FALSE
diff --git a/R/utils-latency.R b/R/utils-latency.R
index d854cb9cf..3c50e210f 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -34,6 +34,7 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @importFrom tidyr drop_na
 #' @importFrom utils capture.output
 get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns = NULL) {
+
   if (is.null(columns)) {
     columns <- info %>%
       filter(source == "original") %>%
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 9c9558093..25a652ac0 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -7,27 +7,39 @@
 \alias{epi_recipe.epi_df}
 \title{Create a epi_recipe for preprocessing data}
 \usage{
-epi_recipe(x, forecast_date = NULL, ...)
+epi_recipe(x, ...)
 
-\method{epi_recipe}{epi_df}(x, formula = NULL, ..., vars = NULL, roles = NULL)
+\method{epi_recipe}{epi_df}(
+  x,
+  reference_date = NULL,
+  formula = NULL,
+  ...,
+  vars = NULL,
+  roles = NULL
+)
 
-\method{epi_recipe}{formula}(formula, data, ...)
+\method{epi_recipe}{formula}(formula, data, reference_date = NULL, ...)
 }
 \arguments{
 \item{x, data}{An epi_df of the \emph{template} data set (see below).}
 
-\item{forecast_date}{Either a date of the same class as the \code{time_value}
-column in the \code{epi_df} or \code{NULL}. If a date, it gives the date on which the
-forecast is created. This is often the same as the most recent date of
-data availability, but when data is "latent" (reported after the date to
-which it corresponds), the \code{forecast_date} may come later. Setting this
-to a value BEFORE the most recent data is no longer a true "forecast"
-because future data is being used to create the prediction. If \code{NULL},
-the \code{forecast_date} is}
-
 \item{...}{Further arguments passed to or from other methods (not currently
 used).}
 
+\item{reference_date}{Either a date of the same class as the \code{time_value}
+column in the \code{epi_df} or \code{NULL}. If a date, it gives the date to which all
+operations are relative. Typically, in real-time tasks this is the date that
+the model is created (and presumably trained). In forecasting, this is
+often the same the most recent date of
+data availability, but when data is "latent" (reported after the date to
+which it corresponds), or if performing a nowcast, the \code{reference_date} may
+be later than this. Setting \code{reference_date}
+to a value BEFORE the most recent data is not a true "forecast",
+because future data is being used to create the model But this may be
+reasonable in model building, nowcasting (predicting finalized values from
+preliminary data), or if producing a backcast. If \code{NULL}, it will be set
+to the \code{as_of} date of the \code{epi_df}.}
+
 \item{formula}{A model formula. No in-line functions should be used here
 (e.g. \code{log(x)}, \code{x:y}, etc.) and minus signs are not allowed. These types of
 transformations should be enacted using \code{step} functions in this package.
diff --git a/tests/testthat/_snaps/epi_recipe.md b/tests/testthat/_snaps/epi_recipe.md
index 24b046678..c63bf8f07 100644
--- a/tests/testthat/_snaps/epi_recipe.md
+++ b/tests/testthat/_snaps/epi_recipe.md
@@ -3,8 +3,8 @@
     Code
       epi_recipe(tib)
     Condition
-      Error in `epi_recipe()`:
-      ! `x` must be an <epi_df> or a <formula>, not a <tbl_df>.
+      Error in `UseMethod()`:
+      ! no applicable method for 'epi_recipe' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"
 
 ---
 
@@ -19,8 +19,8 @@
     Code
       epi_recipe(m)
     Condition
-      Error in `epi_recipe()`:
-      ! `x` must be an <epi_df> or a <formula>, not a <matrix>.
+      Error in `UseMethod()`:
+      ! no applicable method for 'epi_recipe' applied to an object of class "c('matrix', 'array', 'character')"
 
 # add/update/adjust/remove epi_recipe works as intended
 

From 5662a950fc144e66abdf5772458ce976a8838bc9 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 10:44:34 -0800
Subject: [PATCH 195/387] bump version

---
 DESCRIPTION | 2 +-
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 017084322..1b52822a5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.6
+Version: 0.1.7
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index e5e1bb819..2f138fd96 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -19,6 +19,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Make key column inference more consistent within the package and with current `epiprocess`.
 - Fix `quantile_reg()` producing error when asked to output just median-level predictions.
 - (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
+- Add `reference_date` as an argument to `epi_recipe()`
 
 ## Bug fixes
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`

From 7b629f4751ce69e9fbcbbf397ad4c352fb59993b Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 10:59:02 -0800
Subject: [PATCH 196/387] styler

---
 R/epi_recipe.R    | 146 +++++++++++++++++++++++-----------------------
 R/utils-latency.R |   1 -
 2 files changed, 73 insertions(+), 74 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index c3d384099..722679978 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -66,88 +66,88 @@ epi_recipe.epi_df <- function(x,
                               ...,
                               vars = NULL,
                               roles = NULL) {
-    attr(x, "decay_to_tibble") <- FALSE
-    if (!is.null(formula)) {
-      if (!is.null(vars)) {
-        cli_abort(paste0(
-          "This `vars` specification will be ignored ",
+  attr(x, "decay_to_tibble") <- FALSE
+  if (!is.null(formula)) {
+    if (!is.null(vars)) {
+      cli_abort(paste0(
+        "This `vars` specification will be ignored ",
+        "when a formula is used"
+      ))
+    }
+    if (!is.null(roles)) {
+      cli_abort(
+        paste0(
+          "This `roles` specification will be ignored ",
           "when a formula is used"
-        ))
-      }
-      if (!is.null(roles)) {
-        cli_abort(
-          paste0(
-            "This `roles` specification will be ignored ",
-            "when a formula is used"
-          )
         )
-      }
-
-      obj <- epi_recipe.formula(formula, x, ...)
-      return(obj)
-    }
-    if (is.null(vars)) vars <- colnames(x)
-    if (any(table(vars) > 1)) {
-      cli_abort("`vars` should have unique members")
-    }
-    if (any(!(vars %in% colnames(x)))) {
-      cli_abort("1 or more elements of `vars` are not in the data")
+      )
     }
 
-    keys <- key_colnames(x) # we know x is an epi_df
+    obj <- epi_recipe.formula(formula, x, ...)
+    return(obj)
+  }
+  if (is.null(vars)) vars <- colnames(x)
+  if (any(table(vars) > 1)) {
+    cli_abort("`vars` should have unique members")
+  }
+  if (any(!(vars %in% colnames(x)))) {
+    cli_abort("1 or more elements of `vars` are not in the data")
+  }
 
-    var_info <- tibble(variable = vars)
-    key_roles <- c("geo_value", rep("key", length(keys) - 2), "time_value")
+  keys <- key_colnames(x) # we know x is an epi_df
 
-    ## Check and add roles when available
-    if (!is.null(roles)) {
-      if (length(roles) != length(vars)) {
-        cli_abort(paste0(
-          "The number of roles should be the same as the number of ",
-          "variables."
-        ))
-      }
-      var_info$role <- roles
-    } else {
-      var_info <- var_info %>% filter(!(variable %in% keys))
-      var_info$role <- "raw"
-    }
-    ## Now we add the keys when necessary
-    var_info <- dplyr::union(
-      var_info,
-      tibble::tibble(variable = keys, role = key_roles)
-    )
+  var_info <- tibble(variable = vars)
+  key_roles <- c("geo_value", rep("key", length(keys) - 2), "time_value")
 
-    ## Add types
-    var_info <- full_join(recipes:::get_types(x), var_info, by = "variable")
-    var_info$source <- "original"
-
-    ## arrange to easy order
-    var_info <- var_info %>%
-      arrange(factor(
-        role,
-        levels = union(
-          c("predictor", "outcome", "time_value", "geo_value", "key"),
-          unique(role)
-        ) # anything else
+  ## Check and add roles when available
+  if (!is.null(roles)) {
+    if (length(roles) != length(vars)) {
+      cli_abort(paste0(
+        "The number of roles should be the same as the number of ",
+        "variables."
       ))
-
-    ## Return final object of class `recipe`
-    max_time_value = max(x$time_value)
-    reference_date <- reference_date %||% attr(x, "metadata")$as_of
-    out <- list(
-      var_info = var_info,
-      term_info = var_info,
-      steps = NULL,
-      template = x[1, ],
-      max_time_value = max_time_value,
-      reference_date = reference_date,
-      levels = NULL,
-      retained = NA
-    )
-    class(out) <- c("epi_recipe", "recipe")
-    out
+    }
+    var_info$role <- roles
+  } else {
+    var_info <- var_info %>% filter(!(variable %in% keys))
+    var_info$role <- "raw"
   }
+  ## Now we add the keys when necessary
+  var_info <- dplyr::union(
+    var_info,
+    tibble::tibble(variable = keys, role = key_roles)
+  )
+
+  ## Add types
+  var_info <- full_join(recipes:::get_types(x), var_info, by = "variable")
+  var_info$source <- "original"
+
+  ## arrange to easy order
+  var_info <- var_info %>%
+    arrange(factor(
+      role,
+      levels = union(
+        c("predictor", "outcome", "time_value", "geo_value", "key"),
+        unique(role)
+      ) # anything else
+    ))
+
+  ## Return final object of class `recipe`
+  max_time_value <- max(x$time_value)
+  reference_date <- reference_date %||% attr(x, "metadata")$as_of
+  out <- list(
+    var_info = var_info,
+    term_info = var_info,
+    steps = NULL,
+    template = x[1, ],
+    max_time_value = max_time_value,
+    reference_date = reference_date,
+    levels = NULL,
+    retained = NA
+  )
+  class(out) <- c("epi_recipe", "recipe")
+  out
+}
 
 
 #' @rdname epi_recipe
diff --git a/R/utils-latency.R b/R/utils-latency.R
index 3c50e210f..d854cb9cf 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -34,7 +34,6 @@ construct_shift_tibble <- function(terms_used, recipe, rel_step_type, shift_name
 #' @importFrom tidyr drop_na
 #' @importFrom utils capture.output
 get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns = NULL) {
-
   if (is.null(columns)) {
     columns <- info %>%
       filter(source == "original") %>%

From b7faa9a23843f5221e97874c9f6c849b1bf38802 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 11:12:55 -0800
Subject: [PATCH 197/387] fix snapshots with epidatasets 0.0.2, proj id

---
 epipredict.Rproj                             | 1 +
 tests/testthat/_snaps/snapshots.md           | 6 +++---
 tests/testthat/_snaps/step_adjust_latency.md | 4 ++--
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/epipredict.Rproj b/epipredict.Rproj
index 69fafd4b6..c1a626913 100644
--- a/epipredict.Rproj
+++ b/epipredict.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 51641a05-0347-438c-a50e-466a31e886c2
 
 RestoreWorkspace: No
 SaveWorkspace: No
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index f3e7e5737..9fd339ded 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1094,7 +1094,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1117,7 +1117,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1141,7 +1141,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 8d09248f3..70ca51b4f 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -79,6 +79,6 @@
       
       -- Operations 
       1. Lagging: death_rate by 0, 7, 14 | Trained
-      2. extend_ahead: case_rate, ... with forecast date 2022-05-31 | Trained
-      3. Leading: death_rate by -158, (lat adj) | Trained
+      2. extend_ahead: case_rate, ... with forecast date 2023-03-10 | Trained
+      3. Leading: death_rate by -441, (lat adj) | Trained
 

From d65b1223544ece0cfb3610f9446d1821af1a0bc5 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 11:12:55 -0800
Subject: [PATCH 198/387] fix snapshots with epidatasets 0.0.2, proj id

---
 epipredict.Rproj                             | 1 +
 tests/testthat/_snaps/snapshots.md           | 6 +++---
 tests/testthat/_snaps/step_adjust_latency.md | 4 ++--
 tests/testthat/test-layer_threshold_preds.R  | 3 ++-
 4 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/epipredict.Rproj b/epipredict.Rproj
index 69fafd4b6..c1a626913 100644
--- a/epipredict.Rproj
+++ b/epipredict.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 51641a05-0347-438c-a50e-466a31e886c2
 
 RestoreWorkspace: No
 SaveWorkspace: No
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index f3e7e5737..9fd339ded 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1094,7 +1094,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1117,7 +1117,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1141,7 +1141,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 8d09248f3..70ca51b4f 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -79,6 +79,6 @@
       
       -- Operations 
       1. Lagging: death_rate by 0, 7, 14 | Trained
-      2. extend_ahead: case_rate, ... with forecast date 2022-05-31 | Trained
-      3. Leading: death_rate by -158, (lat adj) | Trained
+      2. extend_ahead: case_rate, ... with forecast date 2023-03-10 | Trained
+      3. Leading: death_rate by -441, (lat adj) | Trained
 
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 324f60a1b..31659150f 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -20,7 +20,7 @@ test_that("Default pred_lower and pred_upper work as intended", {
   expect_equal(ncol(p), 3L)
   expect_s3_class(p, "epi_df")
   expect_equal(nrow(p), 3L)
-  expect_equal(round(p$.pred, digits = 3), c(0.180, 0, 0.764))
+  expect_equal(round(p$.pred, digits = 3), c(0.179, 0, 0.765))
   # expect_named(p, c("time_value", "geo_value", ".pred"))
   expect_named(p, c("geo_value", "time_value", ".pred"))
 })
@@ -61,3 +61,4 @@ test_that("thresholds additional columns", {
   expect_equal(round(p$values, digits = 3), c(0.180, 0.31, 0.180, .18, 0.310, .31))
   expect_equal(p$quantile_levels, rep(c(.1, .9), times = 3))
 })
+

From c8c87bb2fa54b7761488a5ed02fa4701ae648f6c Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 31 Jan 2025 11:57:36 -0800
Subject: [PATCH 199/387] fu styler

---
 tests/testthat/test-layer_threshold_preds.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 31659150f..f3e90c21a 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -61,4 +61,3 @@ test_that("thresholds additional columns", {
   expect_equal(round(p$values, digits = 3), c(0.180, 0.31, 0.180, .18, 0.310, .31))
   expect_equal(p$quantile_levels, rep(c(.1, .9), times = 3))
 })
-

From 1a67c5dc8cd1422648724632fc176422af3775a2 Mon Sep 17 00:00:00 2001
From: Daniel McDonald <dajmcdon@gmail.com>
Date: Sat, 1 Feb 2025 13:48:30 -0800
Subject: [PATCH 200/387] Apply suggestions from code review

Co-authored-by: David Weber <david.weber2@pm.me>
---
 R/epi_recipe.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 722679978..c82ac1676 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -24,12 +24,12 @@ epi_recipe <- function(x, ...) {
 #'   column in the `epi_df` or `NULL`. If a date, it gives the date to which all
 #'   operations are relative. Typically, in real-time tasks this is the date that
 #'   the model is created (and presumably trained). In forecasting, this is
-#'   often the same the most recent date of
+#'   often the same as the most recent date of
 #'   data availability, but when data is "latent" (reported after the date to
 #'   which it corresponds), or if performing a nowcast, the `reference_date` may
 #'   be later than this. Setting `reference_date`
 #'   to a value BEFORE the most recent data is not a true "forecast",
-#'   because future data is being used to create the model But this may be
+#'   because future data is being used to create the model, but this may be
 #'   reasonable in model building, nowcasting (predicting finalized values from
 #'   preliminary data), or if producing a backcast. If `NULL`, it will be set
 #'   to the `as_of` date of the `epi_df`.

From c650d8e5c713b99714d522b0a54b8d8135e9c2f4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 3 Feb 2025 09:33:20 -0800
Subject: [PATCH 201/387] upstream hardhat changes in test snaps

---
 tests/testthat/_snaps/bake-method.md        | 2 +-
 tests/testthat/_snaps/population_scaling.md | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/testthat/_snaps/bake-method.md b/tests/testthat/_snaps/bake-method.md
index eee28cc4b..0e8aeb78b 100644
--- a/tests/testthat/_snaps/bake-method.md
+++ b/tests/testthat/_snaps/bake-method.md
@@ -3,7 +3,7 @@
     Code
       bake(prep(r, edf), NULL, composition = "matrix")
     Condition
-      Error in `hardhat::recompose()`:
+      Error in `juice()`:
       ! `data` must only contain numeric columns.
       i These columns aren't numeric: "geo_value" and "time_value".
 
diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index 9263e8e1e..99714e71c 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -4,7 +4,7 @@
       wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
     Condition
       Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'a'.
+      ! The required column "a" is missing.
 
 ---
 
@@ -12,5 +12,5 @@
       forecast(wf)
     Condition
       Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'nothere'.
+      ! The required column "nothere" is missing.
 

From 4814377c5a296345d99dfd59299f1a2bd8510a3c Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 3 Feb 2025 09:33:30 -0800
Subject: [PATCH 202/387] redocument

---
 man/epi_recipe.Rd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 25a652ac0..98775eae6 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -30,12 +30,12 @@ used).}
 column in the \code{epi_df} or \code{NULL}. If a date, it gives the date to which all
 operations are relative. Typically, in real-time tasks this is the date that
 the model is created (and presumably trained). In forecasting, this is
-often the same the most recent date of
+often the same as the most recent date of
 data availability, but when data is "latent" (reported after the date to
 which it corresponds), or if performing a nowcast, the \code{reference_date} may
 be later than this. Setting \code{reference_date}
 to a value BEFORE the most recent data is not a true "forecast",
-because future data is being used to create the model But this may be
+because future data is being used to create the model, but this may be
 reasonable in model building, nowcasting (predicting finalized values from
 preliminary data), or if producing a backcast. If \code{NULL}, it will be set
 to the \code{as_of} date of the \code{epi_df}.}

From a66d07f55033693e13953280b0fc59961757a6c3 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 4 Feb 2025 15:49:41 -0800
Subject: [PATCH 203/387] climate prep/bake steps work as planned. does not
 adjust for aheads

---
 NEWS.md                       |  1 +
 R/step_climate.R              | 26 ++++++++++++++------------
 R/step_epi_shift.R            |  1 -
 R/utils-latency.R             |  5 ++---
 man/step_adjust_latency.Rd    |  4 ++--
 man/step_climate.Rd           |  2 +-
 tests/testthat/test-climate.R | 11 +++++++++++
 7 files changed, 31 insertions(+), 19 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index e5e1bb819..e50083fb0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `data(<dataset name>)`, but can be accessed with 
   `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>` 
   or, after loading the package, the name of the dataset alone (#382).
+- `step_adjust_latency()` no longer allows empty column selection.
 
 ## Improvements
 
diff --git a/R/step_climate.R b/R/step_climate.R
index 4b4f8ed01..2173c7c40 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -127,23 +127,25 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   col_names <- recipes_eval_select(x$terms, training, info)
   recipes::check_type(training[, col_names], types = c("double", "integer"))
   wts <- recipes::get_case_weights(info, training)
-  were_weights_used <- recipes::are_weights_used(wts, unsupervised = TRUE)
-  if (isFALSE(were_weights_used)) {
-    wts <- rep(1, nrow(training))
-  }
+  wts_used <- !is.null(wts)
+  wts <- wts %||% rep(1, nrow(training))
 
   modulus <- switch(x$time_type, epiweek = 7L, week = 7L, month = 12L, day = 365L)
 
   result <- training %>%
     mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
     select(.idx, .weights, c(col_names, x$epi_keys)) %>%
-    summarize(across(
-      all_of(col_names),
-      ~ roll_modular_multivec(.x, .idx, .weights, x$center_method,
-                              x$window_size, modulus)),
-      .by = x$epi_keys
+    tidyr::pivot_longer(all_of(unname(col_names))) %>%
+    dplyr::reframe(
+      roll_modular_multivec(value, .idx, .weights, x$center_method,
+                              x$window_size, modulus),
+      .by = c("name", x$epi_keys)
+    ) %>%
+    tidyr::pivot_wider(
+      names_from = "name", values_from = "climate_pred",
+      names_prefix = x$prefix
     ) %>%
-    unnest(col_names)
+    rename(.idx = index)
 
   step_climate_new(
     terms = x$terms,
@@ -159,7 +161,7 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
     columns = col_names,
     skip = x$skip,
     id = x$id,
-    case_weights = were_weights_used
+    case_weights = wts_used
   )
 }
 
@@ -168,7 +170,7 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 #' @export
 bake.step_climate <- function(object, new_data, ...) {
   new_data %>%
-    mutate(.idx = object$time_aggr(.target_time_value)) %>%
+    mutate(.idx = object$time_aggr(time_value)) %>%
     left_join(object$result, by = c(".idx", object$epi_keys)) %>%
     select(-.idx)
 }
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index dfc0c2bc0..ae4bd3f31 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -115,7 +115,6 @@ step_epi_ahead <-
       ))
     }
     arg_is_chr_scalar(prefix, id, role)
-    arg_is_nonneg_int(ahead)
 
     recipes::add_step(
       recipe,
diff --git a/R/utils-latency.R b/R/utils-latency.R
index f06965141..398cc9072 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -315,12 +315,11 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   }
   # construct the latency table
   latency_table <- tibble(col_name = names(training)) %>%
-    filter(col_name %nin% c(key_colnames(training))
+    filter(col_name %nin% key_colnames(training))
   if (length(columns) > 0) {
     latency_table <- latency_table %>% filter(col_name %in% columns)
   }
-  training_dropped <- training %>%
-    drop_ignored_keys(keys_to_ignore)
+  training_dropped <- training %>% drop_ignored_keys(keys_to_ignore)
   if (is.null(latency)) {
     latency_table <- latency_table %>%
       rowwise() %>%
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 9761f36b4..1493898b3 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index f6a5bb795..139aa61ee 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -14,7 +14,7 @@ step_climate(
   epi_keys = NULL,
   prefix = "climate_",
   skip = FALSE,
-  id = rand_id("climate_predictor")
+  id = rand_id("climate")
 )
 }
 \arguments{
diff --git a/tests/testthat/test-climate.R b/tests/testthat/test-climate.R
index 75e6d33d0..f7d737e41 100644
--- a/tests/testthat/test-climate.R
+++ b/tests/testthat/test-climate.R
@@ -51,3 +51,14 @@ test_that("roll_modular_multivec works", {
     expected_res
   )
 })
+
+test_that("bake step creates the correct training data", {
+  single_yr <- seq(as.Date("2021-01-01"), as.Date("2021-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    y = rep(dnorm(seq(-3, 3, length = length(single_yr))) * 50 + 5, times = 2L)
+  ) %>%
+    as_epi_df()
+  r <- epi_recipe(x) %>% step_climate(y)
+})

From 10cd85ff675fe71d73e45342e31538e3aaee1d45 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 4 Feb 2025 15:54:02 -0800
Subject: [PATCH 204/387] tests pass

---
 tests/testthat/_snaps/bake-method.md         |  4 +--
 tests/testthat/_snaps/population_scaling.md  |  4 +--
 tests/testthat/_snaps/snapshots.md           |  6 ++---
 tests/testthat/_snaps/step_adjust_latency.md |  4 +--
 tests/testthat/_snaps/step_epi_shift.new.md  | 27 --------------------
 tests/testthat/test-layer_threshold_preds.R  |  2 +-
 6 files changed, 10 insertions(+), 37 deletions(-)
 delete mode 100644 tests/testthat/_snaps/step_epi_shift.new.md

diff --git a/tests/testthat/_snaps/bake-method.md b/tests/testthat/_snaps/bake-method.md
index 252bc23ee..0e8aeb78b 100644
--- a/tests/testthat/_snaps/bake-method.md
+++ b/tests/testthat/_snaps/bake-method.md
@@ -3,7 +3,7 @@
     Code
       bake(prep(r, edf), NULL, composition = "matrix")
     Condition
-      Error in `hardhat::recompose()`:
+      Error in `juice()`:
       ! `data` must only contain numeric columns.
-      i These columns aren't numeric: "geo_value", "time_value", and ".target_time_value".
+      i These columns aren't numeric: "geo_value" and "time_value".
 
diff --git a/tests/testthat/_snaps/population_scaling.md b/tests/testthat/_snaps/population_scaling.md
index 9263e8e1e..99714e71c 100644
--- a/tests/testthat/_snaps/population_scaling.md
+++ b/tests/testthat/_snaps/population_scaling.md
@@ -4,7 +4,7 @@
       wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu) %>% add_frosting(f)
     Condition
       Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'a'.
+      ! The required column "a" is missing.
 
 ---
 
@@ -12,5 +12,5 @@
       forecast(wf)
     Condition
       Error in `hardhat::validate_column_names()`:
-      ! The following required columns are missing: 'nothere'.
+      ! The required column "nothere" is missing.
 
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index f3e7e5737..9fd339ded 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1094,7 +1094,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1117,7 +1117,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
@@ -1141,7 +1141,7 @@
       Training data was an <epi_df> with:
       * Geography: state,
       * Time type: day,
-      * Using data up-to-date as of: 2022-05-31.
+      * Using data up-to-date as of: 2023-03-10.
       * With the last data available on 2021-12-31
       
       -- Predictions -----------------------------------------------------------------
diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 320c6ebb7..139e80842 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -80,6 +80,6 @@
       
       -- Operations 
       1. Lagging: death_rate by 0, 7, 14 | Trained
-      2. Adj. extend_ahead: case_rate, ... w/ forecast date 2022-05-31 | Trained
-      3. Leading: death_rate by -158, (lat adj) | Trained
+      2. Adj. extend_ahead: case_rate, ... w/ forecast date 2023-03-10 | Trained
+      3. Leading: death_rate by -441, (lat adj) | Trained
 
diff --git a/tests/testthat/_snaps/step_epi_shift.new.md b/tests/testthat/_snaps/step_epi_shift.new.md
deleted file mode 100644
index f18e82e9c..000000000
--- a/tests/testthat/_snaps/step_epi_shift.new.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Values for ahead and lag must be integer values
-
-    Code
-      r1 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = 3.6) %>%
-        step_epi_lag(death_rate, lag = 1.9)
-    Condition
-      Error in `step_epi_ahead()`:
-      ! `ahead` must be a non-negative integer.
-
-# A negative lag value should should throw an error
-
-    Code
-      r2 <- epi_recipe(x) %>% step_epi_ahead(death_rate, ahead = 7) %>% step_epi_lag(
-        death_rate, lag = -7)
-    Condition
-      Error in `step_epi_lag()`:
-      ! `lag` must be a non-negative integer.
-
-# Values for ahead and lag cannot be duplicates
-
-    Code
-      slm_fit(r4)
-    Condition
-      Error in `add_shifted_columns()`:
-      ! Name collision occured in <step_epi_lag>
-      The following variable name already exists: "lag_7_death_rate".
-
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 324f60a1b..f3e90c21a 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -20,7 +20,7 @@ test_that("Default pred_lower and pred_upper work as intended", {
   expect_equal(ncol(p), 3L)
   expect_s3_class(p, "epi_df")
   expect_equal(nrow(p), 3L)
-  expect_equal(round(p$.pred, digits = 3), c(0.180, 0, 0.764))
+  expect_equal(round(p$.pred, digits = 3), c(0.179, 0, 0.765))
   # expect_named(p, c("time_value", "geo_value", ".pred"))
   expect_named(p, c("geo_value", "time_value", ".pred"))
 })

From cd18833fc650961e11c7a04b9b32fa8a00e36b52 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Feb 2025 10:17:52 -0800
Subject: [PATCH 205/387] rename objects

---
 R/step_climate.R              | 16 +++++++---------
 tests/testthat/test-climate.R | 32 ++++++++++++++++----------------
 2 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 2173c7c40..ea61e2b6a 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -142,10 +142,8 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
       .by = c("name", x$epi_keys)
     ) %>%
     tidyr::pivot_wider(
-      names_from = "name", values_from = "climate_pred",
-      names_prefix = x$prefix
-    ) %>%
-    rename(.idx = index)
+      names_from = "name", values_from = "climate_pred", names_prefix = x$prefix
+    )
 
   step_climate_new(
     terms = x$terms,
@@ -187,16 +185,16 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
   invisible(x)
 }
 
-roll_modular_multivec <- function(col, index, weights, center_method,
+roll_modular_multivec <- function(col, .idx, weights, center_method,
                                   window_size, modulus) {
   if (center_method == "mean") {
     aggr <- function(x, w) weighted.mean(x, w, na.rm = TRUE)
   } else {
     aggr <- function(x, w) median(x, na.rm = TRUE)
   }
-  tib <- tibble(col = col, weights = weights, index = index) |>
-    arrange(index) |>
-    tidyr::nest(data = c(col, weights), .by = index)
+  tib <- tibble(col = col, weights = weights, .idx = .idx) |>
+    arrange(.idx) |>
+    tidyr::nest(data = c(col, weights), .by = .idx)
   out <- double(nrow(tib))
   for (iter in seq_along(out)) {
     entries <- (iter - window_size):(iter + window_size) %% modulus
@@ -206,7 +204,7 @@ roll_modular_multivec <- function(col, index, weights, center_method,
       aggr(col, weights)
     )
   }
-  tibble(index = unique(tib$index), climate_pred = out)
+  tibble(.idx = unique(tib$.idx), climate_pred = out)
 }
 
 
diff --git a/tests/testthat/test-climate.R b/tests/testthat/test-climate.R
index f7d737e41..9d7b7d598 100644
--- a/tests/testthat/test-climate.R
+++ b/tests/testthat/test-climate.R
@@ -1,53 +1,53 @@
 test_that("roll_modular_multivec works", {
   tib <- tibble(
     col = c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0),
-    indx = c(1, 1, 1, 2, 2, 3, 3, 3, 1, 1),
+    .idx = c(1, 1, 1, 2, 2, 3, 3, 3, 1, 1),
     w = rep(1, 10)
   )
   modulus <- 3L
   # unweighted mean
   expected_res <- tib |>
-    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
-    summarise(climate_pred = weighted.mean(col, w = w), .by = index)
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 0, modulus),
     expected_res
   )
   w_size <- 1L
-  expected_res <- tibble(index = as.double(1:3), climate_pred = mean(tib$col))
+  expected_res <- tibble(.idx = as.double(1:3), climate_pred = mean(tib$col))
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 1L, modulus),
     expected_res
   )
   # weighted mean
   tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
   expected_res <- tib |>
-    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
-    summarise(climate_pred = weighted.mean(col, w = w), .by = index)
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 0, modulus),
     expected_res
   )
   tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
   expected_res <- tibble(
-    index = as.double(1:3),
+    .idx = as.double(1:3),
     climate_pred = weighted.mean(tib$col, tib$w)
   )
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "mean", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 1L, modulus),
     expected_res
   )
   # median
   expected_res <- tib |>
-    mutate(index = indx %% modulus, index = index + (index == 0) * modulus ) |>
-    summarise(climate_pred = median(col), .by = index)
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    summarise(climate_pred = median(col), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "median", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "median", 0, modulus),
     expected_res
   )
-  expected_res <- tibble(index = as.double(1:3), climate_pred = median(tib$col))
+  expected_res <- tibble(.idx = as.double(1:3), climate_pred = median(tib$col))
   expect_equal(
-    roll_modular_multivec(tib$col, tib$indx, tib$w, "median", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, "median", 1L, modulus),
     expected_res
   )
 })

From bcbda6d1d851e229c1ac23225ea2d5399bf39b02 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Feb 2025 17:22:43 -0800
Subject: [PATCH 206/387] climate predictor works as desired

---
 R/step_climate.R              | 69 ++++++++++++++++++++++++++++++-----
 tests/testthat/test-climate.R | 62 ++++++++++++++++++++++++++++++-
 2 files changed, 119 insertions(+), 12 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index ea61e2b6a..1444a79e1 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -36,6 +36,7 @@
 step_climate <-
   function(recipe,
            ...,
+           forecast_ahead = "detect",
            role = "predictor",
            time_type = c("epiweek", "week", "month", "day"),
            center_method = c("median", "mean"),
@@ -47,11 +48,48 @@ step_climate <-
     if (!is_epi_recipe(recipe)) {
       cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
+
+    ## Handle ahead autodetection, single outcome, time type
     n_outcomes <- sum(recipe$var_info$role == "outcome")
+    time_type <- rlang::arg_match(time_type)
+    edf_time_type <- attr(recipe$template, "metadata")$time_type
+    if (edf_time_type == "custom") {
+      cli_abort("This step only works with daily, weekly, or yearmonth data.")
+    }
     if (n_outcomes > 1L) {
       cli_abort("Only one {.var outcome} role can be used with this step.")
     }
-    time_type <- rlang::arg_match(time_type)
+    if (is.character(forecast_ahead)) {
+      forecast_ahead <- rlang::arg_match(forecast_ahead)
+      if (detect_step(recipe, "epi_ahead")) {
+        outcomes <- extract_argument(recipe, "step_epi_ahead", "role") == "outcome"
+        forecast_ahead <- extract_argument(recipe, "step_epi_ahead", "ahead")[outcomes]
+        if (length(forecast_ahead) != 1L) {
+          cli_abort(c(
+            "To detect the `forecast_ahead` automatically, `step_epi_ahead()`
+            with role = 'outcome' must be specified.",
+            i = "Check your recipe, or specify this argument directly in `step_climate()`."
+          ))
+        }
+        ttype_ord <- match(time_type, c("day", "epiweek", "week", "month"))
+        ttype_ord <- ttype_ord - as.integer(ttype_ord > 2)
+        edf_ttype_ord <- match(edf_time_type, c("day", "week", "yearmonth"))
+        if (ttype_ord != edf_ttype_ord) {
+          cli_abort(c("Automatic detection of the `forecast_ahead` is only
+            supported if the original data and the time type for aggregation
+            are in the same units.",
+            i = "Here, the data is in {.val {edf_time_type}} while
+            `time_type` is {.val {time_type}}.",
+            i = "This is resolved most easily by specifying `forecast_ahead`."
+          ))
+        }
+      } else {
+        forecast_ahead <- 0L
+      }
+    }
+    arg_is_int(forecast_ahead)
+
+    # check other args
     center_method <- rlang::arg_match(center_method)
     arg_is_chr(role)
     arg_is_chr(epi_keys, allow_null = TRUE)
@@ -70,12 +108,14 @@ step_climate <-
         terms = enquos(...),
         role = role,
         trained = FALSE,
+        forecast_ahead = forecast_ahead,
         time_type = time_type,
         time_aggr = time_aggr,
+        modulus = NULL,
         center_method = center_method,
         window_size = window_size,
         epi_keys = epi_keys,
-        result = NULL,
+        climate_table = NULL,
         prefix = prefix,
         columns = NULL,
         skip = skip,
@@ -90,12 +130,14 @@ step_climate_new <-
   function(terms,
            role,
            trained,
+           forecast_ahead,
            time_type,
            time_aggr,
+           modulus,
            center_method,
            window_size,
            epi_keys,
-           result,
+           climate_table,
            prefix,
            columns,
            skip,
@@ -106,12 +148,14 @@ step_climate_new <-
       terms = terms,
       role = role,
       trained = trained,
+      forecast_ahead = forecast_ahead,
       time_type = time_type,
       time_aggr = time_aggr,
+      modulus = modulus,
       center_method = center_method,
       window_size = window_size,
       epi_keys = epi_keys,
-      result = result,
+      climate_table = climate_table,
       prefix = prefix,
       columns = columns,
       skip = skip,
@@ -130,9 +174,9 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   wts_used <- !is.null(wts)
   wts <- wts %||% rep(1, nrow(training))
 
-  modulus <- switch(x$time_type, epiweek = 7L, week = 7L, month = 12L, day = 365L)
+  modulus <- switch(x$time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
 
-  result <- training %>%
+  climate_table <- training %>%
     mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
     select(.idx, .weights, c(col_names, x$epi_keys)) %>%
     tidyr::pivot_longer(all_of(unname(col_names))) %>%
@@ -149,12 +193,14 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
     terms = x$terms,
     role = x$role,
     trained = TRUE,
+    forecast_ahead = x$forecast_ahead,
     time_type = x$time_type,
     time_aggr = x$time_aggr,
+    modulus = modulus,
     center_method = x$center_method,
     window_size = x$window_size,
     epi_keys = x$epi_keys,
-    result = result,
+    climate_table = climate_table,
     prefix = x$prefix,
     columns = col_names,
     skip = x$skip,
@@ -167,9 +213,14 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.step_climate <- function(object, new_data, ...) {
+  climate_table <- object$climate_table %>%
+    mutate(
+      .idx = (.idx - object$forecast_ahead) %% object$modulus,
+      .idx = dplyr::case_when(.idx == 0 ~ object$modulus, TRUE ~ .idx)
+    )
   new_data %>%
     mutate(.idx = object$time_aggr(time_value)) %>%
-    left_join(object$result, by = c(".idx", object$epi_keys)) %>%
+    left_join(climate_table, by = c(".idx", object$epi_keys)) %>%
     select(-.idx)
 }
 
@@ -206,5 +257,3 @@ roll_modular_multivec <- function(col, .idx, weights, center_method,
   }
   tibble(.idx = unique(tib$.idx), climate_pred = out)
 }
-
-
diff --git a/tests/testthat/test-climate.R b/tests/testthat/test-climate.R
index 9d7b7d598..c786791ef 100644
--- a/tests/testthat/test-climate.R
+++ b/tests/testthat/test-climate.R
@@ -52,13 +52,71 @@ test_that("roll_modular_multivec works", {
   )
 })
 
-test_that("bake step creates the correct training data", {
+test_that("prep/bake steps create the correct training data", {
   single_yr <- seq(as.Date("2021-01-01"), as.Date("2021-12-31"), by = "1 day")
   x <- tibble(
     time_value = rep(single_yr, times = 2L),
     geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
-    y = rep(dnorm(seq(-3, 3, length = length(single_yr))) * 50 + 5, times = 2L)
+    # shift by 2 days to match the epiweeks of 2021
+    y = rep(c(1, 1, rep(c(1:26, 26:2), each = 7), 1, 1, 1, 1, 1, 1), times = 2L)
   ) %>%
     as_epi_df()
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
   r <- epi_recipe(x) %>% step_climate(y)
+  p <- prep(r, x)
+
+  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
+  expect_equal(p$steps[[1]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expected_bake <- x %>%
+    mutate(.idx = lubridate::epiweek(time_value)) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    select(-.idx)
+  expect_equal(b, expected_bake)
+})
+
+
+test_that("leading the climate predictor works as expected", {
+  single_yr <- seq(as.Date("2021-01-01"), as.Date("2021-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    # shift by 2 days to match the epiweeks of 2021
+    y = rep(c(1, 1, rep(c(1:26, 26:2), each = 7), 1, 1, 1, 1, 1, 1), times = 2L)
+  ) %>%
+    as_epi_df()
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
+  r <- epi_recipe(x) %>%
+    step_epi_ahead(y, ahead = 14L) %>%
+    step_epi_lag(y, lag = c(0, 7L, 14L)) %>%
+    step_climate(y, forecast_ahead = 2L) %>%
+    # matches the response
+    step_epi_naomit()
+  p <- prep(r, x)
+
+  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
+  expect_equal(p$steps[[3]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expect_identical(max(b$time_value), as.Date("2021-12-17")) # last date with no NAs
+  # expected climate predictor should be shifted forward by 2 weeks
+  expected_climate_pred <- x %>%
+    mutate(
+      .idx = (lubridate::epiweek(time_value) + 2) %% 53,
+      .idx = dplyr::case_when(.idx == 0 ~ 53, TRUE ~ .idx)
+    ) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    arrange(time_value, geo_value) %>%
+    filter(time_value %in% unique(b$time_value)) %>%
+    pull(climate_y)
+  expect_identical(b$climate_y, expected_climate_pred)
+
+  # Check if our test data has the right values
+  td <- get_test_data(r, x)
+  expected_test_x <- td %>%
+    filter(time_value == "2021-12-31") %>%
+    mutate(ahead_14_y = NA_real_, lag_0_y = 1, lag_7_y = 2, lag_14_y = 3,
+           climate_y = 2)
+  expect_equal(bake(p, td), expected_test_x)
 })

From 2fabf45bb104e43fdf4fbb0c6330557614713368 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Feb 2025 17:24:04 -0800
Subject: [PATCH 207/387] rename test

---
 tests/testthat/{test-climate.R => test-step_climate.R} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename tests/testthat/{test-climate.R => test-step_climate.R} (100%)

diff --git a/tests/testthat/test-climate.R b/tests/testthat/test-step_climate.R
similarity index 100%
rename from tests/testthat/test-climate.R
rename to tests/testthat/test-step_climate.R

From ced85b439cc9d1af947b389f866551b467d9fef8 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Feb 2025 17:58:03 -0800
Subject: [PATCH 208/387] more detailed documentation

---
 R/step_climate.R    | 60 +++++++++++++++++++++++++++++++++++++++---
 man/step_climate.Rd | 64 +++++++++++++++++++++++++++++++++++++++++----
 2 files changed, 116 insertions(+), 8 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 1444a79e1..b159defa4 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -1,12 +1,64 @@
 #' Calculate a climatological variable based on the history
 #'
 #' `step_climate()` creates a *specification* of a recipe step
-#'   that will generate one or more new columns of derived data. Using this as
-#'   a predictor is most useful when predicting strongly seasonal data. The
-#'   climate timing will automatically be aligned to the outcome at bake time.
+#' that will generate one or more new columns of derived data. This step
+#' examines all available seasons in the training data and calculates the
+#' a measure of center for the "typical" season. Think of this like with
+#' the weather: to predict the temperature in January in Pittsburgh, PA,
+#' I might look at all previous January's on record, average their temperatures,
+#' and include that in my model. So it is important to _align_ the forecast
+#' horizon with the climate. See the details for more information.
+#'
+#' @details
+#' Construction of a climate predictor can be helpful with strongly seasonal
+#' data. But its utility is greatest when the estimated "climate" is aligned
+#' to the forecast horizon.
+#' For example, if today is December 1, and we want
+#' to make a prediction for December 15, we want to know the climate for the
+#' week of December 15 to use in our model. But we also want to align the rest
+#' of our training data with the climate _2 weeks after_ those dates.
+#'
+#' To accomplish
+#' this, if we have daily data, we could use `time_type = "week"` and
+#' `forecast_ahead = 2`. The climate predictor would be created by taking
+#' averages over each week (with a window of a few weeks before and after, as
+#' determined by `window_size`), and then aligning these with the appropriate dates
+#' in the training data so that each `time_value` will "see" the typical climate 2
+#' weeks in the future.
+#'
+#' Alternatively, in the same scenario, we could use `time_type = "day"` and
+#' `forecast_ahead = 14`. The climate predictor would be created by taking
+#' averages over a small window around each _day_, and then aligning these with
+#' the appropriate dates in the training data so that each `time_value` will
+#' "see" the climate 14 days in the future.
+#'
+#' The only differences between these options is the type of averaging performed
+#' over the historical data. In the first case, days in the same week will get
+#' the same value of the climate predictor (because we're looking at weekly
+#' windows), while in the second case, every day in the data will have the
+#' average climate for the _day_ that happens 14 days in the future.
+#'
+#' Autodetecting the forecast horizon can only be guaranteed to work correctly
+#' when the time types are the same: for example using daily data for training
+#' and daily climate calculations. However, using weekly data, predicting 4
+#' weeks ahead, and setting `time_type = "month"` is perfectly reasonable. It's
+#' just that the climate is calculated over _months_ (January, February, March,
+#' etc.) so how to properly align this when producing a forecast for the 5th week
+#' in the year is challenging. For scenarios like these, it may be best to
+#' approximately match the times with `forecast_ahead = 1`, for example.
 #'
 #'
 #' @inheritParams step_growth_rate
+#' @param forecast_ahead The forecast horizon. By default, this step will try to
+#'   detect whether a forecast horizon has already been specified with
+#'   [step_epi_ahead()]. Alternatively, one can specify an explicit
+#'   horizon with a scalar integer. Auto-detection is only possible
+#'   when the time type of the `epi_df` used to create the `epi_recipe` is the
+#'   same as the aggregation
+#'   `time_type` specified in this step (say, both daily or both weekly). If,
+#'   for example, daily data is used with monthly time aggregation, then
+#'   auto-detection is not possible (and may in fact lead to strange behaviour
+#'   even if `forecast_ahead` is specified with an integer). See details below.
 #' @param time_type The duration over which time aggregation should be performed.
 #' @param center_method The measure of center to be calculated over the time
 #'   window.
@@ -20,6 +72,8 @@
 #'   state-level data, a national climate would be calculated if `NULL`, but
 #'   passing `epi_keys = "geo_value"` would calculate the climate separately
 #'   by state.
+#' @param role What role should be assigned for any variables created by this
+#'   step? "predictor" is the most likely choice.
 #' @template step-return
 #'
 #'
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index 139aa61ee..da51af66e 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -7,6 +7,7 @@
 step_climate(
   recipe,
   ...,
+  forecast_ahead = "detect",
   role = "predictor",
   time_type = c("epiweek", "week", "month", "day"),
   center_method = c("median", "mean"),
@@ -24,8 +25,19 @@ sequence of operations for this recipe.}
 \item{...}{One or more selector functions to choose variables
 for this step. See \code{\link[recipes:selections]{recipes::selections()}} for more details.}
 
-\item{role}{For model terms created by this step, what analysis role should
-they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.}
+\item{forecast_ahead}{The forecast horizon. By default, this step will try to
+detect whether a forecast horizon has already been specified with
+\code{\link[=step_epi_ahead]{step_epi_ahead()}}. Alternatively, one can specify an explicit
+horizon with a scalar integer. Auto-detection is only possible
+when the time type of the \code{epi_df} used to create the \code{epi_recipe} is the
+same as the aggregation
+\code{time_type} specified in this step (say, both daily or both weekly). If,
+for example, daily data is used with monthly time aggregation, then
+auto-detection is not possible (and may in fact lead to strange behaviour
+even if \code{forecast_ahead} is specified with an integer). See details below.}
+
+\item{role}{What role should be assigned for any variables created by this
+step? "predictor" is the most likely choice.}
 
 \item{time_type}{The duration over which time aggregation should be performed.}
 
@@ -61,9 +73,51 @@ sequence of any existing operations.
 }
 \description{
 \code{step_climate()} creates a \emph{specification} of a recipe step
-that will generate one or more new columns of derived data. Using this as
-a predictor is most useful when predicting strongly seasonal data. The
-climate timing will automatically be aligned to the outcome at bake time.
+that will generate one or more new columns of derived data. This step
+examines all available seasons in the training data and calculates the
+a measure of center for the "typical" season. Think of this like with
+the weather: to predict the temperature in January in Pittsburgh, PA,
+I might look at all previous January's on record, average their temperatures,
+and include that in my model. So it is important to \emph{align} the forecast
+horizon with the climate. See the details for more information.
+}
+\details{
+Construction of a climate predictor can be helpful with strongly seasonal
+data. But its utility is greatest when the estimated "climate" is aligned
+to the forecast horizon.
+For example, if today is December 1, and we want
+to make a prediction for December 15, we want to know the climate for the
+week of December 15 to use in our model. But we also want to align the rest
+of our training data with the climate \emph{2 weeks after} those dates.
+
+To accomplish
+this, if we have daily data, we could use \code{time_type = "week"} and
+\code{forecast_ahead = 2}. The climate predictor would be created by taking
+averages over each week (with a window of a few weeks before and after, as
+determined by \code{window_size}), and then aligning these with the appropriate dates
+in the training data so that each \code{time_value} will "see" the typical climate 2
+weeks in the future.
+
+Alternatively, in the same scenario, we could use \code{time_type = "day"} and
+\code{forecast_ahead = 14}. The climate predictor would be created by taking
+averages over a small window around each \emph{day}, and then aligning these with
+the appropriate dates in the training data so that each \code{time_value} will
+"see" the climate 14 days in the future.
+
+The only differences between these options is the type of averaging performed
+over the historical data. In the first case, days in the same week will get
+the same value of the climate predictor (because we're looking at weekly
+windows), while in the second case, every day in the data will have the
+average climate for the \emph{day} that happens 14 days in the future.
+
+Autodetecting the forecast horizon can only be guaranteed to work correctly
+when the time types are the same: for example using daily data for training
+and daily climate calculations. However, using weekly data, predicting 4
+weeks ahead, and setting \code{time_type = "month"} is perfectly reasonable. It's
+just that the climate is calculated over \emph{months} (January, February, March,
+etc.) so how to properly align this when producing a forecast for the 5th week
+in the year is challenging. For scenarios like these, it may be best to
+approximately match the times with \code{forecast_ahead = 1}, for example.
 }
 \examples{
 r <- epi_recipe(covid_case_death_rates) \%>\%

From 01182d7a0383eb35e41f3bcd8dece59a5b304ab1 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Feb 2025 18:00:29 -0800
Subject: [PATCH 209/387] add another example

---
 R/step_climate.R    | 12 ++++++++++++
 man/step_climate.Rd | 12 ++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/R/step_climate.R b/R/step_climate.R
index b159defa4..cbeea504f 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -79,6 +79,7 @@
 #'
 #' @export
 #' @examples
+#' # automatically detects the horizon
 #' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_climate(death_rate, time_type = "day")
@@ -87,6 +88,17 @@
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
 #'   bake(new_data = NULL)
+#'
+#' # same idea, but using weekly climate
+#' r <- epi_recipe(covid_case_death_rates) %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   step_climate(death_rate, forecast_ahead = 1, time_type = "epiweek",
+#'                window_size = 1L)
+#' r
+#'
+#' r %>%
+#'   prep(covid_case_death_rates) %>%
+#'   bake(new_data = NULL)
 step_climate <-
   function(recipe,
            ...,
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index da51af66e..0a8bb57d0 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -120,11 +120,23 @@ in the year is challenging. For scenarios like these, it may be best to
 approximately match the times with \code{forecast_ahead = 1}, for example.
 }
 \examples{
+# automatically detects the horizon
 r <- epi_recipe(covid_case_death_rates) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_climate(death_rate, time_type = "day")
 r
 
+r \%>\%
+  prep(covid_case_death_rates) \%>\%
+  bake(new_data = NULL)
+
+# same idea, but using weekly climate
+r <- epi_recipe(covid_case_death_rates) \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
+  step_climate(death_rate, forecast_ahead = 1, time_type = "epiweek",
+               window_size = 1L)
+r
+
 r \%>\%
   prep(covid_case_death_rates) \%>\%
   bake(new_data = NULL)

From 6eaad66b896211d38374e4ee914d1120b56d03f6 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 13:11:03 -0600
Subject: [PATCH 210/387] adding 0.5 as a default canned quantile

---
 R/arx_forecaster.R                  |   2 +-
 R/autoplot.R                        |  13 +-
 R/flatline_forecaster.R             |   2 +-
 R/layer_quantile_distn.R            |   2 +-
 R/layer_residual_quantiles.R        |   2 +-
 man/arx_args_list.Rd                |   2 +-
 man/flatline_args_list.Rd           |   2 +-
 man/layer_quantile_distn.Rd         |   2 +-
 man/layer_residual_quantiles.Rd     |   2 +-
 man/step_adjust_latency.Rd          |   4 +-
 tests/testthat/_snaps/snapshots.md  | 262 ++++++++++++++--------------
 tests/testthat/test-arx_args_list.R |   2 +-
 12 files changed, 153 insertions(+), 144 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index b386fe456..09e009cd4 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -295,7 +295,7 @@ arx_args_list <- function(
     target_date = NULL,
     adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
     warn_latency = TRUE,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.5, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
diff --git a/R/autoplot.R b/R/autoplot.R
index 870dcb8d8..65e68f6c1 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -236,19 +236,20 @@ plot_bands <- function(
     alpha = 0.6,
     linewidth = 0.05) {
   innames <- names(predictions)
-  n <- length(levels)
-  alpha <- alpha / (n - 1)
-  l <- (1 - levels) / 2
-  l <- c(rev(l), 1 - l)
+  na_levels <- length(levels)
+  alpha <- alpha / (n_levels - 1)
+  # generate the corresponding level that is 1 - level
+  levels <- (1 - levels) / 2
+  levels <- c(rev(levels), 1 - levels)
 
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, l), l)) %>%
+    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, levels), levels)) %>%
     pivot_quantiles_wider(.pred_distn)
   qnames <- setdiff(names(predictions), innames)
 
-  for (i in 1:n) {
+  for (i in 1:n_levels) {
     bottom <- qnames[i]
     top <- rev(qnames)[i]
     if (i == 1) {
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 7efda3efd..b3578b1be 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -108,7 +108,7 @@ flatline_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.5, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index f7bc9259d..2f0357bf5 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -44,7 +44,7 @@
 #' p
 layer_quantile_distn <- function(frosting,
                                  ...,
-                                 quantile_levels = c(.25, .75),
+                                 quantile_levels = c(0.25, 0.5, 0.75),
                                  truncate = c(-Inf, Inf),
                                  name = ".pred_distn",
                                  id = rand_id("quantile_distn")) {
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index e9b5b7c19..624932ce6 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -48,7 +48,7 @@
 #' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.5, 0.95),
     symmetrize = TRUE,
     by_key = character(0L),
     name = ".pred_distn",
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index f28cdefab..7a9cd592b 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -12,7 +12,7 @@ arx_args_list(
   target_date = NULL,
   adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
   warn_latency = TRUE,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.5, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 401850efe..634d30e5d 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -9,7 +9,7 @@ flatline_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.5, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index 3a5cb60e2..a0de7b669 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -7,7 +7,7 @@
 layer_quantile_distn(
   frosting,
   ...,
-  quantile_levels = c(0.25, 0.75),
+  quantile_levels = c(0.25, 0.5, 0.75),
   truncate = c(-Inf, Inf),
   name = ".pred_distn",
   id = rand_id("quantile_distn")
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index a7deded71..40e7b0303 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -7,7 +7,7 @@
 layer_residual_quantiles(
   frosting,
   ...,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.5, 0.95),
   symmetrize = TRUE,
   by_key = character(0L),
   name = ".pred_distn",
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 0078de100..53098504e 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 9fd339ded..213f6b7be 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -3,24 +3,24 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
+    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
+    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18999, 18999, 
-    18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -28,24 +28,24 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0.084583345, 
-    0.194105055), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.1393442, 0.194105055), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.048438145, 0.157959855), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.257363545, 0.366885255
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.048438145, 0.103199, 0.157959855), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.257363545, 
+    0.3121244, 0.366885255), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.367085245, 0.476606955), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.677223545, 0.786745255
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.367085245, 0.4218461, 0.476606955), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.677223545, 
+    0.7319844, 0.786745255), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.142781745, 0.252303455), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18993, 18993, 
-    18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
+        values = c(0.142781745, 0.1975426, 0.252303455), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -53,24 +53,24 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
+    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
+    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18999, 18999, 
-    18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -78,24 +78,24 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
+    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
+    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18993, 18993, 
-    18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
+        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
+        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 # cdc_baseline_forecaster snapshots
@@ -981,26 +981,28 @@
 # arx_forecaster snapshots
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328, 
-    1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
-        structure(list(values = c(0.171022956902288, 0.535003760656582
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.353013358779434, 0.648525432444876, 0.667670289394327, 
+    1.1418673907239, 0.830448695683588, 0.329799431948648), .pred_distn = structure(list(
+        structure(list(values = c(0.171022956902287, 0.353013358779434, 
+        0.535003760656581), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.46653503056773, 0.830515834322024), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
-        0.849660691271475), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.466535030567729, 0.648525432444876, 0.830515834322023
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.959876988846753, 1.32385779260105), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.64845829380644, 
-        1.01243909756073), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.48567988751718, 0.667670289394327, 0.849660691271474
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.147809030071502, 0.511789833825796), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+            values = c(0.959876988846751, 1.1418673907239, 1.32385779260105
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.648458293806441, 0.830448695683588, 1.01243909756074
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.147809030071501, 0.329799431948648, 0.511789833825795
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -1008,77 +1010,83 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
-    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398633, 0.202494988128882
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.470345577837144, 0.725986105412009, 0.212686665274006), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398626, 0.149303403634372, 
+        0.202494988128881), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
-        0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.0865730800114375, 0.139764664505947, 0.192956249000456
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.672794520917498, 
-        0.779177689906517), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.279994736572135, 0.333186321066645, 0.386377905561154
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.159495080779498, 0.265878249768516), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+            values = c(0.417153993342635, 0.470345577837144, 0.523537162331653
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.6727945209175, 0.725986105412009, 0.779177689906518
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.159495080779497, 0.212686665274006, 0.265878249768516
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
-    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.469979623951498
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
+    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c(0.136509784083987, 0.303244704017743, 
+        0.469979623951498), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.364597933377326, 0.531332853311082, 0.698067773244837
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
-        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.422093024752224, 0.588827944685979, 0.755562864619735
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
-        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.821955329282474, 0.988690249216229, 1.15542516914998
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
-    18997, 18997), class = "Date"), target_date = structure(c(18998, 
+            values = c(0.628067077067883, 0.794801997001639, 0.961536916935394
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.140160537291566, 0.306895457225321, 0.473630377159077
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
+    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
-    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.469979623951498
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
+    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c(0.136509784083987, 0.303244704017743, 
+        0.469979623951498), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.364597933377326, 0.531332853311082, 0.698067773244837
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(0.422093024752224, 0.588827944685979, 0.755562864619735
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
-        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.821955329282474, 0.988690249216229, 1.15542516914998
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
-        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.628067077067883, 0.794801997001639, 0.961536916935394
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
-    18997, 18997), class = "Date"), target_date = structure(c(18998, 
+            values = c(0.140160537291566, 0.306895457225321, 0.473630377159077
+            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
+    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 226444e37..8102c31d8 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -41,7 +41,7 @@ test_that("arx forecaster disambiguates quantiles", {
   tlist <- eval(formals(quantile_reg)$quantile_levels)
   expect_identical( # both default
     compare_quantile_args(alist, tlist),
-    sort(c(alist, tlist))
+    c(0.05, 0.5, 0.95)
   )
   expect_snapshot(
     error = TRUE,

From 5126b72bd68cc0a18502320a883e3cd11256c7d2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 13:12:39 -0600
Subject: [PATCH 211/387] news & description

---
 DESCRIPTION | 2 +-
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1b52822a5..e45633b0e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.7
+Version: 0.1.8
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 2f138fd96..84aaffadc 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,6 +25,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 - Quantiles produced by `grf` were sometimes out of order.
 - dist_quantiles can have all `NA` values without causing unrelated errors
+- add `0.5` as a default quantile for canned forecasters to avoid strange thresholding behavior
 
 # epipredict 0.1
 

From aa121d342cde58404d57f8e2cc26c3b43edaa247 Mon Sep 17 00:00:00 2001
From: David Weber <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 12:21:51 -0800
Subject: [PATCH 212/387] Update R/autoplot.R

Co-authored-by: Dmitry Shemetov <dmtryshmtv@gmail.com>
---
 R/autoplot.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 65e68f6c1..6761ce6f7 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -236,7 +236,7 @@ plot_bands <- function(
     alpha = 0.6,
     linewidth = 0.05) {
   innames <- names(predictions)
-  na_levels <- length(levels)
+  n_levels <- length(levels)
   alpha <- alpha / (n_levels - 1)
   # generate the corresponding level that is 1 - level
   levels <- (1 - levels) / 2

From 42a74f548ce03bedc6536365d86720018ea8e51b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 14:54:31 -0600
Subject: [PATCH 213/387] autoplot doesn't extrapolate by default

---
 R/autoplot.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 6761ce6f7..8ae9226bf 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -85,7 +85,7 @@ NULL
 #' @rdname autoplot-epipred
 autoplot.epi_workflow <- function(
     object, predictions = NULL,
-    .levels = c(.5, .8, .95), ...,
+    .levels = c(.5, .8, .9), ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
@@ -231,7 +231,7 @@ starts_with_impl <- function(x, vars) {
 
 plot_bands <- function(
     base_plot, predictions,
-    levels = c(.5, .8, .95),
+    levels = c(.5, .8, .9),
     fill = "blue4",
     alpha = 0.6,
     linewidth = 0.05) {

From 7d31ee3bf02156f3f91f51177018f5797d8b3006 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 15:28:15 -0600
Subject: [PATCH 214/387] docs mismatch, missing `dplyr::`

---
 R/autoplot.R            | 2 +-
 man/autoplot-epipred.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 8ae9226bf..6b8a5d9c8 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -183,7 +183,7 @@ autoplot.epi_workflow <- function(
   }
 
   if (".pred" %in% names(predictions)) {
-    ntarget_dates <- n_distinct(predictions$time_value)
+    ntarget_dates <- dplyr::n_distinct(predictions$time_value)
     if (ntarget_dates > 1L) {
       bp <- bp +
         geom_line(
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 1025759b3..c3b3e902b 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -9,7 +9,7 @@
 \method{autoplot}{epi_workflow}(
   object,
   predictions = NULL,
-  .levels = c(0.5, 0.8, 0.95),
+  .levels = c(0.5, 0.8, 0.9),
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),

From 83a6ec8158a2d44d8befd1deabb73f8229c49ccb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 13:44:11 -0800
Subject: [PATCH 215/387] use all_of in tidyselect

---
 R/step_climate.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index cbeea504f..cf3f2cc22 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -244,7 +244,7 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 
   climate_table <- training %>%
     mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
-    select(.idx, .weights, c(col_names, x$epi_keys)) %>%
+    select(.idx, .weights, all_of(c(col_names, x$epi_keys))) %>%
     tidyr::pivot_longer(all_of(unname(col_names))) %>%
     dplyr::reframe(
       roll_modular_multivec(value, .idx, .weights, x$center_method,

From 94de99d9f327d91a522007c60e7b73127b1f889c Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 13:49:36 -0800
Subject: [PATCH 216/387] better defaults in layer_residual_quantiles, force
 0.5 and document

---
 R/layer_residual_quantiles.R | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 624932ce6..09a374579 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -3,7 +3,8 @@
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
 #' @param quantile_levels numeric vector of probabilities with values in (0,1)
-#'   referring to the desired quantile.
+#'   referring to the desired quantile. Note that 0.5 will always be included
+#'   even if left out by the user.
 #' @param symmetrize logical. If `TRUE` then interval will be symmetric.
 #' @param by_key A character vector of keys to group the residuals by before
 #'   calculating quantiles. The default, `c()` performs no grouping.
@@ -28,7 +29,7 @@
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_residual_quantiles(
-#'     quantile_levels = c(0.0275, 0.975),
+#'     quantile_levels = c(0.025, 0.975),
 #'     symmetrize = FALSE
 #'   ) %>%
 #'   layer_naomit(.pred)
@@ -48,7 +49,7 @@
 #' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
-    quantile_levels = c(0.05, 0.5, 0.95),
+    quantile_levels = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
     symmetrize = TRUE,
     by_key = character(0L),
     name = ".pred_distn",
@@ -59,6 +60,7 @@ layer_residual_quantiles <- function(
   arg_is_chr(by_key, allow_empty = TRUE)
   arg_is_probabilities(quantile_levels)
   arg_is_lgl(symmetrize)
+  quantile_levels <- sort(unique(c(0.5, quantile_levels)))
   add_layer(
     frosting,
     layer_residual_quantiles_new(

From 521756db0c796b998abf966acb860a590292f6b7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 13:55:54 -0800
Subject: [PATCH 217/387] redocument, pass layer test

---
 man/layer_residual_quantiles.Rd                | 7 ++++---
 man/step_adjust_latency.Rd                     | 4 ++--
 tests/testthat/test-layer_residual_quantiles.R | 8 ++++----
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index 40e7b0303..96a9da56d 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -7,7 +7,7 @@
 layer_residual_quantiles(
   frosting,
   ...,
-  quantile_levels = c(0.05, 0.5, 0.95),
+  quantile_levels = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
   symmetrize = TRUE,
   by_key = character(0L),
   name = ".pred_distn",
@@ -20,7 +20,8 @@ layer_residual_quantiles(
 \item{...}{Unused, include for consistency with other layers.}
 
 \item{quantile_levels}{numeric vector of probabilities with values in (0,1)
-referring to the desired quantile.}
+referring to the desired quantile. Note that 0.5 will always be included
+even if left out by the user.}
 
 \item{symmetrize}{logical. If \code{TRUE} then interval will be symmetric.}
 
@@ -53,7 +54,7 @@ wf <- epi_workflow(r, linear_reg()) \%>\% fit(jhu)
 f <- frosting() \%>\%
   layer_predict() \%>\%
   layer_residual_quantiles(
-    quantile_levels = c(0.0275, 0.975),
+    quantile_levels = c(0.025, 0.975),
     symmetrize = FALSE
   ) \%>\%
   layer_naomit(.pred)
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 53098504e..0078de100 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 12e44809e..498b80f95 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -26,8 +26,8 @@ test_that("Returns expected number or rows and columns", {
   nested <- p %>% dplyr::mutate(.quantiles = nested_quantiles(.pred_distn))
   unnested <- nested %>% tidyr::unnest(.quantiles)
 
-  expect_equal(nrow(unnested), 9L)
-  expect_equal(unique(unnested$quantile_levels), c(.0275, .8, .95))
+  expect_equal(nrow(unnested), 12L)
+  expect_equal(unique(unnested$quantile_levels), c(.0275, .5, .8, .95))
 })
 
 
@@ -65,9 +65,9 @@ test_that("Grouping by keys is supported", {
   expect_warning(p2 <- forecast(wf2))
 
   pivot1 <- pivot_quantiles_wider(p1, .pred_distn) %>%
-    mutate(width = `0.95` - `0.05`)
+    mutate(width = `0.9` - `0.1`)
   pivot2 <- pivot_quantiles_wider(p2, .pred_distn) %>%
-    mutate(width = `0.95` - `0.05`)
+    mutate(width = `0.9` - `0.1`)
   expect_equal(pivot1$width, rep(pivot1$width[1], nrow(pivot1)))
   expect_false(all(pivot2$width == pivot2$width[1]))
 })

From f9c1d3294338ac8be84cbdd6e007d5f755f8e466 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 14:12:02 -0800
Subject: [PATCH 218/387] default quantiles are the same everywhere

---
 R/arx_forecaster.R                             | 8 ++++----
 R/autoplot.R                                   | 4 +---
 R/extract.R                                    | 2 +-
 R/flatline_forecaster.R                        | 2 +-
 R/layer_quantile_distn.R                       | 2 +-
 R/layer_residual_quantiles.R                   | 2 +-
 R/make_grf_quantiles.R                         | 2 +-
 R/make_quantile_reg.R                          | 6 ++++--
 R/make_smooth_quantile_reg.R                   | 9 ++-------
 tests/testthat/test-layer_residual_quantiles.R | 4 ++--
 vignettes/epipredict.Rmd                       | 7 ++++---
 vignettes/panel-data.Rmd                       | 1 -
 vignettes/preprocessing-and-models.Rmd         | 2 +-
 13 files changed, 23 insertions(+), 28 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 09e009cd4..228fa1217 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -253,8 +253,8 @@ arx_fcast_epi_workflow <- function(
 #'   the last day of data. For example, if the last day of data was 3 days ago,
 #'   the ahead becomes `ahead+3`.
 #'   - `"extend_lags"`: increase the lags so they're relative to the actual
-#'   forecast date. For example, if the lags are `c(0,7,14)` and the last day of
-#'   data was 3 days ago, the lags become `c(3,10,17)`.
+#'   forecast date. For example, if the lags are `c(0, 7, 14)` and the last day of
+#'   data was 3 days ago, the lags become `c(3, 10, 17)`.
 #' @param warn_latency by default, `step_adjust_latency` warns the user if the
 #'   latency is large. If this is `FALSE`, that warning is turned off.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
@@ -295,7 +295,7 @@ arx_args_list <- function(
     target_date = NULL,
     adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
     warn_latency = TRUE,
-    quantile_levels = c(0.05, 0.5, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
@@ -362,7 +362,7 @@ compare_quantile_args <- function(alist, tlist, train_method = c("qr", "grf")) {
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
   default_tlist <- switch(train_method,
     "qr" = eval(formals(quantile_reg)$quantile_levels),
-    "grf" = c(.1, .5, .9)
+    "grf" = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
   )
   if (setequal(alist, default_alist)) {
     if (setequal(tlist, default_tlist)) {
diff --git a/R/autoplot.R b/R/autoplot.R
index 6b8a5d9c8..c0e3c68dd 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -39,9 +39,7 @@ ggplot2::autoplot
 #'   step_epi_naomit()
 #'
 #' f <- frosting() %>%
-#'   layer_residual_quantiles(
-#'     quantile_levels = c(.025, .1, .25, .75, .9, .975)
-#'   ) %>%
+#'   layer_residual_quantiles() %>%
 #'   layer_threshold(starts_with(".pred")) %>%
 #'   layer_add_target_date()
 #'
diff --git a/R/extract.R b/R/extract.R
index e227b59b1..2e06567e2 100644
--- a/R/extract.R
+++ b/R/extract.R
@@ -13,7 +13,7 @@
 #' @examples
 #' f <- frosting() %>%
 #'   layer_predict() %>%
-#'   layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) %>%
+#'   layer_residual_quantiles(symmetrize = FALSE) %>%
 #'   layer_naomit(.pred)
 #'
 #' extract_argument(f, "layer_residual_quantiles", "symmetrize")
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index b3578b1be..7faad31b3 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -108,7 +108,7 @@ flatline_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    quantile_levels = c(0.05, 0.5, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 2f0357bf5..b39b58f4a 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -44,7 +44,7 @@
 #' p
 layer_quantile_distn <- function(frosting,
                                  ...,
-                                 quantile_levels = c(0.25, 0.5, 0.75),
+                                 quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
                                  truncate = c(-Inf, Inf),
                                  name = ".pred_distn",
                                  id = rand_id("quantile_distn")) {
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 09a374579..96ad88411 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -49,7 +49,7 @@
 #' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
-    quantile_levels = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     by_key = character(0L),
     name = ".pred_distn",
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 00e7d0e71..fbd221d22 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -141,7 +141,7 @@ make_grf_quantiles <- function() {
       data = c(x = "X", y = "Y"),
       func = c(pkg = "grf", fun = "quantile_forest"),
       defaults = list(
-        quantiles = c(0.1, 0.5, 0.9),
+        quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         num.threads = 1L,
         seed = rlang::expr(stats::runif(1, 0, .Machine$integer.max))
       )
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 1388dd859..223881c85 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -12,7 +12,7 @@
 #' @param engine Character string naming the fitting function. Currently, only
 #'   "rq" and "grf" are supported.
 #' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#'   quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).
 #' @param method A fitting method used by [quantreg::rq()]. See the
 #'   documentation for a list of options.
 #'
@@ -27,7 +27,9 @@
 #' rq_spec <- quantile_reg(quantile_levels = c(.2, .8)) %>% set_engine("rq")
 #' ff <- rq_spec %>% fit(y ~ ., data = tib)
 #' predict(ff, new_data = tib)
-quantile_reg <- function(mode = "regression", engine = "rq", quantile_levels = 0.5, method = "br") {
+quantile_reg <- function(mode = "regression", engine = "rq",
+                         quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
+                         method = "br") {
   # Check for correct mode
   if (mode != "regression") {
     cli_abort("`mode` must be 'regression'")
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index 448ee0fa5..f31ade3cb 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -5,12 +5,7 @@
 #' the [tidymodels](https://www.tidymodels.org/) framework. Currently, the
 #' only supported engine is [smoothqr::smooth_qr()].
 #'
-#' @param mode A single character string for the type of model.
-#'   The only possible value for this model is "regression".
-#' @param engine Character string naming the fitting function. Currently, only
-#'   "smooth_qr" is supported.
-#' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#' @inheritParams quantile_reg
 #' @param outcome_locations Defaults to the vector `1:ncol(y)` but if the
 #'   responses are observed at a different spacing (or appear in a different
 #'   order), that information should be used here. This
@@ -76,7 +71,7 @@ smooth_quantile_reg <- function(
     mode = "regression",
     engine = "smoothqr",
     outcome_locations = NULL,
-    quantile_levels = 0.5,
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     degree = 3L) {
   # Check for correct mode
   if (mode != "regression") cli_abort("`mode` must be 'regression'")
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 498b80f95..1736ded2d 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -65,9 +65,9 @@ test_that("Grouping by keys is supported", {
   expect_warning(p2 <- forecast(wf2))
 
   pivot1 <- pivot_quantiles_wider(p1, .pred_distn) %>%
-    mutate(width = `0.9` - `0.1`)
+    mutate(width = `0.95` - `0.05`)
   pivot2 <- pivot_quantiles_wider(p2, .pred_distn) %>%
-    mutate(width = `0.9` - `0.1`)
+    mutate(width = `0.95` - `0.05`)
   expect_equal(pivot1$width, rep(pivot1$width[1], nrow(pivot1)))
   expect_false(all(pivot2$width == pivot2$width[1]))
 })
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 2cf7037c7..f5d57a071 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -208,7 +208,7 @@ quantiles.
 ```{r differential-levels}
 out_q <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
   args_list = arx_args_list(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99)
+    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
   )
 )
 ```
@@ -237,7 +237,8 @@ function:
 ```{r, eval = FALSE}
 arx_args_list(
   lags = c(0L, 7L, 14L), ahead = 7L, n_training = Inf,
-  forecast_date = NULL, target_date = NULL, quantile_levels = c(0.05, 0.95),
+  forecast_date = NULL, target_date = NULL, 
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE, nonneg = TRUE, quantile_by_key = character(0L),
   nafill_buffer = Inf
 )
@@ -407,7 +408,7 @@ intervals at 0. The code to do this (inside the forecaster) is
 f <- frosting() %>%
   layer_predict() %>%
   layer_residual_quantiles(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99),
+    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
     symmetrize = TRUE
   ) %>%
   layer_add_forecast_date() %>%
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 1faf5b56f..e99057897 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -364,7 +364,6 @@ f <- frosting() %>%
   layer_threshold(.pred, lower = 0) %>%
   # 90% prediction interval
   layer_residual_quantiles(
-    quantile_levels = c(0.1, 0.9),
     symmetrize = FALSE
   ) %>%
   layer_population_scaling(
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 8d1d2f19f..6bff45611 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -381,7 +381,7 @@ f <- frosting() %>%
     df_pop_col = "pop"
   )
 
-wf <- epi_workflow(r, quantile_reg(quantile_levels = c(.05, .5, .95))) %>%
+wf <- epi_workflow(r, quantile_reg()) %>%
   fit(jhu) %>%
   add_frosting(f)
 

From 82a76743b4abeb5f839da0323e16d09d83d73f1e Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 14:12:46 -0800
Subject: [PATCH 219/387] redocument

---
 man/arx_args_list.Rd            | 6 +++---
 man/arx_class_args_list.Rd      | 4 ++--
 man/autoplot-epipred.Rd         | 4 +---
 man/extract_argument.Rd         | 2 +-
 man/flatline_args_list.Rd       | 2 +-
 man/grf_quantiles.Rd            | 4 ++--
 man/layer_quantile_distn.Rd     | 2 +-
 man/layer_residual_quantiles.Rd | 2 +-
 man/quantile_reg.Rd             | 4 ++--
 man/smooth_quantile_reg.Rd      | 6 +++---
 10 files changed, 17 insertions(+), 19 deletions(-)

diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 7a9cd592b..5ece7109b 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -12,7 +12,7 @@ arx_args_list(
   target_date = NULL,
   adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
   warn_latency = TRUE,
-  quantile_levels = c(0.05, 0.5, 0.95),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
@@ -55,8 +55,8 @@ to shift the model to account for this difference. The options are:
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
 \item \code{"extend_lags"}: increase the lags so they're relative to the actual
-forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
-data was 3 days ago, the lags become \code{c(3,10,17)}.
+forecast date. For example, if the lags are \code{c(0, 7, 14)} and the last day of
+data was 3 days ago, the lags become \code{c(3, 10, 17)}.
 }}
 
 \item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index a229b67c0..dbf275355 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -57,8 +57,8 @@ to shift the model to account for this difference. The options are:
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
 \item \code{"extend_lags"}: increase the lags so they're relative to the actual
-forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
-data was 3 days ago, the lags become \code{c(3,10,17)}.
+forecast date. For example, if the lags are \code{c(0, 7, 14)} and the last day of
+data was 3 days ago, the lags become \code{c(3, 10, 17)}.
 }}
 
 \item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index c3b3e902b..066f55383 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -81,9 +81,7 @@ r <- epi_recipe(jhu) \%>\%
   step_epi_naomit()
 
 f <- frosting() \%>\%
-  layer_residual_quantiles(
-    quantile_levels = c(.025, .1, .25, .75, .9, .975)
-  ) \%>\%
+  layer_residual_quantiles() \%>\%
   layer_threshold(starts_with(".pred")) \%>\%
   layer_add_target_date()
 
diff --git a/man/extract_argument.Rd b/man/extract_argument.Rd
index 69c610c98..a276d59a6 100644
--- a/man/extract_argument.Rd
+++ b/man/extract_argument.Rd
@@ -24,7 +24,7 @@ Extract an argument made to a frosting layer or recipe step
 \examples{
 f <- frosting() \%>\%
   layer_predict() \%>\%
-  layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) \%>\%
+  layer_residual_quantiles(symmetrize = FALSE) \%>\%
   layer_naomit(.pred)
 
 extract_argument(f, "layer_residual_quantiles", "symmetrize")
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 634d30e5d..626bcb6f1 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -9,7 +9,7 @@ flatline_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
-  quantile_levels = c(0.05, 0.5, 0.95),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index f6400edcf..ad33ce11a 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -52,8 +52,8 @@ details, see \href{https://grf-labs.github.io/grf/articles/categorical_inputs.ht
 #> Model fit template:
 #> grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~integer(1), 
 #>     x), num.trees = integer(1), min.node.size = min_rows(~integer(1), 
-#>     x), quantiles = c(0.1, 0.5, 0.9), num.threads = 1L, seed = stats::runif(1, 
-#>     0, .Machine$integer.max))
+#>     x), quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95), 
+#>     num.threads = 1L, seed = stats::runif(1, 0, .Machine$integer.max))
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index a0de7b669..ed4762fa3 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -7,7 +7,7 @@
 layer_quantile_distn(
   frosting,
   ...,
-  quantile_levels = c(0.25, 0.5, 0.75),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   truncate = c(-Inf, Inf),
   name = ".pred_distn",
   id = rand_id("quantile_distn")
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index 96a9da56d..4efea525f 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -7,7 +7,7 @@
 layer_residual_quantiles(
   frosting,
   ...,
-  quantile_levels = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   by_key = character(0L),
   name = ".pred_distn",
diff --git a/man/quantile_reg.Rd b/man/quantile_reg.Rd
index 5079c3434..31a5dd123 100644
--- a/man/quantile_reg.Rd
+++ b/man/quantile_reg.Rd
@@ -7,7 +7,7 @@
 quantile_reg(
   mode = "regression",
   engine = "rq",
-  quantile_levels = 0.5,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   method = "br"
 )
 }
@@ -19,7 +19,7 @@ The only possible value for this model is "regression".}
 "rq" and "grf" are supported.}
 
 \item{quantile_levels}{A scalar or vector of values in (0, 1) to determine which
-quantiles to estimate (default is 0.5).}
+quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).}
 
 \item{method}{A fitting method used by \code{\link[quantreg:rq]{quantreg::rq()}}. See the
 documentation for a list of options.}
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index c6b17dd86..90b2c104f 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -8,7 +8,7 @@ smooth_quantile_reg(
   mode = "regression",
   engine = "smoothqr",
   outcome_locations = NULL,
-  quantile_levels = 0.5,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   degree = 3L
 )
 }
@@ -17,7 +17,7 @@ smooth_quantile_reg(
 The only possible value for this model is "regression".}
 
 \item{engine}{Character string naming the fitting function. Currently, only
-"smooth_qr" is supported.}
+"rq" and "grf" are supported.}
 
 \item{outcome_locations}{Defaults to the vector \code{1:ncol(y)} but if the
 responses are observed at a different spacing (or appear in a different
@@ -25,7 +25,7 @@ order), that information should be used here. This
 argument will be mapped to the \code{ahead} argument of \code{\link[smoothqr:smooth_qr]{smoothqr::smooth_qr()}}.}
 
 \item{quantile_levels}{A scalar or vector of values in (0, 1) to determine which
-quantiles to estimate (default is 0.5).}
+quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).}
 
 \item{degree}{the number of polynomials used for response smoothing. Must
 be no more than the number of responses.}

From a37df952f06fa4fb4b85001684b4e8eec591b815 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 14:31:15 -0800
Subject: [PATCH 220/387] pass all tests

---
 R/arx_forecaster.R                          |   3 +-
 tests/testthat/_snaps/snapshots.md          | 315 ++++++++++++--------
 tests/testthat/test-arx_args_list.R         |   2 +-
 tests/testthat/test-extract_argument.R      |   6 +-
 tests/testthat/test-grf_quantiles.R         |  12 +-
 tests/testthat/test-layer_threshold_preds.R |   5 +-
 6 files changed, 211 insertions(+), 132 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 228fa1217..5397ca881 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -200,7 +200,8 @@ arx_fcast_epi_workflow <- function(
     } else {
       quantile_levels <- sort(compare_quantile_args(
         args_list$quantile_levels,
-        rlang::eval_tidy(trainer$eng_args$quantiles) %||% c(.1, .5, .9),
+        rlang::eval_tidy(trainer$eng_args$quantiles) %||%
+          c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         "grf"
       ))
       trainer$eng_args$quantiles <- rlang::enquo(quantile_levels)
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 213f6b7be..17191e041 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -3,20 +3,28 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
-    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
-    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18999, 
@@ -28,23 +36,30 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0.084583345, 
-    0.1393442, 0.194105055), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    0.1073314, 0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.048438145, 0.103199, 0.157959855), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+        values = c(0.048438145, 0.0711862, 0.0931412, 0.103199, 0.1132568, 
+        0.1352118, 0.157959855), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.257363545, 
-    0.3121244, 0.366885255), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    0.2801116, 0.3020666, 0.3121244, 0.3221822, 0.3441372, 0.366885255
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.367085245, 0.4218461, 0.476606955), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.677223545, 
-    0.7319844, 0.786745255), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.367085245, 0.3898333, 0.4117883, 0.4218461, 
+        0.4319039, 0.4538589, 0.476606955), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.142781745, 0.1975426, 0.252303455), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+        values = c(0.677223545, 0.6999716, 0.7219266, 0.7319844, 
+        0.7420422, 0.7639972, 0.786745255), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0.142781745, 0.1655298, 0.1874848, 0.1975426, 
+        0.2076004, 0.2295554, 0.252303455), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -53,20 +68,28 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
-    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
-    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18999, 
@@ -78,20 +101,28 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.1393442, 0.34820911), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.103199, 0.31206391), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.10325949, 
-    0.3121244, 0.52098931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.4218461, 0.63071101), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.52311949, 
-    0.7319844, 0.94084931), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.1975426, 0.40640751), quantile_levels = c(0.05, 
-        0.5, 0.95)), class = c("dist_quantiles", "dist_default", 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
     "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18993, 
@@ -981,82 +1012,114 @@
 # arx_forecaster snapshots
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.353013358779434, 0.648525432444876, 0.667670289394327, 
-    1.1418673907239, 0.830448695683588, 0.329799431948648), .pred_distn = structure(list(
-        structure(list(values = c(0.171022956902287, 0.353013358779434, 
-        0.535003760656581), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.466535030567729, 0.648525432444876, 0.830515834322023
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328, 
+    1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
+        structure(list(values = c(0.171022956902288, 0.244945899624723, 
+        0.308032696431071, 0.353013358779435, 0.397994021127798, 
+        0.461080817934147, 0.535003760656582), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.48567988751718, 0.667670289394327, 0.849660691271474
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+            values = c(0.46653503056773, 0.540457973290166, 0.603544770096514, 
+            0.648525432444877, 0.693506094793241, 0.756592891599589, 
+            0.830515834322024), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
+        0.559602830239616, 0.622689627045964, 0.667670289394328, 
+        0.712650951742692, 0.77573774854904, 0.849660691271475), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.959876988846753, 
+        1.03379993156919, 1.09688672837554, 1.1418673907239, 1.18684805307226, 
+        1.24993484987861, 1.32385779260105), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.959876988846751, 1.1418673907239, 1.32385779260105
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.648458293806441, 0.830448695683588, 1.01243909756074
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.147809030071501, 0.329799431948648, 0.511789833825795
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
-    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
+            values = c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
+            0.830448695683587, 0.875429358031951, 0.938516154838299, 
+            1.01243909756073), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.147809030071502, 
+        0.221731972793937, 0.284818769600285, 0.329799431948649, 
+        0.374780094297013, 0.437866891103361, 0.511789833825796), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
-    0.470345577837144, 0.725986105412009, 0.212686665274006), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398626, 0.149303403634372, 
-        0.202494988128881), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114375, 0.139764664505947, 0.192956249000456
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.279994736572135, 0.333186321066645, 0.386377905561154
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
+    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398634, 0.118312393281548, 
+        0.13840396557592, 0.149303403634373, 0.160202841692825, 0.180294413987198, 
+        0.202494988128882), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.0865730800114383, 
+        0.108773654153123, 0.128865226447495, 0.139764664505948, 
+        0.1506641025644, 0.170755674858773, 0.192956249000457), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.417153993342635, 0.470345577837144, 0.523537162331653
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.6727945209175, 0.725986105412009, 0.779177689906518
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.159495080779497, 0.212686665274006, 0.265878249768516
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
-    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
+            values = c(0.279994736572136, 0.30219531071382, 0.322286883008193, 
+            0.333186321066645, 0.344085759125097, 0.36417733141947, 
+            0.386377905561154), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.417153993342634, 
+        0.439354567484319, 0.459446139778691, 0.470345577837144, 
+        0.481245015895596, 0.501336588189969, 0.523537162331653), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.672794520917498, 
+        0.694995095059183, 0.715086667353556, 0.725986105412008, 
+        0.73688554347046, 0.756977115764833, 0.779177689906517), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.159495080779498, 
+        0.181695654921182, 0.201787227215555, 0.212686665274007, 
+        0.223586103332459, 0.243677675626832, 0.265878249768516), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
-    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.303244704017743, 
-        0.469979623951498), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.531332853311082, 0.698067773244837
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c(0.136509784083987, 0.202348949370703, 
+        0.263837900408968, 0.303244704017742, 0.342651507626517, 
+        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.422093024752224, 0.588827944685979, 0.755562864619735
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
+            0.531332853311081, 0.570739656919856, 0.632228607958121, 
+            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
+        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282474, 0.988690249216229, 1.15542516914998
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
+            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
+        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.628067077067883, 0.794801997001639, 0.961536916935394
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.306895457225321, 0.473630377159077
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
+            0.306895457225321, 0.346302260834096, 0.407791211872361, 
+            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
     "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
     18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
@@ -1065,26 +1128,36 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017743, 0.531332853311082, 0.588827944685979, 
-    0.988690249216229, 0.794801997001639, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.303244704017743, 
-        0.469979623951498), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.531332853311082, 0.698067773244837
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.422093024752224, 0.588827944685979, 0.755562864619735
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
+    0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
+        structure(list(values = c(0.136509784083987, 0.202348949370703, 
+        0.263837900408968, 0.303244704017742, 0.342651507626517, 
+        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282474, 0.988690249216229, 1.15542516914998
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
+            0.531332853311081, 0.570739656919856, 0.632228607958121, 
+            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
+        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.628067077067883, 0.794801997001639, 0.961536916935394
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
+            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
+            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
+        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.306895457225321, 0.473630377159077
-            ), quantile_levels = c(0.05, 0.5, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
+            0.306895457225321, 0.346302260834096, 0.407791211872361, 
+            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
     "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
     18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 8102c31d8..50379357a 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -41,7 +41,7 @@ test_that("arx forecaster disambiguates quantiles", {
   tlist <- eval(formals(quantile_reg)$quantile_levels)
   expect_identical( # both default
     compare_quantile_args(alist, tlist),
-    c(0.05, 0.5, 0.95)
+    c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
   )
   expect_snapshot(
     error = TRUE,
diff --git a/tests/testthat/test-extract_argument.R b/tests/testthat/test-extract_argument.R
index 7ac160e67..e60632289 100644
--- a/tests/testthat/test-extract_argument.R
+++ b/tests/testthat/test-extract_argument.R
@@ -8,20 +8,20 @@ test_that("layer argument extractor works", {
   expect_snapshot(error = TRUE, extract_argument(f$layers[[1]], "layer_predict", "bubble"))
   expect_identical(
     extract_argument(f$layers[[2]], "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   expect_snapshot(error = TRUE, extract_argument(f, "layer_thresh", "quantile_levels"))
   expect_identical(
     extract_argument(f, "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   wf <- epi_workflow(postprocessor = f)
   expect_snapshot(error = TRUE, extract_argument(epi_workflow(), "layer_residual_quantiles", "quantile_levels"))
   expect_identical(
     extract_argument(wf, "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   expect_snapshot(error = TRUE, extract_argument(wf, "layer_predict", c("type", "opts")))
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index e2cf90cf7..5adbf6518 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -9,7 +9,10 @@ test_that("quantile_rand_forest defaults work", {
   spec <- rand_forest(engine = "grf_quantiles", mode = "regression")
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
-  manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, quantiles = c(0.1, 0.5, 0.9))
+  manual <- quantile_forest(
+    as.matrix(tib[, 2:3]), tib$y,
+    quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
+  )
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 
@@ -43,7 +46,8 @@ test_that("quantile_rand_forest handles alternative quantiles", {
 
 
 test_that("quantile_rand_forest handles allows setting the trees and mtry", {
-  spec <- rand_forest(mode = "regression", mtry = 2, trees = 100, engine = "grf_quantiles")
+  spec <- rand_forest(mode = "regression", mtry = 2, trees = 100) %>%
+    set_engine(engine = "grf_quantiles", quantiles = c(0.1, 0.5, 0.9))
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
   manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, mtry = 2, num.trees = 100)
@@ -68,13 +72,13 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
   spec2 <- parsnip::extract_spec_parsnip(o)
   expect_identical(
     rlang::eval_tidy(spec2$eng_args$quantiles),
-    c(.05, .1, .5, .9, .95) # merged with arx_args default
+    c(.05, .1, 0.25, .5, 0.75, .9, .95) # merged with arx_args default
   )
   df <- epidatasets::counts_subset %>% filter(time_value >= "2021-10-01")
 
   z <- arx_forecaster(df, "cases", "cases", spec2)
   expect_identical(
     nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
-    c(.05, .1, .5, .9, .95)
+    c(.05, .1, 0.25, .5, 0.75, .9, .95)
   )
 })
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index f3e90c21a..2201d7fc4 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -58,6 +58,7 @@ test_that("thresholds additional columns", {
   p <- p %>%
     dplyr::mutate(.quantiles = nested_quantiles(.pred_distn)) %>%
     tidyr::unnest(.quantiles)
-  expect_equal(round(p$values, digits = 3), c(0.180, 0.31, 0.180, .18, 0.310, .31))
-  expect_equal(p$quantile_levels, rep(c(.1, .9), times = 3))
+  expect_equal(round(p$values, digits = 3),
+               c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31))
+  expect_equal(p$quantile_levels, rep(c(.1, 0.5, .9), times = 3))
 })

From 3d3c2b40d36e0d2999e4eb7bcecc00f947ecf11a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 16:38:26 -0800
Subject: [PATCH 221/387] simplify docs, quantiles

---
 R/climatological_forecaster.R | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 92f5b7413..b5d3aedca 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -8,7 +8,7 @@
 #' long history of such periods rather than heavily using recent data.
 #'
 #' The forecast is taken as the quantiles of the `outcome` in a small window
-#' around the target week, computed over the entire available history.
+#' around the target period, computed over the entire available history.
 #'
 #' @inheritParams flatline_forecaster
 #' @param args_list A list of additional arguments as created by the
@@ -116,7 +116,9 @@ climatological_forecaster <- function(
 #' @inheritParams flatline_args_list
 #' @param ahead Vector of integers giving the number of time steps ahead
 #'   (in weeks) of the forecast date for which forecasts should be produced.
-#' @param time_type Character. The duration to which the forecasts correspond.
+#' @param time_type The duration over which time aggregation should be performed.
+#' @param center_method The measure of center to be calculated over the time
+#'   window.
 #' @param window_size Integer. The number of time points on each side of the
 #'   target to include in the calculation.
 #' @param quantile_method One of either `"base8"` or `"epipredict"`. The first
@@ -157,10 +159,10 @@ climatological_forecaster <- function(
 climate_args_list <- function(
     ahead = 1:4,
     forecast_date = NULL,
-    time_type = c("epiweek", "week", "month", "year", "day"),
+    time_type = c("epiweek", "week", "month", "day"),
     window_size = 3L,
     quantile_method = c("base8", "epipredict"),
-    quantile_levels = c(.1, .25, .5, .75, .9),
+    quantile_levels = c(.05, .1, .25, .5, .75, .9, .95),
     symmetrize = FALSE,
     nonneg = TRUE,
     quantile_by_key = character(0L),

From dd6b564c4abf2ed8431a0e3ce08a61835d8db6f8 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 16:40:59 -0800
Subject: [PATCH 222/387] fu styler

---
 tests/testthat/test-layer_threshold_preds.R | 6 ++++--
 vignettes/epipredict.Rmd                    | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 2201d7fc4..2ff43a165 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -58,7 +58,9 @@ test_that("thresholds additional columns", {
   p <- p %>%
     dplyr::mutate(.quantiles = nested_quantiles(.pred_distn)) %>%
     tidyr::unnest(.quantiles)
-  expect_equal(round(p$values, digits = 3),
-               c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31))
+  expect_equal(
+    round(p$values, digits = 3),
+    c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31)
+  )
   expect_equal(p$quantile_levels, rep(c(.1, 0.5, .9), times = 3))
 })
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index f5d57a071..ce0a7e38e 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -237,7 +237,7 @@ function:
 ```{r, eval = FALSE}
 arx_args_list(
   lags = c(0L, 7L, 14L), ahead = 7L, n_training = Inf,
-  forecast_date = NULL, target_date = NULL, 
+  forecast_date = NULL, target_date = NULL,
   quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE, nonneg = TRUE, quantile_by_key = character(0L),
   nafill_buffer = Inf

From 10b5b615655e1d997ddc7ca70c26184e2841a1b6 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 7 Feb 2025 17:02:02 -0800
Subject: [PATCH 223/387] bump news

---
 NEWS.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 84aaffadc..633086cb0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,7 +25,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 - Quantiles produced by `grf` were sometimes out of order.
 - dist_quantiles can have all `NA` values without causing unrelated errors
-- add `0.5` as a default quantile for canned forecasters to avoid strange thresholding behavior
+- adjust default quantiles throughout so that they match.
+- force `layer_residual_quantiles()` to always include `0.5`.
 
 # epipredict 0.1
 

From dab8d43dfe6e608faba10d0d7c82c88b39147b2d Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 10 Feb 2025 11:45:23 -0800
Subject: [PATCH 224/387] merge dev epi_recipe

---
 R/epi_recipe.R | 187 ++++++++++++++++++++++++++-----------------------
 1 file changed, 98 insertions(+), 89 deletions(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index acbbe37dd..c82ac1676 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -13,14 +13,6 @@ epi_recipe <- function(x, ...) {
 }
 
 
-#' @rdname epi_recipe
-#' @export
-epi_recipe.default <- function(x, ...) {
-  cli_abort(paste(
-    "`x` must be an {.cls epi_df} or a {.cls formula},",
-    "not a {.cls {class(x)[[1]]}}."
-  ))
-}
 
 #' @rdname epi_recipe
 #' @inheritParams recipes::recipe
@@ -28,6 +20,19 @@ epi_recipe.default <- function(x, ...) {
 #'   describes a single role that the variable will take. This value could be
 #'   anything but common roles are `"outcome"`, `"predictor"`,
 #'   `"time_value"`, and `"geo_value"`
+#' @param reference_date Either a date of the same class as the `time_value`
+#'   column in the `epi_df` or `NULL`. If a date, it gives the date to which all
+#'   operations are relative. Typically, in real-time tasks this is the date that
+#'   the model is created (and presumably trained). In forecasting, this is
+#'   often the same as the most recent date of
+#'   data availability, but when data is "latent" (reported after the date to
+#'   which it corresponds), or if performing a nowcast, the `reference_date` may
+#'   be later than this. Setting `reference_date`
+#'   to a value BEFORE the most recent data is not a true "forecast",
+#'   because future data is being used to create the model, but this may be
+#'   reasonable in model building, nowcasting (predicting finalized values from
+#'   preliminary data), or if producing a backcast. If `NULL`, it will be set
+#'   to the `as_of` date of the `epi_df`.
 #' @param ... Further arguments passed to or from other methods (not currently
 #'   used).
 #' @param formula A model formula. No in-line functions should be used here
@@ -35,8 +40,7 @@ epi_recipe.default <- function(x, ...) {
 #'  transformations should be enacted using `step` functions in this package.
 #'  Dots are allowed as are simple multivariate outcome terms (i.e. no need for
 #'  `cbind`; see Examples).
-#' @param x,data A data frame, tibble, or epi_df of the *template* data set
-#'   (see below). This is always coerced to the first row to avoid memory issues
+#' @param x,data An epi_df of the *template* data set (see below).
 #' @inherit recipes::recipe return
 #'
 #' @export
@@ -56,100 +60,107 @@ epi_recipe.default <- function(x, ...) {
 #'   step_naomit(all_outcomes(), skip = TRUE)
 #'
 #' r
-epi_recipe.epi_df <-
-  function(x, formula = NULL, ..., vars = NULL, roles = NULL) {
-    attr(x, "decay_to_tibble") <- FALSE
-    if (!is.null(formula)) {
-      if (!is.null(vars)) {
-        cli_abort(paste0(
-          "This `vars` specification will be ignored ",
+epi_recipe.epi_df <- function(x,
+                              reference_date = NULL,
+                              formula = NULL,
+                              ...,
+                              vars = NULL,
+                              roles = NULL) {
+  attr(x, "decay_to_tibble") <- FALSE
+  if (!is.null(formula)) {
+    if (!is.null(vars)) {
+      cli_abort(paste0(
+        "This `vars` specification will be ignored ",
+        "when a formula is used"
+      ))
+    }
+    if (!is.null(roles)) {
+      cli_abort(
+        paste0(
+          "This `roles` specification will be ignored ",
           "when a formula is used"
-        ))
-      }
-      if (!is.null(roles)) {
-        cli_abort(
-          paste0(
-            "This `roles` specification will be ignored ",
-            "when a formula is used"
-          )
         )
-      }
-
-      obj <- epi_recipe.formula(formula, x, ...)
-      return(obj)
-    }
-    if (is.null(vars)) vars <- colnames(x)
-    if (any(table(vars) > 1)) {
-      cli_abort("`vars` should have unique members")
-    }
-    if (any(!(vars %in% colnames(x)))) {
-      cli_abort("1 or more elements of `vars` are not in the data")
+      )
     }
 
-    keys <- key_colnames(x) # we know x is an epi_df
+    obj <- epi_recipe.formula(formula, x, ...)
+    return(obj)
+  }
+  if (is.null(vars)) vars <- colnames(x)
+  if (any(table(vars) > 1)) {
+    cli_abort("`vars` should have unique members")
+  }
+  if (any(!(vars %in% colnames(x)))) {
+    cli_abort("1 or more elements of `vars` are not in the data")
+  }
 
-    var_info <- tibble(variable = vars)
-    key_roles <- c("geo_value", rep("key", length(keys) - 2), "time_value")
+  keys <- key_colnames(x) # we know x is an epi_df
 
-    ## Check and add roles when available
-    if (!is.null(roles)) {
-      if (length(roles) != length(vars)) {
-        cli_abort(paste0(
-          "The number of roles should be the same as the number of ",
-          "variables."
-        ))
-      }
-      var_info$role <- roles
-    } else {
-      var_info <- var_info %>% filter(!(variable %in% keys))
-      var_info$role <- "raw"
-    }
-    ## Now we add the keys when necessary
-    var_info <- dplyr::union(
-      var_info,
-      tibble::tibble(variable = keys, role = key_roles)
-    )
+  var_info <- tibble(variable = vars)
+  key_roles <- c("geo_value", rep("key", length(keys) - 2), "time_value")
 
-    ## Add types
-    var_info <- full_join(recipes:::get_types(x), var_info, by = "variable")
-    var_info$source <- "original"
-
-    ## arrange to easy order
-    var_info <- var_info %>%
-      arrange(factor(
-        role,
-        levels = union(
-          c("predictor", "outcome", "time_value", "geo_value", "key"),
-          unique(role)
-        ) # anything else
+  ## Check and add roles when available
+  if (!is.null(roles)) {
+    if (length(roles) != length(vars)) {
+      cli_abort(paste0(
+        "The number of roles should be the same as the number of ",
+        "variables."
       ))
-
-    ## Return final object of class `recipe`
-    out <- list(
-      var_info = var_info,
-      term_info = var_info,
-      steps = NULL,
-      template = x[1, ],
-      max_time_value = max(x$time_value),
-      levels = NULL,
-      retained = NA
-    )
-    class(out) <- c("epi_recipe", "recipe")
-    out
+    }
+    var_info$role <- roles
+  } else {
+    var_info <- var_info %>% filter(!(variable %in% keys))
+    var_info$role <- "raw"
   }
+  ## Now we add the keys when necessary
+  var_info <- dplyr::union(
+    var_info,
+    tibble::tibble(variable = keys, role = key_roles)
+  )
+
+  ## Add types
+  var_info <- full_join(recipes:::get_types(x), var_info, by = "variable")
+  var_info$source <- "original"
+
+  ## arrange to easy order
+  var_info <- var_info %>%
+    arrange(factor(
+      role,
+      levels = union(
+        c("predictor", "outcome", "time_value", "geo_value", "key"),
+        unique(role)
+      ) # anything else
+    ))
+
+  ## Return final object of class `recipe`
+  max_time_value <- max(x$time_value)
+  reference_date <- reference_date %||% attr(x, "metadata")$as_of
+  out <- list(
+    var_info = var_info,
+    term_info = var_info,
+    steps = NULL,
+    template = x[1, ],
+    max_time_value = max_time_value,
+    reference_date = reference_date,
+    levels = NULL,
+    retained = NA
+  )
+  class(out) <- c("epi_recipe", "recipe")
+  out
+}
 
 
 #' @rdname epi_recipe
 #' @export
-epi_recipe.formula <- function(formula, data, ...) {
+epi_recipe.formula <- function(formula, data, reference_date = NULL, ...) {
   # we ensure that there's only 1 row in the template
   data <- data[1, ]
   # check for minus:
   if (!epiprocess::is_epi_df(data)) {
-    cli_abort(paste(
-      "`epi_recipe()` has been called with a non-{.cls epi_df} object.",
-      "Use `recipe()` instead."
-    ))
+    cli_abort(
+      "`epi_recipe()` has been called with a non-{.cls epi_df} object.
+      Use `recipe()` instead."
+    )
   }
 
   attr(data, "decay_to_tibble") <- FALSE
@@ -437,7 +448,6 @@ prep.epi_recipe <- function(
       "!" = "to avoid addtional warning messages."
     ))
   }
-
   training <- recipes:::check_training_set(training, x, fresh)
   training <- epi_check_training_set(training, x)
   training <- relocate(training, all_of(key_colnames(training)))
@@ -571,7 +581,6 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
   }
 
   new_data <- NextMethod("bake")
-
   if (!is.null(meta)) {
     # Baking should have dropped epi_df-ness and metadata. Re-infer some
     # metadata and assume others remain the same as the object/template:

From d6725a88a3c3b034047eb5152136f00a75a8b3f8 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 10 Feb 2025 14:33:58 -0800
Subject: [PATCH 225/387] refactor the forecaster

---
 NAMESPACE                        |   1 -
 R/arx_forecaster.R               |   3 +-
 R/climatological_forecaster.R    | 194 +++++++++++++++----------------
 R/step_climate.R                 |  17 ++-
 man/arx_args_list.Rd             |   3 +
 man/arx_fcast_epi_workflow.Rd    |   2 +-
 man/climate_args_list.Rd         |  58 ++++-----
 man/climatological_forecaster.Rd |   9 +-
 man/epi_recipe.Rd                |  30 ++++-
 9 files changed, 159 insertions(+), 158 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 89e5683d0..2723e1f0b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -30,7 +30,6 @@ S3method(bake,step_population_scaling)
 S3method(bake,step_training_window)
 S3method(detect_layer,frosting)
 S3method(detect_layer,workflow)
-S3method(epi_recipe,default)
 S3method(epi_recipe,epi_df)
 S3method(epi_recipe,formula)
 S3method(extract_argument,epi_workflow)
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 5397ca881..6d95839a4 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -92,7 +92,7 @@ arx_forecaster <- function(
 #'
 #' @return An unfitted `epi_workflow`.
 #' @export
-#' @seealso [arx_forecaster()]
+#' @seealso [arx_forecaster()], [arx_args_list()]
 #'
 #' @examples
 #' library(dplyr)
@@ -283,6 +283,7 @@ arx_fcast_epi_workflow <- function(
 #'
 #' @return A list containing updated parameter choices with class `arx_flist`.
 #' @export
+#' @seealso [arx_forecaster()]
 #'
 #' @examples
 #' arx_args_list()
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index b5d3aedca..a5cfb7bd3 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -1,7 +1,7 @@
 #' Climatological forecaster
 #'
 #' This is another "baseline" type forecaster, but it is especially appropriate
-#' for strongly seasonal diseases (e.g., influenza). The idea is to forecast
+#' for strongly seasonal diseases (e.g., influenza). The idea is to predict
 #' the "typical season" by summarizing over all available history in the
 #' `epi_data`. This is analogous to a "climate" forecast rather than a "weather"
 #' forecast, essentially predicting "typical January" behavior by relying on a
@@ -17,92 +17,110 @@
 #' @return A data frame of point and interval) forecasts at a all horizons
 #'  for each unique combination of `key_vars`.
 #' @export
+#' @seealso [step_climate()]
 #'
 #' @examples
 #'
 #' 1 + 1
 #' 2 + 2
-climatological_forecaster <- function(
-    epi_data,
-    outcome,
-    args_list = climate_args_list()
-) {
+climatological_forecaster <- function(epi_data,
+                                      outcome,
+                                      args_list = climate_args_list()) {
   if (!is_epi_df(epi_data)) {
-    cli_abort(c(
-      "`epi_data` must be an {.cls epi_df}.",
-      "!" = "This one is a {.cls {class(epi_data)}}."
-    ))
+    cli_abort(
+      "`epi_data` must be an {.cls epi_df}, not a {.cls {class(epi_data)}}."
+    )
+  }
+  edf_time_type <- attr(epi_data, "metadata")$time_type
+  if (edf_time_type == "custom") {
+    cli_abort("This forecaster only works with daily, weekly, or yearmonth data.")
   }
   if (!inherits(args_list, c("climate_fcast", "alist"))) {
     cli_abort("`args_list` was not created using `climate_args_list()`.")
   }
   arg_is_chr_scalar(outcome)
   hardhat::check_column_names(epi_data, c(outcome, args_list$quantile_by_key))
-  ahead <- args_list$ahead
+  forecast_date <- args_list$forecast_date %||% attr(epi_data, "metadata")$as_of
+  horizon <- args_list$forecast_horizon
   window_size <- args_list$window_size
-  names(ahead) <- paste0(".ahead_", ahead)
-  ahead_tib <- tibble(.ahead_set = names(ahead), ahead = ahead)
-  forecast_date <- lubridate::ymd(args_list$forecast_date) %||%
-    max(epi_data$time_value) %||%
-    attributes(epi_data)$metadata$as_of
-  probs <- switch(args_list$quantile_method,
-                  epipredict = c(.25, .5, .75),
-                  base8 = args_list$quantile_values)
-  outcome <- sym(outcome)
+  time_type <- args_list$time_type
+  ttype_ord <- match(time_type, c("day", "epiweek", "week", "month"))
+  ttype_ord <- ttype_ord - as.integer(ttype_ord > 2)
+  edf_ttype_ord <- match(edf_time_type, c("day", "week", "yearmonth"))
+  if (ttype_ord < edf_ttype_ord) {
+    cli_abort(c("Climate forecasts for more granular time types are not possible
+      if the `epi_data` has a higher level of aggregation",
+      i = "Here, the data is in {.val {edf_time_type}}s while
+      `time_type` is {.val {time_type}}."
+    ))
+  }
+  # process the time types
   epi_data <- filter(epi_data, !is.na(!!outcome))
-  target_dates <- forecast_date + lubridate::weeks(ahead)
-  in_window <- map2(
-    target_dates, names(ahead),
-    ~ tibble(!!.y := date_window_mod(.x, epi_data$time_value, window_size, window_size))
-  )
-
-  epi_data <- bind_cols(epi_data, purrr::list_cbind(in_window)) %>%
-    pivot_longer(
-      dplyr::starts_with(".ahead_"), names_to = ".ahead_set",
-      values_to = ".in_window"
+  ttype_dur <- switch(time_type,
+                      epiweek = lubridate::weeks, week = lubridate::weeks,
+                      month = lubridate::months, day = lubridate::days)
+  time_aggr <- switch(time_type,
+                      epiweek = lubridate::epiweek, week = lubridate::week,
+                      month = lubridate::month, day = lubridate::yday)
+  modulus <- switch(time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
+  center_fn <- switch(args_list$center_method,
+                      mean = function(x, w) mean(x, na.rm = TRUE),
+                      median = function(x, w) stats::median(x, na.rm = TRUE))
+  # get the point predictions
+  outcome <- sym(outcome)
+  keys <- key_colnames(epi_data, exclude = "time_value")
+  epi_data <- epi_data %>% mutate(.idx = time_aggr(time_value), .weights = 1)
+  climate_center <- epi_data %>%
+    select(.idx, .weights, outcome, all_of(keys)) %>%
+    dplyr::reframe(
+      roll_modular_multivec(outcome, .idx, .weights, center_fn, window_size,
+                            modulus),
+      .by = keys
     ) %>%
-    filter(.in_window) %>%
-    select(-.in_window) %>%
-    left_join(ahead_tib, by = ".ahead_set")
-
+    rename(.pred = climate_pred)
+  # get the quantiles
   Quantile <- function(x) {
-    x <- x - median(x, na.rm = TRUE)
+    x <- x - stats::median(x, na.rm = TRUE)
     if (args_list$symmetrize) x <- c(x, -x)
-    list(unname(quantile(x, probs = probs, na.rm = TRUE, type = 8)))
-  }
-  quantiles <- group_by(epi_data, .ahead_set, ahead) %>%
-    summarise(
-      .pred_distn = dist_quantiles(Quantile(!!outcome), probs),
-      .by = ,
-      .groups = "drop"
-    )
-
-  epi_data <- epi_data %>%
-    group_by(.ahead_set, ahead, args_list$quantile_by_key) %>%
-    summarise(.pred = median(!!outcome, na.rm = TRUE), .groups = "drop") %>%
-    left_join(quantiles, by = c(".ahead_set", "ahead")) %>%
-    mutate(
-      .pred_distn = .pred_distn + .pred,
-      forecast_date = ymd(forecast_date),
-      target_date = epiweek(forecast_date + weeks(ahead)),
-      .ahead_set = NULL
-    )
-
-  if (args_list$quantile_method == "epipredict") {
-    epi_data <- mutate(
-      epi_data,
-      .pred_distn = extrapolate_quantiles(.pred_distn, args_list$quantile_values)
-    )
+    list(unname(quantile(
+      x, probs = args_list$quantile_levels, na.rm = TRUE, type = 8
+    )))
   }
+  climate_quantiles <- epi_data %>%
+    select(.idx, .weights, outcome, all_of(args_list$quantile_by_key)) %>%
+    dplyr::reframe(
+      roll_modular_multivec(outcome, .idx, .weights, Quantile, window_size,
+                            modulus),
+      .by = args_list$quantile_by_key
+    ) %>%
+    rename(.pred_distn = climate_pred)
+  # combine them together
+  climate_table <- climate_center %>%
+    left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
+    mutate(.pred_distn = .pred_distn - median(.pred_distn) + .pred)
   if (args_list$nonneg) {
-    epi_data <- mutate(
-      epi_data,
+    climate_table <- mutate(
+      climate_table,
       .pred = snap(.pred, 0, Inf),
       .pred_distn = snap(.pred_distn, 0, Inf)
     )
   }
+  # create the predictions
+  predictions <- epi_data %>%
+    select(all_of(keys)) %>%
+    dplyr::distinct() %>%
+    mutate(forecast_date = forecast_date, .idx = time_aggr(forecast_date))
+  predictions <- map(horizon, ~ {
+      predictions %>%
+        mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
+    }) %>%
+    purrr::list_rbind() %>%
+    left_join(climate_table, by = c(".idx", keys)) %>%
+    select(-.idx)
+
   structure(list(
-    predictions = epi_data,
+    predictions = predictions,
+    epi_workflow = epi_workflow(),
     metadata = list(
       training = attr(epi_data, "metadata"),
       forecast_created = Sys.time()
@@ -113,55 +131,30 @@ climatological_forecaster <- function(
 
 #' Climatological forecaster argument constructor
 #'
+#' @inheritParams epi_recipe
+#' @param prediction_horizon Vector of integers giving the number of time steps,
+#'   in units of the `time_type`,
+#'   from the `reference_date` for which predictions should be produced.
+#' @inheritParams step_climate
 #' @inheritParams flatline_args_list
-#' @param ahead Vector of integers giving the number of time steps ahead
-#'   (in weeks) of the forecast date for which forecasts should be produced.
-#' @param time_type The duration over which time aggregation should be performed.
-#' @param center_method The measure of center to be calculated over the time
-#'   window.
-#' @param window_size Integer. The number of time points on each side of the
-#'   target to include in the calculation.
-#' @param quantile_method One of either `"base8"` or `"epipredict"`. The first
-#'   case uses the quantiles of the observed history within the window, calculated
-#'   using `type = 8`. See `?stats::quantile` for additional information.
-#'   Alternatively, `"epipredict"` computes only the quartiles of the observed
-#'   data and interpolates (or extrapolates the remainder). Those quantiles
-#'   between .25 and .75 are interpolated with a cubic spline, while those
-#'   outside this range are extrapolated on the logistic-linear scale. This
-#'   produces a "parametric" quantile estimate with tails that are heavier than
-#'   a normal distribution. See the Examples for this comparison.
 #'
 #' @return A list containing updated parameter choices with class `climate_alist`.
 #' @export
+#' @seealso [climatological_predictor()], [step_climate()]
 #'
 #' @examples
-#'
 #' climate_args_list()
 #' climate_args_list(
 #'   ahead = 0:10,
 #'   quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
 #' )
 #'
-#' # To visualize the quantiles produced with the `epipredict` method
-#' tau <- c(.01, .025, 1:19 / 20, .975, .99)
-#' sm_tau <- 5:15 / 20 # values between .25 and .75
-#' distn <- dist_quantiles(qnorm(sm_tau), sm_tau)
-#' epipredict_quantiles <- extrapolate_quantiles(distn, tau) %>%
-#'   nested_quantiles %>%
-#'   purrr::pluck(1, "values")
-#'
-#' plot(
-#'   qnorm(tau),
-#'   epipredict_quantiles,
-#'   pch = 16, col = 4, ylab = "epipredict", xlab = "Normal quantiles"
-#' )
-#' abline(0, 1, col = 2)
 climate_args_list <- function(
-    ahead = 1:4,
     forecast_date = NULL,
+    forecast_horizon = 0:4,
     time_type = c("epiweek", "week", "month", "day"),
+    center_method = c("median", "mean"),
     window_size = 3L,
-    quantile_method = c("base8", "epipredict"),
     quantile_levels = c(.05, .1, .25, .5, .75, .9, .95),
     symmetrize = FALSE,
     nonneg = TRUE,
@@ -170,20 +163,21 @@ climate_args_list <- function(
 
   rlang::check_dots_empty()
   time_type <- arg_match(time_type)
-  quantile_method <- arg_match(quantile_method)
   if (time_type != "epiweek") {
     cli_abort("Only epiweek forecasts are currently supported.")
   }
-  arg_is_scalar(ahead, window_size, symmetrize, nonneg)
+  center_method <- rlang::arg_match(center_method)
+  arg_is_scalar(window_size, symmetrize, nonneg)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
   arg_is_scalar(forecast_date, allow_null = TRUE)
   arg_is_date(forecast_date, allow_null = TRUE)
-  arg_is_nonneg_int(ahead, window_size)
+  arg_is_nonneg_int(forecast_horizon, window_size)
   arg_is_lgl(symmetrize, nonneg)
   arg_is_probabilities(quantile_levels)
+  quantile_levels <- sort(unique(c(0.5, quantile_levels)))
 
   structure(enlist(
-    ahead, forecast_date, time_type, window_size, quantile_method,
+    forecast_date, forecast_horizon, time_type, center_method, window_size,
     quantile_levels, symmetrize, nonneg, quantile_by_key),
     class = c("climate_fcast", "alist")
   )
diff --git a/R/step_climate.R b/R/step_climate.R
index cf3f2cc22..c5686a037 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -144,7 +144,7 @@ step_climate <-
           cli_abort(c("Automatic detection of the `forecast_ahead` is only
             supported if the original data and the time type for aggregation
             are in the same units.",
-            i = "Here, the data is in {.val {edf_time_type}} while
+            i = "Here, the data is in {.val {edf_time_type}}s while
             `time_type` is {.val {time_type}}.",
             i = "This is resolved most easily by specifying `forecast_ahead`."
           ))
@@ -242,13 +242,16 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 
   modulus <- switch(x$time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
 
+  fn <- switch(center_method,
+               mean = function(x, w) weighted.mean(x, w, na.rm = TRUE),
+               median = function(x, w) median(x, na.rm = TRUE))
+
   climate_table <- training %>%
     mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
     select(.idx, .weights, all_of(c(col_names, x$epi_keys))) %>%
     tidyr::pivot_longer(all_of(unname(col_names))) %>%
     dplyr::reframe(
-      roll_modular_multivec(value, .idx, .weights, x$center_method,
-                              x$window_size, modulus),
+      roll_modular_multivec(value, .idx, .weights, fn, x$window_size, modulus),
       .by = c("name", x$epi_keys)
     ) %>%
     tidyr::pivot_wider(
@@ -302,13 +305,7 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
   invisible(x)
 }
 
-roll_modular_multivec <- function(col, .idx, weights, center_method,
-                                  window_size, modulus) {
-  if (center_method == "mean") {
-    aggr <- function(x, w) weighted.mean(x, w, na.rm = TRUE)
-  } else {
-    aggr <- function(x, w) median(x, na.rm = TRUE)
-  }
+roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus) {
   tib <- tibble(col = col, weights = weights, .idx = .idx) |>
     arrange(.idx) |>
     tidyr::nest(data = c(col, weights), .by = .idx)
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 5ece7109b..650c4a614 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -101,3 +101,6 @@ arx_args_list()
 arx_args_list(symmetrize = FALSE)
 arx_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
 }
+\seealso{
+\code{\link[=arx_forecaster]{arx_forecaster()}}
+}
diff --git a/man/arx_fcast_epi_workflow.Rd b/man/arx_fcast_epi_workflow.Rd
index c2e38218f..61d293ba1 100644
--- a/man/arx_fcast_epi_workflow.Rd
+++ b/man/arx_fcast_epi_workflow.Rd
@@ -58,5 +58,5 @@ arx_fcast_epi_workflow(jhu, "death_rate",
 )
 }
 \seealso{
-\code{\link[=arx_forecaster]{arx_forecaster()}}
+\code{\link[=arx_forecaster]{arx_forecaster()}}, \code{\link[=arx_args_list]{arx_args_list()}}
 }
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
index 325b2c348..171364202 100644
--- a/man/climate_args_list.Rd
+++ b/man/climate_args_list.Rd
@@ -1,16 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/climatological_forecaster.R
+% Please edit documentation in R/climatological_predictor.R
 \name{climate_args_list}
 \alias{climate_args_list}
 \title{Climatological forecaster argument constructor}
 \usage{
 climate_args_list(
-  ahead = 1:4,
   forecast_date = NULL,
-  time_type = c("epiweek", "week", "month", "year", "day"),
+  forecast_horizon = 0:4,
+  time_type = c("epiweek", "week", "month", "day"),
+  center_method = c("median", "mean"),
   window_size = 3L,
-  quantile_method = c("base8", "epipredict"),
-  quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = FALSE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
@@ -18,9 +18,6 @@ climate_args_list(
 )
 }
 \arguments{
-\item{ahead}{Vector of integers giving the number of time steps ahead
-(in weeks) of the forecast date for which forecasts should be produced.}
-
 \item{forecast_date}{Date. The date from which the forecast is occurring.
 The default \code{NULL} will determine this automatically from either
 \enumerate{
@@ -30,20 +27,16 @@ adjustment (the default case), or
 non-\code{NULL}.
 }}
 
-\item{time_type}{Character. The duration to which the forecasts correspond.}
+\item{time_type}{The duration over which time aggregation should be performed.}
 
-\item{window_size}{Integer. The number of time points on each side of the
-target to include in the calculation.}
+\item{center_method}{The measure of center to be calculated over the time
+window.}
 
-\item{quantile_method}{One of either \code{"base8"} or \code{"epipredict"}. The first
-case uses the quantiles of the observed history within the window, calculated
-using \code{type = 8}. See \code{?stats::quantile} for additional information.
-Alternatively, \code{"epipredict"} computes only the quartiles of the observed
-data and interpolates (or extrapolates the remainder). Those quantiles
-between .25 and .75 are interpolated with a cubic spline, while those
-outside this range are extrapolated on the logistic-linear scale. This
-produces a "parametric" quantile estimate with tails that are heavier than
-a normal distribution. See the Examples for this comparison.}
+\item{window_size}{Scalar integer. How many time units on each side should
+be included. For example, if \code{window_size = 3} and \code{time_type = "day"},
+then on each day in the data, the center will be calculated using 3 days
+before and three days after. So, in this case, it operates like a weekly
+rolling average, centered at each day.}
 
 \item{quantile_levels}{Vector or \code{NULL}. A vector of probabilities to produce
 prediction intervals. These are created by computing the quantiles of
@@ -64,7 +57,12 @@ before calculating residual quantiles. See the \code{by_key} argument to
 residual quantiles are used. It is not applicable with
 \code{trainer = quantile_reg()}, for example.}
 
-\item{...}{Space to handle future expansions (unused).}
+\item{...}{Further arguments passed to or from other methods (not currently
+used).}
+
+\item{prediction_horizon}{Vector of integers giving the number of time steps,
+in units of the \code{time_type},
+from the \code{reference_date} for which predictions should be produced.}
 }
 \value{
 A list containing updated parameter choices with class \code{climate_alist}.
@@ -73,25 +71,13 @@ A list containing updated parameter choices with class \code{climate_alist}.
 Climatological forecaster argument constructor
 }
 \examples{
-
 climate_args_list()
 climate_args_list(
   ahead = 0:10,
   quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
 )
 
-# To visualize the quantiles produced with the `epipredict` method
-tau <- c(.01, .025, 1:19 / 20, .975, .99)
-sm_tau <- 5:15 / 20 # values between .25 and .75
-distn <- dist_quantiles(qnorm(sm_tau), sm_tau)
-epipredict_quantiles <- extrapolate_quantiles(distn, tau) \%>\%
-  nested_quantiles \%>\%
-  purrr::pluck(1, "values")
-
-plot(
-  qnorm(tau),
-  epipredict_quantiles,
-  pch = 16, col = 4, ylab = "epipredict", xlab = "Normal quantiles"
-)
-abline(0, 1, col = 2)
+}
+\seealso{
+\code{\link[=climatological_predictor]{climatological_predictor()}}, \code{\link[=step_climate]{step_climate()}}
 }
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
index 249c37d8b..3968f5ab7 100644
--- a/man/climatological_forecaster.Rd
+++ b/man/climatological_forecaster.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/climatological_forecaster.R
+% Please edit documentation in R/climatological_predictor.R
 \name{climatological_forecaster}
 \alias{climatological_forecaster}
 \title{Climatological forecaster}
@@ -20,7 +20,7 @@ for each unique combination of \code{key_vars}.
 }
 \description{
 This is another "baseline" type forecaster, but it is especially appropriate
-for strongly seasonal diseases (e.g., influenza). The idea is to forecast
+for strongly seasonal diseases (e.g., influenza). The idea is to predict
 the "typical season" by summarizing over all available history in the
 \code{epi_data}. This is analogous to a "climate" forecast rather than a "weather"
 forecast, essentially predicting "typical January" behavior by relying on a
@@ -28,10 +28,13 @@ long history of such periods rather than heavily using recent data.
 }
 \details{
 The forecast is taken as the quantiles of the \code{outcome} in a small window
-around the target week, computed over the entire available history.
+around the target period, computed over the entire available history.
 }
 \examples{
 
 1 + 1
 2 + 2
 }
+\seealso{
+\code{\link[=step_climate]{step_climate()}}
+}
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 9ef5eb288..98775eae6 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -9,19 +9,37 @@
 \usage{
 epi_recipe(x, ...)
 
-\method{epi_recipe}{default}(x, ...)
+\method{epi_recipe}{epi_df}(
+  x,
+  reference_date = NULL,
+  formula = NULL,
+  ...,
+  vars = NULL,
+  roles = NULL
+)
 
-\method{epi_recipe}{epi_df}(x, formula = NULL, ..., vars = NULL, roles = NULL)
-
-\method{epi_recipe}{formula}(formula, data, ...)
+\method{epi_recipe}{formula}(formula, data, reference_date = NULL, ...)
 }
 \arguments{
-\item{x, data}{A data frame, tibble, or epi_df of the \emph{template} data set
-(see below). This is always coerced to the first row to avoid memory issues}
+\item{x, data}{An epi_df of the \emph{template} data set (see below).}
 
 \item{...}{Further arguments passed to or from other methods (not currently
 used).}
 
+\item{reference_date}{Either a date of the same class as the \code{time_value}
+column in the \code{epi_df} or \code{NULL}. If a date, it gives the date to which all
+operations are relative. Typically, in real-time tasks this is the date that
+the model is created (and presumably trained). In forecasting, this is
+often the same as the most recent date of
+data availability, but when data is "latent" (reported after the date to
+which it corresponds), or if performing a nowcast, the \code{reference_date} may
+be later than this. Setting \code{reference_date}
+to a value BEFORE the most recent data is not a true "forecast",
+because future data is being used to create the model, but this may be
+reasonable in model building, nowcasting (predicting finalized values from
+preliminary data), or if producing a backcast. If \code{NULL}, it will be set
+to the \code{as_of} date of the \code{epi_df}.}
+
 \item{formula}{A model formula. No in-line functions should be used here
 (e.g. \code{log(x)}, \code{x:y}, etc.) and minus signs are not allowed. These types of
 transformations should be enacted using \code{step} functions in this package.

From 0be6acf1dd1dce2d53c6f7d03947ee5d000f57bb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 10 Feb 2025 15:48:14 -0800
Subject: [PATCH 226/387] missing autoplot branch

---
 R/autoplot.R | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index c0e3c68dd..ce6f4ce51 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -120,12 +120,13 @@ autoplot.epi_workflow <- function(
     shift <- -as.numeric(old_name_y[2])
     new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
     edf <- rename(edf, !!new_name_y := !!names(y))
+  } else {
+    new_name_y <- names(y)
+    shift <- 0
   }
 
-  if (!is.null(shift)) {
-    edf <- mutate(edf, time_value = time_value + shift)
-  }
-  other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
+  edf <- mutate(edf, time_value = time_value + shift)
+  other_keys <- key_colnames(object, exclude = c("geo_value", "time_value"))
   edf <- as_epi_df(edf,
     as_of = object$fit$meta$as_of,
     other_keys = other_keys

From d464c199486ac3a62291ab1277c2a73d9e393c74 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 09:23:14 -0800
Subject: [PATCH 227/387] test args list

---
 R/climatological_forecaster.R                 | 142 +++++++-----------
 man/climate_args_list.Rd                      |   4 +-
 man/climatological_forecaster.Rd              |   2 +-
 .../_snaps/climatological_forecaster.md       | 112 ++++++++++++++
 tests/testthat/test-climatological_baseline.R |  35 -----
 .../testthat/test-climatological_forecaster.R |  28 ++++
 6 files changed, 196 insertions(+), 127 deletions(-)
 create mode 100644 tests/testthat/_snaps/climatological_forecaster.md
 delete mode 100644 tests/testthat/test-climatological_baseline.R
 create mode 100644 tests/testthat/test-climatological_forecaster.R

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index a5cfb7bd3..47d2b618f 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -20,9 +20,12 @@
 #' @seealso [step_climate()]
 #'
 #' @examples
+#' jhu <- covid_case_death_rates
+#' # set as_of to the last day in the data
+#' attr(jhu, "metadata")$as_of <- as.Date("2021-12-31")
+#' fcast <- climatological_forecaster(jhu, "death_rate")
 #'
-#' 1 + 1
-#' 2 + 2
+
 climatological_forecaster <- function(epi_data,
                                       outcome,
                                       args_list = climate_args_list()) {
@@ -55,31 +58,40 @@ climatological_forecaster <- function(epi_data,
     ))
   }
   # process the time types
-  epi_data <- filter(epi_data, !is.na(!!outcome))
-  ttype_dur <- switch(time_type,
-                      epiweek = lubridate::weeks, week = lubridate::weeks,
-                      month = lubridate::months, day = lubridate::days)
-  time_aggr <- switch(time_type,
-                      epiweek = lubridate::epiweek, week = lubridate::week,
-                      month = lubridate::month, day = lubridate::yday)
-  modulus <- switch(time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
-  center_fn <- switch(args_list$center_method,
-                      mean = function(x, w) mean(x, na.rm = TRUE),
-                      median = function(x, w) stats::median(x, na.rm = TRUE))
+  epi_data <- epi_data[!is.na(epi_data[outcome]), ] %>%
+    select(key_colnames(epi_data), outcome)
+  if (time_type %in% c("week", "epiweek")) {
+    ttype_dur <- lubridate::weeks
+    time_aggr <- ifelse(time_type == "week", lubridate::epiweek, lubridate::isoweek)
+    modulus <- 53L
+  } else if (time_type == "month") {
+    ttype_dur <- lubridate::months
+    time_aggr <- lubridate::month
+    modulus <- 12L
+  } else if (time_type == "day") {
+    ttype_dur <- lubridate::days
+    time_aggr <- lubridate::yday
+    modulus <- 365L
+  }
+  center_fn <- switch(
+    args_list$center_method,
+    mean = function(x, w) mean(x, na.rm = TRUE),
+    median = function(x, w) stats::median(x, na.rm = TRUE)
+  )
   # get the point predictions
-  outcome <- sym(outcome)
+  sym_outcome <- sym(outcome)
   keys <- key_colnames(epi_data, exclude = "time_value")
   epi_data <- epi_data %>% mutate(.idx = time_aggr(time_value), .weights = 1)
   climate_center <- epi_data %>%
     select(.idx, .weights, outcome, all_of(keys)) %>%
     dplyr::reframe(
-      roll_modular_multivec(outcome, .idx, .weights, center_fn, window_size,
+      roll_modular_multivec(!!sym_outcome, .idx, .weights, center_fn, window_size,
                             modulus),
       .by = keys
     ) %>%
     rename(.pred = climate_pred)
   # get the quantiles
-  Quantile <- function(x) {
+  Quantile <- function(x, w) {
     x <- x - stats::median(x, na.rm = TRUE)
     if (args_list$symmetrize) x <- c(x, -x)
     list(unname(quantile(
@@ -89,15 +101,16 @@ climatological_forecaster <- function(epi_data,
   climate_quantiles <- epi_data %>%
     select(.idx, .weights, outcome, all_of(args_list$quantile_by_key)) %>%
     dplyr::reframe(
-      roll_modular_multivec(outcome, .idx, .weights, Quantile, window_size,
+      roll_modular_multivec(!!sym_outcome, .idx, .weights, Quantile, window_size,
                             modulus),
       .by = args_list$quantile_by_key
     ) %>%
-    rename(.pred_distn = climate_pred)
+    rename(.pred_distn = climate_pred) %>%
+    mutate(.pred_distn = dist_quantiles(.pred_distn, args_list$quantile_levels))
   # combine them together
   climate_table <- climate_center %>%
     left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
-    mutate(.pred_distn = .pred_distn - median(.pred_distn) + .pred)
+    mutate(.pred_distn = .pred_distn + .pred)
   if (args_list$nonneg) {
     climate_table <- mutate(
       climate_table,
@@ -115,12 +128,29 @@ climatological_forecaster <- function(epi_data,
         mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
     }) %>%
     purrr::list_rbind() %>%
+    mutate(.idx = .idx %% modulus,
+           .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
+    ) %>%
     left_join(climate_table, by = c(".idx", keys)) %>%
     select(-.idx)
+  # fill in some extras for plotting methods, etc.
+  ewf <- epi_workflow()
+  ewf$trained <- TRUE
+  ewf$pre <- list(mold = list(
+    outcomes = select(epi_data, outcome),
+    extras = list(roles = list(
+      geo_value = select(epi_data, geo_value),
+      time_value = select(epi_data, time_value)
+    ))
+  ))
+  other_keys <- key_colnames(epi_data, exclude = c("time_value", "geo_value"))
+  if (length(other_keys) > 0) {
+    ewf$pre$mold$extras$roles$key = epi_data %>% select(all_of(other_keys))
+  }
 
   structure(list(
     predictions = predictions,
-    epi_workflow = epi_workflow(),
+    epi_workflow = ewf,
     metadata = list(
       training = attr(epi_data, "metadata"),
       forecast_created = Sys.time()
@@ -145,7 +175,7 @@ climatological_forecaster <- function(epi_data,
 #' @examples
 #' climate_args_list()
 #' climate_args_list(
-#'   ahead = 0:10,
+#'   forecast_horizon = 0:10,
 #'   quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
 #' )
 #'
@@ -163,15 +193,13 @@ climate_args_list <- function(
 
   rlang::check_dots_empty()
   time_type <- arg_match(time_type)
-  if (time_type != "epiweek") {
-    cli_abort("Only epiweek forecasts are currently supported.")
-  }
   center_method <- rlang::arg_match(center_method)
   arg_is_scalar(window_size, symmetrize, nonneg)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
   arg_is_scalar(forecast_date, allow_null = TRUE)
   arg_is_date(forecast_date, allow_null = TRUE)
-  arg_is_nonneg_int(forecast_horizon, window_size)
+  arg_is_nonneg_int(window_size)
+  arg_is_int(forecast_horizon)
   arg_is_lgl(symmetrize, nonneg)
   arg_is_probabilities(quantile_levels)
   quantile_levels <- sort(unique(c(0.5, quantile_levels)))
@@ -182,67 +210,3 @@ climate_args_list <- function(
     class = c("climate_fcast", "alist")
   )
 }
-
-
-#' @importFrom lubridate epiyear epiweek ymd
-units_in_year <- function(years, units = c("epiweek", "week", "month", "day")) {
-  units <- arg_match(units)
-  if (units == "month") return(rep(12L, length(years)))
-  if (units == "day") return(c(365, 366)[lubridate::leap_year(years) + 1])
-  unitf <- switch(units, epiweek = lubridate::epiweek, week = lubridate::isoweek)
-  mat <- outer(years, 25:31, function(x, y) paste0(x, "12", y))
-  apply(mat, 1, function(x) max(unitf(lubridate::ymd(x))))
-}
-
-date_window_mod <- function(t0, time_value, left = 1L, right = 1L,
-                            type = c("epiweek", "isoweek", "month", "day")) {
-  checkmate::assert_integerish(left, lower = 0, len = 1)
-  checkmate::assert_integerish(right, lower = 0, len = 1)
-  type <- arg_match(type)
-  unitf <- switch(
-    type,
-    epiweek = lubridate::epiweek,
-    iso = lubridate::isoweek,
-    month = lubridate::month,
-    day = lubridate::day
-  )
-  yearf <- switch(
-    type,
-    epiweek = lubridate::epiyear,
-    isoweek = lubridate::isoyear,
-    month = lubridate::year,
-    day = lubridate::year
-  )
-  cur_years <- yearf(unique(c(t0, time_value)))
-  ll <- vctrs::vec_recycle_common(t0 = t0, time_value = time_value)
-  t0 <- ll$t0
-  time_value <- ll$time_value
-
-  back_years <- cur_years - 1
-  all_years <- sort(unique(c(cur_years, back_years)))
-  year_nunits <- units_in_year(all_years, type)
-  back_nunits <- year_nunits[match(yearf(time_value) - 1, all_years)]
-  forward_nunits <- year_nunits[match(yearf(time_value), all_years)]
-
-  unit_diff <- unitf(t0) - unitf(time_value)
-  in_window <- ((unit_diff >= 0) & (unit_diff %% back_nunits <= left)) |
-    ((unit_diff <= 0) & (unit_diff %% forward_nunits <= right))
-
-  in_window
-}
-
-
-episeason <- function(time_value) {
-  time_value <- time_value - lubridate::dmonths(6)
-  paste0(
-    strftime(time_value, "%Y"),
-    "/",
-    strftime(time_value + lubridate::years(1), "%y")
-  )
-}
-
-season_week <- function(time_value, season_start_epiweek = 39) {
-  stopifnot(season_start_epiweek >= 0L, season_start_epiweek <= 53L)
-  time_value <- time_value - lubridate::weeks(season_start_epiweek)
-  lubridate::epiweek(time_value)
-}
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
index 171364202..3216d098c 100644
--- a/man/climate_args_list.Rd
+++ b/man/climate_args_list.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/climatological_predictor.R
+% Please edit documentation in R/climatological_forecaster.R
 \name{climate_args_list}
 \alias{climate_args_list}
 \title{Climatological forecaster argument constructor}
@@ -73,7 +73,7 @@ Climatological forecaster argument constructor
 \examples{
 climate_args_list()
 climate_args_list(
-  ahead = 0:10,
+  forecast_horizon = 0:10,
   quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
 )
 
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
index 3968f5ab7..64dff04cb 100644
--- a/man/climatological_forecaster.Rd
+++ b/man/climatological_forecaster.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/climatological_predictor.R
+% Please edit documentation in R/climatological_forecaster.R
 \name{climatological_forecaster}
 \alias{climatological_forecaster}
 \title{Climatological forecaster}
diff --git a/tests/testthat/_snaps/climatological_forecaster.md b/tests/testthat/_snaps/climatological_forecaster.md
new file mode 100644
index 000000000..78043a20b
--- /dev/null
+++ b/tests/testthat/_snaps/climatological_forecaster.md
@@ -0,0 +1,112 @@
+# climate args list validates properly
+
+    Code
+      climate_args_list(forecast_date = 12345)
+    Condition
+      Error in `climate_args_list()`:
+      ! `forecast_date` must be a date.
+
+---
+
+    Code
+      climate_args_list(forecast_date = as.Date(c("2021-01-10", "2024-01-22")))
+    Condition
+      Error in `climate_args_list()`:
+      ! `forecast_date` must be a scalar.
+
+---
+
+    Code
+      climate_args_list(forecast_horizon = 1.3)
+    Condition
+      Error in `climate_args_list()`:
+      ! `forecast_horizon` must be a integer.
+
+---
+
+    Code
+      climate_args_list(window_size = -1)
+    Condition
+      Error in `climate_args_list()`:
+      ! `window_size` must be a non-negative integer.
+
+---
+
+    Code
+      climate_args_list(window_size = 2.5)
+    Condition
+      Error in `climate_args_list()`:
+      ! `window_size` must be a non-negative integer.
+
+---
+
+    Code
+      climate_args_list(window_size = 1:3)
+    Condition
+      Error in `climate_args_list()`:
+      ! `window_size` must be a scalar.
+
+---
+
+    Code
+      climate_args_list(quantile_levels = -1)
+    Condition
+      Error in `climate_args_list()`:
+      ! `quantile_levels` must lie in [0, 1].
+
+---
+
+    Code
+      climate_args_list(quantile_levels = 1.3)
+    Condition
+      Error in `climate_args_list()`:
+      ! `quantile_levels` must lie in [0, 1].
+
+---
+
+    Code
+      climate_args_list(symmetrize = 2.5)
+    Condition
+      Error in `climate_args_list()`:
+      ! `symmetrize` must be of type <logical>.
+
+---
+
+    Code
+      climate_args_list(symmetrize = c(TRUE, TRUE))
+    Condition
+      Error in `climate_args_list()`:
+      ! `symmetrize` must be a scalar.
+
+---
+
+    Code
+      climate_args_list(nonneg = 2.5)
+    Condition
+      Error in `climate_args_list()`:
+      ! `nonneg` must be of type <logical>.
+
+---
+
+    Code
+      climate_args_list(nonneg = c(TRUE, TRUE))
+    Condition
+      Error in `climate_args_list()`:
+      ! `nonneg` must be a scalar.
+
+---
+
+    Code
+      climate_args_list(quantile_by_key = TRUE)
+    Condition
+      Error in `climate_args_list()`:
+      ! `quantile_by_key` must be of type <character>.
+
+---
+
+    Code
+      climate_args_list(quantile_by_key = 2:3)
+    Condition
+      Error in `climate_args_list()`:
+      ! `quantile_by_key` must be of type <character>.
+
diff --git a/tests/testthat/test-climatological_baseline.R b/tests/testthat/test-climatological_baseline.R
deleted file mode 100644
index e4fa52077..000000000
--- a/tests/testthat/test-climatological_baseline.R
+++ /dev/null
@@ -1,35 +0,0 @@
-test_that("epiwindow function works", {
-  skip()
-
-  t0 <- ymd("2024-01-08")
-  time_value <- ymd(c("2024-01-01", "2023-12-30", "2023-12-01", "2022-01-08"))
-  expect_snapshot(error = TRUE, epiwindow(t0, time_value, 1.5))
-  expect_snapshot(error = TRUE, epiwindow(t0, time_value, 1, 1.5))
-  expect_snapshot(error = TRUE, epiwindow(t0, time_value, seasons = "other"))
-  expect_snapshot(error = TRUE, epiwindow(c(t0, t0), time_value))
-
-  expect_identical(epiwindow(t0, time_value), c(TRUE, FALSE, FALSE, TRUE))
-  time_value <- ymd(c("2024-01-01", "2023-12-31", "2023-12-01", "2022-01-08"))
-  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
-  t0 <- ymd("20231231")
-  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
-  expect_identical(
-    epiwindow(t0, time_value, seasons = "current"),
-    c(TRUE, TRUE, FALSE, FALSE)
-  )
-
-  # 2025 has 53 epiweeks in the year
-  t0 <- ymd("2026-01-08") # week 1
-  # second is week 53
-  time_value <- ymd(c("2026-01-01", "2025-12-27", "2025-12-01", "2022-01-08"))
-  expect_identical(epiwindow(t0, time_value), c(TRUE, FALSE, FALSE, TRUE))
-  # second is week 52
-  time_value <- ymd(c("2025-01-01", "2024-12-30", "2024-12-01", "2022-01-08"))
-  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
-  t0 <- ymd("20251231")
-  expect_identical(epiwindow(t0, time_value), c(TRUE, TRUE, FALSE, TRUE))
-  expect_identical(
-    epiwindow(t0, time_value, seasons = "current"),
-    c(TRUE, TRUE, FALSE, FALSE)
-  )
-})
diff --git a/tests/testthat/test-climatological_forecaster.R b/tests/testthat/test-climatological_forecaster.R
new file mode 100644
index 000000000..3bee13195
--- /dev/null
+++ b/tests/testthat/test-climatological_forecaster.R
@@ -0,0 +1,28 @@
+test_that("climate args list validates properly", {
+  expect_s3_class(climate_args_list(), c("climate_fcast", "alist"))
+  expect_s3_class(
+    climate_args_list(forecast_date = as.Date("2021-01-10")),
+    c("climate_fcast", "alist")
+  )
+  expect_snapshot(error = TRUE, climate_args_list(forecast_date = 12345))
+  expect_snapshot(
+    error = TRUE,
+    climate_args_list(forecast_date = as.Date(c("2021-01-10", "2024-01-22")))
+  )
+  expect_silent(climate_args_list(forecast_horizon = 1L))
+  expect_silent(climate_args_list(forecast_horizon = -1:4))
+  expect_snapshot(error = TRUE, climate_args_list(forecast_horizon = 1.3))
+  expect_snapshot(error = TRUE, climate_args_list(window_size = -1))
+  expect_snapshot(error = TRUE, climate_args_list(window_size = 2.5))
+  expect_snapshot(error = TRUE, climate_args_list(window_size = 1:3))
+  expect_snapshot(error = TRUE, climate_args_list(quantile_levels = -1))
+  expect_snapshot(error = TRUE, climate_args_list(quantile_levels = 1.3))
+  expect_snapshot(error = TRUE, climate_args_list(symmetrize = 2.5))
+  expect_snapshot(error = TRUE, climate_args_list(symmetrize = c(TRUE, TRUE)))
+  expect_snapshot(error = TRUE, climate_args_list(nonneg = 2.5))
+  expect_snapshot(error = TRUE, climate_args_list(nonneg = c(TRUE, TRUE)))
+  expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = TRUE))
+  expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = 2:3))
+})
+
+

From 7afbde8d89473c201001d220fde96c3eaf350ddf Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 09:46:07 -0800
Subject: [PATCH 228/387] add reasonable examples

---
 R/climatological_forecaster.R | 43 ++++++++++++++++++++++++++---------
 1 file changed, 32 insertions(+), 11 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 47d2b618f..65f36f33a 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -20,12 +20,32 @@
 #' @seealso [step_climate()]
 #'
 #' @examples
-#' jhu <- covid_case_death_rates
+#' rates <- cases_deaths_subset
 #' # set as_of to the last day in the data
-#' attr(jhu, "metadata")$as_of <- as.Date("2021-12-31")
-#' fcast <- climatological_forecaster(jhu, "death_rate")
+#' attr(rates, "metadata")$as_of <- as.Date("2021-12-31")
+#' fcast <- climatological_forecaster(rates, "case_rate_7d_av")
+#' autoplot(fcast)
 #'
-
+#' # Compute quantiles separately by location, and a backcast
+#' backcast <- climatological_forecaster(
+#'   rates, "case_rate_7d_av",
+#'   climate_args_list(
+#'     quantile_by_key = "geo_value",
+#'     forecast_date = as.Date("2021-06-01")
+#'   )
+#' )
+#' autoplot(backcast)
+#'
+#' # compute the climate "daily" rather than "weekly"
+#' # use a two week window (on both sides)
+#' daily_fcast <- climatological_forecaster(
+#'   rates, "case_rate_7d_av",
+#'   climate_args_list(
+#'     time_type = "day", window_size = 14L, forecast_horizon = 0:30
+#'   )
+#' )
+#' autoplot(daily_fcast) +
+#'   ggplot2::coord_cartesian(xlim = c(as.Date("2021-10-01"), NA))
 climatological_forecaster <- function(epi_data,
                                       outcome,
                                       args_list = climate_args_list()) {
@@ -111,13 +131,6 @@ climatological_forecaster <- function(epi_data,
   climate_table <- climate_center %>%
     left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
     mutate(.pred_distn = .pred_distn + .pred)
-  if (args_list$nonneg) {
-    climate_table <- mutate(
-      climate_table,
-      .pred = snap(.pred, 0, Inf),
-      .pred_distn = snap(.pred_distn, 0, Inf)
-    )
-  }
   # create the predictions
   predictions <- epi_data %>%
     select(all_of(keys)) %>%
@@ -133,6 +146,14 @@ climatological_forecaster <- function(epi_data,
     ) %>%
     left_join(climate_table, by = c(".idx", keys)) %>%
     select(-.idx)
+  if (args_list$nonneg) {
+    predictions <- mutate(
+      predictions,
+      .pred = snap(.pred, 0, Inf),
+      .pred_distn = snap(.pred_distn, 0, Inf)
+    )
+  }
+
   # fill in some extras for plotting methods, etc.
   ewf <- epi_workflow()
   ewf$trained <- TRUE

From 151d8ea5fd4b8b2f838dae41de75802073bacc08 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 09:54:23 -0800
Subject: [PATCH 229/387] tests pass

---
 R/step_climate.R                              |  2 +-
 .../_snaps/climatological_baseline.md         | 32 -------------------
 tests/testthat/_snaps/epi_recipe.md           |  8 ++---
 tests/testthat/test-step_climate.R            | 16 ++++++----
 4 files changed, 15 insertions(+), 43 deletions(-)
 delete mode 100644 tests/testthat/_snaps/climatological_baseline.md

diff --git a/R/step_climate.R b/R/step_climate.R
index c5686a037..01762b75c 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -242,7 +242,7 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 
   modulus <- switch(x$time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
 
-  fn <- switch(center_method,
+  fn <- switch(x$center_method,
                mean = function(x, w) weighted.mean(x, w, na.rm = TRUE),
                median = function(x, w) median(x, na.rm = TRUE))
 
diff --git a/tests/testthat/_snaps/climatological_baseline.md b/tests/testthat/_snaps/climatological_baseline.md
deleted file mode 100644
index 6270e2822..000000000
--- a/tests/testthat/_snaps/climatological_baseline.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# epiwindow function works
-
-    Code
-      epiwindow(t0, time_value, 1.5)
-    Condition
-      Error in `epiwindow()`:
-      ! Assertion on 'left' failed: Must be of type 'integerish', but element 1 is not close to an integer.
-
----
-
-    Code
-      epiwindow(t0, time_value, 1, 1.5)
-    Condition
-      Error in `epiwindow()`:
-      ! Assertion on 'right' failed: Must be of type 'integerish', but element 1 is not close to an integer.
-
----
-
-    Code
-      epiwindow(t0, time_value, seasons = "other")
-    Condition
-      Error in `epiwindow()`:
-      ! `seasons` must be one of "all" or "current", not "other".
-
----
-
-    Code
-      epiwindow(c(t0, t0), time_value)
-    Condition
-      Error in `epiwindow()`:
-      ! Can't recycle `t0` (size 2) to match `time_value` (size 4).
-
diff --git a/tests/testthat/_snaps/epi_recipe.md b/tests/testthat/_snaps/epi_recipe.md
index 24b046678..c63bf8f07 100644
--- a/tests/testthat/_snaps/epi_recipe.md
+++ b/tests/testthat/_snaps/epi_recipe.md
@@ -3,8 +3,8 @@
     Code
       epi_recipe(tib)
     Condition
-      Error in `epi_recipe()`:
-      ! `x` must be an <epi_df> or a <formula>, not a <tbl_df>.
+      Error in `UseMethod()`:
+      ! no applicable method for 'epi_recipe' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"
 
 ---
 
@@ -19,8 +19,8 @@
     Code
       epi_recipe(m)
     Condition
-      Error in `epi_recipe()`:
-      ! `x` must be an <epi_df> or a <formula>, not a <matrix>.
+      Error in `UseMethod()`:
+      ! no applicable method for 'epi_recipe' applied to an object of class "c('matrix', 'array', 'character')"
 
 # add/update/adjust/remove epi_recipe works as intended
 
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index c786791ef..ab71aa98c 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -5,18 +5,22 @@ test_that("roll_modular_multivec works", {
     w = rep(1, 10)
   )
   modulus <- 3L
+
+  Mean = function(x, w) weighted.mean(x, w, na.rm = TRUE)
+  Median = function(x, w) median(x, na.rm = TRUE)
+
   # unweighted mean
   expected_res <- tib |>
     mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
     summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
     expected_res
   )
   w_size <- 1L
   expected_res <- tibble(.idx = as.double(1:3), climate_pred = mean(tib$col))
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 1L, modulus),
     expected_res
   )
   # weighted mean
@@ -25,7 +29,7 @@ test_that("roll_modular_multivec works", {
     mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
     summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
     expected_res
   )
   tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
@@ -34,7 +38,7 @@ test_that("roll_modular_multivec works", {
     climate_pred = weighted.mean(tib$col, tib$w)
   )
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "mean", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 1L, modulus),
     expected_res
   )
   # median
@@ -42,12 +46,12 @@ test_that("roll_modular_multivec works", {
     mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
     summarise(climate_pred = median(col), .by = .idx)
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "median", 0, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Median, 0, modulus),
     expected_res
   )
   expected_res <- tibble(.idx = as.double(1:3), climate_pred = median(tib$col))
   expect_equal(
-    roll_modular_multivec(tib$col, tib$.idx, tib$w, "median", 1L, modulus),
+    roll_modular_multivec(tib$col, tib$.idx, tib$w, Median, 1L, modulus),
     expected_res
   )
 })

From 381a29850b149c5fb706fffe609aa58e890bb616 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 09:59:02 -0800
Subject: [PATCH 230/387] add snapshot tests

---
 tests/testthat/_snaps/snapshots.md            | 1178 +++++++++++++++++
 .../testthat/test-climatological_forecaster.R |    2 -
 tests/testthat/test-snapshots.R               |   28 +
 3 files changed, 1206 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 17191e041..517d76d27 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1288,3 +1288,1181 @@
         19001, 19001, 19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
     -53L), class = c("tbl_df", "tbl", "data.frame"))
 
+# climatological_forecaster snapshots
+
+    structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx", 
+    "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", "ga", "ny", "pa", 
+    "tx", "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", "ga", "ny", 
+    "pa", "tx"), forecast_date = structure(c(18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992
+    ), class = "Date"), target_date = structure(c(18992, 18992, 18992, 
+    18992, 18992, 18992, 18999, 18999, 18999, 18999, 18999, 18999, 
+    19006, 19006, 19006, 19006, 19006, 19006, 19013, 19013, 19013, 
+    19013, 19013, 19013, 19020, 19020, 19020, 19020, 19020, 19020
+    ), class = "Date"), .pred = c(81.6909143, 46.9476797, 57.1427664, 
+    55.4893801, 64.9577509, 49.3180127, 92.3266162, 51.49632575, 
+    62.0313808, 69.29024775, 65.2064021, 60.734368, 97.9375799, 52.0734516, 
+    66.9333338, 70.0729902, 60.5814898, 62.3893953, 94.69837105, 
+    52.17697935, 69.7197773, 67.80972325, 57.0623769, 66.48962015, 
+    75.3837738, 51.3826096, 67.5469116, 65.9446612, 45.2947493, 67.3274732
+    ), .pred_distn = structure(list(structure(list(values = c(37.581706945, 
+    40.53309523, 68.6068873166667, 81.6909143, 101.23192885, 124.123961743333, 
+    133.701228665), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(2.838472345, 5.78986063, 
+    33.8636527166667, 46.9476797, 66.48869425, 89.3807271433334, 
+    98.957994065), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(13.033559045, 15.98494733, 
+    44.0587394166667, 57.1427664, 76.68378095, 99.5758138433333, 
+    109.153080765), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(11.380172745, 14.33156103, 
+    42.4053531166667, 55.4893801, 75.03039465, 97.9224275433334, 
+    107.499694465), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(20.848543545, 23.79993183, 
+    51.8737239166667, 64.9577509, 84.49876545, 107.390798343333, 
+    116.968065265), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(5.208805345, 8.16019363, 
+    36.2339857166667, 49.3180127, 68.85902725, 91.7510601433333, 
+    101.328327065), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(46.0821344, 60.71441945, 
+    80.2739618916667, 92.3266162, 108.315284833333, 130.664387783333, 
+    140.133233), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(5.25184394999999, 19.884129, 
+    39.4436714416667, 51.49632575, 67.4849943833333, 89.8340973333333, 
+    99.30294255), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(15.786899, 30.41918405, 
+    49.9787264916667, 62.0313808, 78.0200494333333, 100.369152383333, 
+    109.8379976), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(23.04576595, 37.678051, 
+    57.2375934416667, 69.29024775, 85.2789163833333, 107.628019333333, 
+    117.09686455), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(18.9619203, 33.59420535, 
+    53.1537477916667, 65.2064021, 81.1950707333333, 103.544173683333, 
+    113.0130189), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(14.4898862, 29.12217125, 
+    48.6817136916667, 60.734368, 76.7230366333333, 99.0721395833334, 
+    108.5409848), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(71.810054805, 79.4526884866667, 
+    88.220075625, 97.9375799, 114.304113091667, 137.30686307, 146.48749288
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(25.945926505, 33.5885601866667, 42.355947325, 
+        52.0734516, 68.4399847916667, 91.44273477, 100.62336458), 
+        quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95
+        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(40.805808705, 48.4484423866667, 
+    57.215829525, 66.9333338, 83.2998669916667, 106.30261697, 115.48324678
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(43.945465105, 51.5880987866667, 60.355485925, 
+        70.0729902, 86.4395233916667, 109.44227337, 118.62290318), 
+        quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95
+        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(34.453964705, 42.0965983866667, 
+    50.863985525, 60.5814898, 76.9480229916667, 99.95077297, 109.13140278
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(36.261870205, 43.9045038866667, 52.671891025, 
+        62.3893953, 78.7559284916667, 101.75867847, 110.93930828), 
+        quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95
+        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(71.98481162, 75.1198124633333, 
+    84.7910693916667, 94.69837105, 109.307554591667, 131.848629753333, 
+    140.895032215), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(29.46341992, 32.5984207633333, 
+    42.2696776916667, 52.17697935, 66.7861628916667, 89.3272380533333, 
+    98.373640515), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(47.00621787, 50.1412187133333, 
+    59.8124756416667, 69.7197773, 84.3289608416667, 106.870036003333, 
+    115.916438465), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(45.09616382, 48.2311646633333, 
+    57.9024215916667, 67.80972325, 82.4189067916667, 104.959981953333, 
+    114.006384415), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(34.34881747, 37.4838183133333, 
+    47.1550752416667, 57.0623769, 71.6715604416667, 94.2126356033334, 
+    103.259038065), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(43.77606072, 46.9110615633333, 
+    56.5823184916667, 66.48962015, 81.0988036916667, 103.639878853333, 
+    112.686281315), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(46.908072705, 51.4679015966667, 
+    60.7768339583333, 75.3837738, 89.767610275, 102.406549163333, 
+    116.665952285), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(22.906908505, 27.4667373966667, 
+    36.7756697583333, 51.3826096, 65.766446075, 78.4053849633334, 
+    92.664788085), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(39.071210505, 43.6310393966667, 
+    52.9399717583333, 67.5469116, 81.930748075, 94.5696869633334, 
+    108.829090085), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(37.468960105, 42.0287889966667, 
+    51.3377213583333, 65.9446612, 80.328497675, 92.9674365633334, 
+    107.226839685), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(16.819048205, 21.3788770966667, 
+    30.6878094583333, 45.2947493, 59.678585775, 72.3175246633334, 
+    86.576927785), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(38.851772105, 43.4116009966667, 
+    52.7205333583333, 67.3274732, 81.711309675, 94.3502485633334, 
+    108.609651685), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
+    ))), row.names = c(NA, -30L), class = c("tbl_df", "tbl", "data.frame"
+    ))
+
+---
+
+    structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx", 
+    "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", "ga", "ny", "pa", 
+    "tx", "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", "ga", "ny", 
+    "pa", "tx"), forecast_date = structure(c(18779, 18779, 18779, 
+    18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779, 
+    18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779, 
+    18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779, 18779
+    ), class = "Date"), target_date = structure(c(18779, 18779, 18779, 
+    18779, 18779, 18779, 18786, 18786, 18786, 18786, 18786, 18786, 
+    18793, 18793, 18793, 18793, 18793, 18793, 18800, 18800, 18800, 
+    18800, 18800, 18800, 18807, 18807, 18807, 18807, 18807, 18807
+    ), class = "Date"), .pred = c(4.5027417, 6.6356332, 6.06441615, 
+    6.0225729, 5.3742185, 4.7252087, 4.5167124, 7.262058, 5.76229575, 
+    3.88009725, 4.3058565, 5.0086288, 4.7631049, 7.5289294, 5.493522, 
+    3.38954425, 4.0672631, 5.5988237, 6.6727377, 7.9200341, 6.12777475, 
+    3.37439915, 3.8526967, 6.8619285, 7.53783305, 10.69490535, 7.3882769, 
+    3.41133835, 3.62304355, 8.11067975), .pred_distn = structure(list(
+        structure(list(values = c(2.38454615, 2.47051743333333, 3.248099325, 
+        4.5027417, 5.70162214166667, 7.33486465, 8.302502625), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(2.941008575, 3.04656301666667, 3.4802291, 
+            6.6356332, 9.54985604166666, 14.0389050833333, 15.40788105
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(3.084228825, 3.18927068333333, 
+        3.79729063333333, 6.06441615, 6.82660870833333, 7.91693196666667, 
+        8.55974365), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(1.736328725, 
+        1.99287171666667, 3.29602638333333, 6.0225729, 9.486795875, 
+        12.0870102666667, 15.46498015), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(1.857899425, 2.42411633333333, 3.72101395833333, 
+            5.3742185, 8.371536425, 12.3141013833333, 15.1794572), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(2.980046525, 3.21736526666667, 
+        3.60125955, 4.7252087, 7.12831905833333, 8.08197281666667, 
+        9.6703417), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(2.19893535, 
+        2.37792371666667, 2.69084345, 4.5167124, 6.748578625, 9.16127798333333, 
+        13.412516575), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
+        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(2.96812295, 
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+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(1.699020075, 
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+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(1.37680125, 
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(2.9928591, 3.15370095, 
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+        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(1.699020075, 
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+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(1.37680125, 
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+        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(2.992210475, 
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+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(2.17308045, 
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+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(2.9928591, 3.15370095, 3.50495200833333, 7.3882769, 
+            15.6448998, 27.4038069, 31.796668175), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(1.699020075, 1.76237088333333, 2.32421669166667, 
+            3.41133835, 3.99577863333333, 5.58521231666667, 6.239036775
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+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(1.37680125, 1.4762617, 
+        2.18002845, 3.62304355, 4.475069675, 5.69346058333333, 6.07323565
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+        "vctrs_vctr")), structure(list(values = c(3.765229625, 4.06819315, 
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+        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list"))), row.names = c(NA, -30L), class = c("tbl_df", 
+    "tbl", "data.frame"))
+
+---
+
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+    "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", "ga", "ny", "pa", 
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+    "ny", "pa", "tx", "ca", "fl", "ga", "ny", "pa", "tx", "ca", "fl", 
+    "ga", "ny", "pa", "tx"), forecast_date = structure(c(18992, 18992, 
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+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18992, 
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+    19010, 19010, 19010, 19010, 19010, 19011, 19011, 19011, 19011, 
+    19011, 19011, 19012, 19012, 19012, 19012, 19012, 19012, 19013, 
+    19013, 19013, 19013, 19013, 19013, 19014, 19014, 19014, 19014, 
+    19014, 19014, 19015, 19015, 19015, 19015, 19015, 19015, 19016, 
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+    19019, 19019, 19019, 19019, 19019, 19020, 19020, 19020, 19020, 
+    19020, 19020, 19021, 19021, 19021, 19021, 19021, 19021, 19022, 
+    19022, 19022, 19022, 19022, 19022), class = "Date"), .pred = c(101.37020155, 
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+    101.6955195, 59.319734, 64.149558, 76.1701803, 64.8717008, 62.1894199, 
+    101.4527557, 62.0988778, 68.7867389, 78.77218, 64.20397455, 62.69179115, 
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+    64.952956, 78.165809, 78.0370892, 61.5436861, 69.4590841, 100.02084865, 
+    64.19703935, 78.3985684, 77.4833701, 59.9076171, 69.5145517, 
+    99.31687, 63.4411227, 78.6313278, 76.929651, 58.2402571, 69.5700193, 
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+    70.04879245, 99.31687, 62.7667137, 78.6313278, 76.1701803, 57.919525, 
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+    47.2796257, 58.2898901, 63.0818705, 40.4021878, 62.0818906), 
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
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+        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(42.11476082, 
+        45.7715721766667, 53.6209398583333, 63.0818705, 77.3799261083334, 
+        86.1563554266667, 90.655981825), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(19.43507812, 23.0918894766667, 30.9412571583333, 
+            40.4021878, 54.7002434083333, 63.4766727266667, 67.976299125
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(41.11478092, 44.7715922766667, 
+        52.6209599583333, 62.0818906, 76.3799462083334, 85.1563755266667, 
+        89.656001925), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
+        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+        "vctrs_vctr", "list"))), row.names = c(NA, -186L), class = c("tbl_df", 
+    "tbl", "data.frame"))
+
diff --git a/tests/testthat/test-climatological_forecaster.R b/tests/testthat/test-climatological_forecaster.R
index 3bee13195..16f31140c 100644
--- a/tests/testthat/test-climatological_forecaster.R
+++ b/tests/testthat/test-climatological_forecaster.R
@@ -24,5 +24,3 @@ test_that("climate args list validates properly", {
   expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = TRUE))
   expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = 2:3))
 })
-
-
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 3aecfd6b8..505ad5181 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -183,3 +183,31 @@ test_that("arx_classifier snapshots", {
     class = "epipredict__arx_classifier__adjust_latency_unsupported_method"
   )
 })
+
+test_that("climatological_forecaster snapshots", {
+  rates <- cases_deaths_subset
+
+  # set as_of to the last day in the data
+  attr(rates, "metadata")$as_of <- as.Date("2021-12-31")
+  fcast <- climatological_forecaster(rates, "case_rate_7d_av")
+  expect_snapshot_tibble(fcast$predictions)
+
+  # Compute quantiles separately by location, and a backcast
+  backcast <- climatological_forecaster(
+    rates, "case_rate_7d_av",
+    climate_args_list(
+      quantile_by_key = "geo_value",
+      forecast_date = as.Date("2021-06-01")
+    )
+  )
+  expect_snapshot_tibble(backcast$predictions)
+  # compute the climate "daily" rather than "weekly"
+  # use a two week window (on both sides)
+  daily_fcast <- climatological_forecaster(
+    rates, "case_rate_7d_av",
+    climate_args_list(
+      time_type = "day", window_size = 14L, forecast_horizon = 0:30
+    )
+  )
+  expect_snapshot_tibble(daily_fcast$predictions)
+})

From 83e0ad7e5c3373bee7d745a3ebcfcec5d2890fcb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 10:07:06 -0800
Subject: [PATCH 231/387] redocument, add news

---
 NAMESPACE                        |  3 ---
 NEWS.md                          |  2 ++
 R/climatological_forecaster.R    |  4 ++--
 man/climate_args_list.Rd         | 10 +++++-----
 man/climatological_forecaster.Rd | 27 +++++++++++++++++++++++++--
 5 files changed, 34 insertions(+), 12 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 2723e1f0b..d015bd3f0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -278,9 +278,6 @@ importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
-importFrom(lubridate,epiweek)
-importFrom(lubridate,epiyear)
-importFrom(lubridate,ymd)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
diff --git a/NEWS.md b/NEWS.md
index ed44c3462..ce990555e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -20,6 +20,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Make key column inference more consistent within the package and with current `epiprocess`.
 - Fix `quantile_reg()` producing error when asked to output just median-level predictions.
 - (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
+- Add `step_climate()` to create "climate" predictor in forecast workflows
+- Add `climatological_forecaster()` to automatically create climate baselines
 
 ## Bug fixes
 
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 65f36f33a..af4e090b1 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -183,7 +183,7 @@ climatological_forecaster <- function(epi_data,
 #' Climatological forecaster argument constructor
 #'
 #' @inheritParams epi_recipe
-#' @param prediction_horizon Vector of integers giving the number of time steps,
+#' @param forecast_horizon Vector of integers giving the number of time steps,
 #'   in units of the `time_type`,
 #'   from the `reference_date` for which predictions should be produced.
 #' @inheritParams step_climate
@@ -191,7 +191,7 @@ climatological_forecaster <- function(epi_data,
 #'
 #' @return A list containing updated parameter choices with class `climate_alist`.
 #' @export
-#' @seealso [climatological_predictor()], [step_climate()]
+#' @seealso [climatological_forecaster()], [step_climate()]
 #'
 #' @examples
 #' climate_args_list()
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
index 3216d098c..3a889e5c7 100644
--- a/man/climate_args_list.Rd
+++ b/man/climate_args_list.Rd
@@ -27,6 +27,10 @@ adjustment (the default case), or
 non-\code{NULL}.
 }}
 
+\item{forecast_horizon}{Vector of integers giving the number of time steps,
+in units of the \code{time_type},
+from the \code{reference_date} for which predictions should be produced.}
+
 \item{time_type}{The duration over which time aggregation should be performed.}
 
 \item{center_method}{The measure of center to be calculated over the time
@@ -59,10 +63,6 @@ residual quantiles are used. It is not applicable with
 
 \item{...}{Further arguments passed to or from other methods (not currently
 used).}
-
-\item{prediction_horizon}{Vector of integers giving the number of time steps,
-in units of the \code{time_type},
-from the \code{reference_date} for which predictions should be produced.}
 }
 \value{
 A list containing updated parameter choices with class \code{climate_alist}.
@@ -79,5 +79,5 @@ climate_args_list(
 
 }
 \seealso{
-\code{\link[=climatological_predictor]{climatological_predictor()}}, \code{\link[=step_climate]{step_climate()}}
+\code{\link[=climatological_forecaster]{climatological_forecaster()}}, \code{\link[=step_climate]{step_climate()}}
 }
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
index 64dff04cb..26923c9a1 100644
--- a/man/climatological_forecaster.Rd
+++ b/man/climatological_forecaster.Rd
@@ -31,9 +31,32 @@ The forecast is taken as the quantiles of the \code{outcome} in a small window
 around the target period, computed over the entire available history.
 }
 \examples{
+rates <- cases_deaths_subset
+# set as_of to the last day in the data
+attr(rates, "metadata")$as_of <- as.Date("2021-12-31")
+fcast <- climatological_forecaster(rates, "case_rate_7d_av")
+autoplot(fcast)
 
-1 + 1
-2 + 2
+# Compute quantiles separately by location, and a backcast
+backcast <- climatological_forecaster(
+  rates, "case_rate_7d_av",
+  climate_args_list(
+    quantile_by_key = "geo_value",
+    forecast_date = as.Date("2021-06-01")
+  )
+)
+autoplot(backcast)
+
+# compute the climate "daily" rather than "weekly"
+# use a two week window (on both sides)
+daily_fcast <- climatological_forecaster(
+  rates, "case_rate_7d_av",
+  climate_args_list(
+    time_type = "day", window_size = 14L, forecast_horizon = 0:30
+  )
+)
+autoplot(daily_fcast) +
+  ggplot2::coord_cartesian(xlim = c(as.Date("2021-10-01"), NA))
 }
 \seealso{
 \code{\link[=step_climate]{step_climate()}}

From d56d488d394213baec1413831c7509abfba12b93 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:04:38 -0800
Subject: [PATCH 232/387] prefix with recipes, aside to rename object

---
 R/step_adjust_latency.R    | 14 +++++++-------
 man/step_adjust_latency.Rd | 14 +++++++-------
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index 7ce64b302..cfe0ffec8 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -180,22 +180,22 @@
 #' @rdname step_adjust_latency
 #' @export
 #' @examples
-#' jhu <- covid_case_death_rates %>%
+#' rates <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' # setting the `as_of` to something realistic
-#' attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+#' attributes(rates)$metadata$as_of <- max(rates$time_value) + 3
 #'
-#' r <- epi_recipe(covid_case_death_rates) %>%
-#'   step_adjust_latency(has_role("raw"), method = "extend_ahead") %>%
+#' r <- epi_recipe(rates) %>%
+#'   step_adjust_latency(recipes::has_role("raw"), method = "extend_ahead") %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r
 #'
-#' jhu_fit <- epi_workflow() %>%
+#' rates_fit <- epi_workflow() %>%
 #'   add_epi_recipe(r) %>%
 #'   add_model(linear_reg()) %>%
-#'   fit(data = jhu)
-#' jhu_fit
+#'   fit(data = rates)
+#' rates_fit
 #'
 #' @importFrom recipes detect_step
 #' @importFrom rlang enquos is_empty
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 1493898b3..75d674472 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -277,22 +277,22 @@ If you create columns that you then apply lags to (such as
 }
 
 \examples{
-jhu <- covid_case_death_rates \%>\%
+rates <- covid_case_death_rates \%>\%
   dplyr::filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 # setting the `as_of` to something realistic
-attributes(jhu)$metadata$as_of <- max(jhu$time_value) + 3
+attributes(rates)$metadata$as_of <- max(rates$time_value) + 3
 
-r <- epi_recipe(covid_case_death_rates) \%>\%
-  step_adjust_latency(has_role("raw"), method = "extend_ahead") \%>\%
+r <- epi_recipe(rates) \%>\%
+  step_adjust_latency(recipes::has_role("raw"), method = "extend_ahead") \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14))
 r
 
-jhu_fit <- epi_workflow() \%>\%
+rates_fit <- epi_workflow() \%>\%
   add_epi_recipe(r) \%>\%
   add_model(linear_reg()) \%>\%
-  fit(data = jhu)
-jhu_fit
+  fit(data = rates)
+rates_fit
 
 }
 \seealso{

From c1cf5ba821134f861b8bbc387bd2cd684b8097c2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:05:02 -0800
Subject: [PATCH 233/387] muffle dplyr select warnings

---
 R/climatological_forecaster.R | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index af4e090b1..061ee6582 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -78,8 +78,10 @@ climatological_forecaster <- function(epi_data,
     ))
   }
   # process the time types
-  epi_data <- epi_data[!is.na(epi_data[outcome]), ] %>%
-    select(key_colnames(epi_data), outcome)
+  sym_outcome <- sym(outcome)
+  epi_data <- epi_data %>%
+    filter(!is.na(!!outcome)) %>%
+    select(all_of(c(key_colnames(epi_data), outcome)))
   if (time_type %in% c("week", "epiweek")) {
     ttype_dur <- lubridate::weeks
     time_aggr <- ifelse(time_type == "week", lubridate::epiweek, lubridate::isoweek)
@@ -99,15 +101,14 @@ climatological_forecaster <- function(epi_data,
     median = function(x, w) stats::median(x, na.rm = TRUE)
   )
   # get the point predictions
-  sym_outcome <- sym(outcome)
   keys <- key_colnames(epi_data, exclude = "time_value")
   epi_data <- epi_data %>% mutate(.idx = time_aggr(time_value), .weights = 1)
   climate_center <- epi_data %>%
-    select(.idx, .weights, outcome, all_of(keys)) %>%
+    select(.idx, .weights, all_of(c(outcome, keys))) %>%
     dplyr::reframe(
       roll_modular_multivec(!!sym_outcome, .idx, .weights, center_fn, window_size,
                             modulus),
-      .by = keys
+      .by = all_of(keys)
     ) %>%
     rename(.pred = climate_pred)
   # get the quantiles
@@ -119,11 +120,11 @@ climatological_forecaster <- function(epi_data,
     )))
   }
   climate_quantiles <- epi_data %>%
-    select(.idx, .weights, outcome, all_of(args_list$quantile_by_key)) %>%
+    select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
     dplyr::reframe(
       roll_modular_multivec(!!sym_outcome, .idx, .weights, Quantile, window_size,
                             modulus),
-      .by = args_list$quantile_by_key
+      .by = all_of(args_list$quantile_by_key)
     ) %>%
     rename(.pred_distn = climate_pred) %>%
     mutate(.pred_distn = dist_quantiles(.pred_distn, args_list$quantile_levels))
@@ -158,7 +159,7 @@ climatological_forecaster <- function(epi_data,
   ewf <- epi_workflow()
   ewf$trained <- TRUE
   ewf$pre <- list(mold = list(
-    outcomes = select(epi_data, outcome),
+    outcomes = select(epi_data, !!sym_outcome),
     extras = list(roles = list(
       geo_value = select(epi_data, geo_value),
       time_value = select(epi_data, time_value)

From 4a348c8b91b6d50ddbfed14def6b878c06a6200e Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:05:49 -0800
Subject: [PATCH 234/387] shifting must happen before joining to avoid
 duplicated rows

---
 R/step_climate.R                   | 17 ++++++++---------
 tests/testthat/test-step_climate.R |  9 +++++++--
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 01762b75c..cade124a5 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -165,7 +165,7 @@ step_climate <-
     arg_is_lgl_scalar(skip)
 
     time_aggr <- switch(time_type,
-                        epiweek = lubridate::epiweek, week = lubridate::week,
+                        epiweek = lubridate::epiweek, week = lubridate::isoweek,
                         month = lubridate::month, day = lubridate::yday)
 
     recipes::add_step(
@@ -243,11 +243,15 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   modulus <- switch(x$time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
 
   fn <- switch(x$center_method,
-               mean = function(x, w) weighted.mean(x, w, na.rm = TRUE),
+               mean = function(x, w) stats::weighted.mean(x, w, na.rm = TRUE),
                median = function(x, w) median(x, na.rm = TRUE))
 
   climate_table <- training %>%
-    mutate(.idx = x$time_aggr(time_value), .weights = wts) %>%
+    mutate(
+      .idx = x$time_aggr(time_value), .weights = wts,
+      .idx = (.idx - x$forecast_ahead) %% modulus,
+      .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
+    ) %>%
     select(.idx, .weights, all_of(c(col_names, x$epi_keys))) %>%
     tidyr::pivot_longer(all_of(unname(col_names))) %>%
     dplyr::reframe(
@@ -279,14 +283,9 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 }
 
 
-
 #' @export
 bake.step_climate <- function(object, new_data, ...) {
-  climate_table <- object$climate_table %>%
-    mutate(
-      .idx = (.idx - object$forecast_ahead) %% object$modulus,
-      .idx = dplyr::case_when(.idx == 0 ~ object$modulus, TRUE ~ .idx)
-    )
+  climate_table <- object$climate_table
   new_data %>%
     mutate(.idx = object$time_aggr(time_value)) %>%
     left_join(climate_table, by = c(".idx", object$epi_keys)) %>%
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index ab71aa98c..b42a96684 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -99,7 +99,12 @@ test_that("leading the climate predictor works as expected", {
     step_epi_naomit()
   p <- prep(r, x)
 
-  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
+  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2)) %>%
+    mutate(
+      .idx = (.idx - 2L) %% 53,
+      .idx = dplyr::case_when(.idx == 0 ~ 53L, TRUE ~ .idx)
+    ) %>%
+    arrange(.idx)
   expect_equal(p$steps[[3]]$climate_table, expected_res)
 
   b <- bake(p, new_data = NULL)
@@ -107,7 +112,7 @@ test_that("leading the climate predictor works as expected", {
   # expected climate predictor should be shifted forward by 2 weeks
   expected_climate_pred <- x %>%
     mutate(
-      .idx = (lubridate::epiweek(time_value) + 2) %% 53,
+      .idx = lubridate::epiweek(time_value) %% 53,
       .idx = dplyr::case_when(.idx == 0 ~ 53, TRUE ~ .idx)
     ) %>%
     left_join(expected_res, by = join_by(.idx)) %>%

From 46b2a2e99d77a480003ba4547cf63f11d6d80eb2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:21:33 -0800
Subject: [PATCH 235/387] ignore renviron

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index dd821f77e..ae7df4125 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ inst/doc
 .Rprofile
 renv.lock
 renv/
+.Renviron

From 5f76be3ebf3e3966a60a72260944beee74b213fc Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:21:55 -0800
Subject: [PATCH 236/387] months is in base, so we ensure we use lubridate

---
 R/climatological_forecaster.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 061ee6582..2bf5e0898 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -87,7 +87,7 @@ climatological_forecaster <- function(epi_data,
     time_aggr <- ifelse(time_type == "week", lubridate::epiweek, lubridate::isoweek)
     modulus <- 53L
   } else if (time_type == "month") {
-    ttype_dur <- lubridate::months
+    ttype_dur <- function(x) lubridate::period(month = x)
     time_aggr <- lubridate::month
     modulus <- 12L
   } else if (time_type == "day") {

From 77dbcc40830b807aafa9f41d5efae6b4fec9e045 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:33:55 -0800
Subject: [PATCH 237/387] rebuild readme

---
 README.md | 41 +++++++++++++++++++++--------------------
 1 file changed, 21 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 5293a0e9c..1f24bab2b 100644
--- a/README.md
+++ b/README.md
@@ -78,7 +78,7 @@ covid_case_death_rates
 #> An `epi_df` object, 20,496 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
-#> * as_of     = 2022-05-31 12:08:25.791826
+#> * as_of     = 2023-03-10
 #> 
 #> # A tibble: 20,496 × 4
 #>    geo_value time_value case_rate death_rate
@@ -88,11 +88,11 @@ covid_case_death_rates
 #>  3 ar        2020-12-31      66.0      1.27 
 #>  4 as        2020-12-31       0        0    
 #>  5 az        2020-12-31      76.8      1.10 
-#>  6 ca        2020-12-31      96.0      0.751
-#>  7 co        2020-12-31      35.8      0.649
+#>  6 ca        2020-12-31      95.9      0.755
+#>  7 co        2020-12-31      37.8      0.376
 #>  8 ct        2020-12-31      52.1      0.819
 #>  9 dc        2020-12-31      31.0      0.601
-#> 10 de        2020-12-31      65.2      0.807
+#> 10 de        2020-12-31      64.3      0.912
 #> # ℹ 20,486 more rows
 ```
 
@@ -113,19 +113,20 @@ two_week_ahead <- arx_forecaster(
 two_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
 #> 
-#> This forecaster was fit on 2024-11-11 11:38:31.
+#> This forecaster was fit on 2025-02-11 12:32:56.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
-#> • Using data up-to-date as of: 2022-05-31 12:08:25.
+#> • Using data up-to-date as of: 2023-03-10.
+#> • With the last data available on 2021-12-31
 #> 
 #> ── Predictions ─────────────────────────────────────────────────────────────
 #> 
 #> A total of 56 predictions are available for
 #> • 56 unique geographic regions,
 #> • At forecast date: 2021-12-31,
-#> • For target date: 2022-01-14.
+#> • For target date: 2022-01-14,
 #> 
 ```
 
@@ -161,11 +162,11 @@ two_week_ahead$epi_workflow
 #> 
 #> Coefficients:
 #>       (Intercept)    lag_0_case_rate    lag_1_case_rate    lag_2_case_rate  
-#>        -0.0073358          0.0030365          0.0012467          0.0009536  
+#>        -0.0071026          0.0040340          0.0007863          0.0003699  
 #>   lag_3_case_rate    lag_7_case_rate   lag_14_case_rate   lag_0_death_rate  
-#>         0.0011425          0.0012481          0.0003041          0.1351769  
+#>         0.0012887          0.0011980          0.0002527          0.1348573  
 #>  lag_7_death_rate  lag_14_death_rate  
-#>         0.1471127          0.1062473
+#>         0.1479274          0.1067074
 #> 
 #> ── Postprocessor ───────────────────────────────────────────────────────────
 #> 
@@ -188,16 +189,16 @@ two_week_ahead$predictions
 #> # A tibble: 56 × 5
 #>    geo_value .pred        .pred_distn forecast_date target_date
 #>    <chr>     <dbl>             <dist> <date>        <date>     
-#>  1 ak        0.449 quantiles(0.45)[2] 2021-12-31    2022-01-14 
-#>  2 al        0.574 quantiles(0.57)[2] 2021-12-31    2022-01-14 
-#>  3 ar        0.673 quantiles(0.67)[2] 2021-12-31    2022-01-14 
-#>  4 as        0     quantiles(0.12)[2] 2021-12-31    2022-01-14 
-#>  5 az        0.679 quantiles(0.68)[2] 2021-12-31    2022-01-14 
-#>  6 ca        0.575 quantiles(0.57)[2] 2021-12-31    2022-01-14 
-#>  7 co        0.862 quantiles(0.86)[2] 2021-12-31    2022-01-14 
-#>  8 ct        1.07  quantiles(1.07)[2] 2021-12-31    2022-01-14 
-#>  9 dc        2.12  quantiles(2.12)[2] 2021-12-31    2022-01-14 
-#> 10 de        1.09  quantiles(1.09)[2] 2021-12-31    2022-01-14 
+#>  1 ak        0.450 quantiles(0.45)[7] 2021-12-31    2022-01-14 
+#>  2 al        0.602  quantiles(0.6)[7] 2021-12-31    2022-01-14 
+#>  3 ar        0.694 quantiles(0.69)[7] 2021-12-31    2022-01-14 
+#>  4 as        0        quantiles(0)[7] 2021-12-31    2022-01-14 
+#>  5 az        0.699  quantiles(0.7)[7] 2021-12-31    2022-01-14 
+#>  6 ca        0.592 quantiles(0.59)[7] 2021-12-31    2022-01-14 
+#>  7 co        1.47  quantiles(1.47)[7] 2021-12-31    2022-01-14 
+#>  8 ct        1.08  quantiles(1.08)[7] 2021-12-31    2022-01-14 
+#>  9 dc        2.14  quantiles(2.14)[7] 2021-12-31    2022-01-14 
+#> 10 de        1.13  quantiles(1.13)[7] 2021-12-31    2022-01-14 
 #> # ℹ 46 more rows
 ```
 

From c98b47cc11bb869370c3321d6cdf5db547c2b734 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 12:35:51 -0800
Subject: [PATCH 238/387] satisfy styler

---
 R/climatological_forecaster.R      | 56 +++++++++++++++++-------------
 R/step_adjust_latency.R            |  3 +-
 R/step_climate.R                   | 25 +++++++++----
 tests/testthat/test-step_climate.R | 16 +++++----
 4 files changed, 61 insertions(+), 39 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 2bf5e0898..8de21b3f7 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -95,8 +95,7 @@ climatological_forecaster <- function(epi_data,
     time_aggr <- lubridate::yday
     modulus <- 365L
   }
-  center_fn <- switch(
-    args_list$center_method,
+  center_fn <- switch(args_list$center_method,
     mean = function(x, w) mean(x, na.rm = TRUE),
     median = function(x, w) stats::median(x, na.rm = TRUE)
   )
@@ -106,8 +105,10 @@ climatological_forecaster <- function(epi_data,
   climate_center <- epi_data %>%
     select(.idx, .weights, all_of(c(outcome, keys))) %>%
     dplyr::reframe(
-      roll_modular_multivec(!!sym_outcome, .idx, .weights, center_fn, window_size,
-                            modulus),
+      roll_modular_multivec(
+        !!sym_outcome, .idx, .weights, center_fn, window_size,
+        modulus
+      ),
       .by = all_of(keys)
     ) %>%
     rename(.pred = climate_pred)
@@ -116,14 +117,17 @@ climatological_forecaster <- function(epi_data,
     x <- x - stats::median(x, na.rm = TRUE)
     if (args_list$symmetrize) x <- c(x, -x)
     list(unname(quantile(
-      x, probs = args_list$quantile_levels, na.rm = TRUE, type = 8
+      x,
+      probs = args_list$quantile_levels, na.rm = TRUE, type = 8
     )))
   }
   climate_quantiles <- epi_data %>%
     select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
     dplyr::reframe(
-      roll_modular_multivec(!!sym_outcome, .idx, .weights, Quantile, window_size,
-                            modulus),
+      roll_modular_multivec(
+        !!sym_outcome, .idx, .weights, Quantile, window_size,
+        modulus
+      ),
       .by = all_of(args_list$quantile_by_key)
     ) %>%
     rename(.pred_distn = climate_pred) %>%
@@ -138,12 +142,13 @@ climatological_forecaster <- function(epi_data,
     dplyr::distinct() %>%
     mutate(forecast_date = forecast_date, .idx = time_aggr(forecast_date))
   predictions <- map(horizon, ~ {
-      predictions %>%
-        mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
-    }) %>%
+    predictions %>%
+      mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
+  }) %>%
     purrr::list_rbind() %>%
-    mutate(.idx = .idx %% modulus,
-           .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
+    mutate(
+      .idx = .idx %% modulus,
+      .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
     ) %>%
     left_join(climate_table, by = c(".idx", keys)) %>%
     select(-.idx)
@@ -167,16 +172,18 @@ climatological_forecaster <- function(epi_data,
   ))
   other_keys <- key_colnames(epi_data, exclude = c("time_value", "geo_value"))
   if (length(other_keys) > 0) {
-    ewf$pre$mold$extras$roles$key = epi_data %>% select(all_of(other_keys))
+    ewf$pre$mold$extras$roles$key <- epi_data %>% select(all_of(other_keys))
   }
 
-  structure(list(
-    predictions = predictions,
-    epi_workflow = ewf,
-    metadata = list(
-      training = attr(epi_data, "metadata"),
-      forecast_created = Sys.time()
-    )),
+  structure(
+    list(
+      predictions = predictions,
+      epi_workflow = ewf,
+      metadata = list(
+        training = attr(epi_data, "metadata"),
+        forecast_created = Sys.time()
+      )
+    ),
     class = c("climate_fcast", "canned_epipred")
   )
 }
@@ -212,7 +219,6 @@ climate_args_list <- function(
     nonneg = TRUE,
     quantile_by_key = character(0L),
     ...) {
-
   rlang::check_dots_empty()
   time_type <- arg_match(time_type)
   center_method <- rlang::arg_match(center_method)
@@ -226,9 +232,11 @@ climate_args_list <- function(
   arg_is_probabilities(quantile_levels)
   quantile_levels <- sort(unique(c(0.5, quantile_levels)))
 
-  structure(enlist(
-    forecast_date, forecast_horizon, time_type, center_method, window_size,
-    quantile_levels, symmetrize, nonneg, quantile_by_key),
+  structure(
+    enlist(
+      forecast_date, forecast_horizon, time_type, center_method, window_size,
+      quantile_levels, symmetrize, nonneg, quantile_by_key
+    ),
     class = c("climate_fcast", "alist")
   )
 }
diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index cfe0ffec8..ae9db6ef2 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -360,5 +360,6 @@ print.step_adjust_latency <-
     }
     title <- trimws(paste("Adj.", x$method))
     print_epi_step(x$columns, x$terms, x$trained, title,
-                   conjunction = conj, extra_text = extra_text)
+      conjunction = conj, extra_text = extra_text
+    )
   }
diff --git a/R/step_climate.R b/R/step_climate.R
index cade124a5..2f88ca94c 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -92,8 +92,10 @@
 #' # same idea, but using weekly climate
 #' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
-#'   step_climate(death_rate, forecast_ahead = 1, time_type = "epiweek",
-#'                window_size = 1L)
+#'   step_climate(death_rate,
+#'     forecast_ahead = 1, time_type = "epiweek",
+#'     window_size = 1L
+#'   )
 #' r
 #'
 #' r %>%
@@ -165,8 +167,11 @@ step_climate <-
     arg_is_lgl_scalar(skip)
 
     time_aggr <- switch(time_type,
-                        epiweek = lubridate::epiweek, week = lubridate::isoweek,
-                        month = lubridate::month, day = lubridate::yday)
+      epiweek = lubridate::epiweek,
+      week = lubridate::isoweek,
+      month = lubridate::month,
+      day = lubridate::yday
+    )
 
     recipes::add_step(
       recipe,
@@ -240,11 +245,17 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   wts_used <- !is.null(wts)
   wts <- wts %||% rep(1, nrow(training))
 
-  modulus <- switch(x$time_type, epiweek = 53L, week = 53L, month = 12L, day = 365L)
+  modulus <- switch(x$time_type,
+    epiweek = 53L,
+    week = 53L,
+    month = 12L,
+    day = 365L
+  )
 
   fn <- switch(x$center_method,
-               mean = function(x, w) stats::weighted.mean(x, w, na.rm = TRUE),
-               median = function(x, w) median(x, na.rm = TRUE))
+    mean = function(x, w) stats::weighted.mean(x, w, na.rm = TRUE),
+    median = function(x, w) median(x, na.rm = TRUE)
+  )
 
   climate_table <- training %>%
     mutate(
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index b42a96684..045789483 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -6,12 +6,12 @@ test_that("roll_modular_multivec works", {
   )
   modulus <- 3L
 
-  Mean = function(x, w) weighted.mean(x, w, na.rm = TRUE)
-  Median = function(x, w) median(x, na.rm = TRUE)
+  Mean <- function(x, w) weighted.mean(x, w, na.rm = TRUE)
+  Median <- function(x, w) median(x, na.rm = TRUE)
 
   # unweighted mean
   expected_res <- tib |>
-    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus) |>
     summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
@@ -26,7 +26,7 @@ test_that("roll_modular_multivec works", {
   # weighted mean
   tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
   expected_res <- tib |>
-    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus) |>
     summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
   expect_equal(
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
@@ -43,7 +43,7 @@ test_that("roll_modular_multivec works", {
   )
   # median
   expected_res <- tib |>
-    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus ) |>
+    mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus) |>
     summarise(climate_pred = median(col), .by = .idx)
   expect_equal(
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Median, 0, modulus),
@@ -125,7 +125,9 @@ test_that("leading the climate predictor works as expected", {
   td <- get_test_data(r, x)
   expected_test_x <- td %>%
     filter(time_value == "2021-12-31") %>%
-    mutate(ahead_14_y = NA_real_, lag_0_y = 1, lag_7_y = 2, lag_14_y = 3,
-           climate_y = 2)
+    mutate(
+      ahead_14_y = NA_real_, lag_0_y = 1, lag_7_y = 2, lag_14_y = 3,
+      climate_y = 2
+    )
   expect_equal(bake(p, td), expected_test_x)
 })

From 41aa2f173fc3fe9da985a2fac024015a374242ae Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 13:45:00 -0800
Subject: [PATCH 239/387] adjustments for epiprocess 0.10.4 growth_rate

---
 R/step_growth_rate.R                      | 42 +++++++++--------------
 tests/testthat/_snaps/step_growth_rate.md |  9 +++--
 tests/testthat/test-step_growth_rate.R    |  2 +-
 3 files changed, 22 insertions(+), 31 deletions(-)

diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index b3a712313..d45666391 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -22,9 +22,7 @@
 #'   being removed from the data. Alternatively, you could specify arbitrary
 #'   large values, or perhaps zero. Setting this argument to `NULL` will result
 #'   in no replacement.
-#' @param additional_gr_args_list A list of additional arguments used by
-#'   [epiprocess::growth_rate()]. All `...` arguments may be passed here along
-#'   with `dup_rm` and `na_rm`.
+#' @inheritParams epiprocess::growth_rate
 #' @template step-return
 #'
 #'
@@ -46,11 +44,11 @@ step_growth_rate <-
            horizon = 7,
            method = c("rel_change", "linear_reg"),
            log_scale = FALSE,
+           na_rm = TRUE,
            replace_Inf = NA,
            prefix = "gr_",
            skip = FALSE,
-           id = rand_id("growth_rate"),
-           additional_gr_args_list = list()) {
+           id = rand_id("growth_rate")) {
     if (!is_epi_recipe(recipe)) {
       cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
@@ -63,15 +61,7 @@ step_growth_rate <-
     }
     arg_is_chr(role)
     arg_is_chr_scalar(prefix, id)
-    arg_is_lgl_scalar(log_scale, skip)
-
-
-    if (!is.list(additional_gr_args_list)) {
-      cli_abort(c(
-        "`additional_gr_args_list` must be a {.cls list}.",
-        i = "See `?epiprocess::growth_rate` for available options."
-      ))
-    }
+    arg_is_lgl_scalar(log_scale, skip, na_rm)
 
     recipes::add_step(
       recipe,
@@ -82,13 +72,13 @@ step_growth_rate <-
         horizon = horizon,
         method = method,
         log_scale = log_scale,
+        na_rm = na_rm,
         replace_Inf = replace_Inf,
         prefix = prefix,
         keys = key_colnames(recipe),
         columns = NULL,
         skip = skip,
-        id = id,
-        additional_gr_args_list = additional_gr_args_list
+        id = id
       )
     )
   }
@@ -101,13 +91,13 @@ step_growth_rate_new <-
            horizon,
            method,
            log_scale,
+           na_rm,
            replace_Inf,
            prefix,
            keys,
            columns,
            skip,
-           id,
-           additional_gr_args_list) {
+           id) {
     recipes::step(
       subclass = "growth_rate",
       terms = terms,
@@ -116,13 +106,13 @@ step_growth_rate_new <-
       horizon = horizon,
       method = method,
       log_scale = log_scale,
+      na_rm = na_rm,
       replace_Inf = replace_Inf,
       prefix = prefix,
       keys = keys,
       columns = columns,
       skip = skip,
-      id = id,
-      additional_gr_args_list = additional_gr_args_list
+      id = id
     )
   }
 
@@ -137,13 +127,13 @@ prep.step_growth_rate <- function(x, training, info = NULL, ...) {
     horizon = x$horizon,
     method = x$method,
     log_scale = x$log_scale,
+    na_rm = x$na_rm,
     replace_Inf = x$replace_Inf,
     prefix = x$prefix,
     keys = x$keys,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
-    id = x$id,
-    additional_gr_args_list = x$additional_gr_args_list
+    id = x$id
   )
 }
 
@@ -177,10 +167,12 @@ bake.step_growth_rate <- function(object, new_data, ...) {
       across(
         all_of(object$columns),
         ~ epiprocess::growth_rate(
-          time_value, .x,
+          .x,
+          x = time_value,
           method = object$method,
-          h = object$horizon, log_scale = object$log_scale,
-          !!!object$additional_gr_args_list
+          h = object$horizon,
+          log_scale = object$log_scale,
+          na_rm = object$na_rm
         ),
         .names = "{object$prefix}{object$horizon}_{object$method}_{.col}"
       )
diff --git a/tests/testthat/_snaps/step_growth_rate.md b/tests/testthat/_snaps/step_growth_rate.md
index 0912977e3..409aa197e 100644
--- a/tests/testthat/_snaps/step_growth_rate.md
+++ b/tests/testthat/_snaps/step_growth_rate.md
@@ -89,19 +89,18 @@
 ---
 
     Code
-      step_growth_rate(r, value, skip = 1)
+      step_growth_rate(r, value, na_rm = 1)
     Condition
       Error in `step_growth_rate()`:
-      ! `skip` must be a scalar of type <logical>.
+      ! `na_rm` must be a scalar of type <logical>.
 
 ---
 
     Code
-      step_growth_rate(r, value, additional_gr_args_list = 1:5)
+      step_growth_rate(r, value, skip = 1)
     Condition
       Error in `step_growth_rate()`:
-      ! `additional_gr_args_list` must be a <list>.
-      i See `?epiprocess::growth_rate` for available options.
+      ! `skip` must be a scalar of type <logical>.
 
 ---
 
diff --git a/tests/testthat/test-step_growth_rate.R b/tests/testthat/test-step_growth_rate.R
index f2845d812..dc8f04c98 100644
--- a/tests/testthat/test-step_growth_rate.R
+++ b/tests/testthat/test-step_growth_rate.R
@@ -16,8 +16,8 @@ test_that("step_growth_rate validates arguments", {
   expect_snapshot(error = TRUE, step_growth_rate(r, value, prefix = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, id = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, log_scale = 1))
+  expect_snapshot(error = TRUE, step_growth_rate(r, value, na_rm = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, skip = 1))
-  expect_snapshot(error = TRUE, step_growth_rate(r, value, additional_gr_args_list = 1:5))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, replace_Inf = "c"))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, replace_Inf = c(1, 2)))
   expect_silent(step_growth_rate(r, value, replace_Inf = NULL))

From 49271dab411e88fb03ad449b121c3925db6f565a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 13:55:50 -0800
Subject: [PATCH 240/387] geos with all NAs play poorly (can't calculate growth
 rate)

---
 tests/testthat/_snaps/snapshots.md | 84 +++++++++++++++---------------
 tests/testthat/test-snapshots.R    | 12 +++--
 2 files changed, 48 insertions(+), 48 deletions(-)

diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 17191e041..d1cf2df7c 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1237,54 +1237,52 @@
 # arx_classifier snapshots
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
-    "ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in", 
-    "ks", "ky", "la", "ma", "me", "mi", "mn", "mo", "mp", "ms", "mt", 
-    "nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", 
-    "pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", 
-    "wi", "wv", "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 
+    "ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks", 
+    "ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd", 
+    "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr", 
+    "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv", 
+    "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L), levels = c("(-Inf,0.25]", "(0.25, Inf]"), class = "factor"), 
-        forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
-    -53L), class = c("tbl_df", "tbl", "data.frame"))
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]", 
+    "(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+    -51L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
-    "ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in", 
-    "ks", "ky", "la", "ma", "me", "mi", "mn", "mo", "mp", "ms", "mt", 
-    "nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", 
-    "pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", 
-    "wi", "wv", "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
+    "ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks", 
+    "ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd", 
+    "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr", 
+    "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv", 
+    "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L), levels = c("(-Inf,0.25]", "(0.25, Inf]"), class = "factor"), 
-        forecast_date = structure(c(18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994), class = "Date"), target_date = structure(c(19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
-    -53L), class = c("tbl_df", "tbl", "data.frame"))
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]", 
+    "(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994), class = "Date"), target_date = structure(c(19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
+    -51L), class = c("tbl_df", "tbl", "data.frame"))
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 3aecfd6b8..b74ac1192 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -146,16 +146,18 @@ test_that("arx_forecaster output format snapshots", {
 })
 
 test_that("arx_classifier snapshots", {
+  train <- covid_case_death_rates %>%
+    filter(geo_value %nin% c("as", "gu", "mp", "vi"))
   arc1 <- arx_classifier(
-    covid_case_death_rates %>%
+    train %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate")
   )
   expect_snapshot_tibble(arc1$predictions)
-  max_date <- covid_case_death_rates$time_value %>% max()
+  max_date <- train$time_value %>% max()
   arc2 <- arx_classifier(
-    covid_case_death_rates %>%
+    train %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate"),
@@ -164,7 +166,7 @@ test_that("arx_classifier snapshots", {
   expect_snapshot_tibble(arc2$predictions)
   expect_error(
     arc3 <- arx_classifier(
-      covid_case_death_rates %>%
+      train %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),
@@ -174,7 +176,7 @@ test_that("arx_classifier snapshots", {
   )
   expect_error(
     arc4 <- arx_classifier(
-      covid_case_death_rates %>%
+      train %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),

From 4bb4019d3ad70a9499b8f9da5fe57c3cd582e681 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 13:59:21 -0800
Subject: [PATCH 241/387] tests pass

---
 DESCRIPTION                     |  5 ++---
 R/arx_classifier.R              | 35 +++++++++++----------------------
 tests/testthat/test-snapshots.R |  4 ++--
 3 files changed, 16 insertions(+), 28 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e45633b0e..2797b8808 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.8
+Version: 0.1.9
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
@@ -25,7 +25,7 @@ URL: https://github.com/cmu-delphi/epipredict/,
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epidatasets,
-    epiprocess (>= 0.9.0),
+    epiprocess (>= 0.10.4),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:
@@ -73,7 +73,6 @@ Remotes:
     cmu-delphi/epidatasets,
     cmu-delphi/epidatr,
     cmu-delphi/epiprocess,
-    cmu-delphi/epidatasets,
     dajmcdon/smoothqr
 Config/Needs/website: cmu-delphi/delphidocs
 Config/testthat/edition: 3
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 240bc69ee..06d1fedc6 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -58,7 +58,10 @@ arx_classifier <- function(
   if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+      cli_warn(
+        "The specified forecast date {args_list$forecast_date} doesn't match the
+        date from which the forecast is occurring {forecast_date}."
+      )
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -101,7 +104,7 @@ arx_classifier <- function(
 #'
 #' @return An unfit `epi_workflow`.
 #' @export
-#' @seealso [arx_classifier()]
+#' @seealso [arx_classifier()] [arx_class_args_list()]
 #' @examples
 #' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
@@ -154,12 +157,13 @@ arx_class_epi_workflow <- function(
       role = "grp",
       horizon = args_list$horizon,
       method = args_list$method,
-      log_scale = args_list$log_scale,
-      additional_gr_args_list = args_list$additional_gr_args
+      log_scale = args_list$log_scale
     )
   for (l in seq_along(lags)) {
     pred_names <- predictors[l]
-    pred_names <- as.character(glue::glue_data(args_list, "gr_{horizon}_{method}_{pred_names}"))
+    pred_names <- as.character(glue::glue_data(
+      args_list, "gr_{horizon}_{method}_{pred_names}"
+    ))
     r <- step_epi_lag(r, !!pred_names, lag = lags[[l]])
   }
   # ------- outcome
@@ -185,8 +189,7 @@ arx_class_epi_workflow <- function(
           role = "pre-outcome",
           horizon = args_list$horizon,
           method = args_list$method,
-          log_scale = args_list$log_scale,
-          additional_gr_args_list = args_list$additional_gr_args
+          log_scale = args_list$log_scale
         )
     }
   }
@@ -270,9 +273,6 @@ arx_class_epi_workflow <- function(
 #' @param method Character. Options available for growth rate calculation.
 #' @param log_scale Scalar logical. Whether to compute growth rates on the
 #'   log scale.
-#' @param additional_gr_args List. Optional arguments controlling growth rate
-#'   calculation. See [epiprocess::growth_rate()] and the related Vignette for
-#'   more details.
 #' @param check_enough_data_n Integer. A lower limit for the number of rows per
 #'   epi_key that are required for training. If `NULL`, this check is ignored.
 #' @param check_enough_data_epi_keys Character vector. A character vector of
@@ -301,7 +301,6 @@ arx_class_args_list <- function(
     horizon = 7L,
     method = c("rel_change", "linear_reg"),
     log_scale = FALSE,
-    additional_gr_args = list(),
     check_enough_data_n = NULL,
     check_enough_data_epi_keys = NULL,
     ...) {
@@ -320,23 +319,14 @@ arx_class_args_list <- function(
   arg_is_lgl(log_scale)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (!is.list(additional_gr_args)) {
-    cli_abort(c(
-      "`additional_gr_args` must be a {.cls list}.",
-      "!" = "This is a {.cls {class(additional_gr_args)}}.",
-      i = "See `?epiprocess::growth_rate` for available arguments."
-    ))
-  }
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
       cli_warn(
-        paste0(
-          "`forecast_date`  {.val {forecast_date}} +",
-          " `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}."
-        ),
+        "`forecast_date` {.val {forecast_date}} +
+        `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}.",
         class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
       )
     }
@@ -362,7 +352,6 @@ arx_class_args_list <- function(
       horizon,
       method,
       log_scale,
-      additional_gr_args,
       check_enough_data_n,
       check_enough_data_epi_keys
     ),
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index b74ac1192..8b2ae838c 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -148,12 +148,12 @@ test_that("arx_forecaster output format snapshots", {
 test_that("arx_classifier snapshots", {
   train <- covid_case_death_rates %>%
     filter(geo_value %nin% c("as", "gu", "mp", "vi"))
-  arc1 <- arx_classifier(
+  expect_warning(arc1 <- arx_classifier(
     train %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate")
-  )
+  ), "fitted probabilities numerically")
   expect_snapshot_tibble(arc1$predictions)
   max_date <- train$time_value %>% max()
   arc2 <- arx_classifier(

From 45de498cb926063bc5465ed1cdd29073710df708 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 14:34:28 -0800
Subject: [PATCH 242/387] pass all checks, see also cmu-delphi/epiprocess#605

---
 R/arx_classifier.R            |  3 ++-
 R/step_growth_rate.R          |  9 +++++++--
 man/arx_class_args_list.Rd    |  5 -----
 man/arx_class_epi_workflow.Rd |  2 +-
 man/arx_classifier.Rd         |  3 ++-
 man/step_growth_rate.Rd       | 20 ++++++++++++--------
 6 files changed, 24 insertions(+), 18 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 06d1fedc6..1c4f35ef7 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -27,8 +27,9 @@
 #'
 #' @examples
 #' library(dplyr)
+#' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value >= as.Date("2021-11-01"))
+#'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
 #'
 #' out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
 #'
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index d45666391..159c24399 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -30,12 +30,17 @@
 #' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
-#' r <- epi_recipe(covid_case_death_rates) %>%
+#' library(dplyr)
+#' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
+#' rates <- covid_case_death_rates %>%
+#'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
+#'
+#' r <- epi_recipe(rates) %>%
 #'   step_growth_rate(case_rate, death_rate)
 #' r
 #'
 #' r %>%
-#'   prep(covid_case_death_rates) %>%
+#'   prep(rates) %>%
 #'   bake(new_data = NULL)
 step_growth_rate <-
   function(recipe,
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index dbf275355..40bb48ca9 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -17,7 +17,6 @@ arx_class_args_list(
   horizon = 7L,
   method = c("rel_change", "linear_reg"),
   log_scale = FALSE,
-  additional_gr_args = list(),
   check_enough_data_n = NULL,
   check_enough_data_epi_keys = NULL,
   ...
@@ -96,10 +95,6 @@ calculate the growth rate.}
 \item{log_scale}{Scalar logical. Whether to compute growth rates on the
 log scale.}
 
-\item{additional_gr_args}{List. Optional arguments controlling growth rate
-calculation. See \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}} and the related Vignette for
-more details.}
-
 \item{check_enough_data_n}{Integer. A lower limit for the number of rows per
 epi_key that are required for training. If \code{NULL}, this check is ignored.}
 
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index 9f0aae6a1..7497fe95b 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -65,5 +65,5 @@ arx_class_epi_workflow(
 )
 }
 \seealso{
-\code{\link[=arx_classifier]{arx_classifier()}}
+\code{\link[=arx_classifier]{arx_classifier()}} \code{\link[=arx_class_args_list]{arx_class_args_list()}}
 }
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index 94503f3d3..22edc98e9 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -49,8 +49,9 @@ that it estimates a class at a particular target horizon.
 }
 \examples{
 library(dplyr)
+tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 jhu <- covid_case_death_rates \%>\%
-  filter(time_value >= as.Date("2021-11-01"))
+  filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
 
 out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
 
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index 12963f8da..2c98e74e3 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -11,11 +11,11 @@ step_growth_rate(
   horizon = 7,
   method = c("rel_change", "linear_reg"),
   log_scale = FALSE,
+  na_rm = TRUE,
   replace_Inf = NA,
   prefix = "gr_",
   skip = FALSE,
-  id = rand_id("growth_rate"),
-  additional_gr_args_list = list()
+  id = rand_id("growth_rate")
 )
 }
 \arguments{
@@ -41,6 +41,9 @@ growth rates). See \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()
 \item{log_scale}{Should growth rates be estimated using the parameterization
 on the log scale? See details for an explanation. Default is \code{FALSE}.}
 
+\item{na_rm}{Should missing values be removed before the computation? Default
+is \code{FALSE}.}
+
 \item{replace_Inf}{Sometimes, the growth rate calculation can result in
 infinite values (if the denominator is zero, for example). In this case,
 most prediction methods will fail. This argument specifies potential
@@ -59,10 +62,6 @@ Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
 \item{id}{A unique identifier for the step}
-
-\item{additional_gr_args_list}{A list of additional arguments used by
-\code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}}. All \code{...} arguments may be passed here along
-with \code{dup_rm} and \code{na_rm}.}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
@@ -73,12 +72,17 @@ sequence of any existing operations.
 that will generate one or more new columns of derived data.
 }
 \examples{
-r <- epi_recipe(covid_case_death_rates) \%>\%
+library(dplyr)
+tiny_geos <- c("as", "mp", "vi", "gu", "pr")
+rates <- covid_case_death_rates \%>\%
+  filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
+
+r <- epi_recipe(rates) \%>\%
   step_growth_rate(case_rate, death_rate)
 r
 
 r \%>\%
-  prep(covid_case_death_rates) \%>\%
+  prep(rates) \%>\%
   bake(new_data = NULL)
 }
 \seealso{

From 60bdaa88766be41ca1c25253afa2575b77936659 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 14:37:41 -0800
Subject: [PATCH 243/387] mention changes in news

---
 NEWS.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 633086cb0..c8be2132b 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `data(<dataset name>)`, but can be accessed with 
   `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>` 
   or, after loading the package, the name of the dataset alone (#382).
+- Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
+  Corresponding arguments in `step_growth_rate()` have been removed.
 
 ## Improvements
 

From 48ea5ca7b149989ac365504307f9ad5070e9fc08 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 11 Feb 2025 16:29:25 -0800
Subject: [PATCH 244/387] add a print method

---
 NAMESPACE                     |  1 +
 R/canned-epipred.R            | 62 ++++++++++++++++++-----------------
 R/climatological_forecaster.R |  8 +++++
 man/step_climate.Rd           |  6 ++--
 4 files changed, 45 insertions(+), 32 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index d015bd3f0..eb8351050 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -74,6 +74,7 @@ S3method(print,arx_fcast)
 S3method(print,canned_epipred)
 S3method(print,cdc_baseline_fcast)
 S3method(print,check_enough_train_data)
+S3method(print,climate_fcast)
 S3method(print,epi_recipe)
 S3method(print,epi_workflow)
 S3method(print,flat_fcast)
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 8a5baa586..48e984168 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -112,37 +112,39 @@ print.canned_epipred <- function(x, name, ...) {
     "At forecast date{?s}: {.val {fds}},",
     "For target date{?s}: {.val {tds}},"
   ))
-  fit_recipe <- extract_recipe(x$epi_workflow)
-  if (detect_step(fit_recipe, "adjust_latency")) {
-    is_adj_latency <- map_lgl(fit_recipe$steps, function(x) inherits(x, "step_adjust_latency"))
-    latency_step <- fit_recipe$steps[is_adj_latency][[1]]
-    # all steps after adjust_latency
-    later_steps <- fit_recipe$steps[-(1:which(is_adj_latency))]
-    if (latency_step$method == "extend_ahead") {
-      step_names <- "step_epi_ahead"
-      type_str <- "Aheads"
-    } else if (latency_step$method == "extend_lags") {
-      step_names <- "step_epi_lag"
-      type_str <- "Lags"
-    } else {
-      step_names <- ""
-      type_str <- "columns locf"
-    }
-    later_steps[[1]]$columns
-    valid_columns <- later_steps %>%
-      keep(function(x) inherits(x, step_names)) %>%
-      purrr::map("columns") %>%
-      reduce(c)
-    latency_per_base_col <- latency_step$latency_table %>%
-      filter(col_name %in% valid_columns) %>%
-      mutate(latency = abs(latency))
-    if (latency_step$method != "locf" && nrow(latency_per_base_col) > 1) {
-      intro_text <- glue::glue("{type_str} adjusted per column: ")
-    } else if (latency_step$method != "locf") {
-      intro_text <- glue::glue("{type_str} adjusted for ")
+  if ("actions" %in% names(x$pre) && "recipe" %in% names(x$pre$actions)) {
+    fit_recipe <- extract_recipe(x$epi_workflow)
+    if (detect_step(fit_recipe, "adjust_latency")) {
+      is_adj_latency <- map_lgl(fit_recipe$steps, function(x) inherits(x, "step_adjust_latency"))
+      latency_step <- fit_recipe$steps[is_adj_latency][[1]]
+      # all steps after adjust_latency
+      later_steps <- fit_recipe$steps[-(1:which(is_adj_latency))]
+      if (latency_step$method == "extend_ahead") {
+        step_names <- "step_epi_ahead"
+        type_str <- "Aheads"
+      } else if (latency_step$method == "extend_lags") {
+        step_names <- "step_epi_lag"
+        type_str <- "Lags"
+      } else {
+        step_names <- ""
+        type_str <- "columns locf"
+      }
+      later_steps[[1]]$columns
+      valid_columns <- later_steps %>%
+        keep(function(x) inherits(x, step_names)) %>%
+        purrr::map("columns") %>%
+        reduce(c)
+      latency_per_base_col <- latency_step$latency_table %>%
+        filter(col_name %in% valid_columns) %>%
+        mutate(latency = abs(latency))
+      if (latency_step$method != "locf" && nrow(latency_per_base_col) > 1) {
+        intro_text <- glue::glue("{type_str} adjusted per column: ")
+      } else if (latency_step$method != "locf") {
+        intro_text <- glue::glue("{type_str} adjusted for ")
+      }
+      latency_info <- paste0(intro_text, paste(apply(latency_per_base_col, 1, paste0, collapse = "="), collapse = ", "))
+      cli::cli_ul(latency_info)
     }
-    latency_info <- paste0(intro_text, paste(apply(latency_per_base_col, 1, paste0, collapse = "="), collapse = ", "))
-    cli::cli_ul(latency_info)
   }
   cli::cli_text("")
 }
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 8de21b3f7..fb65d3e8a 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -163,6 +163,7 @@ climatological_forecaster <- function(epi_data,
   # fill in some extras for plotting methods, etc.
   ewf <- epi_workflow()
   ewf$trained <- TRUE
+  ewf$original_data <- epi_data
   ewf$pre <- list(mold = list(
     outcomes = select(epi_data, !!sym_outcome),
     extras = list(roles = list(
@@ -240,3 +241,10 @@ climate_args_list <- function(
     class = c("climate_fcast", "alist")
   )
 }
+
+#' @export
+print.climate_fcast <- function(x, ...) {
+  name <- "ARX Forecaster"
+  NextMethod(name = name, ...)
+}
+
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index 0a8bb57d0..7b4fbb726 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -133,8 +133,10 @@ r \%>\%
 # same idea, but using weekly climate
 r <- epi_recipe(covid_case_death_rates) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
-  step_climate(death_rate, forecast_ahead = 1, time_type = "epiweek",
-               window_size = 1L)
+  step_climate(death_rate,
+    forecast_ahead = 1, time_type = "epiweek",
+    window_size = 1L
+  )
 r
 
 r \%>\%

From fb6e07019d0a6383c748fbb2fe814b63e6dff2df Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 12 Feb 2025 14:47:32 -0800
Subject: [PATCH 245/387] fix: update recipes internal function name

---
 R/epi_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index c82ac1676..d1d183cc8 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -448,7 +448,7 @@ prep.epi_recipe <- function(
       "!" = "to avoid addtional warning messages."
     ))
   }
-  training <- recipes:::check_training_set(training, x, fresh)
+  training <- recipes:::validate_training_data(training, x, fresh)
   training <- epi_check_training_set(training, x)
   training <- relocate(training, all_of(key_colnames(training)))
   tr_data <- recipes:::train_info(training)

From d0ca7e24459266e60ba9a3de9613e8850a59f653 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Wed, 12 Feb 2025 14:48:48 -0800
Subject: [PATCH 246/387] doc: bump version

---
 DESCRIPTION | 2 +-
 NEWS.md     | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e45633b0e..593f10386 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.8
+Version: 0.1.9
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 633086cb0..6b98d4d51 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,8 +8,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 - Moved example datasets from being hosted in the package to being loaded
   from the `epidatasets` package. The datasets can no longer be loaded with
-  `data(<dataset name>)`, but can be accessed with 
-  `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>` 
+  `data(<dataset name>)`, but can be accessed with
+  `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>`
   or, after loading the package, the name of the dataset alone (#382).
 
 ## Improvements
@@ -27,6 +27,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - dist_quantiles can have all `NA` values without causing unrelated errors
 - adjust default quantiles throughout so that they match.
 - force `layer_residual_quantiles()` to always include `0.5`.
+- Rename `recipes:::check_training_set()` to `recipes:::validate_training_data()`, as it changed in recipes 1.1.0.
 
 # epipredict 0.1
 

From 7089c2f4c6d66e2cbaf4fd44db44a32a60e16cc6 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Feb 2025 10:44:00 -0600
Subject: [PATCH 247/387] David's suggestions

---
 R/climatological_forecaster.R      |  2 ++
 R/layer_threshold_preds.R          |  1 +
 R/step_climate.R                   | 35 +++++++++++++++++++++++-------
 man/step_adjust_latency.Rd         |  4 ++--
 tests/testthat/test-step_climate.R |  7 ++++--
 5 files changed, 37 insertions(+), 12 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index fb65d3e8a..f1f7d66e1 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -67,6 +67,8 @@ climatological_forecaster <- function(epi_data,
   horizon <- args_list$forecast_horizon
   window_size <- args_list$window_size
   time_type <- args_list$time_type
+  # check that the prediction time type is more granular than epi_data's
+  # time_type
   ttype_ord <- match(time_type, c("day", "epiweek", "week", "month"))
   ttype_ord <- ttype_ord - as.integer(ttype_ord > 2)
   edf_ttype_ord <- match(edf_time_type, c("day", "week", "yearmonth"))
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 7b8ca0252..99f016128 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,6 +61,7 @@ layer_threshold_new <-
 
 
 
+#' restrict various objects to the interval [lower, upper]
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
diff --git a/R/step_climate.R b/R/step_climate.R
index 2f88ca94c..38a5a0ec5 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -1,13 +1,14 @@
 #' Calculate a climatological variable based on the history
 #'
-#' `step_climate()` creates a *specification* of a recipe step
-#' that will generate one or more new columns of derived data. This step
-#' examines all available seasons in the training data and calculates the
-#' a measure of center for the "typical" season. Think of this like with
-#' the weather: to predict the temperature in January in Pittsburgh, PA,
-#' I might look at all previous January's on record, average their temperatures,
-#' and include that in my model. So it is important to _align_ the forecast
-#' horizon with the climate. See the details for more information.
+#' `step_climate()` creates a *specification* of a recipe step that will
+#' generate one or more new columns of derived data. This step examines all
+#' available seasons in the training data and calculates the a measure of center
+#' for the "typical" season. Think of this like with the weather: to predict the
+#' temperature in January in Pittsburgh, PA, I might look at all previous
+#' January's on record, average their temperatures, and include that in my
+#' model. So it is important to _align_ the forecast horizon with the climate.
+#' This step will work best if added after `step_epi_ahead()`, but that is not
+#' strictly required. See the details for more information.
 #'
 #' @details
 #' Construction of a climate predictor can be helpful with strongly seasonal
@@ -101,6 +102,16 @@
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
 #'   bake(new_data = NULL)
+#'
+#' # switching the order is possible if you specify `forecast_ahead`
+#' r <- epi_recipe(covid_case_death_rates) %>%
+#'   step_climate(death_rate, forecast_ahead = 7, time_type = "day") %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   r()
+#'
+#' r %>%
+#'   prep(covid_case_death_rates) %>%
+#'   bake(new_data = NULL)
 step_climate <-
   function(recipe,
            ...,
@@ -315,6 +326,14 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
   invisible(x)
 }
 
+#' group col by .idx values and sum windows around each .idx value
+#' @param .idx the relevant periodic part of time value, e.g. the week number
+#' @param col the list of values indexed by `.idx`
+#' @param weights how much to weigh each particular datapoint
+#' @param aggr the aggregation function, probably Quantile, mean or median
+#' @param window_size the number of .idx entries before and after to include in
+#'   the aggregation
+#' @param modulus the maximum value of `.idx`
 roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus) {
   tib <- tibble(col = col, weights = weights, .idx = .idx) |>
     arrange(.idx) |>
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 75d674472..1fadd3ab3 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 045789483..480eb62b5 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -10,6 +10,7 @@ test_that("roll_modular_multivec works", {
   Median <- function(x, w) median(x, na.rm = TRUE)
 
   # unweighted mean
+  # window of size 0
   expected_res <- tib |>
     mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus) |>
     summarise(climate_pred = weighted.mean(col, w = w), .by = .idx)
@@ -17,13 +18,15 @@ test_that("roll_modular_multivec works", {
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
     expected_res
   )
-  w_size <- 1L
+  # window of size 1, which includes everything
   expected_res <- tibble(.idx = as.double(1:3), climate_pred = mean(tib$col))
   expect_equal(
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 1L, modulus),
     expected_res
   )
+
   # weighted mean
+  # window of size 0
   tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
   expected_res <- tib |>
     mutate(.idx = .idx %% modulus, .idx = .idx + (.idx == 0) * modulus) |>
@@ -32,7 +35,7 @@ test_that("roll_modular_multivec works", {
     roll_modular_multivec(tib$col, tib$.idx, tib$w, Mean, 0, modulus),
     expected_res
   )
-  tib$w <- c(1, 2, 3, 1, 2, 1, 1, 2, 2, 1)
+  # window of size 1
   expected_res <- tibble(
     .idx = as.double(1:3),
     climate_pred = weighted.mean(tib$col, tib$w)

From 60d6aeb5dff90e7ae3f7d2a647ee4472d76250fa Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 13 Feb 2025 14:14:39 -0600
Subject: [PATCH 248/387] time_type defaulting to the epi_df's time_type

---
 R/step_climate.R                   | 7 ++++++-
 tests/testthat/test-step_climate.R | 4 ++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 38a5a0ec5..6169bd2a7 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -117,7 +117,7 @@ step_climate <-
            ...,
            forecast_ahead = "detect",
            role = "predictor",
-           time_type = c("epiweek", "week", "month", "day"),
+           time_type = c("detect", "epiweek", "week", "month", "day"),
            center_method = c("median", "mean"),
            window_size = 3L,
            epi_keys = NULL,
@@ -132,6 +132,11 @@ step_climate <-
     n_outcomes <- sum(recipe$var_info$role == "outcome")
     time_type <- rlang::arg_match(time_type)
     edf_time_type <- attr(recipe$template, "metadata")$time_type
+    if (time_type == "detect") {
+      time_type <- edf_time_type
+    } else {
+      edf_time_type <- attr(recipe$template, "metadata")$time_type
+    }
     if (edf_time_type == "custom") {
       cli_abort("This step only works with daily, weekly, or yearmonth data.")
     }
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 480eb62b5..2544a77ae 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -69,7 +69,7 @@ test_that("prep/bake steps create the correct training data", {
   ) %>%
     as_epi_df()
   # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
-  r <- epi_recipe(x) %>% step_climate(y)
+  r <- epi_recipe(x) %>% step_climate(y, time_type = "epiweek")
   p <- prep(r, x)
 
   expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
@@ -97,7 +97,7 @@ test_that("leading the climate predictor works as expected", {
   r <- epi_recipe(x) %>%
     step_epi_ahead(y, ahead = 14L) %>%
     step_epi_lag(y, lag = c(0, 7L, 14L)) %>%
-    step_climate(y, forecast_ahead = 2L) %>%
+    step_climate(y, forecast_ahead = 2L, time_type = "epiweek") %>%
     # matches the response
     step_epi_naomit()
   p <- prep(r, x)

From dd011a0c700f3472c7e555069d0a6f7cf13615e0 Mon Sep 17 00:00:00 2001
From: Daniel McDonald <dajmcdon@gmail.com>
Date: Fri, 14 Feb 2025 09:31:04 -0800
Subject: [PATCH 249/387] Apply suggestions from code review

Co-authored-by: Dmitry Shemetov <dmtryshmtv@gmail.com>
Co-authored-by: brookslogan <lcbrooks@cs.cmu.edu>
---
 DESCRIPTION | 2 +-
 NEWS.md     | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2797b8808..1994f2cc1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.9
+Version: 0.1.10
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index c8be2132b..d23390855 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -12,7 +12,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>` 
   or, after loading the package, the name of the dataset alone (#382).
 - Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
-  Corresponding arguments in `step_growth_rate()` have been removed.
+  `step_growth_rate()` has lost its `additional_gr_args_list` argument and now
+  has an `na_rm` argument.
 
 ## Improvements
 

From a4ab9312b41ed57c7950e1d9fea7f30ec8788844 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Fri, 14 Feb 2025 09:51:56 -0800
Subject: [PATCH 250/387] snapshot issues following recipes version bump

---
 man/epi_recipe.Rd               | 16 ++++++++--------
 tests/testthat/_snaps/layers.md |  4 ++--
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 98775eae6..b93e4bcf5 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -56,17 +56,17 @@ anything but common roles are \code{"outcome"}, \code{"predictor"},
 }
 \value{
 An object of class \code{recipe} with sub-objects:
-\item{var_info}{A tibble containing information about the original data
-set columns}
+\item{var_info}{A tibble containing information about the original data set
+columns.}
 \item{term_info}{A tibble that contains the current set of terms in the
 data set. This initially defaults to the same data contained in
 \code{var_info}.}
-\item{steps}{A list of \code{step}  or \code{check} objects that define the sequence of
-preprocessing operations that will be applied to data. The default value is
-\code{NULL}}
-\item{template}{A tibble of the data. This is initialized to be the same
-as the data given in the \code{data} argument but can be different after
-the recipe is trained.}
+\item{steps}{A list of \code{step} or \code{check} objects that define the sequence
+of preprocessing operations that will be applied to data. The default value
+is \code{NULL}.}
+\item{template}{A tibble of the data. This is initialized to be the same as
+the data given in the \code{data} argument but can be different after the recipe
+is trained.}
 }
 \description{
 A recipe is a description of the steps to be applied to a data set in
diff --git a/tests/testthat/_snaps/layers.md b/tests/testthat/_snaps/layers.md
index a0474eab6..7f208f2e6 100644
--- a/tests/testthat/_snaps/layers.md
+++ b/tests/testthat/_snaps/layers.md
@@ -3,7 +3,7 @@
     Code
       update(f$layers[[1]], lower = 100)
     Condition
-      Error in `recipes:::update_fields()`:
+      Error in `update()`:
       ! The step you are trying to update, `layer_predict()`, does not have the lower field.
 
 ---
@@ -19,6 +19,6 @@
     Code
       update(f$layers[[2]], bad_param = 100)
     Condition
-      Error in `recipes:::update_fields()`:
+      Error in `update()`:
       ! The step you are trying to update, `layer_threshold()`, does not have the bad_param field.
 

From 759a3b80ea02c389b58cae729c7283c71b58e453 Mon Sep 17 00:00:00 2001
From: David Weber <david.weber2@pm.me>
Date: Fri, 14 Feb 2025 12:41:33 -0800
Subject: [PATCH 251/387] unneeded else branch

Co-authored-by: Daniel McDonald <dajmcdon@gmail.com>
---
 R/step_climate.R | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 6169bd2a7..db66aa32d 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -132,11 +132,7 @@ step_climate <-
     n_outcomes <- sum(recipe$var_info$role == "outcome")
     time_type <- rlang::arg_match(time_type)
     edf_time_type <- attr(recipe$template, "metadata")$time_type
-    if (time_type == "detect") {
-      time_type <- edf_time_type
-    } else {
-      edf_time_type <- attr(recipe$template, "metadata")$time_type
-    }
+    if (time_type == "detect") time_type <- edf_time_type
     if (edf_time_type == "custom") {
       cli_abort("This step only works with daily, weekly, or yearmonth data.")
     }

From 5d062db65c8a553159f4d051ca923768fad37ca7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Sun, 16 Feb 2025 08:41:19 -0800
Subject: [PATCH 252/387] fu styler!!

---
 R/climatological_forecaster.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index f1f7d66e1..7fdc4fa6a 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -249,4 +249,3 @@ print.climate_fcast <- function(x, ...) {
   name <- "ARX Forecaster"
   NextMethod(name = name, ...)
 }
-

From 21c67cd868de94112c4a16988b06805c82ce6f74 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Sun, 16 Feb 2025 08:52:37 -0800
Subject: [PATCH 253/387] deletion from news

---
 NEWS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/NEWS.md b/NEWS.md
index da6a5a1fd..8d0d60651 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -23,6 +23,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Make key column inference more consistent within the package and with current `epiprocess`.
 - Fix `quantile_reg()` producing error when asked to output just median-level predictions.
 - (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
+- Add `reference_date` as an argument to `epi_recipe()`
 - Add `step_climate()` to create "climate" predictor in forecast workflows
 - Add `climatological_forecaster()` to automatically create climate baselines
 

From 05f04906b29bfed2de2a2c2a62bb1767f0f74410 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Sun, 16 Feb 2025 09:30:06 -0800
Subject: [PATCH 254/387] redocument, update snapshots

---
 R/layer_threshold_preds.R          |  2 +-
 R/step_climate.R                   |  4 ++--
 man/roll_modular_multivec.Rd       | 25 +++++++++++++++++++++++++
 man/step_adjust_latency.Rd         |  4 ++--
 man/step_climate.Rd                | 29 ++++++++++++++++++++---------
 tests/testthat/_snaps/snapshots.md |  2 --
 6 files changed, 50 insertions(+), 16 deletions(-)
 create mode 100644 man/roll_modular_multivec.Rd

diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 99f016128..0f7fe7008 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,7 +61,7 @@ layer_threshold_new <-
 
 
 
-#' restrict various objects to the interval [lower, upper]
+# restrict various objects to the interval [lower, upper]
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
diff --git a/R/step_climate.R b/R/step_climate.R
index db66aa32d..72f595e96 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -106,8 +106,8 @@
 #' # switching the order is possible if you specify `forecast_ahead`
 #' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_climate(death_rate, forecast_ahead = 7, time_type = "day") %>%
-#'   step_epi_ahead(death_rate, ahead = 7) %>%
-#'   r()
+#'   step_epi_ahead(death_rate, ahead = 7)
+#' r
 #'
 #' r %>%
 #'   prep(covid_case_death_rates) %>%
diff --git a/man/roll_modular_multivec.Rd b/man/roll_modular_multivec.Rd
new file mode 100644
index 000000000..a26cd5884
--- /dev/null
+++ b/man/roll_modular_multivec.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{roll_modular_multivec}
+\alias{roll_modular_multivec}
+\title{group col by .idx values and sum windows around each .idx value}
+\usage{
+roll_modular_multivec(col, .idx, weights, aggr, window_size, modulus)
+}
+\arguments{
+\item{col}{the list of values indexed by \code{.idx}}
+
+\item{.idx}{the relevant periodic part of time value, e.g. the week number}
+
+\item{weights}{how much to weigh each particular datapoint}
+
+\item{aggr}{the aggregation function, probably Quantile, mean or median}
+
+\item{window_size}{the number of .idx entries before and after to include in
+the aggregation}
+
+\item{modulus}{the maximum value of \code{.idx}}
+}
+\description{
+group col by .idx values and sum windows around each .idx value
+}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 1fadd3ab3..75d674472 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/man/step_climate.Rd b/man/step_climate.Rd
index 7b4fbb726..9b29fdc42 100644
--- a/man/step_climate.Rd
+++ b/man/step_climate.Rd
@@ -9,7 +9,7 @@ step_climate(
   ...,
   forecast_ahead = "detect",
   role = "predictor",
-  time_type = c("epiweek", "week", "month", "day"),
+  time_type = c("detect", "epiweek", "week", "month", "day"),
   center_method = c("median", "mean"),
   window_size = 3L,
   epi_keys = NULL,
@@ -72,14 +72,15 @@ An updated version of \code{recipe} with the new step added to the
 sequence of any existing operations.
 }
 \description{
-\code{step_climate()} creates a \emph{specification} of a recipe step
-that will generate one or more new columns of derived data. This step
-examines all available seasons in the training data and calculates the
-a measure of center for the "typical" season. Think of this like with
-the weather: to predict the temperature in January in Pittsburgh, PA,
-I might look at all previous January's on record, average their temperatures,
-and include that in my model. So it is important to \emph{align} the forecast
-horizon with the climate. See the details for more information.
+\code{step_climate()} creates a \emph{specification} of a recipe step that will
+generate one or more new columns of derived data. This step examines all
+available seasons in the training data and calculates the a measure of center
+for the "typical" season. Think of this like with the weather: to predict the
+temperature in January in Pittsburgh, PA, I might look at all previous
+January's on record, average their temperatures, and include that in my
+model. So it is important to \emph{align} the forecast horizon with the climate.
+This step will work best if added after \code{step_epi_ahead()}, but that is not
+strictly required. See the details for more information.
 }
 \details{
 Construction of a climate predictor can be helpful with strongly seasonal
@@ -139,6 +140,16 @@ r <- epi_recipe(covid_case_death_rates) \%>\%
   )
 r
 
+r \%>\%
+  prep(covid_case_death_rates) \%>\%
+  bake(new_data = NULL)
+
+# switching the order is possible if you specify `forecast_ahead`
+r <- epi_recipe(covid_case_death_rates) \%>\%
+  step_climate(death_rate, forecast_ahead = 7, time_type = "day") \%>\%
+  step_epi_ahead(death_rate, ahead = 7)
+r
+
 r \%>\%
   prep(covid_case_death_rates) \%>\%
   bake(new_data = NULL)
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 53940e1b8..6837f2620 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1207,7 +1207,6 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
-      * Lags adjusted per column: case_rate=3, death_rate=3
       
 
 ---
@@ -1231,7 +1230,6 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
-      * Aheads adjusted for death_rate=3
       
 
 # arx_classifier snapshots

From cfad1f18ba2309c63e84c6a2bd3c1573bdfd241a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Sun, 16 Feb 2025 10:06:35 -0800
Subject: [PATCH 255/387] update snaps for upstream package

---
 tests/testthat/_snaps/step_adjust_latency.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tests/testthat/_snaps/step_adjust_latency.md b/tests/testthat/_snaps/step_adjust_latency.md
index 139e80842..cffb72703 100644
--- a/tests/testthat/_snaps/step_adjust_latency.md
+++ b/tests/testthat/_snaps/step_adjust_latency.md
@@ -36,11 +36,10 @@
       Training data contained 200 data points and no incomplete rows.
       
       -- Operations 
-      1. Adj. extend_lags: case_rate and death_rate w/ forecast date 2021-07-24 |
-      2. Trained
-      3. Lagging: death_rate by 5, 11, 16, (lat adj) | Trained
-      4. Lagging: case_rate by 6, 10, (lat adj) | Trained
-      5. Leading: death_rate by 7 | Trained
+      1. Adj. extend_lags: case_rate death_rate w/ forecast date 2021-07-24 | Trained
+      2. Lagging: death_rate by 5, 11, 16, (lat adj) | Trained
+      3. Lagging: case_rate by 6, 10, (lat adj) | Trained
+      4. Leading: death_rate by 7 | Trained
 
 ---
 

From bece1fe21fd75ea64d2feeffad6798ae7121adc5 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 08:33:50 -0800
Subject: [PATCH 256/387] compute the .pred_distn using residuals. document the
 default

---
 R/climatological_forecaster.R    | 13 +++++++++++--
 man/climatological_forecaster.Rd | 11 +++++++++--
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 7fdc4fa6a..8796d705f 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -7,8 +7,15 @@
 #' forecast, essentially predicting "typical January" behavior by relying on a
 #' long history of such periods rather than heavily using recent data.
 #'
-#' The forecast is taken as the quantiles of the `outcome` in a small window
-#' around the target period, computed over the entire available history.
+#' The point forecast is either the mean or median of the `outcome` in a small
+#' window around the target period, computed over the entire available history,
+#' separately for each key in the `epi_df` (`geo_value` and any additional keys).
+#' The forecast quantiles are computed from the residuals for this point prediction.
+#' By default, the residuals are ungrouped, meaning every key will have the same
+#' shape distribution (though different centers). Note that if your data is not
+#' or comparable scales across keys, this default is likely inappropriate. In that
+#' case, you can choose by which keys quantiles are computed using
+#' `climate_args_list(quantile_by_key = ...)`.
 #'
 #' @inheritParams flatline_forecaster
 #' @param args_list A list of additional arguments as created by the
@@ -124,6 +131,8 @@ climatological_forecaster <- function(epi_data,
     )))
   }
   climate_quantiles <- epi_data %>%
+    left_join(climate_center, by = c(".idx", keys)) %>%
+    mutate(sym_outcome = sym_outcome - .pred) %>%
     select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
     dplyr::reframe(
       roll_modular_multivec(
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
index 26923c9a1..1fff8a0ad 100644
--- a/man/climatological_forecaster.Rd
+++ b/man/climatological_forecaster.Rd
@@ -27,8 +27,15 @@ forecast, essentially predicting "typical January" behavior by relying on a
 long history of such periods rather than heavily using recent data.
 }
 \details{
-The forecast is taken as the quantiles of the \code{outcome} in a small window
-around the target period, computed over the entire available history.
+The point forecast is either the mean or median of the \code{outcome} in a small
+window around the target period, computed over the entire available history,
+separately for each key in the \code{epi_df} (\code{geo_value} and any additional keys).
+The forecast quantiles are computed from the residuals for this point prediction.
+By default, the residuals are ungrouped, meaning every key will have the same
+shape distribution (though different centers). Note that if your data is not
+or comparable scales across keys, this default is likely inappropriate. In that
+case, you can choose by which keys quantiles are computed using
+\code{climate_args_list(quantile_by_key = ...)}.
 }
 \examples{
 rates <- cases_deaths_subset

From 5e3ed566896f126b087324b4415ff2149002dc71 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 08:52:12 -0800
Subject: [PATCH 257/387] fix rlang bug, more detailed examples

---
 R/climatological_forecaster.R    | 24 +++++++++++++++---------
 man/climatological_forecaster.Rd | 19 +++++++++++++------
 2 files changed, 28 insertions(+), 15 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 8796d705f..b8a522b5f 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -27,15 +27,18 @@
 #' @seealso [step_climate()]
 #'
 #' @examples
-#' rates <- cases_deaths_subset
+#' cases <- cases_deaths_subset
 #' # set as_of to the last day in the data
-#' attr(rates, "metadata")$as_of <- as.Date("2021-12-31")
-#' fcast <- climatological_forecaster(rates, "case_rate_7d_av")
+#' # "case_rate_7d_av" is on the same scale for all geographies
+#' attr(cases, "metadata")$as_of <- as.Date("2021-12-31")
+#' fcast <- climatological_forecaster(cases, "case_rate_7d_av")
 #' autoplot(fcast)
 #'
 #' # Compute quantiles separately by location, and a backcast
+#' # "cases" is on different scales by geography, due to population size
+#' # so, it is better to compute quantiles separately
 #' backcast <- climatological_forecaster(
-#'   rates, "case_rate_7d_av",
+#'   cases, "case_rate_7d_av",
 #'   climate_args_list(
 #'     quantile_by_key = "geo_value",
 #'     forecast_date = as.Date("2021-06-01")
@@ -45,10 +48,14 @@
 #'
 #' # compute the climate "daily" rather than "weekly"
 #' # use a two week window (on both sides)
+#' # "cases" is on different scales by geography, due to population size
 #' daily_fcast <- climatological_forecaster(
-#'   rates, "case_rate_7d_av",
+#'   cases, "cases",
 #'   climate_args_list(
-#'     time_type = "day", window_size = 14L, forecast_horizon = 0:30
+#'     quantile_by_key = "geo_value",
+#'     time_type = "day",
+#'     window_size = 14L,
+#'     forecast_horizon = 0:30
 #'   )
 #' )
 #' autoplot(daily_fcast) +
@@ -87,7 +94,7 @@ climatological_forecaster <- function(epi_data,
     ))
   }
   # process the time types
-  sym_outcome <- sym(outcome)
+  sym_outcome <- rlang::data_sym(outcome)
   epi_data <- epi_data %>%
     filter(!is.na(!!outcome)) %>%
     select(all_of(c(key_colnames(epi_data), outcome)))
@@ -123,7 +130,6 @@ climatological_forecaster <- function(epi_data,
     rename(.pred = climate_pred)
   # get the quantiles
   Quantile <- function(x, w) {
-    x <- x - stats::median(x, na.rm = TRUE)
     if (args_list$symmetrize) x <- c(x, -x)
     list(unname(quantile(
       x,
@@ -132,7 +138,7 @@ climatological_forecaster <- function(epi_data,
   }
   climate_quantiles <- epi_data %>%
     left_join(climate_center, by = c(".idx", keys)) %>%
-    mutate(sym_outcome = sym_outcome - .pred) %>%
+    mutate({{outcome}} := !!sym_outcome - .pred) %>%
     select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
     dplyr::reframe(
       roll_modular_multivec(
diff --git a/man/climatological_forecaster.Rd b/man/climatological_forecaster.Rd
index 1fff8a0ad..d773ebce6 100644
--- a/man/climatological_forecaster.Rd
+++ b/man/climatological_forecaster.Rd
@@ -38,15 +38,18 @@ case, you can choose by which keys quantiles are computed using
 \code{climate_args_list(quantile_by_key = ...)}.
 }
 \examples{
-rates <- cases_deaths_subset
+cases <- cases_deaths_subset
 # set as_of to the last day in the data
-attr(rates, "metadata")$as_of <- as.Date("2021-12-31")
-fcast <- climatological_forecaster(rates, "case_rate_7d_av")
+# "case_rate_7d_av" is on the same scale for all geographies
+attr(cases, "metadata")$as_of <- as.Date("2021-12-31")
+fcast <- climatological_forecaster(cases, "case_rate_7d_av")
 autoplot(fcast)
 
 # Compute quantiles separately by location, and a backcast
+# "cases" is on different scales by geography, due to population size
+# so, it is better to compute quantiles separately
 backcast <- climatological_forecaster(
-  rates, "case_rate_7d_av",
+  cases, "case_rate_7d_av",
   climate_args_list(
     quantile_by_key = "geo_value",
     forecast_date = as.Date("2021-06-01")
@@ -56,10 +59,14 @@ autoplot(backcast)
 
 # compute the climate "daily" rather than "weekly"
 # use a two week window (on both sides)
+# "cases" is on different scales by geography, due to population size
 daily_fcast <- climatological_forecaster(
-  rates, "case_rate_7d_av",
+  cases, "cases",
   climate_args_list(
-    time_type = "day", window_size = 14L, forecast_horizon = 0:30
+    quantile_by_key = "geo_value",
+    time_type = "day",
+    window_size = 14L,
+    forecast_horizon = 0:30
   )
 )
 autoplot(daily_fcast) +

From 8238703e40ca412b64b9be7127bd1031fca2bf23 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 09:14:20 -0800
Subject: [PATCH 258/387] pass checks

---
 R/climatological_forecaster.R      |    2 +-
 tests/testthat/_snaps/snapshots.md | 1756 +++++++++++++---------------
 tests/testthat/test-snapshots.R    |    5 +-
 3 files changed, 844 insertions(+), 919 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index b8a522b5f..a5d92f093 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -94,7 +94,7 @@ climatological_forecaster <- function(epi_data,
     ))
   }
   # process the time types
-  sym_outcome <- rlang::data_sym(outcome)
+  sym_outcome <- sym(outcome)
   epi_data <- epi_data %>%
     filter(!is.na(!!outcome)) %>%
     select(all_of(c(key_colnames(epi_data), outcome)))
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 6837f2620..45223fa59 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1303,125 +1303,125 @@
     66.9333338, 70.0729902, 60.5814898, 62.3893953, 94.69837105, 
     52.17697935, 69.7197773, 67.80972325, 57.0623769, 66.48962015, 
     75.3837738, 51.3826096, 67.5469116, 65.9446612, 45.2947493, 67.3274732
-    ), .pred_distn = structure(list(structure(list(values = c(37.581706945, 
-    40.53309523, 68.6068873166667, 81.6909143, 101.23192885, 124.123961743333, 
-    133.701228665), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    ), .pred_distn = structure(list(structure(list(values = c(58.912419115, 
+    63.8539116, 78.6221968916667, 87.443016025, 96.4306031625, 110.01340202, 
+    130.62914179), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
     0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(2.838472345, 5.78986063, 
-    33.8636527166667, 46.9476797, 66.48869425, 89.3807271433334, 
-    98.957994065), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    "vctrs_vctr")), structure(list(values = c(24.169184515, 29.110677, 
+    43.8789622916667, 52.699781425, 61.6873685625, 75.2701674200001, 
+    95.88590719), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
     0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(13.033559045, 15.98494733, 
-    44.0587394166667, 57.1427664, 76.68378095, 99.5758138433333, 
-    109.153080765), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    "vctrs_vctr")), structure(list(values = c(34.364271215, 39.3057637, 
+    54.0740489916667, 62.894868125, 71.8824552625, 85.4652541200001, 
+    106.08099389), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
     0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(11.380172745, 14.33156103, 
-    42.4053531166667, 55.4893801, 75.03039465, 97.9224275433334, 
-    107.499694465), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    "vctrs_vctr")), structure(list(values = c(32.710884915, 37.6523774, 
+    52.4206626916667, 61.241481825, 70.2290689625, 83.8118678200001, 
+    104.42760759), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
     0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(20.848543545, 23.79993183, 
-    51.8737239166667, 64.9577509, 84.49876545, 107.390798343333, 
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-            48.4279616, 62.6388481333333, 72.2835951733333, 77.19023169
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(39.87597843, 44.5957134766667, 
-        51.9830325, 61.8139608, 76.0248473333333, 85.6695943733333, 
-        90.57623089), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+            values = c(8127.2, 8802.53333333333, 9759.5, 11042, 12820.6666666667, 
+            14586.2, 16157.6), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(3962.2, 
+        4750.8, 5905.66666666667, 6992, 7636.66666666667, 8200.53333333333, 
+        8292.8), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
         0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(41.89523223, 
-        46.6149672766667, 54.0022863, 63.8332146, 78.0441011333333, 
-        87.6888481733333, 92.59548469), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(10207, 
+        10426.6666666667, 11727.6666666667, 13035, 14644.3333333333, 
+        15178.4666666667, 16052), quantile_levels = c(0.05, 0.1, 
+        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(18.89110323, 23.6108382766667, 30.9981573, 
-            40.8290856, 55.0399721333333, 64.6847191733333, 69.59135569
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(42.00742563, 46.7271606766667, 
-        54.1144797, 63.945408, 78.1562945333333, 87.8010415733333, 
-        92.70767809), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+            values = c(3788.6, 3853.2, 5119, 5465, 6173.33333333333, 
+            6873.33333333333, 7838.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6094.6, 8547.13333333334, 15299, 20127, 23727.6666666667, 
+            26869.7333333333, 29107.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(17030.4, 18103.3333333333, 19360.6666666667, 
+            20459, 22413, 26277, 28184.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(8010.2, 8685.53333333333, 9642, 10925, 12703.6666666667, 
+            14469.2, 16040.6), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(3788.2, 
+        4576.8, 5346.33333333333, 6818, 7462.66666666667, 8026.53333333333, 
+        8118.8), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
         0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(29.53863982, 
-        33.1954511766667, 41.0448188583333, 50.5057495, 64.8038051083333, 
-        73.5802344266667, 78.079860825), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(10036, 
+        10255.6666666667, 11556.6666666667, 12864, 14080.3333333333, 
+        15007.4666666667, 15881), quantile_levels = c(0.05, 0.1, 
+        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(26.31251602, 29.9693273766667, 37.8186950583333, 
-            47.2796257, 61.5776813083333, 70.3541106266667, 74.853737025
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            values = c(3670.6, 3735.2, 4809, 5266, 6055.33333333333, 
+            6755.33333333333, 7720.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6094.6, 8547.13333333334, 15152, 20127, 23727.6666666667, 
+            26869.7333333333, 29107.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(16349.4, 17422.3333333333, 18679.6666666667, 
+            19778, 21732, 25596, 27503.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(7567.2, 8242.53333333333, 9379.66666666667, 
+            10482, 12260.6666666667, 14026.2, 15597.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3747.2, 4535.8, 5711.33333333333, 6777, 7421.66666666667, 
+            7985.53333333333, 8077.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(9698, 9917.66666666667, 11218.6666666667, 
+            12526, 13742.3333333333, 14669.4666666667, 15543), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3670.6, 3908, 5001, 5347, 6224.33333333333, 
+            6755.33333333333, 7720.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6094.6, 8547.13333333334, 15152, 20127, 23727.6666666667, 
+            26869.7333333333, 29107.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(16298.4, 17371.3333333333, 18628.6666666667, 
+            19727, 21681, 25545, 27452.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6850.2, 7525.53333333333, 8662.66666666667, 
+            9765, 11335.6666666667, 13275.3333333333, 14880.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3736.2, 4524.8, 5700.33333333333, 6714, 7410.66666666667, 
+            7974.53333333333, 8066.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(9287, 9506.66666666667, 10599, 11883, 13331.3333333333, 
+            14258.4666666667, 15132), quantile_levels = c(0.05, 0.1, 
+            0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3402.6, 3640, 4733, 4998, 5853.66666666667, 
+            6487.33333333333, 7452.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(5597.6, 8050.13333333334, 14655, 19574, 22723.3333333333, 
+            26372.7333333333, 28610.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(15152.4, 16225.3333333333, 17482.6666666667, 
+            18581, 20535, 24399, 26306.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6628.2, 7303.53333333333, 8440.66666666667, 
+            9543, 11013, 13053.3333333333, 14658.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3110.2, 3898.8, 5074.33333333333, 6140, 6784.66666666667, 
+            7348.53333333333, 7440.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(8807, 9026.66666666667, 10119, 11403, 13193.3333333333, 
+            13778.4666666667, 14652), quantile_levels = c(0.05, 0.1, 
+            0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3349.6, 3587, 4680, 4945, 5712.66666666667, 
+            6263.53333333333, 7399.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(5468.6, 7921.13333333334, 14526, 19172, 21793.1666666667, 
+            26243.7333333333, 28481.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(14243.4, 15316.3333333333, 16573.6666666667, 
+            17672, 19117.3333333333, 21921.5333333333, 24647), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6569.2, 7244.53333333333, 8381.66666666667, 
+            9484, 10946.6666666667, 12994.3333333333, 14599.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(2365.2, 3153.8, 4329.33333333333, 5343, 6039.66666666667, 
+            6603.53333333333, 6695.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(8524, 8743.66666666667, 9836, 11120, 12910.3333333333, 
+            13495.4666666667, 14369), quantile_levels = c(0.05, 0.1, 
+            0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(2761.6, 2999, 4092, 4438, 5124.66666666667, 
+            5675.53333333333, 6811.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(4336.6, 6789.13333333334, 13394, 17898, 20165.6666666667, 
+            25111.7333333333, 27349.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(13648.4, 14721.3333333333, 15978.6666666667, 
+            17077, 18266, 19933.0666666667, 22864), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(5754.2, 6429.53333333333, 7566.66666666667, 
+            8669, 10050.3333333333, 10882.4, 13120.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(2361.2, 3149.8, 4325.33333333333, 5270, 5878.66666666667, 
+            6580.4, 6683.8), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(8434, 
+        8653.66666666667, 9746, 11030, 12458, 13329.8, 13463.8), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(37.32278042, 40.9795917766667, 
-        48.8289594583333, 58.2898901, 72.5879457083334, 81.3643750266667, 
-        85.864001425), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(42.11476082, 
-        45.7715721766667, 53.6209398583333, 63.0818705, 77.3799261083334, 
-        86.1563554266667, 90.655981825), quantile_levels = c(0.05, 
+        "vctrs_vctr")), structure(list(values = c(2669.6, 2907, 4000, 
+        4346, 5032.66666666667, 5583.53333333333, 6719.8), quantile_levels = c(0.05, 
         0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(19.43507812, 23.0918894766667, 30.9412571583333, 
-            40.4021878, 54.7002434083333, 63.4766727266667, 67.976299125
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(41.11478092, 44.7715922766667, 
-        52.6209599583333, 62.0818906, 76.3799462083334, 85.1563755266667, 
-        89.656001925), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-        "vctrs_vctr", "list"))), row.names = c(NA, -186L), class = c("tbl_df", 
+            values = c(3177.6, 5630.13333333334, 12235, 16653, 18812.6666666667, 
+            23952.7333333333, 26190.4), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list"))), row.names = c(NA, -186L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index fcbdf5f28..eb0439c10 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -196,7 +196,7 @@ test_that("climatological_forecaster snapshots", {
 
   # Compute quantiles separately by location, and a backcast
   backcast <- climatological_forecaster(
-    rates, "case_rate_7d_av",
+    rates, "cases",
     climate_args_list(
       quantile_by_key = "geo_value",
       forecast_date = as.Date("2021-06-01")
@@ -206,8 +206,9 @@ test_that("climatological_forecaster snapshots", {
   # compute the climate "daily" rather than "weekly"
   # use a two week window (on both sides)
   daily_fcast <- climatological_forecaster(
-    rates, "case_rate_7d_av",
+    rates, "cases",
     climate_args_list(
+      quantile_by_key = "geo_value",
       time_type = "day", window_size = 14L, forecast_horizon = 0:30
     )
   )

From 08fcd0116ff16083f9f4ae780f3ac254445d919d Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 10:25:48 -0800
Subject: [PATCH 259/387] once more, FU styler

---
 R/climatological_forecaster.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index a5d92f093..8a584f58a 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -138,7 +138,7 @@ climatological_forecaster <- function(epi_data,
   }
   climate_quantiles <- epi_data %>%
     left_join(climate_center, by = c(".idx", keys)) %>%
-    mutate({{outcome}} := !!sym_outcome - .pred) %>%
+    mutate({{ outcome }} := !!sym_outcome - .pred) %>%
     select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
     dplyr::reframe(
       roll_modular_multivec(

From e4c43f84c86d339c49d134abab95d030b6959b8a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:49:55 -0800
Subject: [PATCH 260/387] move epiprocess to imports

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1994f2cc1..8c47a1d44 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -25,7 +25,6 @@ URL: https://github.com/cmu-delphi/epipredict/,
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epidatasets,
-    epiprocess (>= 0.10.4),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:
@@ -33,6 +32,7 @@ Imports:
     cli,
     distributional,
     dplyr,
+    epiprocess (>= 0.10.4),
     generics,
     ggplot2,
     glue,

From b90b9be6ceed77485a9ed06ecd49ab0e9eafdc75 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:51:39 -0800
Subject: [PATCH 261/387] adjust reexports to include epiprocess necessaries,
 also frequently used dplyr, tibble, tidyr

---
 R/reexports-tidymodels.R | 27 ------------------
 R/reexports.R            | 59 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 59 insertions(+), 27 deletions(-)
 delete mode 100644 R/reexports-tidymodels.R
 create mode 100644 R/reexports.R

diff --git a/R/reexports-tidymodels.R b/R/reexports-tidymodels.R
deleted file mode 100644
index 3b28ac5c5..000000000
--- a/R/reexports-tidymodels.R
+++ /dev/null
@@ -1,27 +0,0 @@
-#' @importFrom generics fit
-#' @export
-generics::fit
-
-#' @importFrom generics forecast
-#' @export
-generics::forecast
-
-#' @importFrom recipes prep
-#' @export
-recipes::prep
-
-#' @importFrom recipes bake
-#' @export
-recipes::bake
-
-#' @importFrom recipes rand_id
-#' @export
-recipes::rand_id
-
-#' @importFrom tibble tibble
-#' @export
-tibble::tibble
-
-#' @importFrom generics tidy
-#' @export
-generics::tidy
diff --git a/R/reexports.R b/R/reexports.R
new file mode 100644
index 000000000..02a377f7c
--- /dev/null
+++ b/R/reexports.R
@@ -0,0 +1,59 @@
+#' @importFrom dplyr filter
+#' @export
+dplyr::filter
+
+#' @importFrom dplyr mutate
+#' @export
+dplyr::mutate
+
+#' @importFrom dplyr rename
+#' @export
+dplyr::rename
+
+#' @importFrom dplyr select
+#' @export
+dplyr::select
+
+#' @importFrom epiprocess as_epi_df
+#' @export
+epiprocess::as_epi_df
+
+#' @importFrom epiprocess key_colnames
+#' @export
+epiprocess::key_colnames
+
+#' @importFrom generics fit
+#' @export
+generics::fit
+
+#' @importFrom generics forecast
+#' @export
+generics::forecast
+
+#' @importFrom generics tidy
+#' @export
+generics::tidy
+
+#' @importFrom recipes prep
+#' @export
+recipes::prep
+
+#' @importFrom recipes bake
+#' @export
+recipes::bake
+
+#' @importFrom recipes rand_id
+#' @export
+recipes::rand_id
+
+#' @importFrom tibble as_tibble
+#' @export
+tibble::as_tibble
+
+#' @importFrom tibble tibble
+#' @export
+tibble::tibble
+
+#' @importFrom tidyr unnest
+#' @export
+tidyr::unnest

From 6efe75949daa847f011dc4624505892aaf6571e9 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:54:01 -0800
Subject: [PATCH 262/387] remove unnecessary library calls from examples

---
 R/arx_classifier.R               |  2 --
 R/arx_forecaster.R               |  1 -
 R/autoplot.R                     |  1 -
 R/cdc_baseline_forecaster.R      | 38 ++++++++++++++---------------
 R/epi_recipe.R                   | 19 ++++-----------
 R/frosting.R                     |  3 ---
 R/layer_add_forecast_date.R      |  1 -
 R/layer_add_target_date.R        |  1 -
 R/layer_cdc_flatline_quantiles.R | 41 ++++++++++++++++----------------
 R/layer_naomit.R                 |  1 -
 R/layer_point_from_distn.R       |  1 -
 R/layer_population_scaling.R     |  3 +--
 R/layer_predict.R                |  1 -
 R/layer_quantile_distn.R         |  1 -
 R/layer_residual_quantiles.R     |  1 -
 R/layer_threshold_preds.R        |  2 +-
 R/layers.R                       |  1 -
 R/make_grf_quantiles.R           |  1 -
 R/make_smooth_quantile_reg.R     |  3 +--
 R/model-methods.R                |  1 -
 R/pivot_quantiles.R              |  2 --
 R/step_epi_slide.R               |  1 -
 R/step_growth_rate.R             |  1 -
 R/step_population_scaling.R      |  3 +--
 R/tidy.R                         |  3 +--
 25 files changed, 48 insertions(+), 85 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 1c4f35ef7..d1aa292dd 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -26,7 +26,6 @@
 #' @seealso [arx_class_epi_workflow()], [arx_class_args_list()]
 #'
 #' @examples
-#' library(dplyr)
 #' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
@@ -107,7 +106,6 @@ arx_classifier <- function(
 #' @export
 #' @seealso [arx_classifier()] [arx_class_args_list()]
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 5397ca881..21b1e63e2 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -95,7 +95,6 @@ arx_forecaster <- function(
 #' @seealso [arx_forecaster()]
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-12-01"))
 #'
diff --git a/R/autoplot.R b/R/autoplot.R
index c0e3c68dd..8e902f535 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -28,7 +28,6 @@ ggplot2::autoplot
 #'
 #' @name autoplot-epipred
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index 44090bf79..a97eece87 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -23,6 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
+#' library(epiprocess)
 #' weekly_deaths <- covid_case_death_rates %>%
 #'   select(geo_value, time_value, death_rate) %>%
 #'   left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
@@ -36,25 +37,24 @@
 #' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths_7dsum")
 #' preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 #'
-#' if (require(ggplot2)) {
-#'   forecast_date <- unique(preds$forecast_date)
-#'   four_states <- c("ca", "pa", "wa", "ny")
-#'   preds %>%
-#'     filter(geo_value %in% four_states) %>%
-#'     ggplot(aes(target_date)) +
-#'     geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-#'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-#'     geom_line(aes(y = .pred), color = "orange") +
-#'     geom_line(
-#'       data = weekly_deaths %>% filter(geo_value %in% four_states),
-#'       aes(x = time_value, y = deaths_7dsum)
-#'     ) +
-#'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-#'     labs(x = "Date", y = "Weekly deaths") +
-#'     facet_wrap(~geo_value, scales = "free_y") +
-#'     theme_bw() +
-#'     geom_vline(xintercept = forecast_date)
-#' }
+#' library(ggplot2)
+#' forecast_date <- unique(preds$forecast_date)
+#' four_states <- c("ca", "pa", "wa", "ny")
+#' preds %>%
+#'   filter(geo_value %in% four_states) %>%
+#'   ggplot(aes(target_date)) +
+#'   geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+#'   geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+#'   geom_line(aes(y = .pred), color = "orange") +
+#'   geom_line(
+#'     data = weekly_deaths %>% filter(geo_value %in% four_states),
+#'     aes(x = time_value, y = deaths_7dsum)
+#'   ) +
+#'   scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+#'   labs(x = "Date", y = "Weekly deaths") +
+#'   facet_wrap(~geo_value, scales = "free_y") +
+#'   theme_bw() +
+#'   geom_vline(xintercept = forecast_date)
 cdc_baseline_forecaster <- function(
     epi_data,
     outcome,
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index d1d183cc8..367cf78f0 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -45,19 +45,14 @@ epi_recipe <- function(x, ...) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
-#' library(recipes)
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-08-01") %>%
-#'   arrange(geo_value, time_value)
+#'   filter(time_value > "2021-08-01")
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-#'   step_naomit(all_predictors()) %>%
-#'   # below, `skip` means we don't do this at predict time
-#'   step_naomit(all_outcomes(), skip = TRUE)
+#'   step_epi_naomit(all_predictors())
 #'
 #' r
 epi_recipe.epi_df <- function(x,
@@ -271,19 +266,14 @@ is_epi_recipe <- function(x) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
-#' library(recipes)
-#'
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-08-01") %>%
-#'   arrange(geo_value, time_value)
+#'   filter(time_value > "2021-08-01")
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-#'   step_naomit(all_predictors()) %>%
-#'   step_naomit(all_outcomes(), skip = TRUE)
+#'   step_epi_naomit()
 #'
 #' workflow <- epi_workflow() %>%
 #'   add_epi_recipe(r)
@@ -355,7 +345,6 @@ update_epi_recipe <- function(x, recipe, ..., blueprint = default_epi_recipe_blu
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' library(workflows)
 #'
 #' jhu <- covid_case_death_rates %>%
diff --git a/R/frosting.R b/R/frosting.R
index ef32b4a3b..cb0fa916e 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -8,7 +8,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
@@ -127,7 +126,6 @@ update_frosting <- function(x, frosting, ...) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
@@ -262,7 +260,6 @@ new_frosting <- function() {
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' # Toy example to show that frosting can be created and added for postprocessing
 #' f <- frosting()
 #' wf <- epi_workflow() %>% add_frosting(f)
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 78cbb79a3..3e62bafb0 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -21,7 +21,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index f8b6a06e5..bd97862ca 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -24,7 +24,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 13938d837..5211170ad 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -55,13 +55,13 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
+#' library(recipes)
 #' r <- epi_recipe(covid_case_death_rates) %>%
 #'   # data is "daily", so we fit this to 1 ahead, the result will contain
 #'   # 1 day ahead residuals
 #'   step_epi_ahead(death_rate, ahead = 1L, skip = TRUE) %>%
-#'   recipes::update_role(death_rate, new_role = "predictor") %>%
-#'   recipes::add_role(time_value, geo_value, new_role = "predictor")
+#'   update_role(death_rate, new_role = "predictor") %>%
+#'   add_role(time_value, geo_value, new_role = "predictor")
 #'
 #' forecast_date <- max(covid_case_death_rates$time_value)
 #'
@@ -82,24 +82,23 @@
 #'   pivot_quantiles_wider(.pred_distn) %>%
 #'   mutate(target_date = forecast_date + ahead)
 #'
-#' if (require("ggplot2")) {
-#'   four_states <- c("ca", "pa", "wa", "ny")
-#'   preds %>%
-#'     filter(geo_value %in% four_states) %>%
-#'     ggplot(aes(target_date)) +
-#'     geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-#'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-#'     geom_line(aes(y = .pred), color = "orange") +
-#'     geom_line(
-#'       data = covid_case_death_rates %>% filter(geo_value %in% four_states),
-#'       aes(x = time_value, y = death_rate)
-#'     ) +
-#'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-#'     labs(x = "Date", y = "Death rate") +
-#'     facet_wrap(~geo_value, scales = "free_y") +
-#'     theme_bw() +
-#'     geom_vline(xintercept = forecast_date)
-#' }
+#' library(ggplot2)
+#' four_states <- c("ca", "pa", "wa", "ny")
+#' preds %>%
+#'   filter(geo_value %in% four_states) %>%
+#'   ggplot(aes(target_date)) +
+#'   geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+#'   geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+#'   geom_line(aes(y = .pred), color = "orange") +
+#'   geom_line(
+#'     data = covid_case_death_rates %>% filter(geo_value %in% four_states),
+#'     aes(x = time_value, y = death_rate)
+#'   ) +
+#'   scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+#'   labs(x = "Date", y = "Death rate") +
+#'   facet_wrap(~geo_value, scales = "free_y") +
+#'   theme_bw() +
+#'   geom_vline(xintercept = forecast_date)
 layer_cdc_flatline_quantiles <- function(
     frosting,
     ...,
diff --git a/R/layer_naomit.R b/R/layer_naomit.R
index 2b111a6f9..5071bcc5c 100644
--- a/R/layer_naomit.R
+++ b/R/layer_naomit.R
@@ -11,7 +11,6 @@
 #' @return an updated `frosting` postprocessor
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index c433717bb..5af434892 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -16,7 +16,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index badd073f8..a3183a0ae 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -52,8 +52,7 @@
 #' @return an updated `frosting` postprocessor
 #' @export
 #' @examples
-#' library(dplyr)
-#' jhu <- epidatasets::cases_deaths_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/layer_predict.R b/R/layer_predict.R
index b59be5f03..0f4c33e11 100644
--- a/R/layer_predict.R
+++ b/R/layer_predict.R
@@ -16,7 +16,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index b39b58f4a..2d991a20f 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -22,7 +22,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 96ad88411..9a811e531 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -15,7 +15,6 @@
 #'   residual quantiles added to the prediction
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 7b8ca0252..4bfb5252f 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -22,9 +22,9 @@
 #' @return an updated `frosting` postprocessor
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value < "2021-03-08", geo_value %in% c("ak", "ca", "ar"))
+#'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
diff --git a/R/layers.R b/R/layers.R
index 752f014c5..01cb19dba 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -41,7 +41,6 @@ layer <- function(subclass, ..., .prefix = "layer_") {
 #' in the layer, and the values are the new values to update the layer with.
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index fbd221d22..b1792adfd 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -60,7 +60,6 @@
 #'
 #' # -- a more complicated task
 #'
-#' library(dplyr)
 #' dat <- covid_case_death_rates %>%
 #'   filter(time_value > as.Date("2021-10-01"))
 #' rec <- epi_recipe(dat) %>%
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index f31ade3cb..28576b954 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -31,11 +31,10 @@
 #' y <- sin(x) + rnorm(length(x), sd = .1)
 #' fd <- x[length(x) - 20]
 #' XY <- smoothqr::lagmat(y[1:(length(y) - 20)], c(-20:20))
-#' XY <- tibble::as_tibble(XY)
+#' XY <- as_tibble(XY)
 #' qr_spec <- smooth_quantile_reg(quantile_levels = c(.2, .5, .8), outcome_locations = 20:1)
 #' tt <- qr_spec %>% fit_xy(x = XY[, 21:41], y = XY[, 1:20])
 #'
-#' library(tidyr)
 #' library(dplyr)
 #' pl <- predict(
 #'   object = tt,
diff --git a/R/model-methods.R b/R/model-methods.R
index a575bd591..dd08d3efc 100644
--- a/R/model-methods.R
+++ b/R/model-methods.R
@@ -32,7 +32,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 2a9e0d4e0..d4ef3d57d 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -6,8 +6,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
-#' library(tidyr)
 #' edf <- covid_case_death_rates[1:3, ]
 #' edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
 #'
diff --git a/R/step_epi_slide.R b/R/step_epi_slide.R
index 274ce2451..4cf0e7acf 100644
--- a/R/step_epi_slide.R
+++ b/R/step_epi_slide.R
@@ -36,7 +36,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
 #' rec <- epi_recipe(jhu) %>%
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 159c24399..5497c2957 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -30,7 +30,6 @@
 #' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
-#' library(dplyr)
 #' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 #' rates <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index 6047f5c4c..bb60d039a 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -49,8 +49,7 @@
 #' @return Scales raw data by the population
 #' @export
 #' @examples
-#' library(dplyr)
-#' jhu <- epidatasets::cases_deaths_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'
diff --git a/R/tidy.R b/R/tidy.R
index 47c6efa68..3969f9dd6 100644
--- a/R/tidy.R
+++ b/R/tidy.R
@@ -26,7 +26,6 @@
 #'  `type` (the method, e.g. "predict", "naomit"), and a character column `id`.
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
@@ -37,7 +36,7 @@
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #' latest <- get_test_data(recipe = r, x = jhu)
-
+#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_naomit(.pred)

From 766e79b5123143caad4b506f108013902dd7cbd1 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:54:31 -0800
Subject: [PATCH 263/387] delete layer creator (dev only, unused)

---
 R/create-layer.R       | 45 ------------------------------------------
 inst/templates/layer.R | 44 -----------------------------------------
 2 files changed, 89 deletions(-)
 delete mode 100644 R/create-layer.R
 delete mode 100644 inst/templates/layer.R

diff --git a/R/create-layer.R b/R/create-layer.R
deleted file mode 100644
index 7179cb7e3..000000000
--- a/R/create-layer.R
+++ /dev/null
@@ -1,45 +0,0 @@
-#' Create a new layer
-#'
-#' This function creates the skeleton for a new `frosting` layer. When called
-#' inside a package, it will create an R script in the `R/` directory,
-#' fill in the name of the layer, and open the file.
-#'
-#' @inheritParams usethis::use_test
-#'
-#' @importFrom rlang %||%
-#' @noRd
-#' @keywords internal
-#' @examples
-#' \dontrun{
-#'
-#' # Note: running this will write `layer_strawberry.R` to
-#' # the `R/` directory of your current project
-#' create_layer("strawberry")
-#' }
-#'
-create_layer <- function(name = NULL, open = rlang::is_interactive()) {
-  name <- name %||% usethis:::get_active_r_file(path = "R")
-  if (substr(name, 1, 5) == "layer") {
-    nn <- substring(name, 6)
-    if (substr(nn, 1, 1) == "_") nn <- substring(nn, 2)
-    cli_abort(
-      c('`name` should not begin with "layer" or "layer_".',
-        i = 'Did you mean to use `create_layer("{ nn }")`?'
-      )
-    )
-  }
-  layer_name <- name
-  name <- paste0("layer_", name)
-  name <- usethis:::slug(name, "R")
-  usethis:::check_file_name(name)
-  path <- fs::path("R", name)
-  if (!fs::file_exists(path)) {
-    usethis::use_template(
-      "layer.R",
-      save_as = path,
-      data = list(name = layer_name), open = FALSE,
-      package = "epipredict"
-    )
-  }
-  usethis::edit_file(usethis::proj_path(path), open = open)
-}
diff --git a/inst/templates/layer.R b/inst/templates/layer.R
deleted file mode 100644
index 59556db5f..000000000
--- a/inst/templates/layer.R
+++ /dev/null
@@ -1,44 +0,0 @@
-layer_{{ name }} <- function(frosting, # mandatory
-                             ...,
-                             args, # add as many as you need
-                             more_args,
-                             id = rand_id("{{{ name }}}")) {
-
-  # validate any additional arguments here
-
-  # if you don't need ... then uncomment the line below
-  ## rlang::check_dots_empty()
-  add_layer(
-    frosting,
-    layer_{{{ name }}}_new(
-      terms = dplyr::enquos(...), # remove if ... should be empty
-      args,
-      id = id
-    )
-  )
-}
-
-layer_{{{ name }}}_new <- function(terms, args, more_args, id) {
-  layer("{{{ name }}}",
-        terms = terms,
-        args = args,
-        more_args = more_args,
-        id = id)
-}
-
-#' @export
-slather.layer_{{{ name }}} <-
-  function(object, components, workflow, new_data, ...) {
-    rlang::check_dots_empty()
-
-    # if layer_ used ... in tidyselect, we need to evaluate it now
-    exprs <- rlang::expr(c(!!!object$terms))
-    pos <- tidyselect::eval_select(exprs, components$predictions)
-    col_names <- names(pos)
-    # now can select with `tidyselect::all_of(col_names)`
-
-    # add additional necessary processing steps here
-
-    # always return components
-    components
-  }

From f411be67c4c0f20bd6e150d93add8e4ff31748a5 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:54:55 -0800
Subject: [PATCH 264/387] imports from epiprocess

---
 R/epipredict-package.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index b4b9973bf..9ee832580 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -9,11 +9,11 @@
 #' @importFrom dplyr full_join relocate summarise everything
 #' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
+#' @importFrom epiprocess growth_rate growth_rate_params is_epi_df
 #' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
 #' @importFrom stats poly predict lm residuals quantile
 #' @importFrom tibble as_tibble
-na_chr <- NA_character_
 ## usethis namespace: end
 NULL

From 58980f48d46715189a4dc69035d7dbcb19733d03 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:55:10 -0800
Subject: [PATCH 265/387] switch ::: to :: from epiprocess

---
 R/utils-latency.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils-latency.R b/R/utils-latency.R
index d854cb9cf..353d0fdd0 100644
--- a/R/utils-latency.R
+++ b/R/utils-latency.R
@@ -199,7 +199,7 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   if (is.null(columns_to_complete)) {
     columns_to_complete <- setdiff(names(x), key_colnames(x))
   }
-  itval <- epiprocess:::guess_period(c(x$time_value, end_date), "time_value")
+  itval <- epiprocess::guess_period(c(x$time_value, end_date), "time_value")
   # get the time values we need to fill in
   completed_time_values <- x %>%
     group_by(across(all_of(groups))) %>%

From 9e93bb8719405f062b5628306038cbe06007564a Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:55:46 -0800
Subject: [PATCH 266/387] adjust some vignette libraries

---
 vignettes/backtesting.Rmd |  9 +++++----
 vignettes/update.Rmd      | 19 +++++++++++--------
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 765a684f4..70d18fa30 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -11,8 +11,9 @@ vignette: >
 source("_common.R")
 ```
 
-```{r pkgs, include=FALSE, echo=TRUE}
+```{r pkgs, message=FALSE}
 library(epipredict)
+library(epiprocess)
 library(epidatr)
 library(data.table)
 library(dplyr)
@@ -66,7 +67,7 @@ vignette mentioned above).
 # Select the `percent_cli` column from the data archive
 doctor_visits <- archive_cases_dv_subset$DT %>%
   select(geo_value, time_value, version, percent_cli) %>%
-  tidyr::drop_na(percent_cli) %>%
+  drop_na(percent_cli) %>%
   as_epi_archive(compactify = TRUE)
 ```
 
@@ -166,7 +167,7 @@ forecasts %>%
     doctor_visits %>%
       epix_as_of(doctor_visits$versions_end) %>%
       group_by(geo_value) %>%
-      tidyr::fill(percent_cli),
+      fill(percent_cli),
     by = c("geo_value", "target_date" = "time_value")
   ) %>%
   select(geo_value, forecast_date, .pred, `0.05`, `0.95`, percent_cli)
@@ -376,7 +377,7 @@ linear regression with those from using boosted regression trees.
 
 ```{r get-can-fc, warning = FALSE}
 aheads <- c(7, 14, 21, 28)
-canada_archive <- epidatasets::can_prov_cases
+canada_archive <- can_prov_cases
 canada_archive_faux <- epix_as_of(canada_archive, canada_archive$versions_end) %>%
   mutate(version = time_value) %>%
   as_epi_archive()
diff --git a/vignettes/update.Rmd b/vignettes/update.Rmd
index 3d97da049..02fe71be7 100644
--- a/vignettes/update.Rmd
+++ b/vignettes/update.Rmd
@@ -14,6 +14,9 @@ source("_common.R")
 ```{r setup, message=FALSE}
 library(epipredict)
 library(recipes)
+library(dplyr)
+library(workflows)
+library(parsnip)
 ```
 
 In this vignette, we will state the main goal of the add/update/remove and
@@ -68,7 +71,7 @@ South Carolina.
 
 ```{r}
 jhu <- covid_case_death_rates %>%
-  dplyr::filter(time_value >= as.Date("2021-11-01"), geo_value %in% c("ak", "ca", "ny", "sc"))
+  filter(time_value >= as.Date("2021-11-01"), geo_value %in% c("ak", "ca", "ny", "sc"))
 
 jhu
 ```
@@ -100,7 +103,7 @@ We may then go on to add the fitted linear model to our `epi_workflow`:
 
 ```{r}
 # Fit a linear model
-wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
+wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)
 
 wf
 ```
@@ -139,7 +142,7 @@ using `workflows::remove_recipe()` and then inspect the class of `wf`:
 
 ```{r}
 wf %>% class() # class before
-workflows::remove_recipe(wf) %>% class() # class after removing recipe using workflows function
+remove_recipe(wf) %>% class() # class after removing recipe using workflows function
 ```
 
 We can observe that `wf` is no longer an `epi_workflow` and a `workflow`. It has
@@ -167,7 +170,7 @@ fit as before:
 
 ```{r}
 # fit linear model
-wf <- Update_model(wf, parsnip::linear_reg()) %>% fit(jhu)
+wf <- Update_model(wf, linear_reg()) %>% fit(jhu)
 wf
 ```
 
@@ -243,8 +246,8 @@ number in the order of operations, which can be obtained by inspecting `r2` or
 the tidy summary of it:
 
 ```{r}
-workflows::extract_preprocessor(wf) # step_epi_ahead is the third step in r2
-tidy(workflows::extract_preprocessor(wf)) # tidy tibble summary of r2
+extract_preprocessor(wf) # step_epi_ahead is the third step in r2
+tidy(extract_preprocessor(wf)) # tidy tibble summary of r2
 
 wf <- wf %>% adjust_epi_recipe(which_step = 3, ahead = 14)
 ```
@@ -268,7 +271,7 @@ variable, we would specify the step number of 2 in `which_step`.
 ```{r}
 wf <- wf %>% adjust_epi_recipe(which_step = 2, lag = c(0, 1, 7, 14, 21))
 
-workflows::extract_preprocessor(wf)
+extract_preprocessor(wf)
 ```
 
 We could adjust a recipe directly in the same way as we adjust a recipe in a
@@ -289,7 +292,7 @@ with a new `epi_recipe` that has undergone the adjustment
 ```{r}
 r2 <- adjust_epi_recipe(r2, which_step = 2, lag = 0:21)
 
-workflows::extract_preprocessor(wf)
+extract_preprocessor(wf)
 ```
 
 ## Adjust a single layer of a `frosting`

From cd2c583f2bacc98f961bdec6df3cf5cf9a63b2b4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 11:56:10 -0800
Subject: [PATCH 267/387] redocument

---
 NAMESPACE                           | 12 +++++++++
 man/Add_model.Rd                    |  1 -
 man/add_epi_recipe.Rd               |  9 ++-----
 man/add_frosting.Rd                 |  1 -
 man/adjust_epi_recipe.Rd            |  1 -
 man/adjust_frosting.Rd              |  1 -
 man/arx_class_epi_workflow.Rd       |  1 -
 man/arx_classifier.Rd               |  1 -
 man/arx_fcast_epi_workflow.Rd       |  1 -
 man/autoplot-epipred.Rd             |  1 -
 man/cdc_baseline_forecaster.Rd      | 38 +++++++++++++-------------
 man/epi_recipe.Rd                   |  9 ++-----
 man/frosting.Rd                     |  1 -
 man/grf_quantiles.Rd                |  1 -
 man/layer_add_forecast_date.Rd      |  1 -
 man/layer_add_target_date.Rd        |  1 -
 man/layer_cdc_flatline_quantiles.Rd | 41 ++++++++++++++---------------
 man/layer_naomit.Rd                 |  1 -
 man/layer_point_from_distn.Rd       |  1 -
 man/layer_population_scaling.Rd     |  3 +--
 man/layer_predict.Rd                |  1 -
 man/layer_quantile_distn.Rd         |  1 -
 man/layer_residual_quantiles.Rd     |  1 -
 man/layer_threshold.Rd              |  2 +-
 man/nested_quantiles.Rd             |  2 --
 man/reexports.Rd                    | 20 +++++++++++---
 man/smooth_quantile_reg.Rd          |  3 +--
 man/step_epi_slide.Rd               |  1 -
 man/step_growth_rate.Rd             |  1 -
 man/step_population_scaling.Rd      |  3 +--
 man/tidy.frosting.Rd                |  2 +-
 man/update.layer.Rd                 |  1 -
 32 files changed, 77 insertions(+), 87 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 3a1fed508..003cd48e7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -144,6 +144,8 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
+export(as_epi_df)
+export(as_tibble)
 export(autoplot)
 export(bake)
 export(cdc_baseline_args_list)
@@ -160,6 +162,7 @@ export(extract_argument)
 export(extract_frosting)
 export(extract_layers)
 export(extrapolate_quantiles)
+export(filter)
 export(fit)
 export(flatline)
 export(flatline_args_list)
@@ -171,6 +174,7 @@ export(get_test_data)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
+export(key_colnames)
 export(layer)
 export(layer_add_forecast_date)
 export(layer_add_target_date)
@@ -184,6 +188,7 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(mutate)
 export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
@@ -195,6 +200,8 @@ export(rand_id)
 export(remove_epi_recipe)
 export(remove_frosting)
 export(remove_model)
+export(rename)
+export(select)
 export(slather)
 export(smooth_quantile_reg)
 export(step_adjust_latency)
@@ -208,6 +215,7 @@ export(step_population_scaling)
 export(step_training_window)
 export(tibble)
 export(tidy)
+export(unnest)
 export(update_epi_recipe)
 export(update_frosting)
 export(update_model)
@@ -256,8 +264,12 @@ importFrom(dplyr,summarize)
 importFrom(dplyr,tibble)
 importFrom(dplyr,tribble)
 importFrom(dplyr,ungroup)
+importFrom(epiprocess,as_epi_df)
 importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
+importFrom(epiprocess,growth_rate_params)
+importFrom(epiprocess,is_epi_df)
+importFrom(epiprocess,key_colnames)
 importFrom(generics,augment)
 importFrom(generics,fit)
 importFrom(generics,forecast)
diff --git a/man/Add_model.Rd b/man/Add_model.Rd
index 641bd7676..8f316532e 100644
--- a/man/Add_model.Rd
+++ b/man/Add_model.Rd
@@ -71,7 +71,6 @@ aliases with the lower-case names. However, in the event that
 properly.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/add_epi_recipe.Rd b/man/add_epi_recipe.Rd
index b74267524..0135cfd6f 100644
--- a/man/add_epi_recipe.Rd
+++ b/man/add_epi_recipe.Rd
@@ -38,19 +38,14 @@ Add an \code{epi_recipe} to a workflow
 default blueprint to automatically handle \link[epiprocess:epi_df]{epiprocess::epi_df} data.
 }
 \examples{
-library(dplyr)
-library(recipes)
-
 jhu <- covid_case_death_rates \%>\%
-  filter(time_value > "2021-08-01") \%>\%
-  arrange(geo_value, time_value)
+  filter(time_value > "2021-08-01")
 
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(case_rate, lag = c(0, 7, 14)) \%>\%
-  step_naomit(all_predictors()) \%>\%
-  step_naomit(all_outcomes(), skip = TRUE)
+  step_epi_naomit()
 
 workflow <- epi_workflow() \%>\%
   add_epi_recipe(r)
diff --git a/man/add_frosting.Rd b/man/add_frosting.Rd
index 00b899b7c..825747487 100644
--- a/man/add_frosting.Rd
+++ b/man/add_frosting.Rd
@@ -26,7 +26,6 @@ update_frosting(x, frosting, ...)
 Add frosting to a workflow
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/adjust_epi_recipe.Rd b/man/adjust_epi_recipe.Rd
index 0ed7148a3..b1dc6c60e 100644
--- a/man/adjust_epi_recipe.Rd
+++ b/man/adjust_epi_recipe.Rd
@@ -52,7 +52,6 @@ must be inputted as \code{...}. See the examples below for brief
 illustrations of the different types of updates.
 }
 \examples{
-library(dplyr)
 library(workflows)
 
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/adjust_frosting.Rd b/man/adjust_frosting.Rd
index 3b855a9af..e6b33f3d1 100644
--- a/man/adjust_frosting.Rd
+++ b/man/adjust_frosting.Rd
@@ -35,7 +35,6 @@ must be inputted as \code{...}. See the examples below for brief
 illustrations of the different types of updates.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index 7497fe95b..9b048463c 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -47,7 +47,6 @@ before fitting and predicting. Supplying a trainer to the function
 may alter the returned \code{epi_workflow} object but can be omitted.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index 22edc98e9..d78700df3 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -48,7 +48,6 @@ This is an autoregressive classification model for
 that it estimates a class at a particular target horizon.
 }
 \examples{
-library(dplyr)
 tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
diff --git a/man/arx_fcast_epi_workflow.Rd b/man/arx_fcast_epi_workflow.Rd
index c2e38218f..0d208858a 100644
--- a/man/arx_fcast_epi_workflow.Rd
+++ b/man/arx_fcast_epi_workflow.Rd
@@ -42,7 +42,6 @@ may alter the returned \code{epi_workflow} object (e.g., if you intend to
 use \code{\link[=quantile_reg]{quantile_reg()}}) but can be omitted.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-12-01"))
 
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 066f55383..087f25dba 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -70,7 +70,6 @@ will be shown as well. Unfit workflows will result in an error, (you
 can simply call \code{autoplot()} on the original \code{epi_df}).
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 
diff --git a/man/cdc_baseline_forecaster.Rd b/man/cdc_baseline_forecaster.Rd
index e7cefda2d..c201bd99d 100644
--- a/man/cdc_baseline_forecaster.Rd
+++ b/man/cdc_baseline_forecaster.Rd
@@ -38,6 +38,7 @@ This forecaster is meant to produce exactly the CDC Baseline used for
 }
 \examples{
 library(dplyr)
+library(epiprocess)
 weekly_deaths <- covid_case_death_rates \%>\%
   select(geo_value, time_value, death_rate) \%>\%
   left_join(state_census \%>\% select(pop, abbr), by = c("geo_value" = "abbr")) \%>\%
@@ -51,23 +52,22 @@ weekly_deaths <- covid_case_death_rates \%>\%
 cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths_7dsum")
 preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 
-if (require(ggplot2)) {
-  forecast_date <- unique(preds$forecast_date)
-  four_states <- c("ca", "pa", "wa", "ny")
-  preds \%>\%
-    filter(geo_value \%in\% four_states) \%>\%
-    ggplot(aes(target_date)) +
-    geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-    geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-    geom_line(aes(y = .pred), color = "orange") +
-    geom_line(
-      data = weekly_deaths \%>\% filter(geo_value \%in\% four_states),
-      aes(x = time_value, y = deaths_7dsum)
-    ) +
-    scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-    labs(x = "Date", y = "Weekly deaths") +
-    facet_wrap(~geo_value, scales = "free_y") +
-    theme_bw() +
-    geom_vline(xintercept = forecast_date)
-}
+library(ggplot2)
+forecast_date <- unique(preds$forecast_date)
+four_states <- c("ca", "pa", "wa", "ny")
+preds \%>\%
+  filter(geo_value \%in\% four_states) \%>\%
+  ggplot(aes(target_date)) +
+  geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+  geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+  geom_line(aes(y = .pred), color = "orange") +
+  geom_line(
+    data = weekly_deaths \%>\% filter(geo_value \%in\% four_states),
+    aes(x = time_value, y = deaths_7dsum)
+  ) +
+  scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+  labs(x = "Date", y = "Weekly deaths") +
+  facet_wrap(~geo_value, scales = "free_y") +
+  theme_bw() +
+  geom_vline(xintercept = forecast_date)
 }
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index b93e4bcf5..90f6ea45f 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -75,19 +75,14 @@ around \code{\link[recipes:recipe]{recipes::recipe()}} to properly handle the ad
 columns present in an \code{epi_df}
 }
 \examples{
-library(dplyr)
-library(recipes)
 jhu <- covid_case_death_rates \%>\%
-  filter(time_value > "2021-08-01") \%>\%
-  arrange(geo_value, time_value)
+  filter(time_value > "2021-08-01")
 
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(case_rate, lag = c(0, 7, 14)) \%>\%
-  step_naomit(all_predictors()) \%>\%
-  # below, `skip` means we don't do this at predict time
-  step_naomit(all_outcomes(), skip = TRUE)
+  step_epi_naomit(all_predictors())
 
 r
 }
diff --git a/man/frosting.Rd b/man/frosting.Rd
index 8534bc6d6..e36100bae 100644
--- a/man/frosting.Rd
+++ b/man/frosting.Rd
@@ -22,7 +22,6 @@ to hold steps for postprocessing predictions.
 The arguments are currently placeholders and must be NULL
 }
 \examples{
-library(dplyr)
 # Toy example to show that frosting can be created and added for postprocessing
 f <- frosting()
 wf <- epi_workflow() \%>\% add_frosting(f)
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index ad33ce11a..2e4b8bcbb 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -84,7 +84,6 @@ predict(out, new_data = tib[1:5, ]) \%>\%
 
 # -- a more complicated task
 
-library(dplyr)
 dat <- covid_case_death_rates \%>\%
   filter(time_value > as.Date("2021-10-01"))
 rec <- epi_recipe(dat) \%>\%
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index cc92e9a71..14c7864bc 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -38,7 +38,6 @@ less than the maximum \code{as_of} value (from the data used pre-processing,
 model fitting, and postprocessing), an appropriate warning will be thrown.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index 37416d24e..c9f43c5f1 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -43,7 +43,6 @@ every dataset used (prep, training, and prediction).
 }
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%
diff --git a/man/layer_cdc_flatline_quantiles.Rd b/man/layer_cdc_flatline_quantiles.Rd
index 632fdb65e..35a6c03e8 100644
--- a/man/layer_cdc_flatline_quantiles.Rd
+++ b/man/layer_cdc_flatline_quantiles.Rd
@@ -84,13 +84,13 @@ the future. This version continues to use the same set of residuals, and
 adds them on to produce wider intervals as \code{ahead} increases.
 }
 \examples{
-library(dplyr)
+library(recipes)
 r <- epi_recipe(covid_case_death_rates) \%>\%
   # data is "daily", so we fit this to 1 ahead, the result will contain
   # 1 day ahead residuals
   step_epi_ahead(death_rate, ahead = 1L, skip = TRUE) \%>\%
-  recipes::update_role(death_rate, new_role = "predictor") \%>\%
-  recipes::add_role(time_value, geo_value, new_role = "predictor")
+  update_role(death_rate, new_role = "predictor") \%>\%
+  add_role(time_value, geo_value, new_role = "predictor")
 
 forecast_date <- max(covid_case_death_rates$time_value)
 
@@ -111,22 +111,21 @@ preds <- preds \%>\%
   pivot_quantiles_wider(.pred_distn) \%>\%
   mutate(target_date = forecast_date + ahead)
 
-if (require("ggplot2")) {
-  four_states <- c("ca", "pa", "wa", "ny")
-  preds \%>\%
-    filter(geo_value \%in\% four_states) \%>\%
-    ggplot(aes(target_date)) +
-    geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-    geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-    geom_line(aes(y = .pred), color = "orange") +
-    geom_line(
-      data = covid_case_death_rates \%>\% filter(geo_value \%in\% four_states),
-      aes(x = time_value, y = death_rate)
-    ) +
-    scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-    labs(x = "Date", y = "Death rate") +
-    facet_wrap(~geo_value, scales = "free_y") +
-    theme_bw() +
-    geom_vline(xintercept = forecast_date)
-}
+library(ggplot2)
+four_states <- c("ca", "pa", "wa", "ny")
+preds \%>\%
+  filter(geo_value \%in\% four_states) \%>\%
+  ggplot(aes(target_date)) +
+  geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+  geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+  geom_line(aes(y = .pred), color = "orange") +
+  geom_line(
+    data = covid_case_death_rates \%>\% filter(geo_value \%in\% four_states),
+    aes(x = time_value, y = death_rate)
+  ) +
+  scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+  labs(x = "Date", y = "Death rate") +
+  facet_wrap(~geo_value, scales = "free_y") +
+  theme_bw() +
+  geom_vline(xintercept = forecast_date)
 }
diff --git a/man/layer_naomit.Rd b/man/layer_naomit.Rd
index 06e09d4b0..eea81959f 100644
--- a/man/layer_naomit.Rd
+++ b/man/layer_naomit.Rd
@@ -24,7 +24,6 @@ an updated \code{frosting} postprocessor
 Omit \code{NA}s from predictions or other columns
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/layer_point_from_distn.Rd b/man/layer_point_from_distn.Rd
index bde2323b1..329f62d5b 100644
--- a/man/layer_point_from_distn.Rd
+++ b/man/layer_point_from_distn.Rd
@@ -34,7 +34,6 @@ information, so one should usually call this AFTER \code{layer_quantile_distn()}
 or set the \code{name} argument to something specific.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 083dd6b48..1b4c3d7d9 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -79,8 +79,7 @@ passed will \emph{multiply} the selected variables while the \code{rate_rescalin
 argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
-library(dplyr)
-jhu <- epidatasets::cases_deaths_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/layer_predict.Rd b/man/layer_predict.Rd
index db771b882..9a7f12a46 100644
--- a/man/layer_predict.Rd
+++ b/man/layer_predict.Rd
@@ -58,7 +58,6 @@ postprocessing. This would typically be the first layer in a \code{frosting}
 postprocessor.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index ed4762fa3..21c38b812 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -45,7 +45,6 @@ If these engines were used, then this layer will grab out estimated
 (or extrapolated) quantiles at the requested quantile values.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index 4efea525f..16e16cea8 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -40,7 +40,6 @@ residual quantiles added to the prediction
 Creates predictions based on residual quantiles
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
diff --git a/man/layer_threshold.Rd b/man/layer_threshold.Rd
index 702c5d713..875390ced 100644
--- a/man/layer_threshold.Rd
+++ b/man/layer_threshold.Rd
@@ -40,9 +40,9 @@ smaller than the lower threshold or higher than the upper threshold equal
 to the threshold values.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value < "2021-03-08", geo_value \%in\% c("ak", "ca", "ar"))
+
 r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
index 0fa0fe8cc..b1571450e 100644
--- a/man/nested_quantiles.Rd
+++ b/man/nested_quantiles.Rd
@@ -16,8 +16,6 @@ a list-col
 Turn a vector of quantile distributions into a list-col
 }
 \examples{
-library(dplyr)
-library(tidyr)
 edf <- covid_case_death_rates[1:3, ]
 edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
 
diff --git a/man/reexports.Rd b/man/reexports.Rd
index f6849a53c..f5d28dbc6 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -1,16 +1,24 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/autoplot.R, R/reexports-tidymodels.R
+% Please edit documentation in R/autoplot.R, R/reexports.R
 \docType{import}
 \name{reexports}
 \alias{reexports}
 \alias{autoplot}
+\alias{filter}
+\alias{mutate}
+\alias{rename}
+\alias{select}
+\alias{as_epi_df}
+\alias{key_colnames}
 \alias{fit}
 \alias{forecast}
+\alias{tidy}
 \alias{prep}
 \alias{bake}
 \alias{rand_id}
+\alias{as_tibble}
 \alias{tibble}
-\alias{tidy}
+\alias{unnest}
 \title{Objects exported from other packages}
 \keyword{internal}
 \description{
@@ -18,12 +26,18 @@ These objects are imported from other packages. Follow the links
 below to see their documentation.
 
 \describe{
+  \item{dplyr}{\code{\link[dplyr]{filter}}, \code{\link[dplyr]{mutate}}, \code{\link[dplyr]{rename}}, \code{\link[dplyr]{select}}}
+
+  \item{epiprocess}{\code{\link[epiprocess:epi_df]{as_epi_df}}, \code{\link[epiprocess]{key_colnames}}}
+
   \item{generics}{\code{\link[generics]{fit}}, \code{\link[generics]{forecast}}, \code{\link[generics]{tidy}}}
 
   \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
 
   \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}}
 
-  \item{tibble}{\code{\link[tibble]{tibble}}}
+  \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
+
+  \item{tidyr}{\code{\link[tidyr]{unnest}}}
 }}
 
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 90b2c104f..06a52ad55 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -50,11 +50,10 @@ x <- -99:99 / 100 * 2 * pi
 y <- sin(x) + rnorm(length(x), sd = .1)
 fd <- x[length(x) - 20]
 XY <- smoothqr::lagmat(y[1:(length(y) - 20)], c(-20:20))
-XY <- tibble::as_tibble(XY)
+XY <- as_tibble(XY)
 qr_spec <- smooth_quantile_reg(quantile_levels = c(.2, .5, .8), outcome_locations = 20:1)
 tt <- qr_spec \%>\% fit_xy(x = XY[, 21:41], y = XY[, 1:20])
 
-library(tidyr)
 library(dplyr)
 pl <- predict(
   object = tt,
diff --git a/man/step_epi_slide.Rd b/man/step_epi_slide.Rd
index b8e4cedb1..4e7ba8bed 100644
--- a/man/step_epi_slide.Rd
+++ b/man/step_epi_slide.Rd
@@ -80,7 +80,6 @@ that will generate one or more new columns of derived data by "sliding"
 a computation along existing data.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-01-01"), geo_value \%in\% c("ca", "ny"))
 rec <- epi_recipe(jhu) \%>\%
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index 2c98e74e3..e76da1100 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -72,7 +72,6 @@ sequence of any existing operations.
 that will generate one or more new columns of derived data.
 }
 \examples{
-library(dplyr)
 tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 rates <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index c41ec7fcc..427e896ed 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -92,8 +92,7 @@ passed will \emph{divide} the selected variables while the \code{rate_rescaling}
 argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
-library(dplyr)
-jhu <- epidatasets::cases_deaths_subset \%>\%
+jhu <- cases_deaths_subset \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ca", "ny")) \%>\%
   select(geo_value, time_value, cases)
 
diff --git a/man/tidy.frosting.Rd b/man/tidy.frosting.Rd
index 8152b1440..3f9b0e377 100644
--- a/man/tidy.frosting.Rd
+++ b/man/tidy.frosting.Rd
@@ -37,7 +37,6 @@ method for the operation exists). Note that this is a modified
 version of the \code{tidy} method for a recipe.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 
@@ -48,6 +47,7 @@ r <- epi_recipe(jhu) \%>\%
 
 wf <- epi_workflow(r, parsnip::linear_reg()) \%>\% fit(jhu)
 latest <- get_test_data(recipe = r, x = jhu)
+
 f <- frosting() \%>\%
   layer_predict() \%>\%
   layer_naomit(.pred)
diff --git a/man/update.layer.Rd b/man/update.layer.Rd
index f151beea9..878c9bb11 100644
--- a/man/update.layer.Rd
+++ b/man/update.layer.Rd
@@ -18,7 +18,6 @@ will replace the elements of the same name in the actual post-processing layer.
 Analogous to \code{update.step()} from the \code{recipes} package.
 }
 \examples{
-library(dplyr)
 jhu <- covid_case_death_rates \%>\%
   filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
 r <- epi_recipe(jhu) \%>\%

From 1ce825f27681c8597d346c1e17be708541e66abc Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 12:38:53 -0800
Subject: [PATCH 268/387] pass checks and redocument

---
 NAMESPACE                     | 4 ++++
 R/epi_recipe.R                | 2 +-
 R/flusight_hub_formatter.R    | 1 +
 R/make_smooth_quantile_reg.R  | 1 -
 R/reexports.R                 | 8 ++++++++
 man/epi_recipe.Rd             | 2 +-
 man/flusight_hub_formatter.Rd | 1 +
 man/reexports.Rd              | 4 +++-
 man/smooth_quantile_reg.Rd    | 1 -
 9 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 003cd48e7..565239a6b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -192,8 +192,10 @@ export(mutate)
 export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
+export(pivot_longer)
 export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
+export(pivot_wider)
 export(prep)
 export(quantile_reg)
 export(rand_id)
@@ -328,6 +330,8 @@ importFrom(tidyr,crossing)
 importFrom(tidyr,drop_na)
 importFrom(tidyr,expand_grid)
 importFrom(tidyr,fill)
+importFrom(tidyr,pivot_longer)
+importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 367cf78f0..dae445f53 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -52,7 +52,7 @@ epi_recipe <- function(x, ...) {
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-#'   step_epi_naomit(all_predictors())
+#'   step_epi_naomit()
 #'
 #' r
 epi_recipe.epi_df <- function(x,
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index b3e31822c..feac624be 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -55,6 +55,7 @@ abbr_to_location <- function(abbr) {
 #'
 #' @examples
 #' library(dplyr)
+#' library(epiprocess)
 #' weekly_deaths <- covid_case_death_rates %>%
 #'   filter(
 #'     time_value >= as.Date("2021-09-01"),
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index 28576b954..b56a67b37 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -35,7 +35,6 @@
 #' qr_spec <- smooth_quantile_reg(quantile_levels = c(.2, .5, .8), outcome_locations = 20:1)
 #' tt <- qr_spec %>% fit_xy(x = XY[, 21:41], y = XY[, 1:20])
 #'
-#' library(dplyr)
 #' pl <- predict(
 #'   object = tt,
 #'   new_data = XY[max(which(complete.cases(XY[, 21:41]))), 21:41]
diff --git a/R/reexports.R b/R/reexports.R
index 02a377f7c..2cc7aa9fa 100644
--- a/R/reexports.R
+++ b/R/reexports.R
@@ -54,6 +54,14 @@ tibble::as_tibble
 #' @export
 tibble::tibble
 
+#' @importFrom tidyr pivot_longer
+#' @export
+tidyr::pivot_longer
+
+#' @importFrom tidyr pivot_wider
+#' @export
+tidyr::pivot_wider
+
 #' @importFrom tidyr unnest
 #' @export
 tidyr::unnest
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 90f6ea45f..fefd162e1 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -82,7 +82,7 @@ r <- epi_recipe(jhu) \%>\%
   step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
   step_epi_ahead(death_rate, ahead = 7) \%>\%
   step_epi_lag(case_rate, lag = c(0, 7, 14)) \%>\%
-  step_epi_naomit(all_predictors())
+  step_epi_naomit()
 
 r
 }
diff --git a/man/flusight_hub_formatter.Rd b/man/flusight_hub_formatter.Rd
index f48d33d65..2c283d7b5 100644
--- a/man/flusight_hub_formatter.Rd
+++ b/man/flusight_hub_formatter.Rd
@@ -42,6 +42,7 @@ format for this forecast task is \href{https://github.com/cdcepi/FluSight-foreca
 }
 \examples{
 library(dplyr)
+library(epiprocess)
 weekly_deaths <- covid_case_death_rates \%>\%
   filter(
     time_value >= as.Date("2021-09-01"),
diff --git a/man/reexports.Rd b/man/reexports.Rd
index f5d28dbc6..aedd9e46b 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -18,6 +18,8 @@
 \alias{rand_id}
 \alias{as_tibble}
 \alias{tibble}
+\alias{pivot_longer}
+\alias{pivot_wider}
 \alias{unnest}
 \title{Objects exported from other packages}
 \keyword{internal}
@@ -38,6 +40,6 @@ below to see their documentation.
 
   \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
 
-  \item{tidyr}{\code{\link[tidyr]{unnest}}}
+  \item{tidyr}{\code{\link[tidyr]{pivot_longer}}, \code{\link[tidyr]{pivot_wider}}, \code{\link[tidyr]{unnest}}}
 }}
 
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 06a52ad55..eb715be63 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -54,7 +54,6 @@ XY <- as_tibble(XY)
 qr_spec <- smooth_quantile_reg(quantile_levels = c(.2, .5, .8), outcome_locations = 20:1)
 tt <- qr_spec \%>\% fit_xy(x = XY[, 21:41], y = XY[, 1:20])
 
-library(dplyr)
 pl <- predict(
   object = tt,
   new_data = XY[max(which(complete.cases(XY[, 21:41]))), 21:41]

From 3903960bea4459694fefa557ae2feb51add08ef2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 12:42:29 -0800
Subject: [PATCH 269/387] add to news and bump version

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 8c47a1d44..b8d83a6a6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.10
+Version: 0.1.11
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 28f5bb99e..014ea3066 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -14,6 +14,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
   `step_growth_rate()` has lost its `additional_gr_args_list` argument and now
   has an `na_rm` argument.
+- Moves `epiprocess` out of depends (#440). No internals have changed, but downstream
+  users may need to add `library(epiprocess)` to existing code.
 
 ## Improvements
 

From a5dea728b90788e43d32aba1aeca8e544c52713e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 27 Feb 2025 13:39:06 -0600
Subject: [PATCH 270/387] step_climate leap weeks/days

---
 R/step_climate.R                   | 84 +++++++++++++++++++++++++-----
 tests/testthat/test-step_climate.R | 74 ++++++++++++++++++++++++--
 2 files changed, 142 insertions(+), 16 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 72f595e96..5bb2556a1 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -179,10 +179,10 @@ step_climate <-
     arg_is_lgl_scalar(skip)
 
     time_aggr <- switch(time_type,
-      epiweek = lubridate::epiweek,
-      week = lubridate::isoweek,
+      epiweek = epiweek_leap,
+      week = isoweek_leap,
       month = lubridate::month,
-      day = lubridate::yday
+      day = yday_leap
     )
 
     recipes::add_step(
@@ -258,22 +258,31 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   wts <- wts %||% rep(1, nrow(training))
 
   modulus <- switch(x$time_type,
-    epiweek = 53L,
-    week = 53L,
+    epiweek = 52L, # only sometimes true
+    week = 52L,
     month = 12L,
-    day = 365L
+    day = 365L # only sometimes true
   )
 
   fn <- switch(x$center_method,
     mean = function(x, w) stats::weighted.mean(x, w, na.rm = TRUE),
     median = function(x, w) median(x, na.rm = TRUE)
   )
-
-  climate_table <- training %>%
+  # suppose it's week 52, and there is no week 53 this year; then
+  # as originally written for 1 week ahead this grabs from week 52+1 %% 53
+  # which will be week 53, not week 1.
+  ahead_period <- switch(x$time_type,
+    epiweek = lubridate::weeks(x$forecast_ahead),
+    week = lubridate::weeks(x$forecast_ahead),
+    month = lubridate::months(x$forecast_ahead),
+    day = lubridate::days(x$forecast_ahead),
+  )
+  climate_table <-
+    training %>%
     mutate(
-      .idx = x$time_aggr(time_value), .weights = wts,
-      .idx = (.idx - x$forecast_ahead) %% modulus,
-      .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
+      .idx = time_value %m-% ahead_period,
+      .idx = x$time_aggr(.idx),
+      .weights = wts
     ) %>%
     select(.idx, .weights, all_of(c(col_names, x$epi_keys))) %>%
     tidyr::pivot_longer(all_of(unname(col_names))) %>%
@@ -341,8 +350,33 @@ roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus
     tidyr::nest(data = c(col, weights), .by = .idx)
   out <- double(nrow(tib))
   for (iter in seq_along(out)) {
-    entries <- (iter - window_size):(iter + window_size) %% modulus
+    # +1 from 1-indexing
+    entries <- ((iter - window_size):(iter + window_size) %% modulus)
     entries[entries == 0] <- modulus
+    # note that because we are 1-indexing, we're looking for indices that are 1
+    # larger than the actual day/week in the year
+    if (modulus == 365) {
+      # we need to grab just the window around the leap day on the leap day
+      if (iter == 366) {
+        entries <- ((60 - window_size):(60 + window_size - 1) %% modulus)
+        entries <- c(entries, 366)
+      } else if ((60 %in% entries) || (61 %in% entries)) {
+        # if we're on the Feb/March boundary for daily data, we need to add in the
+        # leap day data
+        entries <- c(entries, 366)
+      }
+    } else if (modulus == 52) {
+      # we need to grab just the window around the leap week on the leap week
+      if (iter == 53) {
+        entries <- ((53 - window_size):(53 + window_size - 1) %% 52)
+        entries[entries == 0] <- 52
+        entries <- c(entries, 53)
+      } else if ((52 %in% entries) || (1 %in% entries)) {
+        # if we're on the year boundary for weekly data, we need to add in the
+        # leap week data (which is the extra week at the end)
+        entries <- c(entries, 53)
+      }
+    }
     out[iter] <- with(
       purrr::list_rbind(tib$data[entries]),
       aggr(col, weights)
@@ -350,3 +384,29 @@ roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus
   }
   tibble(.idx = unique(tib$.idx), climate_pred = out)
 }
+
+
+#' a function that assigns Feb 29th to -1, and aligns all other dates the same
+#' number in the year, regardless of whether it's a leap year
+#' @keywords internal
+yday_leap <- function(time_value) {
+  ifelse(
+    !lubridate::leap_year(time_value),
+    lubridate::yday(time_value),
+    ifelse(
+      (lubridate::month(time_value) == 2) & lubridate::day(time_value) == 29,
+      999,
+      lubridate::yday(time_value) - (lubridate::month(time_value) > 2)
+    )
+  )
+}
+#' epiweek, but it assigns week 53 the value of -1 instead so it mirrors the assignments in yday_leap
+#' @keywords internal
+epiweek_leap <- function(time_value) {
+  lubridate::epiweek(time_value) %>% ifelse(. == 53, 999, .)
+}
+#' isoweek, but it assigns week 53 the value of -1 instead so it mirrors the assignments in yday_leap
+#' @keywords internal
+isoweek_leap <- function(time_value) {
+  lubridate::isoweek(time_value) %>% ifelse(. == 53, 999, .)
+}
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 2544a77ae..3fa0fd475 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -1,3 +1,17 @@
+test_that("yday_leap works", {
+  # feburary 29th is assigned a negative value
+  expect_equal(yday_leap(as.Date("2024-02-29")), 999)
+  # before that is normal
+  expect_equal(yday_leap(as.Date("2024-02-28")), 31 + 28)
+
+  # after that is decreased by 1 (so matches non leap years)
+  expect_equal(
+    yday_leap(as.Date("2024-05-28")),
+    lubridate::yday(as.Date("2022-05-28"))
+  )
+  # off leap years have the right value
+  expect_equal(yday_leap(as.Date("2023-05-28")), 31 + 28 + 31 + 30 + 28)
+})
 test_that("roll_modular_multivec works", {
   tib <- tibble(
     col = c(1, 2, 3, 3.5, 4, 1, -2, 4, 1, 0),
@@ -72,7 +86,32 @@ test_that("prep/bake steps create the correct training data", {
   r <- epi_recipe(x) %>% step_climate(y, time_type = "epiweek")
   p <- prep(r, x)
 
-  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
+  expected_res <- tibble(.idx = c(1:52, 999), climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2))
+  expect_equal(p$steps[[1]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expected_bake <- x %>%
+    mutate(.idx = epiweek_leap(time_value)) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    select(-.idx)
+  expect_equal(b, expected_bake)
+})
+
+
+test_that("prep/bake steps create the correct training data for non leapweeks", {
+  single_yr <- seq(as.Date("2023-01-01"), as.Date("2023-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    # shift by 2 days to match the epiweeks of 2021
+    y = rep(c(1, 1, rep(c(1:26, 26:2), each = 7), 1, 1, 1, 1, 1, 1), times = 2L)
+  ) %>%
+    as_epi_df()
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
+  r <- epi_recipe(x) %>% step_climate(y, time_type = "epiweek")
+  p <- prep(r, x)
+
+  expected_res <- tibble(.idx = 1:52, climate_y = c(2, 2:25, 25, 25, 25:2, 2))
   expect_equal(p$steps[[1]]$climate_table, expected_res)
 
   b <- bake(p, new_data = NULL)
@@ -84,6 +123,34 @@ test_that("prep/bake steps create the correct training data", {
 })
 
 
+
+test_that("prep/bake steps create the correct training data for daily data", {
+  single_yr <- seq(as.Date("2020-01-01"), as.Date("2020-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    y = rep(c(1:183, 184:2), times = 2L)
+  ) %>%
+    as_epi_df()
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
+  r <- epi_recipe(x) %>% step_climate(y, time_type = "day")
+  p <- prep(r, x)
+
+  expected_res <- tibble(
+    .idx = c(1:365, 999),
+    climate_y = c(3, 3, 3:(60 - 4), 57.5:64.5, 66:181, rep(182, 5), 181:3, 3, 60)
+  )
+  expect_equal(p$steps[[1]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expected_bake <- x %>%
+    mutate(.idx = yday_leap(time_value)) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    select(-.idx)
+  expect_equal(b, expected_bake)
+})
+
+
 test_that("leading the climate predictor works as expected", {
   single_yr <- seq(as.Date("2021-01-01"), as.Date("2021-12-31"), by = "1 day")
   x <- tibble(
@@ -102,10 +169,9 @@ test_that("leading the climate predictor works as expected", {
     step_epi_naomit()
   p <- prep(r, x)
 
-  expected_res <- tibble(.idx = 1:53, climate_y = c(2, 2:25, 25, 25, 25:2, 2, 2)) %>%
+  expected_res <- tibble(.idx = c(1:52, 999), climate_y = c(2, 2, 3, 4, 4.5, 5.5, 7:25, 25, 25, 25:2, 2, 2)) %>%
     mutate(
-      .idx = (.idx - 2L) %% 53,
-      .idx = dplyr::case_when(.idx == 0 ~ 53L, TRUE ~ .idx)
+      climate_y = climate_y[c(3:53, 1:2)]
     ) %>%
     arrange(.idx)
   expect_equal(p$steps[[3]]$climate_table, expected_res)

From b3db00797b80a09ad7fb1bed1385bcb798b8735d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 27 Feb 2025 16:48:26 -0600
Subject: [PATCH 271/387] same for climatological_forecaster

---
 R/climatological_forecaster.R                 |    6 +-
 tests/testthat/_snaps/snapshots.md            | 1241 +++++++++--------
 .../testthat/test-climatological_forecaster.R |   26 +
 3 files changed, 651 insertions(+), 622 deletions(-)

diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 8a584f58a..7df853252 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -100,15 +100,15 @@ climatological_forecaster <- function(epi_data,
     select(all_of(c(key_colnames(epi_data), outcome)))
   if (time_type %in% c("week", "epiweek")) {
     ttype_dur <- lubridate::weeks
-    time_aggr <- ifelse(time_type == "week", lubridate::epiweek, lubridate::isoweek)
-    modulus <- 53L
+    time_aggr <- ifelse(time_type == "week", epiweek_leap, isoweek_leap)
+    modulus <- 52L
   } else if (time_type == "month") {
     ttype_dur <- function(x) lubridate::period(month = x)
     time_aggr <- lubridate::month
     modulus <- 12L
   } else if (time_type == "day") {
     ttype_dur <- lubridate::days
-    time_aggr <- lubridate::yday
+    time_aggr <- yday_leap
     modulus <- 365L
   }
   center_fn <- switch(args_list$center_method,
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 45223fa59..855cbb457 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1297,135 +1297,142 @@
     18992, 18992, 18992, 18999, 18999, 18999, 18999, 18999, 18999, 
     19006, 19006, 19006, 19006, 19006, 19006, 19013, 19013, 19013, 
     19013, 19013, 19013, 19020, 19020, 19020, 19020, 19020, 19020
-    ), class = "Date"), .pred = c(81.6909143, 46.9476797, 57.1427664, 
-    55.4893801, 64.9577509, 49.3180127, 92.3266162, 51.49632575, 
-    62.0313808, 69.29024775, 65.2064021, 60.734368, 97.9375799, 52.0734516, 
-    66.9333338, 70.0729902, 60.5814898, 62.3893953, 94.69837105, 
-    52.17697935, 69.7197773, 67.80972325, 57.0623769, 66.48962015, 
-    75.3837738, 51.3826096, 67.5469116, 65.9446612, 45.2947493, 67.3274732
-    ), .pred_distn = structure(list(structure(list(values = c(58.912419115, 
-    63.8539116, 78.6221968916667, 87.443016025, 96.4306031625, 110.01340202, 
-    130.62914179), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(24.169184515, 29.110677, 
-    43.8789622916667, 52.699781425, 61.6873685625, 75.2701674200001, 
-    95.88590719), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(34.364271215, 39.3057637, 
-    54.0740489916667, 62.894868125, 71.8824552625, 85.4652541200001, 
-    106.08099389), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(32.710884915, 37.6523774, 
-    52.4206626916667, 61.241481825, 70.2290689625, 83.8118678200001, 
-    104.42760759), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(42.179255715, 47.1207482, 
-    61.8890334916667, 70.709852625, 79.6974397625, 93.2802386200001, 
-    113.89597839), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(26.539517515, 31.48101, 
-    46.2492952916667, 55.070114425, 64.0577015625, 77.6405004200001, 
-    98.25624019), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(70.0311977125, 75.2240245166667, 
-    91.1454313916667, 98.454914125, 107.373545891667, 123.554305783333, 
-    144.7359469125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(29.2009072625, 34.3937340666667, 
-    50.3151409416667, 57.624623675, 66.5432554416667, 82.7240153333334, 
-    103.9056564625), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(39.7359623125, 44.9287891166667, 
-    60.8501959916667, 68.159678725, 77.0783104916667, 93.2590703833334, 
-    114.4407115125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(46.9948292625, 52.1876560666667, 
-    68.1090629416667, 75.418545675, 84.3371774416667, 100.517937333333, 
-    121.6995784625), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(42.9109836125, 48.1038104166667, 
-    64.0252172916667, 71.334700025, 80.2533317916667, 96.4340916833333, 
-    117.6157328125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(38.4389495125, 43.6317763166667, 
-    59.5531831916667, 66.862665925, 75.7812976916667, 91.9620575833334, 
-    113.1436987125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(82.576852205, 90.8680012616667, 
-    96.763374925, 102.9301931, 111.617195108333, 125.002081223333, 
-    154.43090355), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(36.712723905, 45.0038729616667, 
-    50.899246625, 57.0660648, 65.7530668083333, 79.1379529233334, 
-    108.56677525), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(51.572606105, 59.8637551616667, 
-    65.759128825, 71.925947, 80.6129490083333, 93.9978351233334, 
-    123.42665745), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(54.712262505, 63.0034115616667, 
-    68.898785225, 75.0656034, 83.7526054083333, 97.1374915233334, 
-    126.56631385), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(45.220762105, 53.5119111616667, 
-    59.407284825, 65.574103, 74.2611050083333, 87.6459911233334, 
-    117.07481345), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
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 ---
 
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+        "vctrs_vctr")), structure(list(values = c(3690.2, 3922.66666666667, 
+        5198, 5463, 6255.33333333333, 7378.46666666667, 8258.6), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(6967.6, 9420.13333333334, 
+        16689.3333333333, 21695, 24506.6666666667, 27742.7333333333, 
+        29980.4), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
         0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(10207, 
-        10426.6666666667, 11727.6666666667, 13035, 14644.3333333333, 
-        15178.4666666667, 16052), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(17730.4, 
+        18803.3333333333, 20114.6666666667, 21531, 23113, 26977, 
+        28232), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(8127.2, 8802.53333333333, 
+        9686, 11042, 12587, 14552.3333333333, 16157.6), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3962.2, 4750.8, 5779, 6992, 7636.66666666667, 
+            8200.53333333333, 8292.8), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(3788.6, 3853.2, 5119, 5465, 6173.33333333333, 
+            values = c(10207, 10426.6666666667, 11727.6666666667, 
+            12896, 14513, 15178.4666666667, 16052), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(3611.2, 3843.66666666667, 5119, 5384, 6039.33333333333, 
             6873.33333333333, 7838.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(6094.6, 8547.13333333334, 15299, 20127, 23727.6666666667, 
+            values = c(6094.6, 8547.13333333334, 15152, 20127, 23361.1666666667, 
             26869.7333333333, 29107.4), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(17030.4, 18103.3333333333, 19360.6666666667, 
-            20459, 22413, 26277, 28184.8), quantile_levels = c(0.05, 
+            20459, 22413, 26277, 27532), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(8010.2, 8685.53333333333, 9642, 10925, 12703.6666666667, 
-            14469.2, 16040.6), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(3788.2, 
-        4576.8, 5346.33333333333, 6818, 7462.66666666667, 8026.53333333333, 
-        8118.8), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(10036, 
-        10255.6666666667, 11556.6666666667, 12864, 14080.3333333333, 
-        15007.4666666667, 15881), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+            values = c(8010.2, 8685.53333333333, 9518, 10925, 12470, 
+            14435.3333333333, 16040.6), quantile_levels = c(0.05, 
+            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(3670.6, 3735.2, 4809, 5266, 6055.33333333333, 
-            6755.33333333333, 7720.8), quantile_levels = c(0.05, 
+            values = c(3788.2, 4576.8, 5325.33333333333, 6818, 7462.66666666667, 
+            8026.53333333333, 8118.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(6094.6, 8547.13333333334, 15152, 20127, 23727.6666666667, 
-            26869.7333333333, 29107.4), quantile_levels = c(0.05, 
+            values = c(10036, 10255.6666666667, 11556.6666666667, 
+            12725, 14080.3333333333, 15007.4666666667, 15881), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(16349.4, 17422.3333333333, 18679.6666666667, 
-            19778, 21732, 25596, 27503.8), quantile_levels = c(0.05, 
+            values = c(3493.2, 3725.66666666667, 4809, 5266, 5921.33333333333, 
+            6755.33333333333, 7720.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+            values = c(6094.6, 8547.13333333334, 14977.6666666667, 
+            20127, 23361.1666666667, 26869.7333333333, 29107.4), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(16349.4, 17422.3333333333, 
+        18679.6666666667, 19778, 21732, 25596, 26851), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(7567.2, 8242.53333333333, 9379.66666666667, 
-            10482, 12260.6666666667, 14026.2, 15597.6), quantile_levels = c(0.05, 
+            10482, 12027, 13992.3333333333, 15597.6), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(3747.2, 4535.8, 5711.33333333333, 6777, 7421.66666666667, 
@@ -2277,23 +2280,23 @@
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(9698, 9917.66666666667, 11218.6666666667, 
-            12526, 13742.3333333333, 14669.4666666667, 15543), quantile_levels = c(0.05, 
+            12387, 13742.3333333333, 14669.4666666667, 15543), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(3670.6, 3908, 5001, 5347, 6224.33333333333, 
+            values = c(3670.6, 3908, 5001, 5347, 6121.66666666667, 
             6755.33333333333, 7720.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(6094.6, 8547.13333333334, 15152, 20127, 23727.6666666667, 
+            values = c(6094.6, 8547.13333333334, 15152, 20127, 23361.1666666667, 
             26869.7333333333, 29107.4), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(16298.4, 17371.3333333333, 18628.6666666667, 
-            19727, 21681, 25545, 27452.8), quantile_levels = c(0.05, 
+            19727, 21681, 25545, 26800), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(6850.2, 7525.53333333333, 8662.66666666667, 
-            9765, 11335.6666666667, 13275.3333333333, 14880.6), quantile_levels = c(0.05, 
+            9765, 11235, 13275.3333333333, 14880.6), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(3736.2, 4524.8, 5700.33333333333, 6714, 7410.66666666667, 
@@ -2308,12 +2311,12 @@
             6487.33333333333, 7452.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(5597.6, 8050.13333333334, 14655, 19574, 22723.3333333333, 
+            values = c(5597.6, 8050.13333333334, 14655, 19574, 21943, 
             26372.7333333333, 28610.4), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(15152.4, 16225.3333333333, 17482.6666666667, 
-            18581, 20535, 24399, 26306.8), quantile_levels = c(0.05, 
+            18581, 20535, 24399, 25654), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
             values = c(6628.2, 7303.53333333333, 8440.66666666667, 
@@ -2332,7 +2335,7 @@
             6263.53333333333, 7399.8), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(5468.6, 7921.13333333334, 14526, 19172, 21793.1666666667, 
+            values = c(5468.6, 7921.13333333334, 14526, 19172, 21297.6666666667, 
             26243.7333333333, 28481.4), quantile_levels = c(0.05, 
             0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
diff --git a/tests/testthat/test-climatological_forecaster.R b/tests/testthat/test-climatological_forecaster.R
index 16f31140c..24f99c26f 100644
--- a/tests/testthat/test-climatological_forecaster.R
+++ b/tests/testthat/test-climatological_forecaster.R
@@ -24,3 +24,29 @@ test_that("climate args list validates properly", {
   expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = TRUE))
   expect_snapshot(error = TRUE, climate_args_list(quantile_by_key = 2:3))
 })
+
+test_that("climatological_forecaster works as expected", {
+  single_yr <- seq(as.Date("2020-01-01"), as.Date("2020-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    y = rep(c(1:183, 184:2), times = 2L)
+  ) %>%
+    as_epi_df(as_of = max(single_yr))
+  clim_forecast <- climatological_forecaster(x, "y", args_list = climate_args_list(time_type = "day"))
+  preds <- clim_forecast$predictions %>%
+    mutate(
+      quant_med = median(.pred_distn)
+    )
+  expect_equal(preds$.pred, preds$quant_med)
+
+  expected_res <- tibble(
+    geo_value = rep(c("reg1", "reg2"), 5),
+    forecast_date = as.Date("2020-12-31"),
+    target_date = c(
+      rep(as.Date("2020-12-31"), 2), rep(as.Date("2021-01-01"), 2), rep(as.Date("2021-01-02"), 2), rep(as.Date("2021-01-03"), 2), rep(as.Date("2021-01-04"), 2)
+    ),
+    .pred = c(rep(3, 8), rep(4, 2))
+  )
+  expect_equal(preds %>% select(geo_value, forecast_date, target_date, .pred), expected_res)
+})

From fda832f77362fc28aaff2601be0c17541a3be571 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 27 Feb 2025 17:25:41 -0600
Subject: [PATCH 272/387] feb 29 is day 60, so the boundary is 59-60

---
 R/step_climate.R                   | 4 ++--
 tests/testthat/test-step_climate.R | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 5bb2556a1..44a9114e0 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -358,9 +358,9 @@ roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus
     if (modulus == 365) {
       # we need to grab just the window around the leap day on the leap day
       if (iter == 366) {
-        entries <- ((60 - window_size):(60 + window_size - 1) %% modulus)
+        entries <- ((59 - window_size):(59 + window_size - 1) %% modulus)
         entries <- c(entries, 366)
-      } else if ((60 %in% entries) || (61 %in% entries)) {
+      } else if ((59 %in% entries) || (60 %in% entries)) {
         # if we're on the Feb/March boundary for daily data, we need to add in the
         # leap day data
         entries <- c(entries, 366)
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 3fa0fd475..008df5f2c 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -138,7 +138,7 @@ test_that("prep/bake steps create the correct training data for daily data", {
 
   expected_res <- tibble(
     .idx = c(1:365, 999),
-    climate_y = c(3, 3, 3:(60 - 4), 57.5:64.5, 66:181, rep(182, 5), 181:3, 3, 60)
+    climate_y = c(3, 3, 3:(59 - 4), 56.5:63.5, 65:181, rep(182, 5), 181:3, 3, 59)
   )
   expect_equal(p$steps[[1]]$climate_table, expected_res)
 

From 49882f21bb3bec70981cef1d411612ce0ca9edf1 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 3 Mar 2025 12:16:38 -0600
Subject: [PATCH 273/387] Dan's feedback, < a full year, test monthly agg

---
 NAMESPACE                          |  4 +++
 R/step_climate.R                   | 53 +++++++++++++++++-------------
 man/step_adjust_latency.Rd         |  4 +--
 tests/testthat/test-step_climate.R | 47 ++++++++++++++++++++++++++
 4 files changed, 83 insertions(+), 25 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index eb8351050..40cc8232a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -279,6 +279,10 @@ importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lubridate,"%m-%")
+importFrom(lubridate,leap_year)
+importFrom(lubridate,month)
+importFrom(lubridate,yday)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
diff --git a/R/step_climate.R b/R/step_climate.R
index 44a9114e0..eb2e78d18 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -274,12 +274,13 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
   ahead_period <- switch(x$time_type,
     epiweek = lubridate::weeks(x$forecast_ahead),
     week = lubridate::weeks(x$forecast_ahead),
-    month = lubridate::months(x$forecast_ahead),
+    month = months(x$forecast_ahead),
     day = lubridate::days(x$forecast_ahead),
   )
   climate_table <-
     training %>%
     mutate(
+      # subtracts a month w/o rollover (usual behavior on weeks/days)
       .idx = time_value %m-% ahead_period,
       .idx = x$time_aggr(.idx),
       .weights = wts
@@ -344,69 +345,75 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
 #' @param window_size the number of .idx entries before and after to include in
 #'   the aggregation
 #' @param modulus the maximum value of `.idx`
+#' @importFrom lubridate %m-%
 roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus) {
   tib <- tibble(col = col, weights = weights, .idx = .idx) |>
     arrange(.idx) |>
     tidyr::nest(data = c(col, weights), .by = .idx)
-  out <- double(nrow(tib))
+  out <- double(modulus + 1)
   for (iter in seq_along(out)) {
     # +1 from 1-indexing
-    entries <- ((iter - window_size):(iter + window_size) %% modulus)
+    entries <- (iter - window_size):(iter + window_size) %% modulus
     entries[entries == 0] <- modulus
     # note that because we are 1-indexing, we're looking for indices that are 1
     # larger than the actual day/week in the year
     if (modulus == 365) {
       # we need to grab just the window around the leap day on the leap day
       if (iter == 366) {
-        entries <- ((59 - window_size):(59 + window_size - 1) %% modulus)
-        entries <- c(entries, 366)
+        # there's an extra data point in front of the leap day
+        entries <- (59 - window_size):(59 + window_size - 1) %% modulus
+        entries[entries == 0] <- modulus
+        # adding in the leap day itself
+        entries <- c(entries, 999)
       } else if ((59 %in% entries) || (60 %in% entries)) {
         # if we're on the Feb/March boundary for daily data, we need to add in the
         # leap day data
-        entries <- c(entries, 366)
+        entries <- c(entries, 999)
       }
     } else if (modulus == 52) {
       # we need to grab just the window around the leap week on the leap week
       if (iter == 53) {
-        entries <- ((53 - window_size):(53 + window_size - 1) %% 52)
+        entries <- (53 - window_size):(53 + window_size - 1) %% 52
         entries[entries == 0] <- 52
-        entries <- c(entries, 53)
+        entries <- c(entries, 999)
       } else if ((52 %in% entries) || (1 %in% entries)) {
         # if we're on the year boundary for weekly data, we need to add in the
         # leap week data (which is the extra week at the end)
-        entries <- c(entries, 53)
+        entries <- c(entries, 999)
       }
     }
     out[iter] <- with(
-      purrr::list_rbind(tib$data[entries]),
+      purrr::list_rbind(tib |> filter(.idx %in% entries) |> pull(data)),
       aggr(col, weights)
     )
   }
-  tibble(.idx = unique(tib$.idx), climate_pred = out)
+  tibble(.idx = unique(tib$.idx), climate_pred = out[seq_len(nrow(tib))])
 }
 
 
 #' a function that assigns Feb 29th to -1, and aligns all other dates the same
 #' number in the year, regardless of whether it's a leap year
 #' @keywords internal
+#' @importFrom lubridate yday month leap_year
 yday_leap <- function(time_value) {
-  ifelse(
-    !lubridate::leap_year(time_value),
-    lubridate::yday(time_value),
-    ifelse(
-      (lubridate::month(time_value) == 2) & lubridate::day(time_value) == 29,
-      999,
-      lubridate::yday(time_value) - (lubridate::month(time_value) > 2)
-    )
+  dplyr::case_when(
+    !leap_year(time_value) ~ yday(time_value),
+    leap_day(time_value) ~ 999,
+    TRUE ~ yday(time_value) - as.numeric(month(time_value) > 2L)
   )
 }
-#' epiweek, but it assigns week 53 the value of -1 instead so it mirrors the assignments in yday_leap
+leap_day <- function(x) lubridate::month(x) == 2 & lubridate::day(x) == 29
+#' epiweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
 #' @keywords internal
 epiweek_leap <- function(time_value) {
-  lubridate::epiweek(time_value) %>% ifelse(. == 53, 999, .)
+  week_values <- lubridate::epiweek(time_value)
+  week_values[week_values == 53] <- 999
+  week_values
 }
-#' isoweek, but it assigns week 53 the value of -1 instead so it mirrors the assignments in yday_leap
+#' isoweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
 #' @keywords internal
 isoweek_leap <- function(time_value) {
-  lubridate::isoweek(time_value) %>% ifelse(. == 53, 999, .)
+  week_values <- lubridate::isoweek(time_value)
+  week_values[week_values == 53] <- 999
+  week_values
 }
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 75d674472..1fadd3ab3 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 008df5f2c..3dca9ec67 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -97,6 +97,29 @@ test_that("prep/bake steps create the correct training data", {
   expect_equal(b, expected_bake)
 })
 
+test_that("prep/bake steps create the correct training data with an incomplete year", {
+  single_yr <- seq(as.Date("2021-01-01"), as.Date("2021-10-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+    # shift by 2 days to match the epiweeks of 2021
+    y = rep(c(1, 1, rep(c(1:26, 26:2), each = 7), 1, 1, 1, 1, 1, 1)[1:length(single_yr)], times = 2L)
+  ) %>%
+    as_epi_df()
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
+  r <- epi_recipe(x) %>% step_climate(y, time_type = "epiweek")
+  p <- prep(r, x)
+
+  expected_res <- tibble(.idx = c(1:44, 999), climate_y = c(2, 3, 3, 4:25, 25, 25, 25:12, 12, 11, 11, 10))
+  expect_equal(p$steps[[1]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expected_bake <- x %>%
+    mutate(.idx = epiweek_leap(time_value)) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    select(-.idx)
+  expect_equal(b, expected_bake)
+})
 
 test_that("prep/bake steps create the correct training data for non leapweeks", {
   single_yr <- seq(as.Date("2023-01-01"), as.Date("2023-12-31"), by = "1 day")
@@ -122,6 +145,30 @@ test_that("prep/bake steps create the correct training data for non leapweeks",
   expect_equal(b, expected_bake)
 })
 
+test_that("prep/bake steps create the correct training data months", {
+  single_yr <- seq(as.Date("2021-01-01"), as.Date("2023-12-31"), by = "1 day")
+  x <- tibble(
+    time_value = rep(single_yr, times = 2L),
+    geo_value = rep(c("reg1", "reg2"), each = length(single_yr)),
+  ) %>%
+    # 1 2 3 4 5 6 6 5 4 3 2 1, assigned based on the month
+    mutate(y = pmin(13 - month(time_value), month(time_value))) %>%
+    as_epi_df()
+
+  # epiweeks 1, 52, and 53 are all 1, note that there are days in wk 52, 2 in wk 53
+  r <- epi_recipe(x) %>% step_climate(y, time_type = "month", window_size = 1)
+  p <- prep(r, x)
+
+  expected_res <- tibble(.idx = 1:12, climate_y = c(1:6, 6:1))
+  expect_equal(p$steps[[1]]$climate_table, expected_res)
+
+  b <- bake(p, new_data = NULL)
+  expected_bake <- x %>%
+    mutate(.idx = month(time_value)) %>%
+    left_join(expected_res, by = join_by(.idx)) %>%
+    select(-.idx)
+  expect_equal(b, expected_bake)
+})
 
 
 test_that("prep/bake steps create the correct training data for daily data", {

From 6424f392df68cd23ec634727f412e0bbd346eed7 Mon Sep 17 00:00:00 2001
From: David Weber <david.weber2@pm.me>
Date: Wed, 5 Mar 2025 12:43:41 -0800
Subject: [PATCH 274/387] pipe R dependency

Co-authored-by: Daniel McDonald <dajmcdon@gmail.com>
---
 R/step_climate.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index eb2e78d18..6feb31a8a 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -383,7 +383,7 @@ roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus
       }
     }
     out[iter] <- with(
-      purrr::list_rbind(tib |> filter(.idx %in% entries) |> pull(data)),
+      purrr::list_rbind(tib %>% filter(.idx %in% entries) %>% pull(data)),
       aggr(col, weights)
     )
   }

From 7c9d1c6882ba20116afb0ed8ea15c799330df728 Mon Sep 17 00:00:00 2001
From: David Weber <david.weber2@pm.me>
Date: Wed, 5 Mar 2025 12:43:54 -0800
Subject: [PATCH 275/387] -1 -> 999

Co-authored-by: Daniel McDonald <dajmcdon@gmail.com>
---
 R/step_climate.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 6feb31a8a..800de5357 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -391,7 +391,7 @@ roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus
 }
 
 
-#' a function that assigns Feb 29th to -1, and aligns all other dates the same
+#' a function that assigns Feb 29th to 999, and aligns all other dates the same
 #' number in the year, regardless of whether it's a leap year
 #' @keywords internal
 #' @importFrom lubridate yday month leap_year

From 03d50b2a5457ea047502d5fc5ca26e2ac9c6ecad Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 11 Mar 2025 12:53:50 -0500
Subject: [PATCH 276/387] version bump, minor doc update

---
 DESCRIPTION                | 2 +-
 man/step_adjust_latency.Rd | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1a713fa3a..d56033357 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.10
+Version: 0.1.11
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 1fadd3ab3..9e1bafbd5 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -143,7 +143,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 8 x 4
 #>   geo_value time_value     a     b
-#> * <chr>     <date>     <dbl> <dbl>
+#>   <chr>     <date>     <dbl> <dbl>
 #> 1 ca        2015-01-11   100     5
 #> 2 ca        2015-01-12   103    10
 #> 3 ca        2015-01-13   103    10
@@ -179,7 +179,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 21 x 7
 #>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-10    NA    NA      NA      NA       100
 #>  2 ca        2015-01-11   100     5      NA      NA       103
 #>  3 ca        2015-01-12   103    10      NA      NA        NA
@@ -227,7 +227,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 10 x 6
 #>    geo_value time_value     a     b lag_0_a ahead_3_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-08    NA    NA      NA       100
 #>  2 ca        2015-01-09    NA    NA      NA       103
 #>  3 ca        2015-01-11   100     5     100        NA

From 6e76e2b379b107f72fd3c88e8b9f1289d02bf4a1 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 30 Sep 2024 16:12:09 -0700
Subject: [PATCH 277/387] skeleton

---
 DESCRIPTION                          |   2 +-
 NAMESPACE                            |  24 +--
 R/dist_quantiles.R                   | 259 ++++-----------------------
 man/dist_quantiles.Rd                |  37 ----
 tests/testthat/test-dist_quantiles.R |  43 ++---
 5 files changed, 53 insertions(+), 312 deletions(-)
 delete mode 100644 man/dist_quantiles.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 9c6f6b38a..9746d81da 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -36,7 +36,7 @@ Imports:
     generics,
     ggplot2,
     glue,
-    hardhat (>= 1.3.0),
+    hardhat (>= 1.4.0.9002),
     lifecycle,
     lubridate,
     magrittr,
diff --git a/NAMESPACE b/NAMESPACE
index 076714517..4ce906754 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,8 +2,6 @@
 
 S3method(Add_model,epi_workflow)
 S3method(Add_model,workflow)
-S3method(Math,dist_quantiles)
-S3method(Ops,dist_quantiles)
 S3method(Remove_model,epi_workflow)
 S3method(Remove_model,workflow)
 S3method(Update_model,epi_workflow)
@@ -48,13 +46,9 @@ S3method(fit,epi_workflow)
 S3method(flusight_hub_formatter,canned_epipred)
 S3method(flusight_hub_formatter,data.frame)
 S3method(forecast,epi_workflow)
-S3method(format,dist_quantiles)
-S3method(is.na,dist_quantiles)
-S3method(is.na,distribution)
 S3method(key_colnames,epi_workflow)
 S3method(key_colnames,recipe)
-S3method(mean,dist_quantiles)
-S3method(median,dist_quantiles)
+S3method(mean,quantile_pred)
 S3method(predict,epi_workflow)
 S3method(predict,flatline)
 S3method(prep,check_enough_train_data)
@@ -101,7 +95,7 @@ S3method(print,step_lag_difference)
 S3method(print,step_naomit)
 S3method(print,step_population_scaling)
 S3method(print,step_training_window)
-S3method(quantile,dist_quantiles)
+S3method(quantile,quantile_pred)
 S3method(refresh_blueprint,default_epi_recipe_blueprint)
 S3method(residuals,flatline)
 S3method(run_mold,default_epi_recipe_blueprint)
@@ -125,8 +119,6 @@ S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
-S3method(vec_ptype_abbr,dist_quantiles)
-S3method(vec_ptype_full,dist_quantiles)
 S3method(weighted_interval_score,default)
 S3method(weighted_interval_score,dist_default)
 S3method(weighted_interval_score,dist_quantiles)
@@ -160,7 +152,6 @@ export(climate_args_list)
 export(climatological_forecaster)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
-export(dist_quantiles)
 export(epi_recipe)
 export(epi_recipe_blueprint)
 export(epi_workflow)
@@ -230,7 +221,6 @@ export(update_frosting)
 export(update_model)
 export(validate_layer)
 export(weighted_interval_score)
-import(distributional)
 import(epidatasets)
 import(epiprocess)
 import(parsnip)
@@ -325,14 +315,11 @@ importFrom(rlang,list2)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
 importFrom(stats,as.formula)
-importFrom(stats,family)
 importFrom(stats,lm)
-importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,na.omit)
 importFrom(stats,poly)
 importFrom(stats,predict)
-importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
@@ -346,13 +333,6 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
-importFrom(vctrs,as_list_of)
-importFrom(vctrs,field)
-importFrom(vctrs,new_rcrd)
-importFrom(vctrs,new_vctr)
 importFrom(vctrs,vec_cast)
 importFrom(vctrs,vec_data)
-importFrom(vctrs,vec_ptype_abbr)
-importFrom(vctrs,vec_ptype_full)
-importFrom(vctrs,vec_recycle_common)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index d40a1d481..db8e62d10 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -1,209 +1,68 @@
-#' @importFrom vctrs field vec_cast new_rcrd
-new_quantiles <- function(values = double(1), quantile_levels = double(1)) {
-  arg_is_probabilities(quantile_levels)
-
-  vec_cast(values, double())
-  vec_cast(quantile_levels, double())
-  values <- unname(values)
-  if (length(values) == 0L) {
-    return(new_rcrd(
-      list(
-        values = rep(NA_real_, length(quantile_levels)),
-        quantile_levels = quantile_levels
-      ),
-      class = c("dist_quantiles", "dist_default")
-    ))
-  }
-  stopifnot(length(values) == length(quantile_levels))
-
-  stopifnot(!vctrs::vec_duplicate_any(quantile_levels))
-  if (is.unsorted(quantile_levels)) {
-    o <- vctrs::vec_order(quantile_levels)
-    values <- values[o]
-    quantile_levels <- quantile_levels[o]
-  }
-  if (is.unsorted(values, na.rm = TRUE)) {
-    cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
-  }
-
-  new_rcrd(list(values = values, quantile_levels = quantile_levels),
-    class = c("dist_quantiles", "dist_default")
-  )
-}
-
-
-
-#' @importFrom vctrs vec_ptype_abbr vec_ptype_full
-#' @export
-vec_ptype_abbr.dist_quantiles <- function(x, ...) "dist_qntls"
-#' @export
-vec_ptype_full.dist_quantiles <- function(x, ...) "dist_quantiles"
-
-#' @export
-format.dist_quantiles <- function(x, digits = 2, ...) {
-  m <- suppressWarnings(median(x))
-  paste0("quantiles(", round(m, digits), ")[", vctrs::vec_size(x), "]")
-}
-
-
-#' A distribution parameterized by a set of quantiles
-#'
-#' @param values A vector (or list of vectors) of values.
-#' @param quantile_levels A vector (or list of vectors) of probabilities
-#'   corresponding to `values`.
-#'
-#' When creating multiple sets of `values`/`quantile_levels` resulting in
-#' different distributions, the sizes must match. See the examples below.
-#'
-#' @return A vector of class `"distribution"`.
-#'
-#' @export
-#'
-#' @examples
-#' dist_quantiles(1:4, 1:4 / 5)
-#' dist_quantiles(list(1:3, 1:4), list(1:3 / 4, 1:4 / 5))
-#' dstn <- dist_quantiles(list(1:4, 8:11), c(.2, .4, .6, .8))
-#' dstn
-#'
-#' quantile(dstn, p = c(.1, .25, .5, .9))
-#' median(dstn)
-#'
-#' # it's a bit annoying to inspect the data
-#' distributional::parameters(dstn[1])
-#' nested_quantiles(dstn[1])[[1]]
-#'
-#' @importFrom vctrs as_list_of vec_recycle_common new_vctr
-dist_quantiles <- function(values, quantile_levels) {
-  if (!is.list(quantile_levels)) {
-    assert_numeric(quantile_levels, lower = 0, upper = 1, any.missing = FALSE, min.len = 1L)
-    quantile_levels <- list(quantile_levels)
-  }
-  if (!is.list(values)) {
-    if (length(values) == 0L) values <- NA_real_
-    values <- list(values)
-  }
-
-  values <- as_list_of(values, .ptype = double())
-  quantile_levels <- as_list_of(quantile_levels, .ptype = double())
-  args <- vec_recycle_common(values = values, quantile_levels = quantile_levels)
-
-  qntls <- as_list_of(
-    map2(args$values, args$quantile_levels, new_quantiles),
-    .ptype = new_quantiles(NA_real_, 0.5)
-  )
-  new_vctr(qntls, class = "distribution")
-}
-
-validate_dist_quantiles <- function(values, quantile_levels) {
-  map(quantile_levels, arg_is_probabilities)
-  common_length <- vctrs::vec_size_common( # aborts internally
-    values = values,
-    quantile_levels = quantile_levels
-  )
-  length_diff <- vctrs::list_sizes(values) != vctrs::list_sizes(quantile_levels)
-  if (any(length_diff)) {
-    cli_abort(c(
-      "`values` and `quantile_levels` must have common length.",
-      i = "Mismatches found at position(s): {.val {which(length_diff)}}."
-    ))
-  }
-  level_duplication <- map_lgl(quantile_levels, vctrs::vec_duplicate_any)
-  if (any(level_duplication)) {
-    cli_abort(c(
-      "`quantile_levels` must not be duplicated.",
-      i = "Duplicates found at position(s): {.val {which(level_duplication)}}."
-    ))
-  }
-}
-
-
-is_dist_quantiles <- function(x) {
-  is_distribution(x) & all(stats::family(x) == "quantiles")
-}
-
-
-
-#' @export
-#' @importFrom stats median qnorm family
-median.dist_quantiles <- function(x, na.rm = FALSE, ..., middle = c("cubic", "linear")) {
-  quantile_levels <- field(x, "quantile_levels")
-  values <- field(x, "values")
-  # we have exactly that quantile
-  if (0.5 %in% quantile_levels) {
-    return(values[match(0.5, quantile_levels)])
-  }
-  # if there's only 1 quantile_level (and it isn't 0.5), or the smallest quantile is larger than 0.5 or the largest smaller than 0.5, or if every value is NA, return NA
-  if (length(quantile_levels) < 2 || min(quantile_levels) > 0.5 || max(quantile_levels) < 0.5 || all(is.na(values))) {
-    return(NA)
-  }
-  if (length(quantile_levels) < 3 || min(quantile_levels) > .25 || max(quantile_levels) < .75) {
-    return(stats::approx(quantile_levels, values, xout = 0.5)$y)
-  }
-  quantile(x, 0.5, ..., middle = middle)
-}
 
 # placeholder to avoid errors, but not ideal
 #' @export
-mean.dist_quantiles <- function(x, na.rm = FALSE, ..., middle = c("cubic", "linear")) {
-  median(x, ..., middle = middle)
+mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
+  median(x, ...)
 }
 
 #' @export
 #' @importFrom stats quantile
-#' @import distributional
-quantile.dist_quantiles <- function(x, p, ..., middle = c("cubic", "linear")) {
+quantile.quantile_pred <- function(x, p, ..., middle = c("cubic", "linear")) {
   arg_is_probabilities(p)
   p <- sort(p)
-  middle <- match.arg(middle)
+  middle <- rlang::arg_match(middle)
   quantile_extrapolate(x, p, middle)
 }
 
 
 quantile_extrapolate <- function(x, tau_out, middle) {
-  tau <- field(x, "quantile_levels")
-  qvals <- field(x, "values")
-  nas <- is.na(qvals)
-  if (all(nas)) {
-    return(rep(NA, times = length(tau_out)))
-  }
-  qvals_out <- rep(NA, length(tau_out))
-  qvals <- qvals[!nas]
-  tau <- tau[!nas]
+  tau <- x %@% "quantile_levels"
+  qvals <- as.matrix(x)
 
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
   if (all(tau_out %in% tau)) {
-    return(qvals[match(tau_out, tau)])
+    return(hardhat::quantile_pred(
+      qvals[ ,match(tau_out, tau), drop = FALSE], tau_out
+    ))
   }
   if (length(tau) < 2) {
-    cli_abort(
-      "Quantile extrapolation is not possible with fewer than 2 quantiles."
-    )
-    return(qvals_out)
+    cli_abort(paste(
+      "Quantile extrapolation is not possible when fewer than 2 quantiles",
+      "are available."
+    ))
   }
+  qvals_out <- map(
+    vctrs::vec_chop(qvals),
+    ~ extrapolate_quantiles_single(.x, tau, tau_out, middle)
+  )
 
+  hardhat::quantile_pred(qvals_out, tau_out)
+}
+
+extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   indl <- tau_out < min(tau)
   indr <- tau_out > max(tau)
   indm <- !indl & !indr
+  qvals_out <- rep(NA, length(tau_out))
 
   if (middle == "cubic") {
     method <- "cubic"
-    result <- tryCatch(
-      {
-        Q <- stats::splinefun(tau, qvals, method = "hyman")
-        quartiles <- Q(c(.25, .5, .75))
-      },
-      error = function(e) {
-        return(NA)
-      }
-    )
+    result <- tryCatch({
+      Q <- stats::splinefun(tau, qvals, method = "hyman")
+      quartiles <- Q(c(.25, .5, .75))
+    },
+    error = function(e) {
+      return(NA)
+    })
   }
   if (middle == "linear" || any(is.na(result))) {
     method <- "linear"
     quartiles <- stats::approx(tau, qvals, c(.25, .5, .75))$y
   }
   if (any(indm)) {
-    qvals_out[indm] <- switch(method,
+    qvals_out[indm] <- switch(
+      method,
       linear = stats::approx(tau, qvals, tau_out[indm])$y,
       cubic = Q(tau_out[indm])
     )
@@ -242,59 +101,3 @@ tail_extrapolate <- function(tau_out, qv) {
   m <- diff(y) / diff(x)
   m * (x0 - x[1]) + y[1]
 }
-
-
-#' @method Math dist_quantiles
-#' @export
-Math.dist_quantiles <- function(x, ...) {
-  quantile_levels <- field(x, "quantile_levels")
-  values <- field(x, "values")
-  values <- vctrs::vec_math(.Generic, values, ...)
-  new_quantiles(values = values, quantile_levels = quantile_levels)
-}
-
-#' @method Ops dist_quantiles
-#' @export
-Ops.dist_quantiles <- function(e1, e2) {
-  is_quantiles <- c(
-    inherits(e1, "dist_quantiles"),
-    inherits(e2, "dist_quantiles")
-  )
-  is_dist <- c(inherits(e1, "dist_default"), inherits(e2, "dist_default"))
-  tau1 <- tau2 <- NULL
-  if (is_quantiles[1]) {
-    q1 <- field(e1, "values")
-    tau1 <- field(e1, "quantile_levels")
-  }
-  if (is_quantiles[2]) {
-    q2 <- field(e2, "values")
-    tau2 <- field(e2, "quantile_levels")
-  }
-  tau <- union(tau1, tau2)
-  if (all(is_dist)) {
-    cli_abort(
-      "You can't perform arithmetic between two distributions like this."
-    )
-  } else {
-    if (is_quantiles[1]) {
-      q2 <- e2
-    } else {
-      q1 <- e1
-    }
-  }
-  q <- vctrs::vec_arith(.Generic, q1, q2)
-  new_quantiles(values = q, quantile_levels = tau)
-}
-
-#' @method is.na distribution
-#' @export
-is.na.distribution <- function(x) {
-  sapply(vec_data(x), is.na)
-}
-
-#' @method is.na dist_quantiles
-#' @export
-is.na.dist_quantiles <- function(x) {
-  q <- field(x, "values")
-  all(is.na(q))
-}
diff --git a/man/dist_quantiles.Rd b/man/dist_quantiles.Rd
deleted file mode 100644
index 1a3226e36..000000000
--- a/man/dist_quantiles.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/dist_quantiles.R
-\name{dist_quantiles}
-\alias{dist_quantiles}
-\title{A distribution parameterized by a set of quantiles}
-\usage{
-dist_quantiles(values, quantile_levels)
-}
-\arguments{
-\item{values}{A vector (or list of vectors) of values.}
-
-\item{quantile_levels}{A vector (or list of vectors) of probabilities
-corresponding to \code{values}.
-
-When creating multiple sets of \code{values}/\code{quantile_levels} resulting in
-different distributions, the sizes must match. See the examples below.}
-}
-\value{
-A vector of class \code{"distribution"}.
-}
-\description{
-A distribution parameterized by a set of quantiles
-}
-\examples{
-dist_quantiles(1:4, 1:4 / 5)
-dist_quantiles(list(1:3, 1:4), list(1:3 / 4, 1:4 / 5))
-dstn <- dist_quantiles(list(1:4, 8:11), c(.2, .4, .6, .8))
-dstn
-
-quantile(dstn, p = c(.1, .25, .5, .9))
-median(dstn)
-
-# it's a bit annoying to inspect the data
-distributional::parameters(dstn[1])
-nested_quantiles(dstn[1])[[1]]
-
-}
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 78d74c812..dd078c881 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -1,36 +1,31 @@
-suppressPackageStartupMessages(library(distributional))
-
-test_that("constructor returns reasonable quantiles", {
-  expect_snapshot(error = TRUE, new_quantiles(rnorm(5), c(-2, -1, 0, 1, 2)))
-  expect_silent(new_quantiles(sort(rnorm(5)), sort(runif(5))))
-  expect_snapshot(error = TRUE, new_quantiles(sort(rnorm(5)), sort(runif(2))))
-  expect_silent(new_quantiles(1:5, 1:5 / 10))
-  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .1, .2, .5, .8)))
-  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
-  expect_snapshot(error = TRUE, new_quantiles(c(1, 2, 3), c(.1, .2, 3)))
-})
-
-
-test_that("single dist_quantiles works, quantiles are accessible", {
-  z <- new_quantiles(values = 1:5, quantile_levels = c(.2, .4, .5, .6, .8))
-  expect_s3_class(z, "dist_quantiles")
+test_that("single quantile_pred works, quantiles are accessible", {
+  z <- hardhat::quantile_pred(
+    values = matrix(1:5, nrow = 1),
+    quantile_levels = c(.2, .4, .5, .6, .8)
+  )
   expect_equal(median(z), 3)
-  expect_equal(quantile(z, c(.2, .4, .5, .6, .8)), 1:5)
-  expect_equal(quantile(z, c(.3, .7), middle = "linear"), c(1.5, 4.5))
+  expect_equal(
+    quantile(z, c(.2, .4, .5, .6, .8)),
+    hardhat::quantile_pred(matrix(1:5, nrow = 1), c(.2, .4, .5, .6, .8))
+  )
+  expect_equal(
+    quantile(z, c(.3, .7), middle = "linear"),
+    hardhat::quantile_pred(matrix(c(1.5, 4.5), nrow = 1), c(.3, .7))
+  )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
   expect_equal(quantile(z, c(.3, .7), middle = "cubic"), Q(c(.3, .7)))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
-    new_quantiles(values = c(1, 1.5, 2, 3, 4, 4.5, 5), quantile_levels = 2:8 / 10)
+    hardhat::quantile_pred(c(1, 1.5, 2, 3, 4, 4.5, 5), 2:8 / 10)
   )
   # empty values slot results in a length zero distribution
   # see issue #361
-  expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
-  expect_identical(
-    dist_quantiles(list(), c(.1, .9)),
-    distributional::dist_degenerate(double())
-  )
+  # expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
+  # expect_identical(
+  #   dist_quantiles(list(), c(.1, .9)),
+  #   distributional::dist_degenerate(double())
+  # )
 })
 
 

From 7a87ae62465ed8e78304b6072142eb414ee887c4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:04:05 -0800
Subject: [PATCH 278/387] add math ops and related tests

---
 NAMESPACE                                     | 12 ++-
 R/extrapolate_quantiles.R                     | 40 ++++----
 ...st_quantiles.R => quantile_pred-methods.R} | 75 +++++++++++++--
 tests/testthat/test-dist_quantiles.R          | 96 ++++++++-----------
 4 files changed, 131 insertions(+), 92 deletions(-)
 rename R/{dist_quantiles.R => quantile_pred-methods.R} (52%)

diff --git a/NAMESPACE b/NAMESPACE
index 4ce906754..3733d5ae2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -39,9 +39,7 @@ S3method(extract_frosting,default)
 S3method(extract_frosting,epi_workflow)
 S3method(extract_layers,frosting)
 S3method(extract_layers,workflow)
-S3method(extrapolate_quantiles,dist_default)
-S3method(extrapolate_quantiles,dist_quantiles)
-S3method(extrapolate_quantiles,distribution)
+S3method(extrapolate_quantiles,quantile_pred)
 S3method(fit,epi_workflow)
 S3method(flusight_hub_formatter,canned_epipred)
 S3method(flusight_hub_formatter,data.frame)
@@ -119,6 +117,10 @@ S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
+S3method(vec_arith,quantile_pred)
+S3method(vec_arith.numeric,quantile_pred)
+S3method(vec_arith.quantile_pred,numeric)
+S3method(vec_math,quantile_pred)
 S3method(weighted_interval_score,default)
 S3method(weighted_interval_score,dist_default)
 S3method(weighted_interval_score,dist_quantiles)
@@ -333,6 +335,8 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
+importFrom(vctrs,vec_arith)
+importFrom(vctrs,vec_arith.numeric)
 importFrom(vctrs,vec_cast)
-importFrom(vctrs,vec_data)
+importFrom(vctrs,vec_math)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index 3362e339e..d9e899ef6 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -32,34 +32,28 @@ extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
 }
 
 #' @export
-#' @importFrom vctrs vec_data
-extrapolate_quantiles.distribution <- function(x, probs, replace_na = TRUE, ...) {
-  rlang::check_dots_empty()
+extrapolate_quantiles.quantile_pred <- function(x, probs, replace_na = TRUE, ...) {
   arg_is_lgl_scalar(replace_na)
   arg_is_probabilities(probs)
   if (is.unsorted(probs)) probs <- sort(probs)
-  dstn <- lapply(vec_data(x), extrapolate_quantiles, probs = probs, replace_na = replace_na)
-  new_vctr(dstn, vars = NULL, class = "distribution")
-}
-
-#' @export
-extrapolate_quantiles.dist_default <- function(x, probs, replace_na = TRUE, ...) {
-  values <- quantile(x, probs, ...)
-  new_quantiles(values = values, quantile_levels = probs)
-}
+  orig_probs <- x %@% "quantile_levels"
+  orig_values <- as.matrix(x)
 
-#' @export
-extrapolate_quantiles.dist_quantiles <- function(x, probs, replace_na = TRUE, ...) {
-  orig_probs <- field(x, "quantile_levels")
-  orig_values <- field(x, "values")
-  new_probs <- c(orig_probs, probs)
-  dups <- duplicated(new_probs)
   if (!replace_na || !anyNA(orig_values)) {
-    new_values <- c(orig_values, quantile(x, probs, ...))
+    all_values <- cbind(orig_values, quantile(x, probs, ...))
   } else {
-    nas <- is.na(orig_values)
-    orig_values[nas] <- quantile(x, orig_probs[nas], ...)
-    new_values <- c(orig_values, quantile(x, probs, ...))
+    newx <- quantile(x, orig_probs, ...) %>%
+      hardhat::quantile_pred(orig_probs)
+    all_values <- cbind(as.matrix(newx), quantile(newx, probs, ...))
   }
-  new_quantiles(new_values[!dups], new_probs[!dups])
+  all_probs <- c(orig_probs, probs)
+  dups <- duplicated(all_probs)
+  all_values <- all_values[, !dups, drop = FALSE]
+  all_probs <- all_probs[!dups]
+  o <- order(all_probs)
+
+  hardhat::quantile_pred(
+    all_values[, o, drop = FALSE],
+    quantile_levels = all_probs[o]
+  )
 }
diff --git a/R/dist_quantiles.R b/R/quantile_pred-methods.R
similarity index 52%
rename from R/dist_quantiles.R
rename to R/quantile_pred-methods.R
index db8e62d10..20379b46e 100644
--- a/R/dist_quantiles.R
+++ b/R/quantile_pred-methods.R
@@ -5,26 +5,29 @@ mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
 }
 
+
+# quantiles by treating quantile_pred like a distribution -----------------
+
+
 #' @export
 #' @importFrom stats quantile
-quantile.quantile_pred <- function(x, p, ..., middle = c("cubic", "linear")) {
+quantile.quantile_pred <- function(x, p, na.rm = FALSE, ...,
+                                   middle = c("cubic", "linear")) {
   arg_is_probabilities(p)
   p <- sort(p)
   middle <- rlang::arg_match(middle)
-  quantile_extrapolate(x, p, middle)
+  quantile_internal(x, p, middle)
 }
 
 
-quantile_extrapolate <- function(x, tau_out, middle) {
+quantile_internal <- function(x, tau_out, middle) {
   tau <- x %@% "quantile_levels"
   qvals <- as.matrix(x)
 
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
-  if (all(tau_out %in% tau)) {
-    return(hardhat::quantile_pred(
-      qvals[ ,match(tau_out, tau), drop = FALSE], tau_out
-    ))
+  if (all(tau_out %in% tau) && !anyNA(qvals)) {
+    return(qvals[ , match(tau_out, tau), drop = FALSE])
   }
   if (length(tau) < 2) {
     cli_abort(paste(
@@ -36,15 +39,26 @@ quantile_extrapolate <- function(x, tau_out, middle) {
     vctrs::vec_chop(qvals),
     ~ extrapolate_quantiles_single(.x, tau, tau_out, middle)
   )
-
-  hardhat::quantile_pred(qvals_out, tau_out)
+  qvals_out <- do.call(rbind, qvals_out) # ensure a matrix of the proper dims
+  qvals_out
 }
 
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
+  qvals_out <- rep(NA, length(tau_out))
+  good <- !is.na(qvals)
+  qvals <- qvals[good]
+  tau <- tau[good]
+
+  # in case we only have one point, and it matches something we wanted
+  if (length(good) < 2) {
+    matched_one <- tau_out %in% tau
+    qvals_out[matched_one] <- qvals[matched_one]
+    return(qvals_out)
+  }
+
   indl <- tau_out < min(tau)
   indr <- tau_out > max(tau)
   indm <- !indl & !indr
-  qvals_out <- rep(NA, length(tau_out))
 
   if (middle == "cubic") {
     method <- "cubic"
@@ -101,3 +115,44 @@ tail_extrapolate <- function(tau_out, qv) {
   m <- diff(y) / diff(x)
   m * (x0 - x[1]) + y[1]
 }
+
+
+# mathematical operations on the values -----------------------------------
+
+
+#' @importFrom vctrs vec_math
+#' @export
+#' @method vec_math quantile_pred
+vec_math.quantile_pred <- function(.fn, .x, ...) {
+  fn <- .fn
+  .fn <- getExportedValue("base", .fn)
+  if (fn %in% c("any", "all", "prod", "sum", "cumsum", "cummax", "cummin", "cumprod")) {
+    cli_abort("{.fn {fn}} is not a supported operation for {.cls quantile_pred}.")
+  }
+  quantile_levels <- .x %@% "quantile_levels"
+  .x <- as.matrix(.x)
+  hardhat::quantile_pred(.fn(.x), quantile_levels)
+}
+
+#' @importFrom vctrs vec_arith vec_arith.numeric
+#' @export
+#' @method vec_arith quantile_pred
+vec_arith.quantile_pred <- function(op, x, y, ...) {
+  UseMethod("vec_arith.quantile_pred", y)
+}
+
+#' @export
+#' @method vec_arith.quantile_pred numeric
+vec_arith.quantile_pred.numeric <- function(op, x, y, ...) {
+  op_fn <- getExportedValue("base", op)
+  out <- op_fn(as.matrix(x), y)
+  hardhat::quantile_pred(out, x %@% "quantile_levels")
+}
+
+#' @export
+#' @method vec_arith.numeric quantile_pred
+vec_arith.numeric.quantile_pred <- function(op, x, y, ...) {
+  op_fn <- getExportedValue("base", op)
+  out <- op_fn(x, as.matrix(y))
+  hardhat::quantile_pred(out, y %@% "quantile_levels")
+}
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index dd078c881..170a36438 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -4,53 +4,38 @@ test_that("single quantile_pred works, quantiles are accessible", {
     quantile_levels = c(.2, .4, .5, .6, .8)
   )
   expect_equal(median(z), 3)
-  expect_equal(
-    quantile(z, c(.2, .4, .5, .6, .8)),
-    hardhat::quantile_pred(matrix(1:5, nrow = 1), c(.2, .4, .5, .6, .8))
-  )
+  expect_equal(quantile(z, c(.2, .4, .5, .6, .8)), matrix(1:5, nrow = 1))
   expect_equal(
     quantile(z, c(.3, .7), middle = "linear"),
-    hardhat::quantile_pred(matrix(c(1.5, 4.5), nrow = 1), c(.3, .7))
+    matrix(c(1.5, 4.5), nrow = 1)
   )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
-  expect_equal(quantile(z, c(.3, .7), middle = "cubic"), Q(c(.3, .7)))
+  expect_equal(quantile(z, c(.3, .7)), Q(c(.3, .7)))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
-    hardhat::quantile_pred(c(1, 1.5, 2, 3, 4, 4.5, 5), 2:8 / 10)
+    hardhat::quantile_pred(matrix(c(1, 1.5, 2, 3, 4, 4.5, 5), nrow = 1), 2:8 / 10)
   )
-  # empty values slot results in a length zero distribution
-  # see issue #361
-  # expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
-  # expect_identical(
-  #   dist_quantiles(list(), c(.1, .9)),
-  #   distributional::dist_degenerate(double())
-  # )
 })
 
 
 test_that("quantile extrapolator works", {
-  dstn <- dist_normal(c(10, 2), c(5, 10))
-  qq <- extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-  expect_s3_class(qq, "distribution")
-  expect_s3_class(vctrs::vec_data(qq[1])[[1]], "dist_quantiles")
-  expect_length(parameters(qq[1])$quantile_levels[[1]], 3L)
-
-
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    c(.2, .4, .6, .8)
+  )
   qq <- extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-  expect_s3_class(qq, "distribution")
-  expect_s3_class(vctrs::vec_data(qq[1])[[1]], "dist_quantiles")
-  expect_length(parameters(qq[1])$quantile_levels[[1]], 7L)
+  expect_s3_class(qq, c("quantile_pred", "vctrs_vctr", "list"))
+  expect_length(qq %@% "quantile_levels", 7L)
 
-  dstn <- dist_quantiles(1:4, 1:4 / 5)
+  dstn <- hardhat::quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5)
   qq <- extrapolate_quantiles(dstn, 1:9 / 10)
-  dstn_na <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+  dstn_na <- hardhat::quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
   qq2 <- extrapolate_quantiles(dstn_na, 1:9 / 10)
   expect_equal(qq, qq2)
   qq3 <- extrapolate_quantiles(dstn_na, 1:9 / 10, replace_na = FALSE)
-  qq2_vals <- field(vec_data(qq2)[[1]], "values")
-  qq3_vals <- field(vec_data(qq3)[[1]], "values")
+  qq2_vals <- unlist(qq2)
+  qq3_vals <- unlist(qq3)
   qq2_vals[6] <- NA
   expect_equal(qq2_vals, qq3_vals)
 })
@@ -60,7 +45,7 @@ test_that("small deviations of quantile requests work", {
   v <- c(0.0890306, 0.1424997, 0.1971793, 0.2850978, 0.3832912, 0.4240479)
   badl <- l
   badl[1] <- badl[1] - 1e-14
-  distn <- dist_quantiles(list(v), list(l))
+  distn <- hardhat::quantile_pred(matrix(v, nrow = 1), l)
 
   # was broken before, now works
   expect_equal(quantile(distn, l), quantile(distn, badl))
@@ -69,50 +54,51 @@ test_that("small deviations of quantile requests work", {
   # the smallest (largest) values or we could end up unsorted
   l <- 1:9 / 10
   v <- 1:9
-  distn <- dist_quantiles(list(v), list(l))
-  expect_equal(quantile(distn, c(.25, .75)), list(c(2.5, 7.5)))
-  expect_equal(quantile(distn, c(.1, .9)), list(c(1, 9)))
+  distn <- hardhat::quantile_pred(matrix(v, nrow = 1), l)
+  expect_equal(quantile(distn, c(.25, .75)), matrix(c(2.5, 7.5), nrow = 1))
+  expect_equal(quantile(distn, c(.1, .9)), matrix(c(1, 9), nrow = 1))
   qv <- data.frame(q = l, v = v)
   expect_equal(
-    unlist(quantile(distn, c(.01, .05))),
+    drop(quantile(distn, c(.01, .05))),
     tail_extrapolate(c(.01, .05), head(qv, 2))
   )
   expect_equal(
-    unlist(quantile(distn, c(.99, .95))),
+    drop(quantile(distn, c(.99, .95))),
     tail_extrapolate(c(.95, .99), tail(qv, 2))
   )
 })
 
 test_that("unary math works on quantiles", {
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-  dstn2 <- dist_quantiles(list(log(1:4), log(8:11)), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    1:4 / 5
+  )
+  dstn2 <- hardhat::quantile_pred(
+    log(matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE)),
+    1:4 / 5
+  )
   expect_identical(log(dstn), dstn2)
 
-  dstn2 <- dist_quantiles(list(cumsum(1:4), cumsum(8:11)), list(c(.2, .4, .6, .8)))
-  expect_identical(cumsum(dstn), dstn2)
 })
 
 test_that("arithmetic works on quantiles", {
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-  dstn2 <- dist_quantiles(list(1:4 + 1, 8:11 + 1), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    1:4 / 5
+  )
+  dstn2 <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE) + 1,
+    1:4 / 5
+  )
   expect_identical(dstn + 1, dstn2)
   expect_identical(1 + dstn, dstn2)
 
-  dstn2 <- dist_quantiles(list(1:4 / 4, 8:11 / 4), list(c(.2, .4, .6, .8)))
+  dstn2 <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE) / 4,
+    1:4 / 5
+  )
   expect_identical(dstn / 4, dstn2)
   expect_identical((1 / 4) * dstn, dstn2)
 
-  expect_snapshot(error = TRUE, sum(dstn))
-  expect_snapshot(error = TRUE, suppressWarnings(dstn + distributional::dist_normal()))
-})
-
-test_that("quantile.dist_quantile works for NA vectors", {
-  distn <- dist_quantiles(
-    list(c(NA, NA)),
-    list(1:2 / 3)
-  )
-  expect_true(is.na(quantile(distn, p = 0.5)))
-  expect_true(is.na(median(distn)))
-  expect_true(is.na(mean(distn)))
-  expect_equal(format(distn), "quantiles(NA)[2]")
+  expect_error(sum(dstn))
 })

From dcca30138bb86de81326592e88878ca0a930b7e2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:06:11 -0800
Subject: [PATCH 279/387] rename tests

---
 .../testthat/{test-dist_quantiles.R => test-quantile_pred.R}  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename tests/testthat/{test-dist_quantiles.R => test-quantile_pred.R} (96%)

diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-quantile_pred.R
similarity index 96%
rename from tests/testthat/test-dist_quantiles.R
rename to tests/testthat/test-quantile_pred.R
index 170a36438..90710c6e1 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-quantile_pred.R
@@ -11,7 +11,7 @@ test_that("single quantile_pred works, quantiles are accessible", {
   )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
-  expect_equal(quantile(z, c(.3, .7)), Q(c(.3, .7)))
+  expect_equal(quantile(z, c(.3, .7)), matrix(Q(c(.3, .7)), nrow = 1))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
     hardhat::quantile_pred(matrix(c(1, 1.5, 2, 3, 4, 4.5, 5), nrow = 1), 2:8 / 10)
@@ -100,5 +100,5 @@ test_that("arithmetic works on quantiles", {
   expect_identical(dstn / 4, dstn2)
   expect_identical((1 / 4) * dstn, dstn2)
 
-  expect_error(sum(dstn))
+  expect_snapshot(error = TRUE, sum(dstn))
 })

From 316065b8cfe52a64f2822bc8b2d98e745abc8351 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:06:59 -0800
Subject: [PATCH 280/387] quantile pred tests pass

---
 tests/testthat/_snaps/quantile_pred.md | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 tests/testthat/_snaps/quantile_pred.md

diff --git a/tests/testthat/_snaps/quantile_pred.md b/tests/testthat/_snaps/quantile_pred.md
new file mode 100644
index 000000000..dd13dcb86
--- /dev/null
+++ b/tests/testthat/_snaps/quantile_pred.md
@@ -0,0 +1,8 @@
+# arithmetic works on quantiles
+
+    Code
+      sum(dstn)
+    Condition
+      Error in `vec_math()`:
+      ! `sum()` is not a supported operation for <quantile_pred>.
+

From d508069c0191a699066a01dd8ace427628faabfb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:08:46 -0800
Subject: [PATCH 281/387] updates to WIS

---
 NAMESPACE                      |   5 +-
 R/weighted_interval_score.R    | 100 +++++++++++----------------------
 man/weighted_interval_score.Rd |  19 ++-----
 3 files changed, 38 insertions(+), 86 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 3733d5ae2..90dcc43c4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -121,10 +121,7 @@ S3method(vec_arith,quantile_pred)
 S3method(vec_arith.numeric,quantile_pred)
 S3method(vec_arith.quantile_pred,numeric)
 S3method(vec_math,quantile_pred)
-S3method(weighted_interval_score,default)
-S3method(weighted_interval_score,dist_default)
-S3method(weighted_interval_score,dist_quantiles)
-S3method(weighted_interval_score,distribution)
+S3method(weighted_interval_score,quantile_pred)
 export("%>%")
 export(Add_model)
 export(Remove_model)
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index 48741de7d..567ae4e69 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -13,6 +13,16 @@
 #' @param actual double. Actual value(s)
 #' @param quantile_levels probabilities. If specified, the score will be
 #'   computed at this set of levels.
+#' @param na_handling character. Determines how `quantile_levels` without a
+#'   corresponding `value` are handled. For `"impute"`, missing values will be
+#'   calculated if possible using the available quantiles. For `"drop"`,
+#'   explicitly missing values are ignored in the calculation of the score, but
+#'   implicitly missing values are imputed if possible.
+#'   For `"propogate"`, the resulting score will be `NA` if any missing values
+#'   exist in the original `quantile_levels`. Finally, if
+#'   `quantile_levels` is specified, `"fail"` will result in
+#'   the score being `NA` when any required quantile levels (implicit or explicit)
+#'   are do not have corresponding values.
 #' @param ... not used
 #'
 #' @return a vector of nonnegative scores.
@@ -44,13 +54,13 @@
 #'
 #' # Using some actual forecasts --------
 #' library(dplyr)
-#' jhu <- covid_case_death_rates %>%
+#' jhu <- case_death_rate_subset %>%
 #'   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 #' preds <- flatline_forecaster(
 #'   jhu, "death_rate",
 #'   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 #' )$predictions
-#' actuals <- covid_case_death_rates %>%
+#' actuals <- case_death_rate_subset %>%
 #'   filter(time_value == as.Date("2021-12-01") + 7) %>%
 #'   select(geo_value, time_value, actual = death_rate)
 #' preds <- left_join(preds, actuals,
@@ -58,90 +68,44 @@
 #' ) %>%
 #'   mutate(wis = weighted_interval_score(.pred_distn, actual))
 #' preds
-weighted_interval_score <- function(x, actual, quantile_levels = NULL, ...) {
+weighted_interval_score <- function(
+    x,
+    actual,
+    quantile_levels = NULL,
+    na_handling = c("impute", "drop", "propagate", "fail"),
+    ...) {
   UseMethod("weighted_interval_score")
 }
 
-#' @export
-weighted_interval_score.default <- function(x, actual,
-                                            quantile_levels = NULL, ...) {
-  cli_abort(c(
-    "Weighted interval score can only be calculated if `x`",
-    "has class {.cls distribution}."
-  ))
-}
-
-#' @export
-weighted_interval_score.distribution <- function(
-    x, actual,
-    quantile_levels = NULL, ...) {
-  assert_numeric(actual, finite = TRUE)
-  l <- vctrs::vec_recycle_common(x = x, actual = actual)
-  map2_dbl(
-    .x = vctrs::vec_data(l$x),
-    .y = l$actual,
-    .f = weighted_interval_score,
-    quantile_levels = quantile_levels,
-    ...
-  )
-}
-
-#' @export
-weighted_interval_score.dist_default <- function(x, actual,
-                                                 quantile_levels = NULL, ...) {
-  rlang::check_dots_empty()
-  if (is.null(quantile_levels)) {
-    cli_warn(c(
-      "Weighted interval score isn't implemented for {.cls {class(x)}}",
-      "as we don't know what set of quantile levels to use.",
-      "Use a {.cls dist_quantiles} or pass `quantile_levels`.",
-      "The result for this element will be `NA`."
-    ))
-    return(NA)
-  }
-  x <- extrapolate_quantiles(x, probs = quantile_levels)
-  weighted_interval_score(x, actual, quantile_levels = NULL)
-}
 
-#' @param na_handling character. Determines how `quantile_levels` without a
-#'   corresponding `value` are handled. For `"impute"`, missing values will be
-#'   calculated if possible using the available quantiles. For `"drop"`,
-#'   explicitly missing values are ignored in the calculation of the score, but
-#'   implicitly missing values are imputed if possible.
-#'   For `"propogate"`, the resulting score will be `NA` if any missing values
-#'   exist in the original `quantile_levels`. Finally, if
-#'   `quantile_levels` is specified, `"fail"` will result in
-#'   the score being `NA` when any required quantile levels (implicit or explicit)
-#'   are do not have corresponding values.
-#' @describeIn weighted_interval_score Weighted interval score with
-#'   `dist_quantiles` allows for different `NA` behaviours.
 #' @export
-weighted_interval_score.dist_quantiles <- function(
+weighted_interval_score.quantile_pred <- function(
     x, actual,
     quantile_levels = NULL,
     na_handling = c("impute", "drop", "propagate", "fail"),
     ...) {
   rlang::check_dots_empty()
-  if (is.na(actual)) {
-    return(NA)
-  }
-  if (all(is.na(vctrs::field(x, "values")))) {
-    return(NA)
-  }
+  n <- vctrs::vec_size(x)
+  if (length(actual) == 1L) actual <- rep(actual, n)
+  assert_numeric(actual, finite = TRUE, len = n)
+  assert_numeric(quantile_levels, lower = 0, upper = 1, null.ok = TRUE)
   na_handling <- rlang::arg_match(na_handling)
-  old_quantile_levels <- field(x, "quantile_levels")
+  old_quantile_levels <- x %@% "quantile_levels"
   if (na_handling == "fail") {
     if (is.null(quantile_levels)) {
       cli_abort('`na_handling = "fail"` requires `quantile_levels` to be specified.')
     }
-    old_values <- field(x, "values")
-    if (!all(quantile_levels %in% old_quantile_levels) || any(is.na(old_values))) {
-      return(NA)
+    if (!all(quantile_levels %in% old_quantile_levels)) {
+      return(rep(NA_real_, n))
     }
   }
   tau <- quantile_levels %||% old_quantile_levels
-  x <- extrapolate_quantiles(x, probs = tau, replace_na = (na_handling == "impute"))
-  q <- field(x, "values")[field(x, "quantile_levels") %in% tau]
+  x <- extrapolate_quantiles(x, tau, replace_na = (na_handling == "impute"))
+  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau]
   na_rm <- (na_handling == "drop")
+  map2_dbl(vctrs::vec_chop(x), actual, ~ wis_one_quantile(.x, tau, .y, na_rm))
+}
+
+wis_one_quantile <- function(q, tau, actual, na_rm) {
   2 * mean(pmax(tau * (actual - q), (1 - tau) * (q - actual)), na.rm = na_rm)
 }
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 4aac20e7d..4b7c796ea 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -2,12 +2,9 @@
 % Please edit documentation in R/weighted_interval_score.R
 \name{weighted_interval_score}
 \alias{weighted_interval_score}
-\alias{weighted_interval_score.dist_quantiles}
 \title{Compute weighted interval score}
 \usage{
-weighted_interval_score(x, actual, quantile_levels = NULL, ...)
-
-\method{weighted_interval_score}{dist_quantiles}(
+weighted_interval_score(
   x,
   actual,
   quantile_levels = NULL,
@@ -25,8 +22,6 @@ contains \code{dist_quantiles()}, though other distributions are supported when
 \item{quantile_levels}{probabilities. If specified, the score will be
 computed at this set of levels.}
 
-\item{...}{not used}
-
 \item{na_handling}{character. Determines how \code{quantile_levels} without a
 corresponding \code{value} are handled. For \code{"impute"}, missing values will be
 calculated if possible using the available quantiles. For \code{"drop"},
@@ -37,6 +32,8 @@ exist in the original \code{quantile_levels}. Finally, if
 \code{quantile_levels} is specified, \code{"fail"} will result in
 the score being \code{NA} when any required quantile levels (implicit or explicit)
 are do not have corresponding values.}
+
+\item{...}{not used}
 }
 \value{
 a vector of nonnegative scores.
@@ -48,12 +45,6 @@ approximation of the commonly-used continuous ranked probability score
 generalization of absolute error. For example, see \href{https://arxiv.org/abs/2005.12881}{Bracher et al. (2020)} for discussion in the context
 of COVID-19 forecasting.
 }
-\section{Methods (by class)}{
-\itemize{
-\item \code{weighted_interval_score(dist_quantiles)}: Weighted interval score with
-\code{dist_quantiles} allows for different \code{NA} behaviours.
-
-}}
 \examples{
 quantile_levels <- c(.2, .4, .6, .8)
 predq_1 <- 1:4 #
@@ -80,13 +71,13 @@ weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
 
 # Using some actual forecasts --------
 library(dplyr)
-jhu <- covid_case_death_rates \%>\%
+jhu <- case_death_rate_subset \%>\%
   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 preds <- flatline_forecaster(
   jhu, "death_rate",
   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 )$predictions
-actuals <- covid_case_death_rates \%>\%
+actuals <- case_death_rate_subset \%>\%
   filter(time_value == as.Date("2021-12-01") + 7) \%>\%
   select(geo_value, time_value, actual = death_rate)
 preds <- left_join(preds, actuals,

From 7278c69053317202b7ddbab1ab071f16a7961375 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:55:16 -0800
Subject: [PATCH 282/387] remove the distributional package

---
 DESCRIPTION                                   |   2 +-
 NAMESPACE                                     |   8 +-
 R/autoplot.R                                  |   2 +-
 R/extrapolate_quantiles.R                     |  32 ++--
 R/flusight_hub_formatter.R                    |   7 +-
 R/layer_cdc_flatline_quantiles.R              |   7 +-
 R/layer_predictive_distn.R                    |   6 +
 R/layer_quantile_distn.R                      |  20 ++-
 R/layer_residual_quantiles.R                  |  15 +-
 R/layer_threshold_preds.R                     |  25 +--
 R/make_grf_quantiles.R                        |   4 +-
 R/make_quantile_reg.R                         |  20 +--
 R/make_smooth_quantile_reg.R                  |   4 +-
 R/pivot_quantiles.R                           | 156 +++++-------------
 R/quantile_pred-methods.R                     |  14 +-
 R/weighted_interval_score.R                   |  34 ++--
 man/extrapolate_quantiles.Rd                  |  32 ++--
 man/layer_cdc_flatline_quantiles.Rd           |   2 +-
 man/nested_quantiles.Rd                       |  24 ---
 man/pivot_quantiles_longer.Rd                 |  26 +--
 man/pivot_quantiles_wider.Rd                  |  20 +--
 man/smooth_quantile_reg.Rd                    |   2 +-
 man/weighted_interval_score.Rd                |  32 ++--
 tests/testthat/_snaps/pivot_quantiles.md      |  33 ++--
 tests/testthat/_snaps/wis-quantile_pred.md    |  16 ++
 .../testthat/test-layer_residual_quantiles.R  |   6 +-
 tests/testthat/test-layer_threshold_preds.R   |   9 +-
 tests/testthat/test-pivot_quantiles.R         |  72 +++-----
 ...t-quantiles.R => test-wis-quantile_pred.R} |  27 ++-
 29 files changed, 244 insertions(+), 413 deletions(-)
 delete mode 100644 man/nested_quantiles.Rd
 create mode 100644 tests/testthat/_snaps/wis-quantile_pred.md
 rename tests/testthat/{test-wis-dist-quantiles.R => test-wis-quantile_pred.R} (65%)

diff --git a/DESCRIPTION b/DESCRIPTION
index 9746d81da..01e07520d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -30,7 +30,6 @@ Depends:
 Imports:
     checkmate,
     cli,
-    distributional,
     dplyr,
     epiprocess (>= 0.10.4),
     generics,
@@ -51,6 +50,7 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
+    distributional,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/NAMESPACE b/NAMESPACE
index 90dcc43c4..204175a04 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -110,9 +110,7 @@ S3method(slather,layer_residual_quantiles)
 S3method(slather,layer_threshold)
 S3method(slather,layer_unnest)
 S3method(snap,default)
-S3method(snap,dist_default)
-S3method(snap,dist_quantiles)
-S3method(snap,distribution)
+S3method(snap,quantile_pred)
 S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
@@ -184,8 +182,6 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
-export(mutate)
-export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(pivot_longer)
@@ -202,6 +198,7 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
+export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
@@ -280,6 +277,7 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
+importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(lubridate,"%m-%")
diff --git a/R/autoplot.R b/R/autoplot.R
index 60b395a7d..4e52ac3e3 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -243,7 +243,7 @@ plot_bands <- function(
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, levels), levels)) %>%
+    mutate(.pred_distn = quantile_pred(quantile(.pred_distn, levels), levels)) %>%
     pivot_quantiles_wider(.pred_distn)
   qnames <- setdiff(names(predictions), innames)
 
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index d9e899ef6..82116c1d3 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -1,32 +1,26 @@
 #' Summarize a distribution with a set of quantiles
 #'
-#' @param x a `distribution` vector
+#' This function takes a `quantile_pred` vector and returns the same
+#' type of object, expanded to include
+#' *additional* quantiles computed at `probs`. If you want behaviour more
+#' similar to [stats::quantile()], then `quantile(x,...)` may be more
+#' appropriate.
+#'
+#' @param x A vector of class `quantile_pred`.
 #' @param probs a vector of probabilities at which to calculate quantiles
 #' @param replace_na logical. If `x` contains `NA`'s, these are imputed if
-#'   possible (if `TRUE`) or retained (if `FALSE`). This only effects
-#'   elements of class `dist_quantiles`.
+#'   possible (if `TRUE`) or retained (if `FALSE`).
 #' @param ... additional arguments passed on to the `quantile` method
 #'
-#' @return a `distribution` vector containing `dist_quantiles`. Any elements
-#'   of `x` which were originally `dist_quantiles` will now have a superset
+#' @return a `quantile_pred` vector. Each element
+#'   of `x` will now have a superset
 #'   of the original `quantile_values` (the union of those and `probs`).
 #' @export
 #'
 #' @examples
-#' library(distributional)
-#' dstn <- dist_normal(c(10, 2), c(5, 10))
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-#'
-#' dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-#' # because this distribution is already quantiles, any extra quantiles are
-#' # appended
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-#'
-#' dstn <- c(
-#'   dist_normal(c(10, 2), c(5, 10)),
-#'   dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-#' )
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
+#' dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+#' # extra quantiles are appended
+#' as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
   UseMethod("extrapolate_quantiles")
 }
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index feac624be..3deba254e 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -104,12 +104,11 @@ flusight_hub_formatter.data.frame <- function(
 
   object <- object %>%
     # combine the predictions and the distribution
-    mutate(.pred_distn = nested_quantiles(.pred_distn)) %>%
-    tidyr::unnest(.pred_distn) %>%
+    pivot_quantiles_longer(.pred_distn) %>%
     # now we create the correct column names
     rename(
-      value = values,
-      output_type_id = quantile_levels,
+      value = .pred_distn_value,
+      output_type_id = .pred_distn_quantile_level,
       reference_date = forecast_date
     ) %>%
     # convert to fips codes, and add any constant cols passed in ...
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 5211170ad..9c08ef768 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -51,7 +51,7 @@
 #'   in an additional `<list-col>` named `.pred_distn_all` containing 2-column
 #'   [tibble::tibble()]'s. For each
 #'   desired combination of `key`'s, the tibble will contain one row per ahead
-#'   with the associated [dist_quantiles()].
+#'   with the associated [quantile_pred()].
 #' @export
 #'
 #' @examples
@@ -265,11 +265,10 @@ propagate_samples <- function(
     }
   }
   res <- res[aheads]
+  res_quantiles <- map(res, quantile, probs = quantile_levels)
   list(tibble(
     ahead = aheads,
-    .pred_distn = map_vec(
-      res, ~ dist_quantiles(quantile(.x, quantile_levels), quantile_levels)
-    )
+    .pred_distn = quantile_pred(do.call(rbind, res_quantiles), quantile_levels)
   ))
 }
 
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 2b18fbf8e..8ce55e1c3 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -45,6 +45,12 @@ layer_predictive_distn <- function(frosting,
                                    truncate = c(-Inf, Inf),
                                    name = ".pred_distn",
                                    id = rand_id("predictive_distn")) {
+  if (!requireNamespace("distributional", quietly = TRUE)) {
+    cli_abort(paste(
+      "You must install the {.pkg distributional} package for",
+      "this functionality."
+    ))
+  }
   rlang::check_dots_empty()
   arg_is_chr_scalar(name, id)
   dist_type <- match.arg(dist_type)
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 2d991a20f..67fbfa28c 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -78,15 +78,23 @@ layer_quantile_distn_new <- function(quantile_levels, truncate, name, id) {
 slather.layer_quantile_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    if (!inherits(dstn, "distribution")) {
-      cli_abort(c(
-        "`layer_quantile_distn()` requires distributional predictions.",
-        "These are of class {.cls {class(dstn)}}."
-      ))
+    is_supported <- inherits(dstn, "distribution") ||
+      inherits(dstn, "quantile_pred")
+    if (!is_supported) {
+      cli_abort(
+        "`layer_quantile_distn()` requires distributional or quantile
+        predictions. These are of class {.cls {class(dstn)}}."
+      )
+    }
+    if (inherits(dstn, "distribution") && !requireNamespace("distributional", quietly = TRUE)) {
+      cli_abort(
+        "You must install the {.pkg distributional} package for this
+        functionality."
+      )
     }
     rlang::check_dots_empty()
 
-    dstn <- dist_quantiles(
+    dstn <- quantile_pred(
       quantile(dstn, object$quantile_levels),
       object$quantile_levels
     )
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 9a811e531..0517a0ffd 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -125,14 +125,11 @@ slather.layer_residual_quantiles <-
     }
 
     r <- r %>%
-      summarise(
-        dstn = list(quantile(
-          c(.resid, s * .resid),
-          probs = object$quantile_levels, na.rm = TRUE
-        ))
-      )
+      summarize(dstn = quantile_pred(matrix(quantile(
+        c(.resid, s * .resid), probs = object$quantile_levels, na.rm = TRUE
+      ), nrow = 1), quantile_levels = object$quantile_levels))
     # Check for NA
-    if (any(sapply(r$dstn, is.na))) {
+    if (anyNA(as.matrix(r$dstn)))  {
       cli_abort(c(
         "Residual quantiles could not be calculated due to missing residuals.",
         i = "This may be due to `n_train` < `ahead` in your {.cls epi_recipe}."
@@ -140,9 +137,7 @@ slather.layer_residual_quantiles <-
     }
 
     estimate <- components$predictions$.pred
-    res <- tibble(
-      .pred_distn = dist_quantiles(map2(estimate, r$dstn, "+"), object$quantile_levels)
-    )
+    res <- tibble(.pred_distn = r$dstn + estimate)
     res <- check_pname(res, components$predictions, object)
     components$predictions <- mutate(components$predictions, !!!res)
     components
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index d480019a9..c0903ffda 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -62,6 +62,7 @@ layer_threshold_new <-
 
 
 # restrict various objects to the interval [lower, upper]
+#' @export
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
@@ -74,25 +75,11 @@ snap.default <- function(x, lower, upper, ...) {
 }
 
 #' @export
-snap.distribution <- function(x, lower, upper, ...) {
-  rlang::check_dots_empty()
-  arg_is_scalar(lower, upper)
-  dstn <- lapply(vec_data(x), snap, lower = lower, upper = upper)
-  distributional:::wrap_dist(dstn)
-}
-
-#' @export
-snap.dist_default <- function(x, lower, upper, ...) {
-  rlang::check_dots_empty()
-  x
-}
-
-#' @export
-snap.dist_quantiles <- function(x, lower, upper, ...) {
-  values <- field(x, "values")
-  quantile_levels <- field(x, "quantile_levels")
-  values <- snap(values, lower, upper)
-  new_quantiles(values = values, quantile_levels = quantile_levels)
+snap.quantile_pred <- function(x, lower, upper, ...) {
+  values <- as.matrix(x)
+  quantile_levels <- x %@% "quantile_levels"
+  values <- map(vctrs::vec_chop(values), ~ snap(.x, lower, upper))
+  quantile_pred(do.call(rbind, values), quantile_levels = quantile_levels)
 }
 
 #' @export
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index b1792adfd..78c855831 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -162,12 +162,12 @@ make_grf_quantiles <- function() {
     )
   )
 
-  # turn the predictions into a tibble with a dist_quantiles column
+  # turn the predictions into a tibble with a quantile_pred column
   process_qrf_preds <- function(x, object) {
     quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
-    out <- dist_quantiles(out, list(quantile_levels))
+    out <- hardhat::quantile_pred(do.call(rbind, out), quantile_levels)
     return(dplyr::tibble(.pred = out))
   }
 
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 223881c85..bc2d322bf 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -110,21 +110,11 @@ make_quantile_reg <- function() {
 
   process_rq_preds <- function(x, object) {
     object <- parsnip::extract_fit_engine(object)
-    type <- class(object)[1]
-
-    # can't make a method because object is second
-    out <- switch(type,
-      rq = dist_quantiles(unname(as.list(x)), object$tau), # one quantile
-      rqs = {
-        x <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
-        dist_quantiles(x, list(object$tau))
-      },
-      cli_abort(c(
-        "Prediction is not implemented for this `rq` type.",
-        i = "See {.fun quantreg::rq}."
-      ))
-    )
-    return(dplyr::tibble(.pred = out))
+    if (!is.matrix(x)) x <- as.matrix(x)
+    rownames(x) <- NULL
+    n_pred_quantiles <- ncol(x)
+    quantile_levels <- object$tau
+    tibble(.pred = hardhat::quantile_pred(x, quantile_levels))
   }
 
   parsnip::set_pred(
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index b56a67b37..1ca019f75 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -47,7 +47,7 @@
 #'     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
 #'     ahead = NULL
 #'   ) %>%
-#'   pivot_wider(names_from = quantile_levels, values_from = values)
+#'   pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
 #' plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 #' curve(sin(x), add = TRUE)
 #' abline(v = fd, lty = 2)
@@ -171,7 +171,7 @@ make_smooth_quantile_reg <- function() {
       x <- lapply(unname(split(
         p, seq(nrow(p))
       )), function(q) unname(sort(q, na.last = TRUE)))
-      dist_quantiles(x, list(object$tau))
+      quantile_pred(do.call(rbind, x), object$tau)
     })
     n_preds <- length(list_of_pred_distns[[1]])
     nout <- length(list_of_pred_distns)
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index d4ef3d57d..37f08b46f 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -1,144 +1,65 @@
-#' Turn a vector of quantile distributions into a list-col
+#' Pivot a column containing `quantile_pred` longer
 #'
-#' @param x a `distribution` containing `dist_quantiles`
-#'
-#' @return a list-col
-#' @export
-#'
-#' @examples
-#' edf <- covid_case_death_rates[1:3, ]
-#' edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
-#'
-#' edf_nested <- edf %>% mutate(q = nested_quantiles(q))
-#' edf_nested %>% unnest(q)
-nested_quantiles <- function(x) {
-  stopifnot(is_dist_quantiles(x))
-  distributional:::dist_apply(x, .f = function(z) {
-    as_tibble(vec_data(z)) %>%
-      mutate(across(everything(), as.double)) %>%
-      vctrs::list_of()
-  })
-}
-
-
-#' Pivot columns containing `dist_quantile` longer
-#'
-#' Selected columns that contain `dist_quantiles` will be "lengthened" with
+#' A column that contains `quantile_pred` will be "lengthened" with
 #' the quantile levels serving as 1 column and the values as another. If
 #' multiple columns are selected, these will be prefixed with the column name.
 #'
 #' @param .data A data frame, or a data frame extension such as a tibble or
 #'   epi_df.
-#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
+#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One unquoted
 #'   expressions separated by commas. Variable names can be used as if they
-#'   were positions in the data frame, so expressions like `x:y` can
-#'   be used to select a range of variables.
-#' @param .ignore_length_check If multiple columns are selected, as long as
-#'   each row has contains the same number of quantiles, the result will be
-#'   reasonable. But if, for example, `var1[1]` has 5 quantiles while `var2[1]`
-#'   has 7, then the only option would be to recycle everything, creating a
-#'   _very_ long result. By default, this would throw an error. But if this is
-#'   really the goal, then the error can be bypassed by setting this argument
-#'   to `TRUE`. The quantiles in the first selected column will vary the fastest.
+#'   were positions in the data frame. Note that only one variable
+#'   can be selected for this operation.
 #'
 #' @return An object of the same class as `.data`.
 #' @export
 #'
 #' @examples
-#' d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-#' d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
+#' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+#' d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
 #' tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 #'
 #' pivot_quantiles_longer(tib, "d1")
 #' pivot_quantiles_longer(tib, dplyr::ends_with("1"))
-#' pivot_quantiles_longer(tib, d1, d2)
-pivot_quantiles_longer <- function(.data, ..., .ignore_length_check = FALSE) {
-  cols <- validate_pivot_quantiles(.data, ...)
-  .data <- .data %>% mutate(across(all_of(cols), nested_quantiles))
-  if (length(cols) > 1L) {
-    lengths_check <- .data %>%
-      dplyr::transmute(across(all_of(cols), ~ map_int(.x, vctrs::vec_size))) %>%
-      as.matrix() %>%
-      apply(1, function(x) dplyr::n_distinct(x) == 1L) %>%
-      all()
-    if (lengths_check) {
-      .data <- tidyr::unnest(.data, all_of(cols), names_sep = "_")
-    } else {
-      if (.ignore_length_check) {
-        for (col in cols) {
-          .data <- .data %>% tidyr::unnest(all_of(col), names_sep = "_")
-        }
-      } else {
-        cli_abort(paste(
-          "Some selected columns contain different numbers of quantiles.",
-          "The result would be a {.emph very} long {.cls tibble}.",
-          "To do this anyway, rerun with `.ignore_length_check = TRUE`."
-        ))
-      }
-    }
-  } else {
-    .data <- .data %>% tidyr::unnest(all_of(cols))
-  }
-  .data
+#' pivot_quantiles_longer(tib, d2)
+pivot_quantiles_longer <- function(.data, ...) {
+  col <- validate_pivot_quantiles(.data, ...)
+  .data$.row <- seq_len(vctrs::vec_size(.data))
+  long_tib <- as_tibble(.data[[col]])
+  .data <- select(.data, !all_of(col))
+  names(long_tib)[1:2] <- c(glue::glue("{col}_value"), glue::glue("{col}_quantile_level"))
+  left_join(.data, long_tib, by = ".row") %>%
+    select(!.row)
 }
 
-#' Pivot columns containing `dist_quantile` wider
+#' Pivot a column containing `quantile_pred` wider
 #'
-#' Any selected columns that contain `dist_quantiles` will be "widened" with
+#' Any selected columns that contain `quantile_pred` will be "widened" with
 #' the "taus" (quantile) serving as names and the values in the data frame.
 #' When pivoting multiple columns, the original column name will be used as
 #' a prefix.
 #'
-#' @param .data A data frame, or a data frame extension such as a tibble or
-#'   epi_df.
-#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
-#'   expressions separated by commas. Variable names can be used as if they
-#'   were positions in the data frame, so expressions like `x:y` can
-#'   be used to select a range of variables.
+#' @inheritParams pivot_quantiles_longer
 #'
 #' @return An object of the same class as `.data`
 #' @export
 #'
 #' @examples
-#' d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-#' d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-#' tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+#' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+#' d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+#' tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 #'
-#' pivot_quantiles_wider(tib, c("d1", "d2"))
-#' pivot_quantiles_wider(tib, dplyr::starts_with("d"))
+#' pivot_quantiles_wider(tib, "d1")
+#' pivot_quantiles_wider(tib, dplyr::ends_with("2"))
 #' pivot_quantiles_wider(tib, d2)
 pivot_quantiles_wider <- function(.data, ...) {
-  cols <- validate_pivot_quantiles(.data, ...)
-  .data <- .data %>% mutate(across(all_of(cols), nested_quantiles))
-  checks <- map_lgl(cols, ~ diff(range(vctrs::list_sizes(.data[[.x]]))) == 0L)
-  if (!all(checks)) {
-    nms <- cols[!checks]
-    cli_abort(c(
-      "Quantiles must be the same length and have the same set of taus.",
-      i = "Check failed for variables(s) {.var {nms}}."
-    ))
-  }
-
-  # tidyr::pivot_wider can crash if there are duplicates, this generally won't
-  # happen in our context. To avoid, silently add an index column and remove it
-  # later
-  .hidden_index <- seq_len(nrow(.data))
-  .data <- tibble::add_column(.data, .hidden_index = .hidden_index)
-  if (length(cols) > 1L) {
-    for (col in cols) {
-      .data <- .data %>%
-        tidyr::unnest(all_of(col)) %>%
-        tidyr::pivot_wider(
-          names_from = "quantile_levels", values_from = "values",
-          names_prefix = paste0(col, "_")
-        )
-    }
-  } else {
-    .data <- .data %>%
-      tidyr::unnest(all_of(cols)) %>%
-      tidyr::pivot_wider(names_from = "quantile_levels", values_from = "values")
-  }
-  select(.data, -.hidden_index)
+  col <- validate_pivot_quantiles(.data, ...)
+  .data$.row <- seq_len(vctrs::vec_size(.data))
+  wide_tib <- as_tibble(.data[[col]]) %>%
+    tidyr::pivot_wider(names_from = .quantile_levels, values_from = .pred_quantile)
+  .data <- select(.data, !all_of(col))
+  left_join(.data, wide_tib, by = ".row") %>%
+    select(!.row)
 }
 
 pivot_quantiles <- function(.data, ...) {
@@ -149,14 +70,19 @@ pivot_quantiles <- function(.data, ...) {
   lifecycle::deprecate_stop(msg)
 }
 
-validate_pivot_quantiles <- function(.data, ...) {
+validate_pivot_quantiles <- function(.data, ..., call = caller_env()) {
   expr <- rlang::expr(c(...))
   cols <- names(tidyselect::eval_select(expr, .data))
-  dqs <- map_lgl(cols, ~ is_dist_quantiles(.data[[.x]]))
-  if (!all(dqs)) {
-    nms <- cols[!dqs]
+  if (length(cols) > 1L) {
+    cli_abort(
+      "Only one column can be pivotted. Can not pivot all of: {.var {cols}}.",
+      call = call
+    )
+  }
+  if (!inherits(.data[[cols]], "quantile_pred")) {
     cli_abort(
-      "Variables(s) {.var {nms}} are not `dist_quantiles`. Cannot pivot them."
+      "{.var {cols}} is not {.cls `quantile_pred`}. Cannot pivot it.",
+      call = call
     )
   }
   cols
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 20379b46e..75c2da70c 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -1,5 +1,6 @@
 
 # placeholder to avoid errors, but not ideal
+#' @importFrom hardhat quantile_pred
 #' @export
 mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
@@ -46,6 +47,7 @@ quantile_internal <- function(x, tau_out, middle) {
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   qvals_out <- rep(NA, length(tau_out))
   good <- !is.na(qvals)
+  if (!any(good)) return(qvals_out)
   qvals <- qvals[good]
   tau <- tau[good]
 
@@ -131,7 +133,7 @@ vec_math.quantile_pred <- function(.fn, .x, ...) {
   }
   quantile_levels <- .x %@% "quantile_levels"
   .x <- as.matrix(.x)
-  hardhat::quantile_pred(.fn(.x), quantile_levels)
+  quantile_pred(.fn(.x), quantile_levels)
 }
 
 #' @importFrom vctrs vec_arith vec_arith.numeric
@@ -145,14 +147,16 @@ vec_arith.quantile_pred <- function(op, x, y, ...) {
 #' @method vec_arith.quantile_pred numeric
 vec_arith.quantile_pred.numeric <- function(op, x, y, ...) {
   op_fn <- getExportedValue("base", op)
-  out <- op_fn(as.matrix(x), y)
-  hardhat::quantile_pred(out, x %@% "quantile_levels")
+  l <- vctrs::vec_recycle_common(x = x, y = y)
+  out <- op_fn(as.matrix(l$x), l$y)
+  quantile_pred(out, x %@% "quantile_levels")
 }
 
 #' @export
 #' @method vec_arith.numeric quantile_pred
 vec_arith.numeric.quantile_pred <- function(op, x, y, ...) {
   op_fn <- getExportedValue("base", op)
-  out <- op_fn(x, as.matrix(y))
-  hardhat::quantile_pred(out, y %@% "quantile_levels")
+  l <- vctrs::vec_recycle_common(x = x, y = y)
+  out <- op_fn(l$x, as.matrix(l$y))
+  quantile_pred(out, y %@% "quantile_levels")
 }
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index 567ae4e69..aa0c816a3 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -7,22 +7,21 @@
 #' al. (2020)](https://arxiv.org/abs/2005.12881) for discussion in the context
 #' of COVID-19 forecasting.
 #'
-#' @param x distribution. A vector of class distribution. Ideally, this vector
-#'   contains `dist_quantiles()`, though other distributions are supported when
-#'   `quantile_levels` is specified. See below.
+#' @param x A vector of class `quantile_pred`.
 #' @param actual double. Actual value(s)
 #' @param quantile_levels probabilities. If specified, the score will be
-#'   computed at this set of levels.
-#' @param na_handling character. Determines how `quantile_levels` without a
-#'   corresponding `value` are handled. For `"impute"`, missing values will be
+#'   computed at this set of levels. Otherwise, those present in `x` will be
+#'   used.
+#' @param na_handling character. Determines missing values are handled.
+#'   For `"impute"`, missing values will be
 #'   calculated if possible using the available quantiles. For `"drop"`,
 #'   explicitly missing values are ignored in the calculation of the score, but
 #'   implicitly missing values are imputed if possible.
 #'   For `"propogate"`, the resulting score will be `NA` if any missing values
-#'   exist in the original `quantile_levels`. Finally, if
+#'   exist. Finally, if
 #'   `quantile_levels` is specified, `"fail"` will result in
 #'   the score being `NA` when any required quantile levels (implicit or explicit)
-#'   are do not have corresponding values.
+#'   do not have corresponding values.
 #' @param ... not used
 #'
 #' @return a vector of nonnegative scores.
@@ -30,24 +29,23 @@
 #' @export
 #' @examples
 #' quantile_levels <- c(.2, .4, .6, .8)
-#' predq_1 <- 1:4 #
-#' predq_2 <- 8:11
-#' dstn <- dist_quantiles(list(predq_1, predq_2), quantile_levels)
+#' predq1 <- 1:4 #
+#' predq2 <- 8:11
+#' dstn <- quantile_pred(rbind(predq1, predq2), quantile_levels)
 #' actual <- c(3.3, 7.1)
 #' weighted_interval_score(dstn, actual)
 #' weighted_interval_score(dstn, actual, c(.25, .5, .75))
 #'
-#' library(distributional)
-#' dstn <- dist_normal(c(.75, 2))
-#' weighted_interval_score(dstn, 1, c(.25, .5, .75))
-#'
 #' # Missing value behaviours
-#' dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+#' dstn <- quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
 #' weighted_interval_score(dstn, 2.5)
 #' weighted_interval_score(dstn, 2.5, 1:9 / 10)
 #' weighted_interval_score(dstn, 2.5, 1:9 / 10, na_handling = "drop")
 #' weighted_interval_score(dstn, 2.5, na_handling = "propagate")
-#' weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
+#' weighted_interval_score(
+#'   quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5),
+#'   actual = 2.5,
+#'   quantile_levels = 1:9 / 10,
 #'   na_handling = "fail"
 #' )
 #'
@@ -101,7 +99,7 @@ weighted_interval_score.quantile_pred <- function(
   }
   tau <- quantile_levels %||% old_quantile_levels
   x <- extrapolate_quantiles(x, tau, replace_na = (na_handling == "impute"))
-  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau]
+  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau, drop = FALSE]
   na_rm <- (na_handling == "drop")
   map2_dbl(vctrs::vec_chop(x), actual, ~ wis_one_quantile(.x, tau, .y, na_rm))
 }
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index 4b1d1282c..b645b85fa 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -7,37 +7,29 @@
 extrapolate_quantiles(x, probs, replace_na = TRUE, ...)
 }
 \arguments{
-\item{x}{a \code{distribution} vector}
+\item{x}{A vector of class \code{quantile_pred}.}
 
 \item{probs}{a vector of probabilities at which to calculate quantiles}
 
 \item{replace_na}{logical. If \code{x} contains \code{NA}'s, these are imputed if
-possible (if \code{TRUE}) or retained (if \code{FALSE}). This only effects
-elements of class \code{dist_quantiles}.}
+possible (if \code{TRUE}) or retained (if \code{FALSE}).}
 
 \item{...}{additional arguments passed on to the \code{quantile} method}
 }
 \value{
-a \code{distribution} vector containing \code{dist_quantiles}. Any elements
-of \code{x} which were originally \code{dist_quantiles} will now have a superset
+a \code{quantile_pred} vector. Each element
+of \code{x} will now have a superset
 of the original \code{quantile_values} (the union of those and \code{probs}).
 }
 \description{
-Summarize a distribution with a set of quantiles
+This function takes a \code{quantile_pred} vector and returns the same
+type of object, expanded to include
+\emph{additional} quantiles computed at \code{probs}. If you want behaviour more
+similar to \code{\link[stats:quantile]{stats::quantile()}}, then \code{quantile(x,...)} may be more
+appropriate.
 }
 \examples{
-library(distributional)
-dstn <- dist_normal(c(10, 2), c(5, 10))
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-
-dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-# because this distribution is already quantiles, any extra quantiles are
-# appended
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-
-dstn <- c(
-  dist_normal(c(10, 2), c(5, 10)),
-  dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-)
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
+dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+# extra quantiles are appended
+as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 }
diff --git a/man/layer_cdc_flatline_quantiles.Rd b/man/layer_cdc_flatline_quantiles.Rd
index 35a6c03e8..12b173da1 100644
--- a/man/layer_cdc_flatline_quantiles.Rd
+++ b/man/layer_cdc_flatline_quantiles.Rd
@@ -62,7 +62,7 @@ an updated \code{frosting} postprocessor. Calling \code{\link[=predict]{predict(
 in an additional \verb{<list-col>} named \code{.pred_distn_all} containing 2-column
 \code{\link[tibble:tibble]{tibble::tibble()}}'s. For each
 desired combination of \code{key}'s, the tibble will contain one row per ahead
-with the associated \code{\link[=dist_quantiles]{dist_quantiles()}}.
+with the associated \code{\link[=quantile_pred]{quantile_pred()}}.
 }
 \description{
 This layer creates quantile forecasts by taking a sample from the
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
deleted file mode 100644
index b1571450e..000000000
--- a/man/nested_quantiles.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/pivot_quantiles.R
-\name{nested_quantiles}
-\alias{nested_quantiles}
-\title{Turn a vector of quantile distributions into a list-col}
-\usage{
-nested_quantiles(x)
-}
-\arguments{
-\item{x}{a \code{distribution} containing \code{dist_quantiles}}
-}
-\value{
-a list-col
-}
-\description{
-Turn a vector of quantile distributions into a list-col
-}
-\examples{
-edf <- covid_case_death_rates[1:3, ]
-edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
-
-edf_nested <- edf \%>\% mutate(q = nested_quantiles(q))
-edf_nested \%>\% unnest(q)
-}
diff --git a/man/pivot_quantiles_longer.Rd b/man/pivot_quantiles_longer.Rd
index 9879d5d07..b872d0c97 100644
--- a/man/pivot_quantiles_longer.Rd
+++ b/man/pivot_quantiles_longer.Rd
@@ -2,41 +2,33 @@
 % Please edit documentation in R/pivot_quantiles.R
 \name{pivot_quantiles_longer}
 \alias{pivot_quantiles_longer}
-\title{Pivot columns containing \code{dist_quantile} longer}
+\title{Pivot a column containing \code{quantile_pred} longer}
 \usage{
-pivot_quantiles_longer(.data, ..., .ignore_length_check = FALSE)
+pivot_quantiles_longer(.data, ...)
 }
 \arguments{
 \item{.data}{A data frame, or a data frame extension such as a tibble or
 epi_df.}
 
-\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One or more unquoted
+\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One unquoted
 expressions separated by commas. Variable names can be used as if they
-were positions in the data frame, so expressions like \code{x:y} can
-be used to select a range of variables.}
-
-\item{.ignore_length_check}{If multiple columns are selected, as long as
-each row has contains the same number of quantiles, the result will be
-reasonable. But if, for example, \code{var1[1]} has 5 quantiles while \code{var2[1]}
-has 7, then the only option would be to recycle everything, creating a
-\emph{very} long result. By default, this would throw an error. But if this is
-really the goal, then the error can be bypassed by setting this argument
-to \code{TRUE}. The quantiles in the first selected column will vary the fastest.}
+were positions in the data frame. Note that only one variable
+can be selected for this operation.}
 }
 \value{
 An object of the same class as \code{.data}.
 }
 \description{
-Selected columns that contain \code{dist_quantiles} will be "lengthened" with
+A column that contains \code{quantile_pred} will be "lengthened" with
 the quantile levels serving as 1 column and the values as another. If
 multiple columns are selected, these will be prefixed with the column name.
 }
 \examples{
-d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
+d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
 tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
 pivot_quantiles_longer(tib, "d1")
 pivot_quantiles_longer(tib, dplyr::ends_with("1"))
-pivot_quantiles_longer(tib, d1, d2)
+pivot_quantiles_longer(tib, d2)
 }
diff --git a/man/pivot_quantiles_wider.Rd b/man/pivot_quantiles_wider.Rd
index e477777ca..01f85a70a 100644
--- a/man/pivot_quantiles_wider.Rd
+++ b/man/pivot_quantiles_wider.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/pivot_quantiles.R
 \name{pivot_quantiles_wider}
 \alias{pivot_quantiles_wider}
-\title{Pivot columns containing \code{dist_quantile} wider}
+\title{Pivot a column containing \code{quantile_pred} wider}
 \usage{
 pivot_quantiles_wider(.data, ...)
 }
@@ -10,26 +10,26 @@ pivot_quantiles_wider(.data, ...)
 \item{.data}{A data frame, or a data frame extension such as a tibble or
 epi_df.}
 
-\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One or more unquoted
+\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One unquoted
 expressions separated by commas. Variable names can be used as if they
-were positions in the data frame, so expressions like \code{x:y} can
-be used to select a range of variables.}
+were positions in the data frame. Note that only one variable
+can be selected for this operation.}
 }
 \value{
 An object of the same class as \code{.data}
 }
 \description{
-Any selected columns that contain \code{dist_quantiles} will be "widened" with
+Any selected columns that contain \code{quantile_pred} will be "widened" with
 the "taus" (quantile) serving as names and the values in the data frame.
 When pivoting multiple columns, the original column name will be used as
 a prefix.
 }
 \examples{
-d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
-pivot_quantiles_wider(tib, c("d1", "d2"))
-pivot_quantiles_wider(tib, dplyr::starts_with("d"))
+pivot_quantiles_wider(tib, "d1")
+pivot_quantiles_wider(tib, dplyr::ends_with("2"))
 pivot_quantiles_wider(tib, d2)
 }
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index eb715be63..973a85879 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -66,7 +66,7 @@ pl <- pl \%>\%
     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
     ahead = NULL
   ) \%>\%
-  pivot_wider(names_from = quantile_levels, values_from = values)
+  pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
 plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 curve(sin(x), add = TRUE)
 abline(v = fd, lty = 2)
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 4b7c796ea..22a616b70 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -13,25 +13,24 @@ weighted_interval_score(
 )
 }
 \arguments{
-\item{x}{distribution. A vector of class distribution. Ideally, this vector
-contains \code{dist_quantiles()}, though other distributions are supported when
-\code{quantile_levels} is specified. See below.}
+\item{x}{A vector of class \code{quantile_pred}.}
 
 \item{actual}{double. Actual value(s)}
 
 \item{quantile_levels}{probabilities. If specified, the score will be
-computed at this set of levels.}
+computed at this set of levels. Otherwise, those present in \code{x} will be
+used.}
 
-\item{na_handling}{character. Determines how \code{quantile_levels} without a
-corresponding \code{value} are handled. For \code{"impute"}, missing values will be
+\item{na_handling}{character. Determines missing values are handled.
+For \code{"impute"}, missing values will be
 calculated if possible using the available quantiles. For \code{"drop"},
 explicitly missing values are ignored in the calculation of the score, but
 implicitly missing values are imputed if possible.
 For \code{"propogate"}, the resulting score will be \code{NA} if any missing values
-exist in the original \code{quantile_levels}. Finally, if
+exist. Finally, if
 \code{quantile_levels} is specified, \code{"fail"} will result in
 the score being \code{NA} when any required quantile levels (implicit or explicit)
-are do not have corresponding values.}
+do not have corresponding values.}
 
 \item{...}{not used}
 }
@@ -47,24 +46,23 @@ of COVID-19 forecasting.
 }
 \examples{
 quantile_levels <- c(.2, .4, .6, .8)
-predq_1 <- 1:4 #
-predq_2 <- 8:11
-dstn <- dist_quantiles(list(predq_1, predq_2), quantile_levels)
+predq1 <- 1:4 #
+predq2 <- 8:11
+dstn <- quantile_pred(rbind(predq1, predq2), quantile_levels)
 actual <- c(3.3, 7.1)
 weighted_interval_score(dstn, actual)
 weighted_interval_score(dstn, actual, c(.25, .5, .75))
 
-library(distributional)
-dstn <- dist_normal(c(.75, 2))
-weighted_interval_score(dstn, 1, c(.25, .5, .75))
-
 # Missing value behaviours
-dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+dstn <- quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
 weighted_interval_score(dstn, 2.5)
 weighted_interval_score(dstn, 2.5, 1:9 / 10)
 weighted_interval_score(dstn, 2.5, 1:9 / 10, na_handling = "drop")
 weighted_interval_score(dstn, 2.5, na_handling = "propagate")
-weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
+weighted_interval_score(
+  quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5),
+  actual = 2.5,
+  quantile_levels = 1:9 / 10,
   na_handling = "fail"
 )
 
diff --git a/tests/testthat/_snaps/pivot_quantiles.md b/tests/testthat/_snaps/pivot_quantiles.md
index ca775a18f..13dd81916 100644
--- a/tests/testthat/_snaps/pivot_quantiles.md
+++ b/tests/testthat/_snaps/pivot_quantiles.md
@@ -3,47 +3,38 @@
     Code
       pivot_quantiles_wider(tib, a)
     Condition
-      Error in `UseMethod()`:
-      ! no applicable method for 'family' applied to an object of class "c('integer', 'numeric')"
+      Error in `pivot_quantiles_wider()`:
+      ! `a` is not <`quantile_pred`>. Cannot pivot it.
 
 ---
 
     Code
-      pivot_quantiles_wider(tib, c)
+      pivot_quantiles_wider(tib, d1, d2)
     Condition
-      Error in `validate_pivot_quantiles()`:
-      ! Variables(s) `c` are not `dist_quantiles`. Cannot pivot them.
+      Error in `pivot_quantiles_wider()`:
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
 ---
 
     Code
-      pivot_quantiles_wider(tib, d1)
+      pivot_quantiles_longer(tib, d1, d2)
     Condition
-      Error in `pivot_quantiles_wider()`:
-      ! Quantiles must be the same length and have the same set of taus.
-      i Check failed for variables(s) `d1`.
+      Error in `pivot_quantiles_longer()`:
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
 # quantile pivotting longer behaves
 
     Code
       pivot_quantiles_longer(tib, a)
     Condition
-      Error in `UseMethod()`:
-      ! no applicable method for 'family' applied to an object of class "c('integer', 'numeric')"
-
----
-
-    Code
-      pivot_quantiles_longer(tib, c)
-    Condition
-      Error in `validate_pivot_quantiles()`:
-      ! Variables(s) `c` are not `dist_quantiles`. Cannot pivot them.
+      Error in `pivot_quantiles_longer()`:
+      ! `a` is not <`quantile_pred`>. Cannot pivot it.
 
 ---
 
     Code
-      pivot_quantiles_longer(tib, d1, d3)
+      pivot_quantiles_longer(tib, d1, d2)
     Condition
       Error in `pivot_quantiles_longer()`:
-      ! Some selected columns contain different numbers of quantiles. The result would be a very long <tibble>. To do this anyway, rerun with `.ignore_length_check = TRUE`.
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
diff --git a/tests/testthat/_snaps/wis-quantile_pred.md b/tests/testthat/_snaps/wis-quantile_pred.md
new file mode 100644
index 000000000..f13bd74db
--- /dev/null
+++ b/tests/testthat/_snaps/wis-quantile_pred.md
@@ -0,0 +1,16 @@
+# wis dispatches and produces the correct values
+
+    Code
+      weighted_interval_score(1:10, 10)
+    Condition
+      Error in `UseMethod()`:
+      ! no applicable method for 'weighted_interval_score' applied to an object of class "c('integer', 'numeric')"
+
+---
+
+    Code
+      weighted_interval_score(quantile_pred(rbind(1:4, 8:11), 1:4 / 5), 1:3)
+    Condition
+      Error in `weighted_interval_score.quantile_pred()`:
+      ! Assertion on 'actual' failed: Must have length 2, but has length 3.
+
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 1736ded2d..2941037f5 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -23,11 +23,9 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(nrow(p), 3L)
   expect_named(p, c("geo_value", "time_value", ".pred", ".pred_distn"))
 
-  nested <- p %>% dplyr::mutate(.quantiles = nested_quantiles(.pred_distn))
-  unnested <- nested %>% tidyr::unnest(.quantiles)
-
+  unnested <- p %>% pivot_quantiles_longer(.pred_distn)
   expect_equal(nrow(unnested), 12L)
-  expect_equal(unique(unnested$quantile_levels), c(.0275, .5, .8, .95))
+  expect_equal(unique(unnested$.pred_distn_quantile_level), c(.0275, 0.5, .8, .95))
 })
 
 
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 2ff43a165..64e8608b2 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -56,11 +56,10 @@ test_that("thresholds additional columns", {
   expect_equal(round(p$.pred, digits = 3), c(0.180, 0.180, 0.310))
   expect_named(p, c("geo_value", "time_value", ".pred", ".pred_distn"))
   p <- p %>%
-    dplyr::mutate(.quantiles = nested_quantiles(.pred_distn)) %>%
-    tidyr::unnest(.quantiles)
+    pivot_quantiles_longer(.pred_distn)
   expect_equal(
-    round(p$values, digits = 3),
-    c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31)
+    round(p$.pred_distn_value, digits = 3),
+    c(0.180, 0.180, 0.31, 0.180, .18, .18, .31, 0.310, .31)
   )
-  expect_equal(p$quantile_levels, rep(c(.1, 0.5, .9), times = 3))
+  expect_equal(p$.pred_distn_quantile_level, rep(c(.1, 0.5, .9), times = 3))
 })
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index 1639058e2..a4362cffb 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -1,76 +1,44 @@
 test_that("quantile pivotting wider behaves", {
   tib <- tibble::tibble(a = 1:5, b = 6:10)
   expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, a))
-  tib$c <- rep(dist_normal(), 5)
-  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, c))
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:5, 1:4 / 5))
-  # different quantiles
-  tib <- tib[1:2, ]
-  tib$d1 <- d1
-  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, d1))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 2:4 / 4))
-  tib$d1 <- d1
-  # would want to error (mismatched quantiles), but hard to check efficiently
-  expect_silent(pivot_quantiles_wider(tib, d1))
+  d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+  d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+  tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-  d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-  tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+  # too many columns
+  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, d1, d2))
+  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d2))
 
 
-  expect_length(pivot_quantiles_wider(tib, c("d1", "d2")), 7L)
-  expect_length(pivot_quantiles_wider(tib, tidyselect::starts_with("d")), 7L)
-  expect_length(pivot_quantiles_wider(tib, d2), 5L)
+  expect_length(pivot_quantiles_wider(tib, d1), 5L)
+  expect_length(pivot_quantiles_wider(tib, tidyselect::ends_with("1")), 5L)
+  expect_equal(vctrs::vec_size(pivot_quantiles_longer(tib, d2)), 6L)
 })
 
 test_that("pivotting wider still works if there are duplicates", {
   # previously this would produce a warning if pivotted because the
   # two rows of the result are identical
-  tb <- tibble(.pred = dist_quantiles(list(1:3, 1:3), list(c(.1, .5, .9))))
+  tb <- tibble(.pred = quantile_pred(rbind(1:3, 1:3), c(.1, .5, .9)))
   res <- tibble(`0.1` = c(1, 1), `0.5` = c(2, 2), `0.9` = c(3, 3))
-  expect_identical(tb %>% pivot_quantiles_wider(.pred), res)
+  expect_equal(tb %>% pivot_quantiles_wider(.pred), res)
 })
 
 
 test_that("quantile pivotting longer behaves", {
   tib <- tibble::tibble(a = 1:5, b = 6:10)
   expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, a))
-  tib$c <- rep(dist_normal(), 5)
-  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, c))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:5, 1:4 / 5))
-  # different quantiles
-  tib <- tib[1:2, ]
-  tib$d1 <- d1
-  expect_length(pivot_quantiles_longer(tib, d1), 5L)
-  expect_identical(nrow(pivot_quantiles_longer(tib, d1)), 7L)
-  expect_identical(pivot_quantiles_longer(tib, d1)$values, as.double(c(1:3, 2:5)))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 2:4 / 4))
-  tib$d1 <- d1
-  expect_silent(pivot_quantiles_longer(tib, d1))
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-  d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-  tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+  d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+  d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+  tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
+  # too many columns
+  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d2))
 
-  expect_length(pivot_quantiles_longer(tib, c("d1", "d2")), 5L)
-  expect_identical(nrow(pivot_quantiles_longer(tib, c("d1", "d2"))), 6L)
-  expect_silent(pivot_quantiles_longer(tib, tidyselect::starts_with("d")))
-  expect_length(pivot_quantiles_longer(tib, d2), 4L)
+  # different quantiles
+  expect_length(pivot_quantiles_longer(tib, d1), 4L)
+  expect_identical(nrow(pivot_quantiles_longer(tib, d1)), 6L)
+  expect_identical(pivot_quantiles_longer(tib, d1)$d1_value, c(1:3, 2:4))
 
-  tib$d3 <- c(dist_quantiles(2:5, 2:5 / 6), dist_quantiles(3:6, 2:5 / 6))
-  # now the cols have different numbers of quantiles
-  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d3))
-  expect_length(
-    pivot_quantiles_longer(tib, d1, d3, .ignore_length_check = TRUE),
-    6L
-  )
-  expect_identical(
-    pivot_quantiles_longer(tib, d1, d3, .ignore_length_check = TRUE)$d1_values,
-    as.double(rep(c(1:3, 2:4), each = 4))
-  )
 })
diff --git a/tests/testthat/test-wis-dist-quantiles.R b/tests/testthat/test-wis-quantile_pred.R
similarity index 65%
rename from tests/testthat/test-wis-dist-quantiles.R
rename to tests/testthat/test-wis-quantile_pred.R
index 937793189..187344af9 100644
--- a/tests/testthat/test-wis-dist-quantiles.R
+++ b/tests/testthat/test-wis-quantile_pred.R
@@ -8,45 +8,38 @@ test_that("wis dispatches and produces the correct values", {
   actual <- 5
   expected <- c(wis_one_pred(q1, tau, actual), wis_one_pred(q2, tau, actual))
 
-  dstn <- dist_quantiles(list(q1, q2), tau)
+  dstn <- quantile_pred(rbind(q1, q2), tau)
   expect_equal(weighted_interval_score(dstn, actual), expected)
 
   # works with a single dstn
   q <- sort(10 * rexp(23))
   tau0 <- c(.01, .025, 1:19 / 20, .975, .99)
-  dst <- dist_quantiles(q, tau0)
+  dst <- quantile_pred(rbind(q), tau0)
   expect_equal(weighted_interval_score(dst, 10), wis_one_pred(q, tau0, 10))
 
   # returns NA when expected
-  dst <- dist_quantiles(rep(NA, 3), c(.2, .5, .95))
+  dst <- quantile_pred(rbind(rep(NA, 3)), c(.2, .5, .95))
   expect_true(is.na(weighted_interval_score(dst, 10)))
   expect_equal(
     weighted_interval_score(dstn, c(NA, actual)),
     c(NA, wis_one_pred(q2, tau, actual))
   )
 
-  # errors for non distributions
+  # errors for non quantile_pred
   expect_snapshot(error = TRUE, weighted_interval_score(1:10, 10))
-  expect_warning(w <- weighted_interval_score(dist_normal(1), 10))
-  expect_true(all(is.na(w)))
-  expect_warning(w <- weighted_interval_score(
-    c(dist_normal(), dist_quantiles(1:5, 1:5 / 6)),
-    10
-  ))
-  expect_equal(w, c(NA, wis_one_pred(1:5, 1:5 / 6, 10)))
 
   # errors if sizes don't match
   expect_snapshot(error = TRUE, weighted_interval_score(
-    dist_quantiles(list(1:4, 8:11), 1:4 / 5), # length 2
+    quantile_pred(rbind(1:4, 8:11), 1:4 / 5), # length 2
     1:3
   ))
 
   #' # Missing value behaviours
-  dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+  dstn <- quantile_pred(rbind(c(1, 2, NA, 4)), 1:4 / 5)
   expect_equal(weighted_interval_score(dstn, 2.5), 0.5)
   expect_equal(weighted_interval_score(dstn, 2.5, c(2, 4, 5, 6, 8) / 10), 0.4)
   expect_equal(
-    weighted_interval_score(dist_quantiles(c(1, 2, NA, 4), 1:4 / 5), 3, na_handling = "drop"),
+    weighted_interval_score(dstn, 3, na_handling = "drop"),
     2 / 3
   )
   expect_equal(
@@ -56,5 +49,9 @@ test_that("wis dispatches and produces the correct values", {
   expect_true(is.na(
     weighted_interval_score(dstn, 2.5, na_handling = "propagate")
   ))
-  weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail")
+  expect_true(is.na(
+    weighted_interval_score(
+      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail"
+    )
+  ))
 })

From b7b441659a431ca5b26ee93e8c66861d614dc176 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 24 Feb 2025 18:01:51 -0800
Subject: [PATCH 283/387] tests pass, checks don't

---
 NAMESPACE                                   |   12 +-
 R/epipredict-package.R                      |    1 +
 R/extrapolate_quantiles.R                   |    4 +-
 R/layer_point_from_distn.R                  |    6 +-
 R/layer_threshold_preds.R                   |    1 +
 R/pivot_quantiles.R                         |   50 +
 R/quantile_pred-methods.R                   |  105 ++
 R/reexports-tidymodels.R                    |   37 +
 R/utils-misc.R                              |    2 +-
 man/dist_quantiles.Rd                       |   28 +
 man/extrapolate_quantiles.Rd                |    4 +-
 man/nested_quantiles.Rd                     |   31 +
 man/reexports.Rd                            |    7 +-
 tests/testthat/_snaps/dist_quantiles.md     |   56 -
 tests/testthat/_snaps/pivot_quantiles.md    |   18 +
 tests/testthat/_snaps/snapshots.md          | 1484 +++++++------------
 tests/testthat/_snaps/wis-dist-quantiles.md |   17 -
 tests/testthat/test-grf_quantiles.R         |    2 +-
 tests/testthat/test-pivot_quantiles.R       |    9 +
 tests/testthat/test-population_scaling.R    |   13 +-
 tests/testthat/test-step_adjust_latency.R   |    8 +-
 21 files changed, 829 insertions(+), 1066 deletions(-)
 create mode 100644 R/reexports-tidymodels.R
 create mode 100644 man/dist_quantiles.Rd
 create mode 100644 man/nested_quantiles.Rd
 delete mode 100644 tests/testthat/_snaps/dist_quantiles.md
 delete mode 100644 tests/testthat/_snaps/wis-dist-quantiles.md

diff --git a/NAMESPACE b/NAMESPACE
index 204175a04..4c43e2f45 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -119,6 +119,7 @@ S3method(vec_arith,quantile_pred)
 S3method(vec_arith.numeric,quantile_pred)
 S3method(vec_arith.quantile_pred,numeric)
 S3method(vec_math,quantile_pred)
+S3method(vec_proxy_equal,quantile_pred)
 S3method(weighted_interval_score,quantile_pred)
 export("%>%")
 export(Add_model)
@@ -137,7 +138,6 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
-export(as_epi_df)
 export(as_tibble)
 export(autoplot)
 export(bake)
@@ -149,12 +149,14 @@ export(climate_args_list)
 export(climatological_forecaster)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
+export(dist_quantiles)
 export(epi_recipe)
 export(epi_recipe_blueprint)
 export(epi_workflow)
 export(extract_argument)
 export(extract_frosting)
 export(extract_layers)
+export(extract_quantile_levels)
 export(extrapolate_quantiles)
 export(filter)
 export(fit)
@@ -182,6 +184,7 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(pivot_longer)
@@ -189,6 +192,7 @@ export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
 export(pivot_wider)
 export(prep)
+export(quantile_pred)
 export(quantile_reg)
 export(rand_id)
 export(remove_epi_recipe)
@@ -276,6 +280,7 @@ importFrom(ggplot2,geom_linerange)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(glue,glue)
+importFrom(hardhat,extract_quantile_levels)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
@@ -284,6 +289,7 @@ importFrom(lubridate,"%m-%")
 importFrom(lubridate,leap_year)
 importFrom(lubridate,month)
 importFrom(lubridate,yday)
+importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
@@ -330,8 +336,12 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
+importFrom(vctrs,as_list_of)
+importFrom(vctrs,new_vctr)
 importFrom(vctrs,vec_arith)
 importFrom(vctrs,vec_arith.numeric)
 importFrom(vctrs,vec_cast)
 importFrom(vctrs,vec_math)
+importFrom(vctrs,vec_proxy_equal)
+importFrom(vctrs,vec_recycle_common)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index 9ee832580..fae8c3c65 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -10,6 +10,7 @@
 #' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
 #' @importFrom epiprocess growth_rate growth_rate_params is_epi_df
+#' @importFrom lifecycle deprecated
 #' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index 82116c1d3..c7a9a3b6b 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -18,9 +18,9 @@
 #' @export
 #'
 #' @examples
-#' dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+#' dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 #' # extra quantiles are appended
-#' as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+#' as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
   UseMethod("extrapolate_quantiles")
 }
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index 5af434892..5d293992f 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -78,10 +78,10 @@ layer_point_from_distn_new <- function(type, name, id) {
 slather.layer_point_from_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    if (!inherits(dstn, "distribution")) {
-      rlang::warn(
+    if (!(inherits(dstn, "quantile_pred") | inherits(dstn, "distribution"))) {
+      cli_warn(
         c("`layer_point_from_distn` requires distributional predictions.",
-          i = "These are of class {class(dstn)}. Ignoring this layer."
+          i = "These are of class {.cls {class(dstn)}}. Ignoring this layer."
         )
       )
       return(components)
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index c0903ffda..9acf06725 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -63,6 +63,7 @@ layer_threshold_new <-
 
 # restrict various objects to the interval [lower, upper]
 #' @export
+#' @keywords internal
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 37f08b46f..8e846d7e6 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -1,3 +1,53 @@
+#' Turn a vector of quantile distributions into a list-col
+#'
+#' `r lifecycle::badge("deprecated")`
+#'
+#' This function is deprecated. The recommended alternative is
+#' [hardhat::quantile_pred()] with [tibble::as_tibble()]
+
+#'
+#' @param x a `distribution` containing `dist_quantiles`
+#'
+#' @return a list-col
+#' @export
+#'
+#' @examples
+#' .pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#' nested_quantiles(.pred_quantile)
+#'
+#' .pred_quantile %>%
+#'   as_tibble() %>%
+#'   tidyr::nest(.by = .row) %>%
+#'   dplyr::select(-.row)
+#'
+nested_quantiles <- function(x) {
+  lifecycle::deprecate_warn("0.1.11", "nested_quantiles()", "hardhat::quantile_pred()")
+  if (inherits(x, "distribution")) {
+    if (requireNamespace("distributional")) {
+      x <- vctrs::vec_data(x)
+      return(distributional:::dist_apply(x, .f = function(z) {
+        as_tibble(vctrs::vec_data(z)) %>%
+          mutate(across(everything(), as.double)) %>%
+          vctrs::list_of()
+      }))
+    } else {
+      cli_abort(c(
+        "`nested_quantiles()` is deprecated and the {.pkg distributional}",
+        `!` = "package is not installed.",
+        i = "See {.fn hardhat::quantile_pred}."
+      ))
+    }
+  }
+  if (inherits(x, "quantile_pred")) {
+    return(x %>% as_tibble() %>% tidyr::nest(.by = .row) %>%
+      dplyr::select(data))
+  }
+  cli_abort(c(
+    "`nested_quantiles()` is deprecated. See {.fn hardhat::quantile_pred}."
+  ))
+}
+
+
 #' Pivot a column containing `quantile_pred` longer
 #'
 #' A column that contains `quantile_pred` will be "lengthened" with
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 75c2da70c..134308da2 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -1,3 +1,40 @@
+#' A distribution parameterized by a set of quantiles
+#'
+#' `r lifecycle::badge("deprecated")`
+#'
+#' This function is deprecated. The recommended alternative is
+#' [hardhat::quantile_pred()].
+#'
+#' @param values A vector (or list of vectors) of values.
+#' @param quantile_levels A vector (or list of vectors) of probabilities
+#'   corresponding to `values`.
+#'
+#' When creating multiple sets of `values`/`quantile_levels` resulting in
+#' different distributions, the sizes must match. See the examples below.
+#'
+#' @return A vector of class `"distribution"`.
+#'
+#' @export
+#' @keywords internal
+#'
+#' @importFrom vctrs as_list_of vec_recycle_common new_vctr
+dist_quantiles <- function(values, quantile_levels) {
+  lifecycle::deprecate_warn("0.1.11", "dist_quantiles()", "hardhat::quantile_pred()")
+  if (is.list(values)) {
+    n <- length(values)
+    values <- unlist(values)
+    return(quantile_pred(matrix(values, nrow = n, byrow = TRUE), quantile_levels))
+  } else if (is.matrix(values)) {
+    return(quantile_pred(values, quantile_levels))
+  } else if (is.vector(values)) {
+    return(quantile_pred(matrix(values, nrow = 1), quantile_levels))
+  }
+  cli_abort(c(
+    "`dist_quantiles()` is deprecated and the format of `values` could not",
+    `!` = "be automatically converted to work with the replacement.",
+    i = "See {.fn hardhat::quantile_pred}."
+  ))
+}
 
 # placeholder to avoid errors, but not ideal
 #' @importFrom hardhat quantile_pred
@@ -6,6 +43,74 @@ mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
 }
 
+# These next 3 functions should probably be added via PR to {hardhat}
+# Only the third is actually *needed* at the moment.
+# The second doesn't work correctly (not sure why), but leaving here for the
+# future.
+#
+# We only export the third.
+#
+# self-self method, should work only if attr(quantile_levels) are compatible
+# #' @importFrom vctrs vec_ptype2 vec_cast
+# #' @importFrom hardhat extract_quantile_levels
+# #' @export
+# #' @keywords internal
+# vec_ptype2.quantile_pred.quantile_pred <- function(
+#     x, y, ..., x_arg = "", y_arg = "", call = caller_env()
+# ) {
+#   if (all(extract_quantile_levels(y) %in% extract_quantile_levels(x))) {
+#     return(x)
+#   }
+#   if (all(extract_quantile_levels(x) %in% extract_quantile_levels(y))) {
+#     return(y)
+#   }
+#   vctrs::stop_incompatible_type(
+#     x, y, x_arg = x_arg, y_arg = y_arg,
+#     details = "`quantile_levels` must be compatible (a superset/subset relation)."
+#   )
+# }
+
+# currently doesn't work
+# #' @export
+# vec_cast.quantile_pred.quantile_pred <- function(
+#     x, to, ..., x_arg = caller_arg(x), to_arg = caller_arg(to),
+#     call = caller_env()
+# ) {
+#   to_ql <- extract_quantile_levels(to)
+#   x_ql <- extract_quantile_levels(x)
+#   x_in_to <- x_ql %in% to_ql
+#   to_in_x <- to_ql %in% x_ql
+#   if (all(x_in_to)) {
+#     mat <- matrix(NA, ncol = length(to_ql))
+#     mat[ , to_in_x] <- c(as.matrix(x))
+#   } else if (all(to_in_x)) {
+#     mat <- as.matrix(x)[ , x_in_to, drop = FALSE]
+#   } else {
+#     vctrs::stop_incompatible_type(
+#       x, to, x_arg = x_arg, y_arg = to_arg,
+#       details = "`quantile_levels` must be compatible (a superset/subset relation)."
+#     )
+#   }
+#   quantile_pred(mat, to_ql)
+# }
+
+
+# Convert the quantile_pred to a data frame (named with the .quantile_levels)
+# This powers vec_proxy_equal (and hence ==, !=, is.na, etc)
+# It also powers vec_proxy_compare, so, given matching colnames, these should
+# work out of the box.
+#
+#' @importFrom vctrs vec_proxy_equal
+#' @export
+vec_proxy_equal.quantile_pred <- function(x, ...) {
+  as_tibble(x) %>%
+    tidyr::pivot_wider(
+      names_from = .quantile_levels,
+      values_from = .pred_quantile
+    ) %>%
+    dplyr::select(-.row)
+}
+
 
 # quantiles by treating quantile_pred like a distribution -----------------
 
diff --git a/R/reexports-tidymodels.R b/R/reexports-tidymodels.R
new file mode 100644
index 000000000..00cd7e4fd
--- /dev/null
+++ b/R/reexports-tidymodels.R
@@ -0,0 +1,37 @@
+#' @importFrom generics fit
+#' @export
+generics::fit
+
+#' @importFrom generics forecast
+#' @export
+generics::forecast
+
+#' @importFrom recipes prep
+#' @export
+recipes::prep
+
+#' @importFrom recipes bake
+#' @export
+recipes::bake
+
+#' @importFrom recipes rand_id
+#' @export
+recipes::rand_id
+
+#' @importFrom tibble tibble as_tibble
+#' @export
+tibble::tibble
+
+#' @export
+tibble::as_tibble
+
+#' @importFrom generics tidy
+#' @export
+generics::tidy
+
+#' @importFrom hardhat quantile_pred extract_quantile_levels
+#' @export
+hardhat::quantile_pred
+
+#' @export
+hardhat::extract_quantile_levels
diff --git a/R/utils-misc.R b/R/utils-misc.R
index fec707913..bdb35a9eb 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -47,7 +47,7 @@ format_varnames <- function(x, empty = "*none*") {
   if (length(x) == 0L) {
     empty
   } else {
-    as.character(syms(x))
+    as.character(rlang::syms(x))
   }
 }
 
diff --git a/man/dist_quantiles.Rd b/man/dist_quantiles.Rd
new file mode 100644
index 000000000..1bfa437b0
--- /dev/null
+++ b/man/dist_quantiles.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/quantile_pred-methods.R
+\name{dist_quantiles}
+\alias{dist_quantiles}
+\title{A distribution parameterized by a set of quantiles}
+\usage{
+dist_quantiles(values, quantile_levels)
+}
+\arguments{
+\item{values}{A vector (or list of vectors) of values.}
+
+\item{quantile_levels}{A vector (or list of vectors) of probabilities
+corresponding to \code{values}.
+
+When creating multiple sets of \code{values}/\code{quantile_levels} resulting in
+different distributions, the sizes must match. See the examples below.}
+}
+\value{
+A vector of class \code{"distribution"}.
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
+This function is deprecated. The recommended alternative is
+\code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}}.
+}
+\keyword{internal}
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index b645b85fa..bd460dbe9 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -29,7 +29,7 @@ similar to \code{\link[stats:quantile]{stats::quantile()}}, then \code{quantile(
 appropriate.
 }
 \examples{
-dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 # extra quantiles are appended
-as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 }
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
new file mode 100644
index 000000000..4b666ee32
--- /dev/null
+++ b/man/nested_quantiles.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/pivot_quantiles.R
+\name{nested_quantiles}
+\alias{nested_quantiles}
+\title{Turn a vector of quantile distributions into a list-col}
+\usage{
+nested_quantiles(x)
+}
+\arguments{
+\item{x}{a \code{distribution} containing \code{dist_quantiles}}
+}
+\value{
+a list-col
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
+This function is deprecated. The recommended alternative is
+\code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}} with \code{\link[tibble:as_tibble]{tibble::as_tibble()}}
+}
+\examples{
+.pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+nested_quantiles(.pred_quantile)
+
+.pred_quantile \%>\%
+  as_tibble() \%>\%
+  tidyr::nest(.by = .row) \%>\%
+  dplyr::select(-.row)
+
+}
diff --git a/man/reexports.Rd b/man/reexports.Rd
index aedd9e46b..ab2cf17be 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -21,6 +21,10 @@
 \alias{pivot_longer}
 \alias{pivot_wider}
 \alias{unnest}
+\alias{as_tibble}
+\alias{tidy}
+\alias{quantile_pred}
+\alias{extract_quantile_levels}
 \title{Objects exported from other packages}
 \keyword{internal}
 \description{
@@ -36,10 +40,11 @@ below to see their documentation.
 
   \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
 
+  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}}
+
   \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}}
 
   \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
 
   \item{tidyr}{\code{\link[tidyr]{pivot_longer}}, \code{\link[tidyr]{pivot_wider}}, \code{\link[tidyr]{unnest}}}
 }}
-
diff --git a/tests/testthat/_snaps/dist_quantiles.md b/tests/testthat/_snaps/dist_quantiles.md
deleted file mode 100644
index 1d626e089..000000000
--- a/tests/testthat/_snaps/dist_quantiles.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# constructor returns reasonable quantiles
-
-    Code
-      new_quantiles(rnorm(5), c(-2, -1, 0, 1, 2))
-    Condition
-      Error in `new_quantiles()`:
-      ! `quantile_levels` must lie in [0, 1].
-
----
-
-    Code
-      new_quantiles(sort(rnorm(5)), sort(runif(2)))
-    Condition
-      Error in `new_quantiles()`:
-      ! length(values) == length(quantile_levels) is not TRUE
-
----
-
-    Code
-      new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.1, 0.2, 0.5, 0.8))
-    Condition
-      Error in `new_quantiles()`:
-      ! !vctrs::vec_duplicate_any(quantile_levels) is not TRUE
-
----
-
-    Code
-      new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.15, 0.2, 0.5, 0.8))
-    Condition
-      Error in `new_quantiles()`:
-      ! `values[order(quantile_levels)]` produces unsorted quantiles.
-
----
-
-    Code
-      new_quantiles(c(1, 2, 3), c(0.1, 0.2, 3))
-    Condition
-      Error in `new_quantiles()`:
-      ! `quantile_levels` must lie in [0, 1].
-
-# arithmetic works on quantiles
-
-    Code
-      sum(dstn)
-    Condition
-      Error in `mapply()`:
-      ! You can't perform arithmetic between two distributions like this.
-
----
-
-    Code
-      suppressWarnings(dstn + distributional::dist_normal())
-    Condition
-      Error:
-      ! non-numeric argument to binary operator
-
diff --git a/tests/testthat/_snaps/pivot_quantiles.md b/tests/testthat/_snaps/pivot_quantiles.md
index 13dd81916..ea027d50c 100644
--- a/tests/testthat/_snaps/pivot_quantiles.md
+++ b/tests/testthat/_snaps/pivot_quantiles.md
@@ -38,3 +38,21 @@
       Error in `pivot_quantiles_longer()`:
       ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
+# nested_quantiles is deprecated, but works where possible
+
+    Code
+      d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5)
+    Condition
+      Warning:
+      `dist_quantiles()` was deprecated in epipredict 0.1.11.
+      i Please use `hardhat::quantile_pred()` instead.
+
+---
+
+    Code
+      o <- nested_quantiles(d)
+    Condition
+      Warning:
+      `nested_quantiles()` was deprecated in epipredict 0.1.11.
+      i Please use `hardhat::quantile_pred()` instead.
+
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 855cbb457..78f629171 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -2,30 +2,16 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
+    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
+    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
+        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
+        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
+        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
+        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
+        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
+    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
@@ -35,31 +21,18 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(structure(list(values = c(0.084583345, 
-    0.1073314, 0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.048438145, 0.0711862, 0.0931412, 0.103199, 0.1132568, 
-        0.1352118, 0.157959855), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.257363545, 
-    0.2801116, 0.3020666, 0.3121244, 0.3221822, 0.3441372, 0.366885255
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.367085245, 0.3898333, 0.4117883, 0.4218461, 
-        0.4319039, 0.4538589, 0.476606955), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.677223545, 0.6999716, 0.7219266, 0.7319844, 
-        0.7420422, 0.7639972, 0.786745255), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.142781745, 0.1655298, 0.1874848, 0.1975426, 
-        0.2076004, 0.2295554, 0.252303455), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
-    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    0.1975426), .pred_distn = structure(list(c(0.084583345, 0.1073314, 
+    0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055), c(0.048438145, 
+    0.0711862, 0.0931412, 0.103199, 0.1132568, 0.1352118, 0.157959855
+    ), c(0.257363545, 0.2801116, 0.3020666, 0.3121244, 0.3221822, 
+    0.3441372, 0.366885255), c(0.367085245, 0.3898333, 0.4117883, 
+    0.4218461, 0.4319039, 0.4538589, 0.476606955), c(0.677223545, 
+    0.6999716, 0.7219266, 0.7319844, 0.7420422, 0.7639972, 0.786745255
+    ), c(0.142781745, 0.1655298, 0.1874848, 0.1975426, 0.2076004, 
+    0.2295554, 0.252303455)), quantile_levels = c(0.05, 0.1, 0.25, 
+    0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -67,30 +40,16 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
+    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
+    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
+        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
+        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
+        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
+        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
+        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
+    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
@@ -100,30 +59,16 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
+    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
+    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
+        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
+        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
+        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
+        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
+        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
+    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
@@ -141,279 +86,160 @@
     0.7319844, 0.7319844, 0.7319844, 0.1975426, 0.1975426, 0.1975426, 
     0.1975426, 0.1975426), ahead = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 
     3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 
-    4L, 5L, 1L, 2L, 3L, 4L, 5L), .pred_distn = structure(list(structure(list(
-        values = c(0, 0, 0, 0.05519342, 0.082372705, 0.0936219, 0.1048711, 
-        0.1157573, 0.12317806, 0.1302723, 0.1353526, 0.1393442, 0.1433358, 
-        0.1484161, 0.15551034, 0.1629311, 0.1738173, 0.1850665, 0.196315695, 
-        0.22349498, 0.309768685, 0.3567520625, 0.439580229), quantile_levels = c(0.01, 
-        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
-        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
-        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.0335550493877939, 
+    4L, 5L, 1L, 2L, 3L, 4L, 5L), .pred_distn = structure(list(c(0, 
+    0, 0, 0.05519342, 0.082372705, 0.0936219, 0.1048711, 0.1157573, 
+    0.12317806, 0.1302723, 0.1353526, 0.1393442, 0.1433358, 0.1484161, 
+    0.15551034, 0.1629311, 0.1738173, 0.1850665, 0.196315695, 0.22349498, 
+    0.309768685, 0.3567520625, 0.439580229), c(0, 0, 0, 0, 0.0335550493877939, 
     0.0604073208819088, 0.0796881899581496, 0.0945180888333883, 0.107218788833388, 
     0.118830788833388, 0.129717088833388, 0.1393442, 0.148949488833388, 
     0.159110072060821, 0.171080110623306, 0.184009705322953, 0.19866346102411, 
     0.218798896615666, 0.250961850618106, 0.300471354816148, 0.368582781136862, 
-    0.43909595699107, 0.520101234797705), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.0310685196688967, 
+    0.43909595699107, 0.520101234797705), c(0, 0, 0, 0, 0, 0.0310685196688967, 
     0.0565901050435504, 0.0768417663716637, 0.0947104815343153, 0.110553706525765, 
     0.125192081534315, 0.1393442, 0.153133424194392, 0.167807181271713, 
     0.183769310145952, 0.202099979390294, 0.224139947221972, 0.252840918770688, 
     0.291417895572206, 0.341073550318203, 0.420604597710477, 0.494523225410904, 
-    0.573647294116801), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
-    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
-    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00623643594225938, 0.0360877950479505, 
-        0.0604332430739307, 0.0824028153516535, 0.102509343235732, 
-        0.121439405653606, 0.1393442, 0.15780837904264, 0.176333479766098, 
-        0.1971089199637, 0.219859545844459, 0.246500872561225, 0.279163385675357, 
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 ---
@@ -428,290 +254,167 @@
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     18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(19006, 19013, 
-    19020, 19027, 19034, 19006, 19013, 19020, 19027, 19034, 19006, 
+    18992, 18992), class = "Date"), target_date = structure(c(19006, 
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+    19027, 19034), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 ---
@@ -726,287 +429,180 @@
     0.7319844, 0.7319844, 0.7319844, 0.1975426, 0.1975426, 0.1975426, 
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+        0, 0, 0, 0.186887482630176, 0.277238777881179, 0.317854348809488, 
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+        0.7319844, 0.73651047, 0.7431597, 0.7487474, 0.7554526, 0.766627925, 
+        0.7822734, 0.8012715, 0.85144875, 0.935151945, 1.0252810475, 
+        1.12578085), c(0.276821846502455, 0.354318476867519, 0.440270225449805, 
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+        0.680831108876989, 0.696223359635746, 0.7096337, 0.7219265, 
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+        1.02188617627186, 1.10971107833641, 1.18626816850867), c(0.202265200637946, 
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+        0.6783427, 0.6984583, 0.715655544727447, 0.7319844, 0.7487473, 
+        0.7666278, 0.785715489951649, 0.8090941, 0.83815, 0.873623567291473, 
+        0.920206978680437, 0.98231174201862, 1.08425930872329, 1.16639411427812, 
+        1.25926838507547), c(0.129193504425124, 0.241744300793533, 
+        0.331949483165032, 0.43649858695157, 0.504472062268773, 0.556141464729147, 
+        0.597172505336053, 0.631406591640416, 0.660898437441874, 
+        0.686684727470375, 0.709633972330423, 0.7319844, 0.753217699696647, 
+        0.77608746100351, 0.8012715950276, 0.830327492252422, 0.86464477397774, 
+        0.906319686121761, 0.956815387818928, 1.02495125855129, 1.13129413647201, 
+        1.21644533535035, 1.32424172966634), c(0.0667682979050189, 
+        0.189580042212397, 0.290485041721667, 0.402951609190092, 
+        0.475328740486855, 0.530590906520765, 0.575504908587586, 
+        0.613421932920829, 0.647285177364573, 0.678099283398734, 
+        0.70593862799773, 0.7319844, 0.758701322488325, 0.786639532920829, 
+        0.816837200234752, 0.850627936753767, 0.888963924063491, 
+        0.933785069065791, 0.988913131611816, 1.06240172852619, 1.16959624730917, 
+        1.2662008825538, 1.38860505690239), c(0, 0, 0.0419413650000001, 
+        0.09882005, 0.1230992, 0.14226962, 0.1600776, 0.1722416, 
+        0.1800265, 0.1880061, 0.1936501, 0.1975426, 0.2014351, 0.2070791, 
+        0.2150587, 0.2228436, 0.2350076, 0.25281558, 0.271986, 0.29626515, 
+        0.353143835, 0.4353357125, 0.545314878), c(0, 0, 0, 0.0438463650372504, 
+        0.0808594787511875, 0.106995615813358, 0.127478232938079, 
+        0.145480846633466, 0.1610508, 0.17461199504795, 0.186668812203222, 
+        0.1975426, 0.208428571374764, 0.2204108, 0.233930283744537, 
+        0.249894552784127, 0.267362348440485, 0.288755575723157, 
+        0.316120297580926, 0.355450425419354, 0.443192503687136, 
+        0.536871211931719, 0.636344785545224), c(0, 0, 0, 0.00188932708477086, 
+        0.0470905919531195, 0.079226864399944, 0.105414109111591, 
+        0.127225815559956, 0.146699420891509, 0.164644114298843, 
+        0.18142942603581, 0.1975426, 0.213933119201142, 0.231001630488804, 
+        0.24941229702312, 0.269578845560456, 0.292362546530965, 0.319632071367214, 
+        0.354433951358713, 0.406915236639266, 0.506944745332152, 
+        0.596044605353528, 0.695533388807317), c(0, 0, 0, 0, 0.0156342454546545, 
+        0.0536811248488485, 0.084228833507335, 0.110407751354614, 
+        0.134410113872139, 0.156669167575476, 0.177701902429674, 
+        0.1975426, 0.217759024165492, 0.238897316673167, 0.261484572608426, 
+        0.286120039498095, 0.313065324705997, 0.345395334882349, 
+        0.386811116673167, 0.44780805303823, 0.550781846423163, 0.644984940689833, 
+        0.752937731654986), c(0, 0, 0, 0, 0, 0.0290260214229144, 
+        0.0653218111708617, 0.0966336637233373, 0.124670861123061, 
+        0.149775978614687, 0.174275935467055, 0.1975426, 0.221291415429954, 
+        0.246723385601356, 0.273144383515685, 0.30101566402084, 0.33204051788793, 
+        0.369730347126771, 0.416909038104281, 0.481925596660567, 
+        0.58989871202142, 0.688635568252056, 0.803906183401304)), quantile_levels = c(0.01, 
     0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.0845659921302213, 
-    0.228553649752897, 0.289236861333113, 0.326073140839108, 0.354785333802038, 
-    0.379166830409904, 0.401230227456875, 0.4218461, 0.442801275729157, 
-    0.465572618600986, 0.490133389090691, 0.520052318734487, 0.558588500497255, 
-    0.62065225601836, 0.788392143304334, 1.05428294678997, 1.55684044507063, 
-    2.01374350966068, 2.37954449328776), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.33818795, 0.4386877525, 
-    0.528816855, 0.61252005, 0.6626973, 0.6816954, 0.697340875, 0.7085162, 
-    0.7152214, 0.7208091, 0.72745833, 0.7319844, 0.73651047, 0.7431597, 
-    0.7487474, 0.7554526, 0.766627925, 0.7822734, 0.8012715, 0.85144875, 
-    0.935151945, 1.0252810475, 1.12578085), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.276821846502455, 
-    0.354318476867519, 0.440270225449805, 0.533132934163242, 0.5900576, 
-    0.631102729748298, 0.660462274661497, 0.680831108876989, 0.696223359635746, 
-    0.7096337, 0.7219265, 0.7319844, 0.7431597, 0.7543351, 0.7677455, 
-    0.783391, 0.804046832839828, 0.833541896886769, 0.873735298798638, 
-    0.929106903073231, 1.02188617627186, 1.10971107833641, 1.18626816850867
-    ), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.202265200637946, 
-    0.298325094034965, 0.380907645938709, 0.481339524857949, 0.543219696138311, 
-    0.589507953775938, 0.6258186, 0.654874580912809, 0.6783427, 0.6984583, 
-    0.715655544727447, 0.7319844, 0.7487473, 0.7666278, 0.785715489951649, 
-    0.8090941, 0.83815, 0.873623567291473, 0.920206978680437, 0.98231174201862, 
-    1.08425930872329, 1.16639411427812, 1.25926838507547), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.129193504425124, 
-    0.241744300793533, 0.331949483165032, 0.43649858695157, 0.504472062268773, 
-    0.556141464729147, 0.597172505336053, 0.631406591640416, 0.660898437441874, 
-    0.686684727470375, 0.709633972330423, 0.7319844, 0.753217699696647, 
-    0.77608746100351, 0.8012715950276, 0.830327492252422, 0.86464477397774, 
-    0.906319686121761, 0.956815387818928, 1.02495125855129, 1.13129413647201, 
-    1.21644533535035, 1.32424172966634), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.0667682979050189, 
-    0.189580042212397, 0.290485041721667, 0.402951609190092, 0.475328740486855, 
-    0.530590906520765, 0.575504908587586, 0.613421932920829, 0.647285177364573, 
-    0.678099283398734, 0.70593862799773, 0.7319844, 0.758701322488325, 
-    0.786639532920829, 0.816837200234752, 0.850627936753767, 0.888963924063491, 
-    0.933785069065791, 0.988913131611816, 1.06240172852619, 1.16959624730917, 
-    1.2662008825538, 1.38860505690239), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.0419413650000001, 
-    0.09882005, 0.1230992, 0.14226962, 0.1600776, 0.1722416, 0.1800265, 
-    0.1880061, 0.1936501, 0.1975426, 0.2014351, 0.2070791, 0.2150587, 
-    0.2228436, 0.2350076, 0.25281558, 0.271986, 0.29626515, 0.353143835, 
-    0.4353357125, 0.545314878), quantile_levels = c(0.01, 0.025, 
-    0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 
-    0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0.0438463650372504, 0.0808594787511875, 
-        0.106995615813358, 0.127478232938079, 0.145480846633466, 
-        0.1610508, 0.17461199504795, 0.186668812203222, 0.1975426, 
-        0.208428571374764, 0.2204108, 0.233930283744537, 0.249894552784127, 
-        0.267362348440485, 0.288755575723157, 0.316120297580926, 
-        0.355450425419354, 0.443192503687136, 0.536871211931719, 
-        0.636344785545224), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0.00188932708477086, 0.0470905919531195, 
-        0.079226864399944, 0.105414109111591, 0.127225815559956, 
-        0.146699420891509, 0.164644114298843, 0.18142942603581, 0.1975426, 
-        0.213933119201142, 0.231001630488804, 0.24941229702312, 0.269578845560456, 
-        0.292362546530965, 0.319632071367214, 0.354433951358713, 
-        0.406915236639266, 0.506944745332152, 0.596044605353528, 
-        0.695533388807317), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0.0156342454546545, 0.0536811248488485, 
-        0.084228833507335, 0.110407751354614, 0.134410113872139, 
-        0.156669167575476, 0.177701902429674, 0.1975426, 0.217759024165492, 
-        0.238897316673167, 0.261484572608426, 0.286120039498095, 
-        0.313065324705997, 0.345395334882349, 0.386811116673167, 
-        0.44780805303823, 0.550781846423163, 0.644984940689833, 0.752937731654986
-        ), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 
-        0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 
-        0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.0290260214229144, 0.0653218111708617, 
-        0.0966336637233373, 0.124670861123061, 0.149775978614687, 
-        0.174275935467055, 0.1975426, 0.221291415429954, 0.246723385601356, 
-        0.273144383515685, 0.30101566402084, 0.33204051788793, 0.369730347126771, 
-        0.416909038104281, 0.481925596660567, 0.58989871202142, 0.688635568252056, 
-        0.803906183401304), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    ), class = c("quantile_pred", "vctrs_vctr", "list")), forecast_date = structure(c(18992, 
     18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
     18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
     18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18997, 19002, 
-    19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017, 18997, 
+    18992, 18992), class = "Date"), target_date = structure(c(18997, 
     19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017, 
     18997, 19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012, 
-    19017), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
+    19017, 18997, 19002, 19007, 19012, 19017, 18997, 19002, 19007, 
+    19012, 19017), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 # arx_forecaster snapshots
@@ -1014,37 +610,23 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328, 
     1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
-        structure(list(values = c(0.171022956902288, 0.244945899624723, 
-        0.308032696431071, 0.353013358779435, 0.397994021127798, 
-        0.461080817934147, 0.535003760656582), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.46653503056773, 0.540457973290166, 0.603544770096514, 
-            0.648525432444877, 0.693506094793241, 0.756592891599589, 
-            0.830515834322024), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
+        c(0.171022956902288, 0.244945899624723, 0.308032696431071, 
+        0.353013358779435, 0.397994021127798, 0.461080817934147, 
+        0.535003760656582), c(0.46653503056773, 0.540457973290166, 
+        0.603544770096514, 0.648525432444877, 0.693506094793241, 
+        0.756592891599589, 0.830515834322024), c(0.485679887517181, 
         0.559602830239616, 0.622689627045964, 0.667670289394328, 
         0.712650951742692, 0.77573774854904, 0.849660691271475), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.959876988846753, 
-        1.03379993156919, 1.09688672837554, 1.1418673907239, 1.18684805307226, 
-        1.24993484987861, 1.32385779260105), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
-            0.830448695683587, 0.875429358031951, 0.938516154838299, 
-            1.01243909756073), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.147809030071502, 
-        0.221731972793937, 0.284818769600285, 0.329799431948649, 
-        0.374780094297013, 0.437866891103361, 0.511789833825796), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+        c(0.959876988846753, 1.03379993156919, 1.09688672837554, 
+        1.1418673907239, 1.18684805307226, 1.24993484987861, 1.32385779260105
+        ), c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
+        0.830448695683587, 0.875429358031951, 0.938516154838299, 
+        1.01243909756073), c(0.147809030071502, 0.221731972793937, 
+        0.284818769600285, 0.329799431948649, 0.374780094297013, 
+        0.437866891103361, 0.511789833825796)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -1053,35 +635,21 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
     0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398634, 0.118312393281548, 
-        0.13840396557592, 0.149303403634373, 0.160202841692825, 0.180294413987198, 
-        0.202494988128882), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.0865730800114383, 
-        0.108773654153123, 0.128865226447495, 0.139764664505948, 
-        0.1506641025644, 0.170755674858773, 0.192956249000457), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.279994736572136, 0.30219531071382, 0.322286883008193, 
-            0.333186321066645, 0.344085759125097, 0.36417733141947, 
-            0.386377905561154), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.417153993342634, 
+        c(0.0961118191398634, 0.118312393281548, 0.13840396557592, 
+        0.149303403634373, 0.160202841692825, 0.180294413987198, 
+        0.202494988128882), c(0.0865730800114383, 0.108773654153123, 
+        0.128865226447495, 0.139764664505948, 0.1506641025644, 0.170755674858773, 
+        0.192956249000457), c(0.279994736572136, 0.30219531071382, 
+        0.322286883008193, 0.333186321066645, 0.344085759125097, 
+        0.36417733141947, 0.386377905561154), c(0.417153993342634, 
         0.439354567484319, 0.459446139778691, 0.470345577837144, 
         0.481245015895596, 0.501336588189969, 0.523537162331653), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.672794520917498, 
-        0.694995095059183, 0.715086667353556, 0.725986105412008, 
-        0.73688554347046, 0.756977115764833, 0.779177689906517), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.159495080779498, 
-        0.181695654921182, 0.201787227215555, 0.212686665274007, 
-        0.223586103332459, 0.243677675626832, 0.265878249768516), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+        c(0.672794520917498, 0.694995095059183, 0.715086667353556, 
+        0.725986105412008, 0.73688554347046, 0.756977115764833, 0.779177689906517
+        ), c(0.159495080779498, 0.181695654921182, 0.201787227215555, 
+        0.212686665274007, 0.223586103332459, 0.243677675626832, 
+        0.265878249768516)), quantile_levels = c(0.05, 0.1, 0.25, 
+    0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
     18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
@@ -1092,34 +660,20 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.202348949370703, 
-        0.263837900408968, 0.303244704017742, 0.342651507626517, 
-        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
-            0.531332853311081, 0.570739656919856, 0.632228607958121, 
-            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        c(0.136509784083987, 0.202348949370703, 0.263837900408968, 
+        0.303244704017742, 0.342651507626517, 0.404140458664782, 
+        0.469979623951498), c(0.364597933377326, 0.430437098664042, 
+        0.491926049702307, 0.531332853311081, 0.570739656919856, 
+        0.632228607958121, 0.698067773244837), c(0.422093024752224, 
         0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
-        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
-            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.689723699333019, 0.755562864619735), c(0.821955329282475, 
+        0.887794494569191, 0.949283445607456, 0.98869024921623, 1.028097052825, 
+        1.08958600386327, 1.15542516914999), c(0.628067077067884, 
         0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
-        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
-            0.306895457225321, 0.346302260834096, 0.407791211872361, 
-            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
+        0.205999702578282, 0.267488653616547, 0.306895457225321, 
+        0.346302260834096, 0.407791211872361, 0.473630377159077)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
     "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
     18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
@@ -1130,34 +684,20 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.202348949370703, 
-        0.263837900408968, 0.303244704017742, 0.342651507626517, 
-        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
-            0.531332853311081, 0.570739656919856, 0.632228607958121, 
-            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        c(0.136509784083987, 0.202348949370703, 0.263837900408968, 
+        0.303244704017742, 0.342651507626517, 0.404140458664782, 
+        0.469979623951498), c(0.364597933377326, 0.430437098664042, 
+        0.491926049702307, 0.531332853311081, 0.570739656919856, 
+        0.632228607958121, 0.698067773244837), c(0.422093024752224, 
         0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
-        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
-            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        0.689723699333019, 0.755562864619735), c(0.821955329282475, 
+        0.887794494569191, 0.949283445607456, 0.98869024921623, 1.028097052825, 
+        1.08958600386327, 1.15542516914999), c(0.628067077067884, 
         0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
-        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
-            0.306895457225321, 0.346302260834096, 0.407791211872361, 
-            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
+        0.205999702578282, 0.267488653616547, 0.306895457225321, 
+        0.346302260834096, 0.407791211872361, 0.473630377159077)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
     "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
     18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
diff --git a/tests/testthat/_snaps/wis-dist-quantiles.md b/tests/testthat/_snaps/wis-dist-quantiles.md
deleted file mode 100644
index fb9cfbdf6..000000000
--- a/tests/testthat/_snaps/wis-dist-quantiles.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# wis dispatches and produces the correct values
-
-    Code
-      weighted_interval_score(1:10, 10)
-    Condition
-      Error in `weighted_interval_score()`:
-      ! Weighted interval score can only be calculated if `x`
-      has class <distribution>.
-
----
-
-    Code
-      weighted_interval_score(dist_quantiles(list(1:4, 8:11), 1:4 / 5), 1:3)
-    Condition
-      Error in `weighted_interval_score()`:
-      ! Can't recycle `x` (size 2) to match `actual` (size 3).
-
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 5adbf6518..32f581d7a 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -78,7 +78,7 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
 
   z <- arx_forecaster(df, "cases", "cases", spec2)
   expect_identical(
-    nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
+    hardhat::extract_quantile_levels(z$predictions$.pred_distn),
     c(.05, .1, 0.25, .5, 0.75, .9, .95)
   )
 })
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index a4362cffb..af6f82727 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -42,3 +42,12 @@ test_that("quantile pivotting longer behaves", {
   expect_identical(pivot_quantiles_longer(tib, d1)$d1_value, c(1:3, 2:4))
 
 })
+
+test_that("nested_quantiles is deprecated, but works where possible", {
+  expect_snapshot(d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5))
+  expect_snapshot(o <- nested_quantiles(d))
+  res <- as_tibble(hardhat::quantile_pred(
+    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5)
+  )
+  expect_identical(o |> mutate(.row = dplyr::row_number()) |> unnest(data), res)
+})
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 6e4cd5df2..f2efde3c0 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -340,7 +340,6 @@ test_that("test joining by default columns with less common keys/classes", {
       pivot_quantiles_wider(.pred),
     dat1 %>%
       select(!"y") %>%
-      as_tibble() %>%
       mutate(`0.5` = c(2 * 5, 2 * 11))
   )
 
@@ -377,7 +376,6 @@ test_that("test joining by default columns with less common keys/classes", {
           pivot_quantiles_wider(.pred),
         dat1b %>%
           select(!"y") %>%
-          as_tibble() %>%
           # geo 1 scaling used for both:
           mutate(`0.5` = c(2 * 5, 2 * 5))
       ),
@@ -419,10 +417,10 @@ test_that("test joining by default columns with less common keys/classes", {
       expect_equal(
         # get_test_data doesn't work with non-`epi_df`s, so provide test data manually:
         predict(fit(ewf1b2, dat1b2), dat1b2) %>%
-          pivot_quantiles_wider(.pred),
+          pivot_quantiles_wider(.pred) %>%
+          as_tibble(),
         dat1b2 %>%
           select(!"y") %>%
-          as_tibble() %>%
           # geo 1 scaling used for both:
           mutate(`0.5` = c(2 * 5, 2 * 5)) %>%
           select(geo_value, age_group, time_value, `0.5`)
@@ -485,7 +483,8 @@ test_that("test joining by default columns with less common keys/classes", {
   )
   expect_equal(
     forecast(fit(ewf2, dat2)) %>%
-      pivot_quantiles_wider(.pred),
+      pivot_quantiles_wider(.pred) %>%
+      as_tibble(),
     dat2 %>%
       select(!"y") %>%
       as_tibble() %>%
@@ -522,7 +521,6 @@ test_that("test joining by default columns with less common keys/classes", {
           pivot_quantiles_wider(.pred),
         dat2b %>%
           select(!"y") %>%
-          as_tibble() %>%
           mutate(`0.5` = c(2 * 5, 2 * 11))
       ),
       class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
@@ -559,8 +557,7 @@ test_that("test joining by default columns with less common keys/classes", {
     # slightly edited copy-pasta due to test time selection:
     dat3 %>%
       select(!"y") %>%
-      as_tibble() %>%
-      slice_max(by = geo_value, time_value) %>%
+      dplyr::slice_max(by = geo_value, time_value) %>%
       mutate(`0.5` = 2 * 11)
   )
 
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 1e9127bbe..9a6910533 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -13,7 +13,6 @@ x <- tibble(
 ) %>%
   as_epi_df(as_of = as.POSIXct("2024-09-17"))
 max_time <- max(x$time_value)
-class(attributes(x)$metadata$as_of)
 as_of <- attributes(x)$metadata$as_of
 ahead <- 7
 latency <- 5
@@ -241,7 +240,12 @@ test_that("epi_adjust_latency extends multiple aheads", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit3 <- fit(epi_wf, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
+  expect_error(
+    expect_warning(
+      fit3 <- fit(epi_wf, data = x),
+      class = "epipredict__prep.step_latency__very_large_latency"),
+    class = "simpleError"
+  )
   # real date example
   fit3 <- fit(epi_wf, data = real_x)
   expect_equal(

From b1d1630432ce11894fa0eb97fbfaec7a1fc4b744 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:00:47 -0800
Subject: [PATCH 284/387] pass local checks

---
 NAMESPACE                      |  1 -
 R/make_smooth_quantile_reg.R   | 10 ++++------
 R/weighted_interval_score.R    |  6 +++---
 man/smooth_quantile_reg.Rd     | 10 ++++------
 man/weighted_interval_score.Rd |  6 +++---
 5 files changed, 14 insertions(+), 19 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 4c43e2f45..4bb3d6fe1 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -202,7 +202,6 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
-export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index 1ca019f75..03d4e19af 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -41,13 +41,11 @@
 #' )
 #' pl <- pl %>%
 #'   unnest(.pred) %>%
-#'   mutate(distn = nested_quantiles(distn)) %>%
-#'   unnest(distn) %>%
+#'   pivot_quantiles_wider(distn) %>%
 #'   mutate(
 #'     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
 #'     ahead = NULL
-#'   ) %>%
-#'   pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
+#'   )
 #' plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 #' curve(sin(x), add = TRUE)
 #' abline(v = fd, lty = 2)
@@ -57,11 +55,11 @@
 #'
 #' library(ggplot2)
 #' ggplot(data.frame(x = x, y = y), aes(x)) +
-#'   geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
+#'   geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "cornflowerblue") +
 #'   geom_point(aes(y = y), colour = "grey") + # observed data
 #'   geom_function(fun = sin, colour = "black") + # truth
 #'   geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
-#'   geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
+#'   geom_line(data = pl, aes(y = `0.5`), colour = "orange") + # median prediction
 #'   theme_bw() +
 #'   coord_cartesian(xlim = c(0, NA)) +
 #'   ylab("y")
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index aa0c816a3..47187fe56 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -52,13 +52,13 @@
 #'
 #' # Using some actual forecasts --------
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' training <- covid_case_death_rates %>%
 #'   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 #' preds <- flatline_forecaster(
-#'   jhu, "death_rate",
+#'   training, "death_rate",
 #'   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 #' )$predictions
-#' actuals <- case_death_rate_subset %>%
+#' actuals <- covid_case_death_rates %>%
 #'   filter(time_value == as.Date("2021-12-01") + 7) %>%
 #'   select(geo_value, time_value, actual = death_rate)
 #' preds <- left_join(preds, actuals,
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 973a85879..5d401c795 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -60,13 +60,11 @@ pl <- predict(
 )
 pl <- pl \%>\%
   unnest(.pred) \%>\%
-  mutate(distn = nested_quantiles(distn)) \%>\%
-  unnest(distn) \%>\%
+  pivot_quantiles_wider(distn) \%>\%
   mutate(
     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
     ahead = NULL
-  ) \%>\%
-  pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
+  )
 plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 curve(sin(x), add = TRUE)
 abline(v = fd, lty = 2)
@@ -76,11 +74,11 @@ lines(pl$x, pl$`0.5`, col = "red")
 
 library(ggplot2)
 ggplot(data.frame(x = x, y = y), aes(x)) +
-  geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
+  geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "cornflowerblue") +
   geom_point(aes(y = y), colour = "grey") + # observed data
   geom_function(fun = sin, colour = "black") + # truth
   geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
-  geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
+  geom_line(data = pl, aes(y = `0.5`), colour = "orange") + # median prediction
   theme_bw() +
   coord_cartesian(xlim = c(0, NA)) +
   ylab("y")
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 22a616b70..ef09d4da1 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -69,13 +69,13 @@ weighted_interval_score(
 
 # Using some actual forecasts --------
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+training <- covid_case_death_rates \%>\%
   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 preds <- flatline_forecaster(
-  jhu, "death_rate",
+  training, "death_rate",
   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 )$predictions
-actuals <- case_death_rate_subset \%>\%
+actuals <- covid_case_death_rates \%>\%
   filter(time_value == as.Date("2021-12-01") + 7) \%>\%
   select(geo_value, time_value, actual = death_rate)
 preds <- left_join(preds, actuals,

From e049766b6c58f8d7c751febd2baa9cb7c44444a7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:05:03 -0800
Subject: [PATCH 285/387] bump news

---
 NEWS.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 9c30f2cf7..aaa0af2c0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -17,6 +17,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   has an `na_rm` argument.
 - Moves `epiprocess` out of depends (#440). No internals have changed, but downstream
   users may need to add `library(epiprocess)` to existing code.
+- Removes dependence on the `distributional` package, replacing the quantiles 
+  with `hardhat::quantile_pred()`. Some associated functions are deprecated with
+  `lifecycle` messages.
 
 ## Improvements
 
@@ -28,6 +31,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Add `reference_date` as an argument to `epi_recipe()`
 - Add `step_climate()` to create "climate" predictor in forecast workflows
 - Add `climatological_forecaster()` to automatically create climate baselines
+- Replace `dist_quantiles()` with `hardhat::quantile_pred()`
 
 ## Bug fixes
 

From 893a52fbcd4223d5332fd5200a629c30116ee8f4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:21:02 -0800
Subject: [PATCH 286/387] style: fu

---
 R/layer_residual_quantiles.R              |  5 +++--
 R/quantile_pred-methods.R                 | 25 +++++++++++++----------
 tests/testthat/test-pivot_quantiles.R     |  5 ++---
 tests/testthat/test-quantile_pred.R       |  1 -
 tests/testthat/test-step_adjust_latency.R |  3 ++-
 tests/testthat/test-wis-quantile_pred.R   |  3 ++-
 6 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 0517a0ffd..b82d6ab91 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -126,10 +126,11 @@ slather.layer_residual_quantiles <-
 
     r <- r %>%
       summarize(dstn = quantile_pred(matrix(quantile(
-        c(.resid, s * .resid), probs = object$quantile_levels, na.rm = TRUE
+        c(.resid, s * .resid),
+        probs = object$quantile_levels, na.rm = TRUE
       ), nrow = 1), quantile_levels = object$quantile_levels))
     # Check for NA
-    if (anyNA(as.matrix(r$dstn)))  {
+    if (anyNA(as.matrix(r$dstn))) {
       cli_abort(c(
         "Residual quantiles could not be calculated due to missing residuals.",
         i = "This may be due to `n_train` < `ahead` in your {.cls epi_recipe}."
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 134308da2..6694a0146 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -133,7 +133,7 @@ quantile_internal <- function(x, tau_out, middle) {
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
   if (all(tau_out %in% tau) && !anyNA(qvals)) {
-    return(qvals[ , match(tau_out, tau), drop = FALSE])
+    return(qvals[, match(tau_out, tau), drop = FALSE])
   }
   if (length(tau) < 2) {
     cli_abort(paste(
@@ -152,7 +152,9 @@ quantile_internal <- function(x, tau_out, middle) {
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   qvals_out <- rep(NA, length(tau_out))
   good <- !is.na(qvals)
-  if (!any(good)) return(qvals_out)
+  if (!any(good)) {
+    return(qvals_out)
+  }
   qvals <- qvals[good]
   tau <- tau[good]
 
@@ -169,21 +171,22 @@ extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
 
   if (middle == "cubic") {
     method <- "cubic"
-    result <- tryCatch({
-      Q <- stats::splinefun(tau, qvals, method = "hyman")
-      quartiles <- Q(c(.25, .5, .75))
-    },
-    error = function(e) {
-      return(NA)
-    })
+    result <- tryCatch(
+      {
+        Q <- stats::splinefun(tau, qvals, method = "hyman")
+        quartiles <- Q(c(.25, .5, .75))
+      },
+      error = function(e) {
+        return(NA)
+      }
+    )
   }
   if (middle == "linear" || any(is.na(result))) {
     method <- "linear"
     quartiles <- stats::approx(tau, qvals, c(.25, .5, .75))$y
   }
   if (any(indm)) {
-    qvals_out[indm] <- switch(
-      method,
+    qvals_out[indm] <- switch(method,
       linear = stats::approx(tau, qvals, tau_out[indm])$y,
       cubic = Q(tau_out[indm])
     )
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index af6f82727..39db31b2f 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -40,14 +40,13 @@ test_that("quantile pivotting longer behaves", {
   expect_length(pivot_quantiles_longer(tib, d1), 4L)
   expect_identical(nrow(pivot_quantiles_longer(tib, d1)), 6L)
   expect_identical(pivot_quantiles_longer(tib, d1)$d1_value, c(1:3, 2:4))
-
 })
 
 test_that("nested_quantiles is deprecated, but works where possible", {
   expect_snapshot(d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5))
   expect_snapshot(o <- nested_quantiles(d))
   res <- as_tibble(hardhat::quantile_pred(
-    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5)
-  )
+    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5
+  ))
   expect_identical(o |> mutate(.row = dplyr::row_number()) |> unnest(data), res)
 })
diff --git a/tests/testthat/test-quantile_pred.R b/tests/testthat/test-quantile_pred.R
index 90710c6e1..70d7c71a5 100644
--- a/tests/testthat/test-quantile_pred.R
+++ b/tests/testthat/test-quantile_pred.R
@@ -78,7 +78,6 @@ test_that("unary math works on quantiles", {
     1:4 / 5
   )
   expect_identical(log(dstn), dstn2)
-
 })
 
 test_that("arithmetic works on quantiles", {
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 9a6910533..80e31dc17 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -243,7 +243,8 @@ test_that("epi_adjust_latency extends multiple aheads", {
   expect_error(
     expect_warning(
       fit3 <- fit(epi_wf, data = x),
-      class = "epipredict__prep.step_latency__very_large_latency"),
+      class = "epipredict__prep.step_latency__very_large_latency"
+    ),
     class = "simpleError"
   )
   # real date example
diff --git a/tests/testthat/test-wis-quantile_pred.R b/tests/testthat/test-wis-quantile_pred.R
index 187344af9..a51a67352 100644
--- a/tests/testthat/test-wis-quantile_pred.R
+++ b/tests/testthat/test-wis-quantile_pred.R
@@ -51,7 +51,8 @@ test_that("wis dispatches and produces the correct values", {
   ))
   expect_true(is.na(
     weighted_interval_score(
-      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail"
+      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10,
+      na_handling = "fail"
     )
   ))
 })

From e8d4e57dd019fe9d9d28495ad63ae6a242959034 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 18:49:35 -0800
Subject: [PATCH 287/387] bump hh version to release

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 01e07520d..af8e94595 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -35,7 +35,7 @@ Imports:
     generics,
     ggplot2,
     glue,
-    hardhat (>= 1.4.0.9002),
+    hardhat (>= 1.4.1),
     lifecycle,
     lubridate,
     magrittr,

From 3895f1355923e487f4caaedfa4f71923cd45b756 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 13:44:35 -0800
Subject: [PATCH 288/387] allow for quantile() to threshold to an interval

---
 R/quantile_pred-methods.R     | 54 ++++++++++++++++++++++++++---
 man/quantile.quantile_pred.Rd | 64 +++++++++++++++++++++++++++++++++++
 2 files changed, 113 insertions(+), 5 deletions(-)
 create mode 100644 man/quantile.quantile_pred.Rd

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 6694a0146..7548b58eb 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -115,14 +115,58 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 # quantiles by treating quantile_pred like a distribution -----------------
 
 
+#' Quantiles from a distribution
+#'
+#' Given a [hardhat::quantile_pred] object, users may wish to compute additional
+#' `quantile_levels` that are not part of the object. This function attempts
+#' to estimate these quantities under some assumptions. Interior probabilities,
+#' those contained within, existing probabilities are interpolated in a manner
+#' controled by the `middle` argument. Those outside existing probabilities
+#' are extrapolated under the assumption that the tails of the distribution
+#' decays exponentially. Optionally, one may constrain _all_ quantiles to be
+#' within some support (say, `[0, Inf)`).
+#'
+#' @inheritParams stats::quantile
+#' @param ... unused
+#' @param lower Scalar. Optional lower bound.
+#' @param upper Scalar. Optional upper bound.
+#' @param middle Controls how extrapolation to "interior" probabilities is
+#'   performed. "cubic" attempts to use [stats::splinefun()] while "linear"
+#'   uses [stats::approx()]. The "linear" method is used as a fallback if
+#'   "cubic" should fail for some reason.
+#'
+#' @returns a matrix with one row for each entry in `x` and one column for each
+#'   value in `probs`. If either has length 1, a vector.
+#' @seealso [extrapolate_quantiles()]
 #' @export
 #' @importFrom stats quantile
-quantile.quantile_pred <- function(x, p, na.rm = FALSE, ...,
-                                   middle = c("cubic", "linear")) {
-  arg_is_probabilities(p)
-  p <- sort(p)
+#'
+#' @examples
+#' qp <- quantile_pred(matrix(1:8, nrow = 2, byrow = TRUE), 1:4 / 5)
+#' quantile(qp)
+#' quantile(qp, lower = 0)
+#' quantile(qp, probs = 0.5)
+#' quantile(qp, probs = 1:9 / 10)
+quantile.quantile_pred <- function(x,
+                                   probs = seq(0, 1, 0.25),
+                                   na.rm = FALSE,
+                                   lower = -Inf,
+                                   upper = Inf,
+                                   middle = c("cubic", "linear"),
+                                   ...
+) {
+  arg_is_probabilities(probs)
+  arg_is_scalar(lower, upper, na.rm)
+  arg_is_numeric(lower, upper)
+  arg_is_lgl(na.rm)
+
+  if (lower > upper) {
+    cli_abort("`lower` must be less than `upper`.")
+  }
+
+  if (is.unsorted(probs)) probs <- sort(probs)
   middle <- rlang::arg_match(middle)
-  quantile_internal(x, p, middle)
+  drop(snap(quantile_internal(x, probs, middle), lower, upper))
 }
 
 
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
new file mode 100644
index 000000000..c83e629b2
--- /dev/null
+++ b/man/quantile.quantile_pred.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/quantile_pred-methods.R
+\name{quantile.quantile_pred}
+\alias{quantile.quantile_pred}
+\title{Quantiles from a distribution}
+\usage{
+\method{quantile}{quantile_pred}(
+  x,
+  probs = seq(0, 1, 0.25),
+  na.rm = FALSE,
+  lower = -Inf,
+  upper = Inf,
+  middle = c("cubic", "linear"),
+  ...
+)
+}
+\arguments{
+\item{x}{numeric vector whose sample quantiles are wanted, or an
+    object of a class for which a method has been defined (see also
+    \sQuote{details}). \code{\link{NA}} and \code{NaN} values are not
+    allowed in numeric vectors unless \code{na.rm} is \code{TRUE}.}
+
+\item{probs}{numeric vector of probabilities with values in
+    \eqn{[0,1]}.  (Values up to \samp{2e-14} outside that
+    range are accepted and moved to the nearby endpoint.)}
+
+\item{na.rm}{logical; if true, any \code{\link{NA}} and \code{NaN}'s
+    are removed from \code{x} before the quantiles are computed.}
+
+\item{lower}{Scalar. Optional lower bound.}
+
+\item{upper}{Scalar. Optional upper bound.}
+
+\item{middle}{Controls how extrapolation to "interior" probabilities is
+performed. "cubic" attempts to use \code{\link[stats:splinefun]{stats::splinefun()}} while "linear"
+uses \code{\link[stats:approxfun]{stats::approx()}}. The "linear" method is used as a fallback if
+"cubic" should fail for some reason.}
+
+\item{...}{unused}
+}
+\value{
+a matrix with one row for each entry in \code{x} and one column for each
+value in \code{probs}. If either has length 1, a vector.
+}
+\description{
+Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional
+\code{quantile_levels} that are not part of the object. This function attempts
+to estimate these quantities under some assumptions. Interior probabilities,
+those contained within, existing probabilities are interpolated in a manner
+controled by the \code{middle} argument. Those outside existing probabilities
+are extrapolated under the assumption that the tails of the distribution
+decays exponentially. Optionally, one may constrain \emph{all} quantiles to be
+within some support (say, \verb{[0, Inf)}).
+}
+\examples{
+qp <- quantile_pred(matrix(1:8, nrow = 2, byrow = TRUE), 1:4 / 5)
+quantile(qp)
+quantile(qp, lower = 0)
+quantile(qp, probs = 0.5)
+quantile(qp, probs = 1:9 / 10)
+}
+\seealso{
+\code{\link[=extrapolate_quantiles]{extrapolate_quantiles()}}
+}

From fb1669967c802cd3d9b69cdb73aafd36bce58cbb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:00:25 -0800
Subject: [PATCH 289/387] closes #434

---
 NEWS.md                       | 1 +
 R/quantile_pred-methods.R     | 4 ++--
 man/quantile.quantile_pred.Rd | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index aaa0af2c0..13a9d93b3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -32,6 +32,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Add `step_climate()` to create "climate" predictor in forecast workflows
 - Add `climatological_forecaster()` to automatically create climate baselines
 - Replace `dist_quantiles()` with `hardhat::quantile_pred()`
+- Allow `quantile()` to threshold to an interval if desired (#434)
 
 ## Bug fixes
 
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 7548b58eb..16b30f941 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -136,7 +136,7 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 #'   "cubic" should fail for some reason.
 #'
 #' @returns a matrix with one row for each entry in `x` and one column for each
-#'   value in `probs`. If either has length 1, a vector.
+#'   value in `probs`
 #' @seealso [extrapolate_quantiles()]
 #' @export
 #' @importFrom stats quantile
@@ -166,7 +166,7 @@ quantile.quantile_pred <- function(x,
 
   if (is.unsorted(probs)) probs <- sort(probs)
   middle <- rlang::arg_match(middle)
-  drop(snap(quantile_internal(x, probs, middle), lower, upper))
+  snap(quantile_internal(x, probs, middle), lower, upper)
 }
 
 
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
index c83e629b2..246a3e987 100644
--- a/man/quantile.quantile_pred.Rd
+++ b/man/quantile.quantile_pred.Rd
@@ -40,7 +40,7 @@ uses \code{\link[stats:approxfun]{stats::approx()}}. The "linear" method is used
 }
 \value{
 a matrix with one row for each entry in \code{x} and one column for each
-value in \code{probs}. If either has length 1, a vector.
+value in \code{probs}
 }
 \description{
 Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional

From 297a65dcb887033bd4bf0cfb9ebfdefde0a32f97 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:05:20 -0800
Subject: [PATCH 290/387] excise lingering distributional internals

---
 DESCRIPTION                   |  1 -
 NAMESPACE                     |  2 -
 R/layer_predictive_distn.R    | 93 ++---------------------------------
 R/layer_quantile_distn.R      | 12 +----
 R/pivot_quantiles.R           | 32 +++---------
 man/layer_predictive_distn.Rd | 24 ++-------
 man/nested_quantiles.Rd       |  6 +--
 7 files changed, 17 insertions(+), 153 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index af8e94595..1ce1f8ef1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -50,7 +50,6 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
-    distributional,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/NAMESPACE b/NAMESPACE
index 4bb3d6fe1..3c59dcb06 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -78,7 +78,6 @@ S3method(print,layer_naomit)
 S3method(print,layer_point_from_distn)
 S3method(print,layer_population_scaling)
 S3method(print,layer_predict)
-S3method(print,layer_predictive_distn)
 S3method(print,layer_quantile_distn)
 S3method(print,layer_residual_quantiles)
 S3method(print,layer_threshold)
@@ -104,7 +103,6 @@ S3method(slather,layer_naomit)
 S3method(slather,layer_point_from_distn)
 S3method(slather,layer_population_scaling)
 S3method(slather,layer_predict)
-S3method(slather,layer_predictive_distn)
 S3method(slather,layer_quantile_distn)
 S3method(slather,layer_residual_quantiles)
 S3method(slather,layer_threshold)
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 8ce55e1c3..824593f8d 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -1,5 +1,7 @@
 #' Returns predictive distributions
 #'
+#' `r lifecycle::badge("deprecated")`
+#'
 #' This function calculates an _approximation_ to a parametric predictive
 #' distribution. Predictive distributions from linear models require
 #' `x* (X'X)^{-1} x*`
@@ -16,102 +18,13 @@
 #'
 #' @return an updated `frosting` postprocessor with additional columns of the
 #'   residual quantiles added to the prediction
-
 #' @export
 #'
-#' @examples
-#' library(dplyr)
-#' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
-#'
-#' r <- epi_recipe(jhu) %>%
-#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-#'   step_epi_ahead(death_rate, ahead = 7) %>%
-#'   step_epi_naomit()
-#'
-#' wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)
-#'
-#' f <- frosting() %>%
-#'   layer_predict() %>%
-#'   layer_predictive_distn() %>%
-#'   layer_naomit(.pred)
-#' wf1 <- wf %>% add_frosting(f)
-#'
-#' p <- forecast(wf1)
-#' p
 layer_predictive_distn <- function(frosting,
                                    ...,
                                    dist_type = c("gaussian", "student_t"),
                                    truncate = c(-Inf, Inf),
                                    name = ".pred_distn",
                                    id = rand_id("predictive_distn")) {
-  if (!requireNamespace("distributional", quietly = TRUE)) {
-    cli_abort(paste(
-      "You must install the {.pkg distributional} package for",
-      "this functionality."
-    ))
-  }
-  rlang::check_dots_empty()
-  arg_is_chr_scalar(name, id)
-  dist_type <- match.arg(dist_type)
-  stopifnot(
-    length(truncate) == 2L, is.numeric(truncate), truncate[1] < truncate[2]
-  )
-
-  add_layer(
-    frosting,
-    layer_predictive_distn_new(
-      dist_type = dist_type,
-      truncate = truncate,
-      name = name,
-      id = id
-    )
-  )
-}
-
-layer_predictive_distn_new <- function(dist_type, truncate, name, id) {
-  layer("predictive_distn",
-    dist_type = dist_type, truncate = truncate,
-    name = name, id = id
-  )
-}
-
-#' @export
-slather.layer_predictive_distn <-
-  function(object, components, workflow, new_data, ...) {
-    the_fit <- workflows::extract_fit_parsnip(workflow)
-    rlang::check_dots_empty()
-
-    m <- components$predictions$.pred
-    r <- grab_residuals(the_fit, components)
-    df <- the_fit$df.residual
-    n <- sum(!is.na(r))
-    papprox <- ncol(components$mold$predictors) + 1
-    if (is.null(df)) df <- n - papprox
-    mse <- sum(r^2, na.rm = TRUE) / df
-    s <- sqrt(mse * (1 + papprox / df)) # E[x (X'X)^1 x] if E[X'X] ~= (n-p) I
-    dstn <- switch(object$dist_type,
-      gaussian = distributional::dist_normal(m, s),
-      student_t = distributional::dist_student_t(df, m, s)
-    )
-    truncate <- object$truncate
-    if (!all(is.infinite(truncate))) {
-      dstn <- distributional::dist_truncated(dstn, truncate[1], truncate[2])
-    }
-    dstn <- tibble(dstn = dstn)
-    dstn <- check_pname(dstn, components$predictions, object)
-    components$predictions <- mutate(components$predictions, !!!dstn)
-    components
-  }
-
-#' @export
-print.layer_predictive_distn <- function(
-    x, width = max(20, options()$width - 30), ...) {
-  title <- "Creating approximate predictive intervals"
-  td <- "<calculated>"
-  td <- rlang::enquos(td)
-  print_layer(td,
-    title = title, width = width, conjunction = "type",
-    extra_text = x$dist_type
-  )
+  lifecycle::deprecate_stop("0.1.11", "layer_predictive_distn()", "layer_residual_quantiles()")
 }
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 67fbfa28c..c6bc217f3 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -78,20 +78,12 @@ layer_quantile_distn_new <- function(quantile_levels, truncate, name, id) {
 slather.layer_quantile_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    is_supported <- inherits(dstn, "distribution") ||
-      inherits(dstn, "quantile_pred")
-    if (!is_supported) {
+    if (!inherits(dstn, "quantile_pred")) {
       cli_abort(
-        "`layer_quantile_distn()` requires distributional or quantile
+        "`layer_quantile_distn()` requires or quantile
         predictions. These are of class {.cls {class(dstn)}}."
       )
     }
-    if (inherits(dstn, "distribution") && !requireNamespace("distributional", quietly = TRUE)) {
-      cli_abort(
-        "You must install the {.pkg distributional} package for this
-        functionality."
-      )
-    }
     rlang::check_dots_empty()
 
     dstn <- quantile_pred(
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 8e846d7e6..e4b5b3320 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -12,39 +12,23 @@
 #' @export
 #'
 #' @examples
-#' .pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
-#' nested_quantiles(.pred_quantile)
+#' pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#' nested_quantiles(pred_quantile)
 #'
-#' .pred_quantile %>%
+#' pred_quantile %>%
 #'   as_tibble() %>%
 #'   tidyr::nest(.by = .row) %>%
 #'   dplyr::select(-.row)
 #'
 nested_quantiles <- function(x) {
   lifecycle::deprecate_warn("0.1.11", "nested_quantiles()", "hardhat::quantile_pred()")
-  if (inherits(x, "distribution")) {
-    if (requireNamespace("distributional")) {
-      x <- vctrs::vec_data(x)
-      return(distributional:::dist_apply(x, .f = function(z) {
-        as_tibble(vctrs::vec_data(z)) %>%
-          mutate(across(everything(), as.double)) %>%
-          vctrs::list_of()
-      }))
-    } else {
-      cli_abort(c(
-        "`nested_quantiles()` is deprecated and the {.pkg distributional}",
-        `!` = "package is not installed.",
-        i = "See {.fn hardhat::quantile_pred}."
-      ))
-    }
-  }
   if (inherits(x, "quantile_pred")) {
     return(x %>% as_tibble() %>% tidyr::nest(.by = .row) %>%
       dplyr::select(data))
   }
-  cli_abort(c(
+  cli_abort(
     "`nested_quantiles()` is deprecated. See {.fn hardhat::quantile_pred}."
-  ))
+  )
 }
 
 
@@ -113,11 +97,7 @@ pivot_quantiles_wider <- function(.data, ...) {
 }
 
 pivot_quantiles <- function(.data, ...) {
-  msg <- c(
-    "{.fn pivot_quantiles} was deprecated in {.pkg epipredict} 0.0.6",
-    i = "Please use {.fn pivot_quantiles_wider} instead."
-  )
-  lifecycle::deprecate_stop(msg)
+  lifecycle::deprecate_stop("0.0.6", "pivot_quantiles()", "pivot_quantiles_wider()")
 }
 
 validate_pivot_quantiles <- function(.data, ..., call = caller_env()) {
diff --git a/man/layer_predictive_distn.Rd b/man/layer_predictive_distn.Rd
index 3bd95425b..0e4b17cdb 100644
--- a/man/layer_predictive_distn.Rd
+++ b/man/layer_predictive_distn.Rd
@@ -31,6 +31,9 @@ an updated \code{frosting} postprocessor with additional columns of the
 residual quantiles added to the prediction
 }
 \description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
 This function calculates an \emph{approximation} to a parametric predictive
 distribution. Predictive distributions from linear models require
 \verb{x* (X'X)^\{-1\} x*}
@@ -38,24 +41,3 @@ along with the degrees of freedom. This function approximates both. It
 should be reasonably accurate for models fit using \code{lm} when the new point
 \verb{x*} isn't too far from the bulk of the data.
 }
-\examples{
-library(dplyr)
-jhu <- covid_case_death_rates \%>\%
-  filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
-
-r <- epi_recipe(jhu) \%>\%
-  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
-  step_epi_ahead(death_rate, ahead = 7) \%>\%
-  step_epi_naomit()
-
-wf <- epi_workflow(r, linear_reg()) \%>\% fit(jhu)
-
-f <- frosting() \%>\%
-  layer_predict() \%>\%
-  layer_predictive_distn() \%>\%
-  layer_naomit(.pred)
-wf1 <- wf \%>\% add_frosting(f)
-
-p <- forecast(wf1)
-p
-}
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
index 4b666ee32..c8c05d94f 100644
--- a/man/nested_quantiles.Rd
+++ b/man/nested_quantiles.Rd
@@ -20,10 +20,10 @@ This function is deprecated. The recommended alternative is
 \code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}} with \code{\link[tibble:as_tibble]{tibble::as_tibble()}}
 }
 \examples{
-.pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
-nested_quantiles(.pred_quantile)
+pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+nested_quantiles(pred_quantile)
 
-.pred_quantile \%>\%
+pred_quantile \%>\%
   as_tibble() \%>\%
   tidyr::nest(.by = .row) \%>\%
   dplyr::select(-.row)

From 65b2ea4d80df65f6ba82b66e78ba42529893bd68 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:28:13 -0800
Subject: [PATCH 291/387] unnecessary lazydata (no data)

---
 DESCRIPTION | 1 -
 1 file changed, 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1ce1f8ef1..0d5398444 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -76,6 +76,5 @@ Remotes:
 Config/Needs/website: cmu-delphi/delphidocs
 Config/testthat/edition: 3
 Encoding: UTF-8
-LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2

From c7f492f420cd3c843c4998b1b24e8ae581bff335 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:31:36 -0800
Subject: [PATCH 292/387] style

---
 R/quantile_pred-methods.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 16b30f941..84412e4dd 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -153,8 +153,7 @@ quantile.quantile_pred <- function(x,
                                    lower = -Inf,
                                    upper = Inf,
                                    middle = c("cubic", "linear"),
-                                   ...
-) {
+                                   ...) {
   arg_is_probabilities(probs)
   arg_is_scalar(lower, upper, na.rm)
   arg_is_numeric(lower, upper)

From cc405c729407a9da01eca16d821c2e3a9b65a48b Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 15:30:39 -0800
Subject: [PATCH 293/387] bump version number

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 0d5398444..8ced21d2d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.11
+Version: 0.1.12
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),

From 161feb785288e50bc9391a76215ebf7216080ee5 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Mar 2025 11:07:49 -0800
Subject: [PATCH 294/387] missing reexports

---
 R/reexports.R | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/R/reexports.R b/R/reexports.R
index 2cc7aa9fa..4166b6e0c 100644
--- a/R/reexports.R
+++ b/R/reexports.R
@@ -65,3 +65,10 @@ tidyr::pivot_wider
 #' @importFrom tidyr unnest
 #' @export
 tidyr::unnest
+
+#' @importFrom hardhat quantile_pred extract_quantile_levels
+#' @export
+hardhat::quantile_pred
+
+#' @export
+hardhat::extract_quantile_levels

From c36b2421971acf7e3660c870881bb007ab55706a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 11 Mar 2025 18:24:21 -0500
Subject: [PATCH 295/387] minor docs, testing multiple pivot_longers

---
 R/quantile_pred-methods.R             | 2 +-
 man/quantile.quantile_pred.Rd         | 2 +-
 tests/testthat/test-pivot_quantiles.R | 5 +++++
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 84412e4dd..293fad902 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -120,7 +120,7 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 #' Given a [hardhat::quantile_pred] object, users may wish to compute additional
 #' `quantile_levels` that are not part of the object. This function attempts
 #' to estimate these quantities under some assumptions. Interior probabilities,
-#' those contained within, existing probabilities are interpolated in a manner
+#' those contained within existing probabilities are interpolated in a manner
 #' controled by the `middle` argument. Those outside existing probabilities
 #' are extrapolated under the assumption that the tails of the distribution
 #' decays exponentially. Optionally, one may constrain _all_ quantiles to be
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
index 246a3e987..1c23daed4 100644
--- a/man/quantile.quantile_pred.Rd
+++ b/man/quantile.quantile_pred.Rd
@@ -46,7 +46,7 @@ value in \code{probs}
 Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional
 \code{quantile_levels} that are not part of the object. This function attempts
 to estimate these quantities under some assumptions. Interior probabilities,
-those contained within, existing probabilities are interpolated in a manner
+those contained within existing probabilities are interpolated in a manner
 controled by the \code{middle} argument. Those outside existing probabilities
 are extrapolated under the assumption that the tails of the distribution
 decays exponentially. Optionally, one may constrain \emph{all} quantiles to be
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index 39db31b2f..d32c16499 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -22,6 +22,11 @@ test_that("pivotting wider still works if there are duplicates", {
   tb <- tibble(.pred = quantile_pred(rbind(1:3, 1:3), c(.1, .5, .9)))
   res <- tibble(`0.1` = c(1, 1), `0.5` = c(2, 2), `0.9` = c(3, 3))
   expect_equal(tb %>% pivot_quantiles_wider(.pred), res)
+  res_longer <- tibble(
+    .pred_value = rep(1:3, 2),
+    .pred_quantile_level = rep(c(0.1, 0.5, 0.9), 2)
+  )
+  expect_equal(tb %>% pivot_quantiles_longer(.pred), res_longer)
 })
 
 

From 6c5e70f6779c339609a8b4c8953961faf73d40fe Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:18:39 -0500
Subject: [PATCH 296/387] updating for climatological

---
 NAMESPACE                          |    5 +-
 R/climatological_forecaster.R      |    2 +-
 R/layer_threshold_preds.R          |    6 +-
 man/reexports.Rd                   |   32 +-
 tests/testthat/_snaps/snapshots.md | 1291 +++++++---------------------
 5 files changed, 356 insertions(+), 980 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 3c59dcb06..939a1f01a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -136,6 +136,7 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
+export(as_epi_df)
 export(as_tibble)
 export(autoplot)
 export(bake)
@@ -182,6 +183,7 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(mutate)
 export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
@@ -200,6 +202,7 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
+export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
@@ -282,11 +285,11 @@ importFrom(hardhat,extract_recipe)
 importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lifecycle,deprecated)
 importFrom(lubridate,"%m-%")
 importFrom(lubridate,leap_year)
 importFrom(lubridate,month)
 importFrom(lubridate,yday)
-importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 7df853252..e29d7d4a3 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -148,7 +148,7 @@ climatological_forecaster <- function(epi_data,
       .by = all_of(args_list$quantile_by_key)
     ) %>%
     rename(.pred_distn = climate_pred) %>%
-    mutate(.pred_distn = dist_quantiles(.pred_distn, args_list$quantile_levels))
+    mutate(.pred_distn = hardhat::quantile_pred(do.call(rbind, .pred_distn), args_list$quantile_levels))
   # combine them together
   climate_table <- climate_center %>%
     left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 9acf06725..2f1720301 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,7 +61,11 @@ layer_threshold_new <-
 
 
 
-# restrict various objects to the interval [lower, upper]
+#' restrict various objects to the interval [lower, upper]
+#' @param x the object to restrict
+#' @param lower numeric, the lower bound
+#' @param upper numeric, the upper bound
+#' @param ... unused
 #' @export
 #' @keywords internal
 snap <- function(x, lower, upper, ...) {
diff --git a/man/reexports.Rd b/man/reexports.Rd
index ab2cf17be..8d3e360f4 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -1,30 +1,29 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/autoplot.R, R/reexports.R
+% Please edit documentation in R/autoplot.R, R/reexports-tidymodels.R,
+%   R/reexports.R
 \docType{import}
 \name{reexports}
 \alias{reexports}
 \alias{autoplot}
-\alias{filter}
-\alias{mutate}
-\alias{rename}
-\alias{select}
-\alias{as_epi_df}
-\alias{key_colnames}
 \alias{fit}
 \alias{forecast}
-\alias{tidy}
 \alias{prep}
 \alias{bake}
 \alias{rand_id}
-\alias{as_tibble}
 \alias{tibble}
-\alias{pivot_longer}
-\alias{pivot_wider}
-\alias{unnest}
 \alias{as_tibble}
 \alias{tidy}
 \alias{quantile_pred}
 \alias{extract_quantile_levels}
+\alias{filter}
+\alias{mutate}
+\alias{rename}
+\alias{select}
+\alias{as_epi_df}
+\alias{key_colnames}
+\alias{pivot_longer}
+\alias{pivot_wider}
+\alias{unnest}
 \title{Objects exported from other packages}
 \keyword{internal}
 \description{
@@ -36,15 +35,16 @@ below to see their documentation.
 
   \item{epiprocess}{\code{\link[epiprocess:epi_df]{as_epi_df}}, \code{\link[epiprocess]{key_colnames}}}
 
-  \item{generics}{\code{\link[generics]{fit}}, \code{\link[generics]{forecast}}, \code{\link[generics]{tidy}}}
+  \item{generics}{\code{\link[generics]{fit}}, \code{\link[generics]{fit}}, \code{\link[generics]{forecast}}, \code{\link[generics]{forecast}}, \code{\link[generics]{tidy}}, \code{\link[generics]{tidy}}}
 
   \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
 
-  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}}
+  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}, \code{\link[hardhat]{quantile_pred}}}
 
-  \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}}
+  \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}, \code{\link[recipes]{rand_id}}}
 
-  \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
+  \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}, \code{\link[tibble]{tibble}}}
 
   \item{tidyr}{\code{\link[tidyr]{pivot_longer}}, \code{\link[tidyr]{pivot_wider}}, \code{\link[tidyr]{unnest}}}
 }}
+
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 78f629171..b2972a8a0 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -843,136 +843,61 @@
     64.1929086, 67.80972325, 58.13744405, 63.10147305, 84.3449863, 
     51.8545645, 66.9333338, 65.8523132, 55.9202576, 64.1234883, 59.50597115, 
     48.9275239, 64.0481843, 63.45754255, 43.7883142, 65.37832155), 
-        .pred_distn = structure(list(structure(list(values = c(55.01884375, 
-        61.9936534, 76.8558811, 85.8705552, 95.7238376875, 107.595355333333, 
-        124.8294820725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(22.4852324, 
-        29.46004205, 44.32226975, 53.33694385, 63.1902263375, 75.0617439833333, 
-        92.2958707225), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(33.03575305, 
-        40.0105627, 54.8727904, 63.8874645, 73.7407469875, 85.6122646333333, 
-        102.8463913725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(31.00572905, 
-        37.9805387, 52.8427664, 61.8574405, 71.7107229875, 83.5822406333333, 
-        100.8163673725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(36.34589965, 
+        .pred_distn = structure(list(c(55.01884375, 61.9936534, 76.8558811, 
+        85.8705552, 95.7238376875, 107.595355333333, 124.8294820725
+        ), c(22.4852324, 29.46004205, 44.32226975, 53.33694385, 63.1902263375, 
+        75.0617439833333, 92.2958707225), c(33.03575305, 40.0105627, 
+        54.8727904, 63.8874645, 73.7407469875, 85.6122646333333, 
+        102.8463913725), c(31.00572905, 37.9805387, 52.8427664, 61.8574405, 
+        71.7107229875, 83.5822406333333, 100.8163673725), c(36.34589965, 
         43.3207093, 58.182937, 67.1976111, 77.0508935875, 88.9224112333333, 
-        106.1565379725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(27.4126191, 
-        34.38742875, 49.24965645, 58.26433055, 68.1176130375, 79.9891306833333, 
-        97.2232574225), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(59.1059257625, 
-        65.4267824, 80.893949775, 88.512741775, 98.9851218875, 111.446583986667, 
-        128.951180125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(26.1435490625, 
+        106.1565379725), c(27.4126191, 34.38742875, 49.24965645, 
+        58.26433055, 68.1176130375, 79.9891306833333, 97.2232574225
+        ), c(59.1059257625, 65.4267824, 80.893949775, 88.512741775, 
+        98.9851218875, 111.446583986667, 128.951180125), c(26.1435490625, 
         32.4644057, 47.931573075, 55.550365075, 66.0227451875, 78.4842072866667, 
-        95.988803425), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(36.8149959625, 
-        43.1358526, 58.603019975, 66.221811975, 76.6941920875, 89.1556541866667, 
-        106.660250325), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(41.8898725625, 
-        48.2107292, 63.677896575, 71.296688575, 81.7690686875, 94.2305307866667, 
-        111.735126925), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(37.4322168625, 
+        95.988803425), c(36.8149959625, 43.1358526, 58.603019975, 
+        66.221811975, 76.6941920875, 89.1556541866667, 106.660250325
+        ), c(41.8898725625, 48.2107292, 63.677896575, 71.296688575, 
+        81.7690686875, 94.2305307866667, 111.735126925), c(37.4322168625, 
         43.7530735, 59.220240875, 66.839032875, 77.3114129875, 89.7728750866667, 
-        107.277471225), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(34.2319594625, 
-        40.5528161, 56.019983475, 63.638775475, 74.1111555875, 86.5726176866667, 
-        104.077213825), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(76.597008525, 
-        83.6285187166667, 89.8466094166667, 96.39787945, 106.5883326375, 
-        118.488928483333, 139.8972249625), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(36.051994425, 43.0835046166667, 49.3015953166667, 
-            55.85286535, 66.0433185375, 77.9439143833333, 99.3522108625
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(48.463300925, 55.4948111166667, 
+        107.277471225), c(34.2319594625, 40.5528161, 56.019983475, 
+        63.638775475, 74.1111555875, 86.5726176866667, 104.077213825
+        ), c(76.597008525, 83.6285187166667, 89.8466094166667, 96.39787945, 
+        106.5883326375, 118.488928483333, 139.8972249625), c(36.051994425, 
+        43.0835046166667, 49.3015953166667, 55.85286535, 66.0433185375, 
+        77.9439143833333, 99.3522108625), c(48.463300925, 55.4948111166667, 
         61.7129018166667, 68.26417185, 78.4546250375, 90.3552208833333, 
-        111.7635173625), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(52.080115575, 
-        59.1116257666667, 65.3297164666667, 71.8809865, 82.0714396875, 
-        93.9720355333333, 115.3803320125), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(42.407836375, 49.4393465666667, 55.6574372666667, 
-            62.2087073, 72.3991604875, 84.2997563333333, 105.7080528125
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(47.371865375, 54.4033755666667, 
-        60.6214662666667, 67.1727363, 77.3631894875, 89.2637853333333, 
-        110.6720818125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(75.151683565, 
-        78.2257355633333, 82.1485257708333, 87.28053515, 96.1483782208333, 
-        106.438732546667, 113.7276053175), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(42.661261765, 45.7353137633333, 49.6581039708333, 
-            54.79011335, 63.6579564208333, 73.9483107466667, 81.2371835175
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(57.740031065, 60.8140830633333, 
-        64.7368732708333, 69.86888265, 78.7367257208333, 89.0270800466667, 
-        96.3159528175), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(56.659010465, 
+        111.7635173625), c(52.080115575, 59.1116257666667, 65.3297164666667, 
+        71.8809865, 82.0714396875, 93.9720355333333, 115.3803320125
+        ), c(42.407836375, 49.4393465666667, 55.6574372666667, 62.2087073, 
+        72.3991604875, 84.2997563333333, 105.7080528125), c(47.371865375, 
+        54.4033755666667, 60.6214662666667, 67.1727363, 77.3631894875, 
+        89.2637853333333, 110.6720818125), c(75.151683565, 78.2257355633333, 
+        82.1485257708333, 87.28053515, 96.1483782208333, 106.438732546667, 
+        113.7276053175), c(42.661261765, 45.7353137633333, 49.6581039708333, 
+        54.79011335, 63.6579564208333, 73.9483107466667, 81.2371835175
+        ), c(57.740031065, 60.8140830633333, 64.7368732708333, 69.86888265, 
+        78.7367257208333, 89.0270800466667, 96.3159528175), c(56.659010465, 
         59.7330624633333, 63.6558526708333, 68.78786205, 77.6557051208333, 
-        87.9460594466667, 95.2349322175), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(46.726954865, 49.8010068633333, 53.7237970708333, 
-            58.85580645, 67.7236495208333, 78.0140038466667, 85.3028766175
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(54.930185565, 58.0042375633333, 
-        61.9270277708333, 67.05903715, 75.9268802208333, 86.2172345466667, 
-        93.5061073175), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(50.3425070425, 
-        53.1286383166667, 57.7775218541667, 61.6092566, 68.5981147666667, 
-        77.2595451983333, 81.44671049), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(39.7640597925, 42.5501910666667, 47.1990746041667, 
-            51.03080935, 58.0196675166667, 66.6810979483333, 70.86826324
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(54.8847201925, 
-        57.6708514666667, 62.3197350041667, 66.15146975, 73.1403279166667, 
-        81.8017583483333, 85.98892364), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(54.2940784425, 57.0802097166667, 61.7290932541667, 
-            65.560828, 72.5496861666667, 81.2111165983333, 85.39828189
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(34.6248500925, 
-        37.4109813666667, 42.0598649041667, 45.89159965, 52.8804578166667, 
-        61.5418882483333, 65.72905354), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(56.2148574425, 59.0009887166667, 63.6498722541667, 
-            67.481607, 74.4704651666667, 83.1318955983333, 87.31906089
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-        "list"))), row.names = c(NA, -30L), class = c("tbl_df", "tbl", 
-    "data.frame"))
+        87.9460594466667, 95.2349322175), c(46.726954865, 49.8010068633333, 
+        53.7237970708333, 58.85580645, 67.7236495208333, 78.0140038466667, 
+        85.3028766175), c(54.930185565, 58.0042375633333, 61.9270277708333, 
+        67.05903715, 75.9268802208333, 86.2172345466667, 93.5061073175
+        ), c(50.3425070425, 53.1286383166667, 57.7775218541667, 61.6092566, 
+        68.5981147666667, 77.2595451983333, 81.44671049), c(39.7640597925, 
+        42.5501910666667, 47.1990746041667, 51.03080935, 58.0196675166667, 
+        66.6810979483333, 70.86826324), c(54.8847201925, 57.6708514666667, 
+        62.3197350041667, 66.15146975, 73.1403279166667, 81.8017583483333, 
+        85.98892364), c(54.2940784425, 57.0802097166667, 61.7290932541667, 
+        65.560828, 72.5496861666667, 81.2111165983333, 85.39828189
+        ), c(34.6248500925, 37.4109813666667, 42.0598649041667, 45.89159965, 
+        52.8804578166667, 61.5418882483333, 65.72905354), c(56.2148574425, 
+        59.0009887166667, 63.6498722541667, 67.481607, 74.4704651666667, 
+        83.1318955983333, 87.31906089)), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+        "vctrs_vctr", "list"))), row.names = c(NA, -30L), class = c("tbl_df", 
+    "tbl", "data.frame"))
 
 ---
 
@@ -990,120 +915,47 @@
     ), class = "Date"), .pred = c(1782, 927.5, 577.5, 935, 635, 1321, 
     1791, 934.5, 561, 765.5, 529.5, 1435, 2153.5, 946.5, 607, 673, 
     476, 1663.5, 2486.5, 1138, 659.5, 637.5, 446.5, 2002, 3236, 1311, 
-    879.5, 666.5, 446.5, 2964), .pred_distn = structure(list(structure(list(
-        values = c(512, 651.333333333334, 1098.45833333333, 1678, 
-        2429.04166666667, 3110.91666666667, 3621.875), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 365.541666666667, 927.5, 2329.29166666667, 
-        3263.83333333333, 3892.5), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(220.125, 253.166666666667, 342, 540.5, 733.375, 
-        854.166666666667, 977), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(435.375, 
-    506.583333333333, 567.875, 776, 1103.75, 1465.83333333333, 1673.125
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(312.25, 377.833333333333, 453.416666666667, 584.25, 
-        726.541666666667, 1037.33333333333, 1516.5), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(149.5, 363.666666666667, 814.333333333333, 1149, 
-        2003.95833333333, 3254.66666666667, 3883.375), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 450.083333333333, 924.75, 1670.5, 2544.375, 
-        3821.08333333334, 4407.25), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 6.41666666666674, 941.5, 2380.375, 4497.16666666667, 
-        9047.75), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-        0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(118.25, 
-    198.583333333333, 288.916666666667, 495.75, 736.625, 962, 1521.5
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(313.375, 350.166666666667, 409.083333333333, 597, 
-        860.791666666666, 1210.41666666667, 1353.25), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(206.75, 258.666666666667, 330.166666666667, 478.75, 
-        598.041666666667, 734.833333333333, 926.25), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 281.083333333333, 790.666666666667, 1242.5, 
-        2162.125, 3593.5, 4306.625), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 121.916666666668, 1184, 2179.25, 3261.875, 
-        4614.33333333333, 5419.75), quantile_levels = c(0.05, 0.1, 
-        0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
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@@ -1189,743 +1041,260 @@
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-            values = c(6569.2, 7244.53333333333, 8381.66666666667, 
-            9484, 10946.6666666667, 12994.3333333333, 14599.6), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(2365.2, 3153.8, 4329.33333333333, 5343, 6039.66666666667, 
-            6603.53333333333, 6695.8), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(8524, 8743.66666666667, 9836, 11120, 12910.3333333333, 
-            13495.4666666667, 14369), quantile_levels = c(0.05, 0.1, 
-            0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(2761.6, 2999, 4092, 4438, 5124.66666666667, 
-            5675.53333333333, 6811.8), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(4336.6, 6789.13333333334, 13394, 17898, 20165.6666666667, 
-            25111.7333333333, 27349.4), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(13648.4, 14721.3333333333, 15978.6666666667, 
-            17077, 18266, 19933.0666666667, 22864), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(5754.2, 6429.53333333333, 7566.66666666667, 
-            8669, 10050.3333333333, 10882.4, 13120.6), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(2361.2, 3149.8, 4325.33333333333, 5270, 5878.66666666667, 
-            6580.4, 6683.8), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(8434, 
-        8653.66666666667, 9746, 11030, 12458, 13329.8, 13463.8), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(2669.6, 2907, 4000, 
-        4346, 5032.66666666667, 5583.53333333333, 6719.8), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(3177.6, 5630.13333333334, 12235, 16653, 18812.6666666667, 
-            23952.7333333333, 26190.4), quantile_levels = c(0.05, 
-            0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list"))), row.names = c(NA, -186L), class = c("tbl_df", 
-    "tbl", "data.frame"))
+        c(6967.6, 9420.13333333334, 16689.3333333333, 21695, 24506.6666666667, 
+        27742.7333333333, 29980.4), c(17730.4, 18803.3333333333, 
+        20114.6666666667, 21531, 23113, 26977, 28232), c(8127.2, 
+        8802.53333333333, 9686, 11042, 12587, 14552.3333333333, 16157.6
+        ), c(3962.2, 4750.8, 5779, 6992, 7636.66666666667, 8200.53333333333, 
+        8292.8), c(10207, 10426.6666666667, 11727.6666666667, 12896, 
+        14513, 15178.4666666667, 16052), c(3611.2, 3843.66666666667, 
+        5119, 5384, 6039.33333333333, 6873.33333333333, 7838.8), 
+        c(6094.6, 8547.13333333334, 15152, 20127, 23361.1666666667, 
+        26869.7333333333, 29107.4), c(17030.4, 18103.3333333333, 
+        19360.6666666667, 20459, 22413, 26277, 27532), c(8010.2, 
+        8685.53333333333, 9518, 10925, 12470, 14435.3333333333, 16040.6
+        ), c(3788.2, 4576.8, 5325.33333333333, 6818, 7462.66666666667, 
+        8026.53333333333, 8118.8), c(10036, 10255.6666666667, 11556.6666666667, 
+        12725, 14080.3333333333, 15007.4666666667, 15881), c(3493.2, 
+        3725.66666666667, 4809, 5266, 5921.33333333333, 6755.33333333333, 
+        7720.8), c(6094.6, 8547.13333333334, 14977.6666666667, 20127, 
+        23361.1666666667, 26869.7333333333, 29107.4), c(16349.4, 
+        17422.3333333333, 18679.6666666667, 19778, 21732, 25596, 
+        26851), c(7567.2, 8242.53333333333, 9379.66666666667, 10482, 
+        12027, 13992.3333333333, 15597.6), c(3747.2, 4535.8, 5711.33333333333, 
+        6777, 7421.66666666667, 7985.53333333333, 8077.8), c(9698, 
+        9917.66666666667, 11218.6666666667, 12387, 13742.3333333333, 
+        14669.4666666667, 15543), c(3670.6, 3908, 5001, 5347, 6121.66666666667, 
+        6755.33333333333, 7720.8), c(6094.6, 8547.13333333334, 15152, 
+        20127, 23361.1666666667, 26869.7333333333, 29107.4), c(16298.4, 
+        17371.3333333333, 18628.6666666667, 19727, 21681, 25545, 
+        26800), c(6850.2, 7525.53333333333, 8662.66666666667, 9765, 
+        11235, 13275.3333333333, 14880.6), c(3736.2, 4524.8, 5700.33333333333, 
+        6714, 7410.66666666667, 7974.53333333333, 8066.8), c(9287, 
+        9506.66666666667, 10599, 11883, 13331.3333333333, 14258.4666666667, 
+        15132), c(3402.6, 3640, 4733, 4998, 5853.66666666667, 6487.33333333333, 
+        7452.8), c(5597.6, 8050.13333333334, 14655, 19574, 21943, 
+        26372.7333333333, 28610.4), c(15152.4, 16225.3333333333, 
+        17482.6666666667, 18581, 20535, 24399, 25654), c(6628.2, 
+        7303.53333333333, 8440.66666666667, 9543, 11013, 13053.3333333333, 
+        14658.6), c(3110.2, 3898.8, 5074.33333333333, 6140, 6784.66666666667, 
+        7348.53333333333, 7440.8), c(8807, 9026.66666666667, 10119, 
+        11403, 13193.3333333333, 13778.4666666667, 14652), c(3349.6, 
+        3587, 4680, 4945, 5712.66666666667, 6263.53333333333, 7399.8
+        ), c(5468.6, 7921.13333333334, 14526, 19172, 21297.6666666667, 
+        26243.7333333333, 28481.4), c(14243.4, 15316.3333333333, 
+        16573.6666666667, 17672, 19117.3333333333, 21921.5333333333, 
+        24647), c(6569.2, 7244.53333333333, 8381.66666666667, 9484, 
+        10946.6666666667, 12994.3333333333, 14599.6), c(2365.2, 3153.8, 
+        4329.33333333333, 5343, 6039.66666666667, 6603.53333333333, 
+        6695.8), c(8524, 8743.66666666667, 9836, 11120, 12910.3333333333, 
+        13495.4666666667, 14369), c(2761.6, 2999, 4092, 4438, 5124.66666666667, 
+        5675.53333333333, 6811.8), c(4336.6, 6789.13333333334, 13394, 
+        17898, 20165.6666666667, 25111.7333333333, 27349.4), c(13648.4, 
+        14721.3333333333, 15978.6666666667, 17077, 18266, 19933.0666666667, 
+        22864), c(5754.2, 6429.53333333333, 7566.66666666667, 8669, 
+        10050.3333333333, 10882.4, 13120.6), c(2361.2, 3149.8, 4325.33333333333, 
+        5270, 5878.66666666667, 6580.4, 6683.8), c(8434, 8653.66666666667, 
+        9746, 11030, 12458, 13329.8, 13463.8), c(2669.6, 2907, 4000, 
+        4346, 5032.66666666667, 5583.53333333333, 6719.8), c(3177.6, 
+        5630.13333333334, 12235, 16653, 18812.6666666667, 23952.7333333333, 
+        26190.4)), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", "list"))), row.names = c(NA, 
+    -186L), class = c("tbl_df", "tbl", "data.frame"))
 

From c3f0c776a0992a312e271cc115a5e8031814547d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:35:12 -0500
Subject: [PATCH 297/387] neglected docs

---
 man/epiweek_leap.Rd | 12 ++++++++++++
 man/isoweek_leap.Rd | 12 ++++++++++++
 man/snap.Rd         | 21 +++++++++++++++++++++
 man/yday_leap.Rd    | 14 ++++++++++++++
 4 files changed, 59 insertions(+)
 create mode 100644 man/epiweek_leap.Rd
 create mode 100644 man/isoweek_leap.Rd
 create mode 100644 man/snap.Rd
 create mode 100644 man/yday_leap.Rd

diff --git a/man/epiweek_leap.Rd b/man/epiweek_leap.Rd
new file mode 100644
index 000000000..4fecbf2ca
--- /dev/null
+++ b/man/epiweek_leap.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{epiweek_leap}
+\alias{epiweek_leap}
+\title{epiweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap}
+\usage{
+epiweek_leap(time_value)
+}
+\description{
+epiweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
+}
+\keyword{internal}
diff --git a/man/isoweek_leap.Rd b/man/isoweek_leap.Rd
new file mode 100644
index 000000000..355d27b1e
--- /dev/null
+++ b/man/isoweek_leap.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{isoweek_leap}
+\alias{isoweek_leap}
+\title{isoweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap}
+\usage{
+isoweek_leap(time_value)
+}
+\description{
+isoweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
+}
+\keyword{internal}
diff --git a/man/snap.Rd b/man/snap.Rd
new file mode 100644
index 000000000..2f5ca57d5
--- /dev/null
+++ b/man/snap.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/layer_threshold_preds.R
+\name{snap}
+\alias{snap}
+\title{restrict various objects to the interval \link{lower, upper}}
+\usage{
+snap(x, lower, upper, ...)
+}
+\arguments{
+\item{x}{the object to restrict}
+
+\item{lower}{numeric, the lower bound}
+
+\item{upper}{numeric, the upper bound}
+
+\item{...}{unused}
+}
+\description{
+restrict various objects to the interval \link{lower, upper}
+}
+\keyword{internal}
diff --git a/man/yday_leap.Rd b/man/yday_leap.Rd
new file mode 100644
index 000000000..1dc873a38
--- /dev/null
+++ b/man/yday_leap.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{yday_leap}
+\alias{yday_leap}
+\title{a function that assigns Feb 29th to 999, and aligns all other dates the same
+number in the year, regardless of whether it's a leap year}
+\usage{
+yday_leap(time_value)
+}
+\description{
+a function that assigns Feb 29th to 999, and aligns all other dates the same
+number in the year, regardless of whether it's a leap year
+}
+\keyword{internal}

From 8387aa2633dad5a6d985ef8617ff0bfe1af8a31e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:50:26 -0500
Subject: [PATCH 298/387] no accidental link

---
 R/layer_threshold_preds.R | 2 +-
 man/snap.Rd               | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 2f1720301..2869fff07 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,7 +61,7 @@ layer_threshold_new <-
 
 
 
-#' restrict various objects to the interval [lower, upper]
+#' restrict various objects to the interval \[lower, upper\]
 #' @param x the object to restrict
 #' @param lower numeric, the lower bound
 #' @param upper numeric, the upper bound
diff --git a/man/snap.Rd b/man/snap.Rd
index 2f5ca57d5..abe4421e8 100644
--- a/man/snap.Rd
+++ b/man/snap.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/layer_threshold_preds.R
 \name{snap}
 \alias{snap}
-\title{restrict various objects to the interval \link{lower, upper}}
+\title{restrict various objects to the interval [lower, upper]}
 \usage{
 snap(x, lower, upper, ...)
 }
@@ -16,6 +16,6 @@ snap(x, lower, upper, ...)
 \item{...}{unused}
 }
 \description{
-restrict various objects to the interval \link{lower, upper}
+restrict various objects to the interval [lower, upper]
 }
 \keyword{internal}

From 928cbe778184eef7979c3b3e5f5c63ca9b6a7936 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 12 Mar 2025 15:33:28 -0700
Subject: [PATCH 299/387] redocument to get pkgdown to build

---
 R/step_climate.R             |  1 +
 man/roll_modular_multivec.Rd |  1 +
 man/step_adjust_latency.Rd   | 10 +++++-----
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/R/step_climate.R b/R/step_climate.R
index 800de5357..2ae2c79d2 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -346,6 +346,7 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
 #'   the aggregation
 #' @param modulus the maximum value of `.idx`
 #' @importFrom lubridate %m-%
+#' @keywords internal
 roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus) {
   tib <- tibble(col = col, weights = weights, .idx = .idx) |>
     arrange(.idx) |>
diff --git a/man/roll_modular_multivec.Rd b/man/roll_modular_multivec.Rd
index a26cd5884..b01304cc6 100644
--- a/man/roll_modular_multivec.Rd
+++ b/man/roll_modular_multivec.Rd
@@ -23,3 +23,4 @@ the aggregation}
 \description{
 group col by .idx values and sum windows around each .idx value
 }
+\keyword{internal}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 9e1bafbd5..75d674472 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -143,7 +143,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 8 x 4
 #>   geo_value time_value     a     b
-#>   <chr>     <date>     <dbl> <dbl>
+#> * <chr>     <date>     <dbl> <dbl>
 #> 1 ca        2015-01-11   100     5
 #> 2 ca        2015-01-12   103    10
 #> 3 ca        2015-01-13   103    10
@@ -179,7 +179,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 21 x 7
 #>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>  * <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-10    NA    NA      NA      NA       100
 #>  2 ca        2015-01-11   100     5      NA      NA       103
 #>  3 ca        2015-01-12   103    10      NA      NA        NA
@@ -227,7 +227,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 10 x 6
 #>    geo_value time_value     a     b lag_0_a ahead_3_a
-#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>  * <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-08    NA    NA      NA       100
 #>  2 ca        2015-01-09    NA    NA      NA       103
 #>  3 ca        2015-01-11   100     5     100        NA
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as

From 93386d86f5c91b40f05fa17e5d48cd8945f21894 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 12 Mar 2025 15:39:08 -0700
Subject: [PATCH 300/387] adjust pkgdown

---
 _pkgdown.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 86b4d16ad..fe34c3b82 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -100,6 +100,7 @@ reference:
   - title: Utilities for quantile distribution processing
     contents:
       - dist_quantiles
+      - contains("quantile_pred")
       - extrapolate_quantiles
       - nested_quantiles
       - weighted_interval_score

From 1953103f9ce9709b0c2cba2552fd66d430a6cef3 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 13 Mar 2025 11:23:15 -0500
Subject: [PATCH 301/387] drop `check_pname` in favor just just `check_name`

---
 R/layer_cdc_flatline_quantiles.R |  1 -
 R/layer_point_from_distn.R       |  7 ++++++-
 R/layer_quantile_distn.R         |  7 ++++++-
 R/layer_residual_quantiles.R     |  2 +-
 R/utils-misc.R                   | 33 --------------------------------
 5 files changed, 13 insertions(+), 37 deletions(-)

diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 9c08ef768..fffacf21a 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -227,7 +227,6 @@ slather.layer_cdc_flatline_quantiles <-
       ) %>%
       select(all_of(c(avail_grps, ".pred_distn_all")))
 
-    # res <- check_pname(res, components$predictions, object)
     components$predictions <- left_join(
       components$predictions,
       res,
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index 5d293992f..a67e3e079 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -93,7 +93,12 @@ slather.layer_point_from_distn <-
       components$predictions$.pred <- dstn
     } else {
       dstn <- tibble(dstn = dstn)
-      dstn <- check_pname(dstn, components$predictions, object)
+      dstn <- check_name(
+        dstn,
+        components$predictions,
+        object,
+        newname = object$name
+      )
       components$predictions <- mutate(components$predictions, !!!dstn)
     }
     components
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index c6bc217f3..e07713e00 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -96,7 +96,12 @@ slather.layer_quantile_distn <-
       dstn <- snap(dstn, truncate[1], truncate[2])
     }
     dstn <- tibble(dstn = dstn)
-    dstn <- check_pname(dstn, components$predictions, object)
+    dstn <- check_name(
+      dstn,
+      components$predictions,
+      object,
+      newname = object$name
+    )
     components$predictions <- mutate(components$predictions, !!!dstn)
     components
   }
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index b82d6ab91..0a6b4fe30 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -139,7 +139,7 @@ slather.layer_residual_quantiles <-
 
     estimate <- components$predictions$.pred
     res <- tibble(.pred_distn = r$dstn + estimate)
-    res <- check_pname(res, components$predictions, object)
+    res <- check_name(res, components$predictions, object, newname = object$name)
     components$predictions <- mutate(components$predictions, !!!res)
     components
   }
diff --git a/R/utils-misc.R b/R/utils-misc.R
index bdb35a9eb..71fd38615 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -1,36 +1,3 @@
-#' Check that newly created variable names don't overlap
-#'
-#' `check_pname` is to be used in a slather method to ensure that
-#'   newly created variable names don't overlap with existing names.
-#'   Throws an warning if check fails, and creates a random string.
-#' @param res A data frame or tibble of the newly created variables.
-#' @param preds An epi_df or tibble containing predictions.
-#' @param object A layer object passed to [slather()].
-#' @param newname A string of variable names if the object doesn't contain a
-#'   $name element
-#'
-#' @keywords internal
-check_pname <- function(res, preds, object, newname = NULL) {
-  if (is.null(newname)) newname <- object$name
-  new_preds_names <- colnames(preds)
-  intersection <- new_preds_names %in% newname
-  if (any(intersection)) {
-    newname <- rand_id(newname)
-    rlang::warn(
-      paste0(
-        "Name collision occured in `",
-        class(object)[1],
-        "`. The following variable names already exists: ",
-        paste0(new_preds_names[intersection], collapse = ", "),
-        ". Result instead has randomly generated string `",
-        newname, "`."
-      )
-    )
-  }
-  names(res) <- newname
-  res
-}
-
 # Copied from `epiprocess`:
 
 #' "Format" a character vector of column/variable names for cli interpolation

From ff2a2a981bc6d062b1a290983a95609d473aa02b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 13 Mar 2025 11:27:53 -0500
Subject: [PATCH 302/387] news & description

---
 DESCRIPTION | 2 +-
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 8ced21d2d..d6f37ef63 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.12
+Version: 0.1.13
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 13a9d93b3..e117f3f52 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -42,6 +42,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - adjust default quantiles throughout so that they match.
 - force `layer_residual_quantiles()` to always include `0.5`.
 - Rename `recipes:::check_training_set()` to `recipes:::validate_training_data()`, as it changed in recipes 1.1.0.
+- A new column name duplicating an existing column name results in an error instead of a random name.
 
 # epipredict 0.1
 

From e1f21d1a361fb3e89cf7cedda98dacff36e8747f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 13 Mar 2025 11:35:47 -0500
Subject: [PATCH 303/387] adding a brief test

---
 R/layer_residual_quantiles.R                   |  7 ++++++-
 tests/testthat/test-layer_residual_quantiles.R | 17 +++++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 0a6b4fe30..6b32d2921 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -139,7 +139,12 @@ slather.layer_residual_quantiles <-
 
     estimate <- components$predictions$.pred
     res <- tibble(.pred_distn = r$dstn + estimate)
-    res <- check_name(res, components$predictions, object, newname = object$name)
+    res <- check_name(
+      res,
+      components$predictions,
+      object,
+      newname = object$name
+    )
     components$predictions <- mutate(components$predictions, !!!res)
     components
   }
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 2941037f5..d04403f6c 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -28,6 +28,23 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(unique(unnested$.pred_distn_quantile_level), c(.0275, 0.5, .8, .95))
 })
 
+tests_that("new name works correctly", {
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_naomit(.pred) %>%
+    layer_residual_quantiles(name = "foo")
+
+  wf1 <- wf %>% add_frosting(f)
+  expect_equal(names(forecast(wf1)), c("geo_value", "time_value", ".pred", "foo"))
+
+  f <- frosting() %>%
+    layer_predict() %>%
+    layer_naomit(.pred) %>%
+    layer_residual_quantiles(name = "geo_value")
+
+  wf1 <- wf %>% add_frosting(f)
+  expect_error(forecast(wf1))
+})
 
 test_that("Errors when used with a classifier", {
   tib <- tibble(

From 416e4c39f6a6d4ea6ff26a1ebe73fff45693f3ad Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 13 Mar 2025 12:14:39 -0500
Subject: [PATCH 304/387] drop docs, test typo

---
 man/check_pname.Rd                            | 24 -------------------
 .../testthat/test-layer_residual_quantiles.R  |  2 +-
 2 files changed, 1 insertion(+), 25 deletions(-)
 delete mode 100644 man/check_pname.Rd

diff --git a/man/check_pname.Rd b/man/check_pname.Rd
deleted file mode 100644
index 6740c6523..000000000
--- a/man/check_pname.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-misc.R
-\name{check_pname}
-\alias{check_pname}
-\title{Check that newly created variable names don't overlap}
-\usage{
-check_pname(res, preds, object, newname = NULL)
-}
-\arguments{
-\item{res}{A data frame or tibble of the newly created variables.}
-
-\item{preds}{An epi_df or tibble containing predictions.}
-
-\item{object}{A layer object passed to \code{\link[=slather]{slather()}}.}
-
-\item{newname}{A string of variable names if the object doesn't contain a
-$name element}
-}
-\description{
-\code{check_pname} is to be used in a slather method to ensure that
-newly created variable names don't overlap with existing names.
-Throws an warning if check fails, and creates a random string.
-}
-\keyword{internal}
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index d04403f6c..3d5883c72 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -28,7 +28,7 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(unique(unnested$.pred_distn_quantile_level), c(.0275, 0.5, .8, .95))
 })
 
-tests_that("new name works correctly", {
+test_that("new name works correctly", {
   f <- frosting() %>%
     layer_predict() %>%
     layer_naomit(.pred) %>%

From f421a2d29be5774970b438c5a6c8ad0de35bbca2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 21 Mar 2025 17:56:25 -0500
Subject: [PATCH 305/387] using check_enough_train_data in practice

---
 R/arx_forecaster.R                   |  4 +++-
 R/check_enough_train_data.R          | 24 +++++++++++++++++++++---
 R/epi_workflow.R                     |  2 +-
 tests/testthat/test-arx_forecaster.R | 19 +++++++++++++++++++
 4 files changed, 44 insertions(+), 5 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index fe9128c00..fd8303658 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -171,7 +171,9 @@ arx_fcast_epi_workflow <- function(
     step_epi_ahead(!!outcome, ahead = args_list$ahead)
   r <- r %>%
     step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training)
+    step_training_window(n_recent = args_list$n_training) %>%
+    check_enough_train_data(all_predictors(), skip = FALSE)
+
   if (!is.null(args_list$check_enough_data_n)) {
     r <- r %>% check_enough_train_data(
       all_predictors(),
diff --git a/R/check_enough_train_data.R b/R/check_enough_train_data.R
index 1279a3712..d71daecc3 100644
--- a/R/check_enough_train_data.R
+++ b/R/check_enough_train_data.R
@@ -47,7 +47,7 @@ check_enough_train_data <-
            role = NA,
            trained = FALSE,
            columns = NULL,
-           skip = TRUE,
+           skip = FALSE,
            id = rand_id("enough_train_data")) {
     recipes::add_check(
       recipe,
@@ -90,7 +90,7 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
   }
 
   if (x$drop_na) {
-    training <- tidyr::drop_na(training)
+    training <- tidyr::drop_na(training, any_of(unname(col_names)))
   }
   cols_not_enough_data <- training %>%
     group_by(across(all_of(.env$x$epi_keys))) %>%
@@ -101,7 +101,8 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
 
   if (length(cols_not_enough_data) > 0) {
     cli_abort(
-      "The following columns don't have enough data to predict: {cols_not_enough_data}."
+      "The following columns don't have enough data to predict: {cols_not_enough_data}.",
+      class = "epipredict__not_enough_train_data"
     )
   }
 
@@ -120,6 +121,23 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
 
 #' @export
 bake.check_enough_train_data <- function(object, new_data, ...) {
+  col_names <- object$columns
+  if (object$drop_na) {
+    newish_data <- tidyr::drop_na(new_data, any_of(unname(col_names)))
+  }
+  cols_not_enough_data <- newish_data %>%
+    group_by(across(all_of(.env$object$epi_keys))) %>%
+    summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$object$n), .groups = "drop") %>%
+    summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
+    unlist() %>%
+    names(.)[.]
+
+  if (length(cols_not_enough_data) > 0) {
+    cli_abort(
+      "The following columns don't have enough data to predict: {cols_not_enough_data}.",
+      class = "epipredict__not_enough_train_data"
+    )
+  }
   new_data
 }
 
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 81b443e7b..826ae7f1b 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -270,6 +270,6 @@ forecast.epi_workflow <- function(object, ..., n_recent = NULL, forecast_date =
     hardhat::extract_preprocessor(object),
     object$original_data
   )
-
+  test_data
   predict(object, new_data = test_data)
 }
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index 0f2b9bd16..cdc21769d 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -24,3 +24,22 @@ test_that("arx_forecaster errors if forecast date, target date, and ahead are in
     class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
   )
 })
+
+test_that("warns if there's not enough data to predict", {
+  edf <- tibble(
+    geo_value = "ct",
+    time_value = seq(as.Date("2020-10-01"), as.Date("2023-05-31"), by = "day"),
+  ) %>%
+    mutate(value = seq_len(nrow(.)) + rnorm(nrow(.))) %>%
+    # Oct to May (flu season, ish) only:
+    filter(!between(as.POSIXlt(time_value)$mon + 1L, 6L, 9L)) %>%
+    # and actually, pretend we're around mid-October 2022:
+    filter(time_value <= as.Date("2022-10-12")) %>%
+    as_epi_df(as_of = as.Date("2022-10-12"))
+  edf %>% filter(time_value > "2022-08-01")
+
+  expect_error(
+    edf %>% arx_forecaster("value"),
+    class = "epipredict__not_enough_train_data"
+  )
+})

From b06e7e68488c2153e868aa74259228a0072abc15 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 21 Mar 2025 17:59:58 -0500
Subject: [PATCH 306/387] news & description

---
 DESCRIPTION | 2 +-
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d6f37ef63..36a84f425 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.13
+Version: 0.1.14
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index e117f3f52..79daf4fdf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -33,6 +33,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Add `climatological_forecaster()` to automatically create climate baselines
 - Replace `dist_quantiles()` with `hardhat::quantile_pred()`
 - Allow `quantile()` to threshold to an interval if desired (#434)
+- `arx_forecaster()` detects if there's enough data to predict
 
 ## Bug fixes
 

From 2940f9576cbc8c4ba0784d03c05f2908b2b5dc54 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 24 Mar 2025 10:39:08 -0500
Subject: [PATCH 307/387] actually passing all the tests

---
 R/arx_forecaster.R                               |  2 +-
 R/check_enough_train_data.R                      |  6 ++++--
 tests/testthat/_snaps/check_enough_train_data.md |  2 +-
 tests/testthat/test-check_enough_train_data.R    | 11 ++++++-----
 tests/testthat/test-layer_residual_quantiles.R   |  2 +-
 5 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index fd8303658..97bd81f08 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -172,7 +172,7 @@ arx_fcast_epi_workflow <- function(
   r <- r %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training) %>%
-    check_enough_train_data(all_predictors(), skip = FALSE)
+    check_enough_train_data(all_predictors(), n = args_list$check_enough_data_n, skip = FALSE)
 
   if (!is.null(args_list$check_enough_data_n)) {
     r <- r %>% check_enough_train_data(
diff --git a/R/check_enough_train_data.R b/R/check_enough_train_data.R
index d71daecc3..3b2af6298 100644
--- a/R/check_enough_train_data.R
+++ b/R/check_enough_train_data.R
@@ -123,9 +123,11 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
 bake.check_enough_train_data <- function(object, new_data, ...) {
   col_names <- object$columns
   if (object$drop_na) {
-    newish_data <- tidyr::drop_na(new_data, any_of(unname(col_names)))
+    non_na_data <- tidyr::drop_na(new_data, any_of(unname(col_names)))
+  } else {
+    non_na_data <- new_data
   }
-  cols_not_enough_data <- newish_data %>%
+  cols_not_enough_data <- non_na_data %>%
     group_by(across(all_of(.env$object$epi_keys))) %>%
     summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$object$n), .groups = "drop") %>%
     summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
diff --git a/tests/testthat/_snaps/check_enough_train_data.md b/tests/testthat/_snaps/check_enough_train_data.md
index 8f2389acb..8f852092a 100644
--- a/tests/testthat/_snaps/check_enough_train_data.md
+++ b/tests/testthat/_snaps/check_enough_train_data.md
@@ -38,7 +38,7 @@
 
     Code
       epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>%
-        check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>% prep(
+        check_enough_train_data(all_predictors(), y, n = 2 * n - 4) %>% prep(
         toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
diff --git a/tests/testthat/test-check_enough_train_data.R b/tests/testthat/test-check_enough_train_data.R
index 9b2ef5f34..11e86a915 100644
--- a/tests/testthat/test-check_enough_train_data.R
+++ b/tests/testthat/test-check_enough_train_data.R
@@ -94,25 +94,26 @@ test_that("check_enough_train_data only checks train data", {
     epiprocess::as_epi_df()
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = n - 2, epi_keys = "geo_value") %>%
+      check_enough_train_data(x, y, n = n - 2, epi_keys = "geo_value", skip = TRUE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = toy_test_data)
   )
   # Same thing, but skip = FALSE
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(y, n = n - 2, epi_keys = "geo_value", skip = FALSE) %>%
+      check_enough_train_data(y, n = n - 2, epi_keys = "geo_value") %>%
       prep(toy_epi_df) %>%
       bake(new_data = toy_test_data)
   )
 })
 
 test_that("check_enough_train_data works with all_predictors() downstream of constructed terms", {
-  # With a lag of 2, we will get 2 * n - 6 non-NA rows
+  # With a lag of 2, we will get 2 * n - 5 non-NA rows (NA's in x but not in the
+  # lags don't count)
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_train_data(all_predictors(), y, n = 2 * n - 6) %>%
+      check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -120,7 +121,7 @@ test_that("check_enough_train_data works with all_predictors() downstream of con
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>%
+      check_enough_train_data(all_predictors(), y, n = 2 * n - 4) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 3d5883c72..2421b8a1c 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -103,7 +103,7 @@ test_that("Canned forecasters work with / without", {
   )
 
   expect_silent(
-    arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"))
+    arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"), args_list = arx_args_list(check_enough_data_n = 1))
   )
   expect_silent(
     flatline_forecaster(

From 7a2a25b25851aa433e682b98bcd7371afc5c13d3 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Mon, 24 Mar 2025 13:34:30 -0700
Subject: [PATCH 308/387] ci: replace auto styler with pr-commands

---
 .github/workflows/pr-commands.yaml | 85 ++++++++++++++++++++++++++++++
 .github/workflows/styler.yml       | 81 ----------------------------
 2 files changed, 85 insertions(+), 81 deletions(-)
 create mode 100644 .github/workflows/pr-commands.yaml
 delete mode 100644 .github/workflows/styler.yml

diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml
new file mode 100644
index 000000000..8bb12b9e2
--- /dev/null
+++ b/.github/workflows/pr-commands.yaml
@@ -0,0 +1,85 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  issue_comment:
+    types: [created]
+
+name: pr-commands.yaml
+
+permissions: read-all
+
+jobs:
+  document:
+    if: ${{ github.event.issue.pull_request && (github.event.comment.author_association == 'COLLABORATOR' || github.event.comment.author_association == 'CONTRIBUTOR' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') && startsWith(github.event.comment.body, '/document') }}
+    name: document
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/pr-fetch@v2
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::roxygen2
+          needs: pr-document
+
+      - name: Document
+        run: roxygen2::roxygenise()
+        shell: Rscript {0}
+
+      - name: commit
+        run: |
+          git config --local user.name "$GITHUB_ACTOR"
+          git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
+          git add man/\* NAMESPACE
+          git commit -m 'Document'
+
+      - uses: r-lib/actions/pr-push@v2
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+  style:
+    if: ${{ github.event.issue.pull_request && (github.event.comment.author_association == 'COLLABORATOR' || github.event.comment.author_association == 'CONTRIBUTOR' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER') && startsWith(github.event.comment.body, '/style') }}
+    name: style
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/pr-fetch@v2
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+      - uses: r-lib/actions/setup-r@v2
+
+      - name: Install dependencies
+        run: install.packages("styler")
+        shell: Rscript {0}
+
+      - name: Style
+        run: styler::style_pkg()
+        shell: Rscript {0}
+
+      - name: commit
+        run: |
+          git config --local user.name "$GITHUB_ACTOR"
+          git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
+          git add \*.R
+          git commit -m 'Style'
+
+      - uses: r-lib/actions/pr-push@v2
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/styler.yml b/.github/workflows/styler.yml
deleted file mode 100644
index 64c2390cd..000000000
--- a/.github/workflows/styler.yml
+++ /dev/null
@@ -1,81 +0,0 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
-# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
-on:
-  workflow_dispatch:
-  pull_request:
-    paths:
-      [
-        "**.[rR]",
-        "**.[qrR]md",
-        "**.[rR]markdown",
-        "**.[rR]nw",
-        "**.[rR]profile",
-      ]
-
-name: Style
-
-jobs:
-  style:
-    runs-on: ubuntu-latest
-    env:
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Setup R
-        uses: r-lib/actions/setup-r@v2
-        with:
-          use-public-rspm: true
-
-      - name: Install dependencies
-        uses: r-lib/actions/setup-r-dependencies@v2
-        with:
-          extra-packages: any::styler, any::roxygen2
-          needs: styler
-
-      - name: Enable styler cache
-        run: styler::cache_activate()
-        shell: Rscript {0}
-
-      - name: Determine cache location
-        id: styler-location
-        run: |
-          cat(
-            "location=",
-            styler::cache_info(format = "tabular")$location,
-            "\n",
-            file = Sys.getenv("GITHUB_OUTPUT"),
-            append = TRUE,
-            sep = ""
-          )
-        shell: Rscript {0}
-
-      - name: Cache styler
-        uses: actions/cache@v3
-        with:
-          path: ${{ steps.styler-location.outputs.location }}
-          key: ${{ runner.os }}-styler-${{ github.sha }}
-          restore-keys: |
-            ${{ runner.os }}-styler-
-            ${{ runner.os }}-
-
-      - name: Style
-        run: styler::style_pkg()
-        shell: Rscript {0}
-
-      - name: Commit and push changes
-        run: |
-          if FILES_TO_COMMIT=($(git diff-index --name-only ${{ github.sha }} \
-              | egrep --ignore-case '\.(R|[qR]md|Rmarkdown|Rnw|Rprofile)$'))
-          then
-            git config --local user.name "$GITHUB_ACTOR"
-            git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com"
-            git commit ${FILES_TO_COMMIT[*]} -m "Style code (GHA)"
-            git pull --ff-only
-            git push origin
-          else
-            echo "No changes to commit."
-          fi

From 5ef9823708c2a4abf84099da4d7e4fe720f71a4f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 24 Mar 2025 19:25:21 -0500
Subject: [PATCH 309/387] rename, old default skip, news, all_outcomes() for
 test, docs

---
 NAMESPACE                                     | 10 ++--
 NEWS.md                                       |  3 ++
 R/arx_classifier.R                            |  2 +-
 R/arx_forecaster.R                            |  6 +--
 ...nough_train_data.R => check_enough_data.R} | 53 ++++++++++---------
 _pkgdown.yml                                  |  2 +-
 ...ugh_train_data.Rd => check_enough_data.Rd} | 37 +++++++------
 man/step_adjust_latency.Rd                    | 10 ++--
 ...ugh_train_data.md => check_enough_data.md} | 25 +++++----
 tests/testthat/test-arx_forecaster.R          |  2 +-
 ..._train_data.R => test-check_enough_data.R} | 26 ++++-----
 11 files changed, 94 insertions(+), 82 deletions(-)
 rename R/{check_enough_train_data.R => check_enough_data.R} (71%)
 rename man/{check_enough_train_data.Rd => check_enough_data.Rd} (54%)
 rename tests/testthat/_snaps/{check_enough_train_data.md => check_enough_data.md} (51%)
 rename tests/testthat/{test-check_enough_train_data.R => test-check_enough_data.R} (74%)

diff --git a/NAMESPACE b/NAMESPACE
index 939a1f01a..f39d9bfbc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -15,7 +15,7 @@ S3method(apply_frosting,epi_workflow)
 S3method(augment,epi_workflow)
 S3method(autoplot,canned_epipred)
 S3method(autoplot,epi_workflow)
-S3method(bake,check_enough_train_data)
+S3method(bake,check_enough_data)
 S3method(bake,epi_recipe)
 S3method(bake,step_adjust_latency)
 S3method(bake,step_climate)
@@ -49,7 +49,7 @@ S3method(key_colnames,recipe)
 S3method(mean,quantile_pred)
 S3method(predict,epi_workflow)
 S3method(predict,flatline)
-S3method(prep,check_enough_train_data)
+S3method(prep,check_enough_data)
 S3method(prep,epi_recipe)
 S3method(prep,step_adjust_latency)
 S3method(prep,step_climate)
@@ -65,7 +65,7 @@ S3method(print,arx_class)
 S3method(print,arx_fcast)
 S3method(print,canned_epipred)
 S3method(print,cdc_baseline_fcast)
-S3method(print,check_enough_train_data)
+S3method(print,check_enough_data)
 S3method(print,climate_fcast)
 S3method(print,epi_recipe)
 S3method(print,epi_workflow)
@@ -109,7 +109,7 @@ S3method(slather,layer_threshold)
 S3method(slather,layer_unnest)
 S3method(snap,default)
 S3method(snap,quantile_pred)
-S3method(tidy,check_enough_train_data)
+S3method(tidy,check_enough_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
@@ -142,7 +142,7 @@ export(autoplot)
 export(bake)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
-export(check_enough_train_data)
+export(check_enough_data)
 export(clean_f_name)
 export(climate_args_list)
 export(climatological_forecaster)
diff --git a/NEWS.md b/NEWS.md
index 79daf4fdf..24c8e3b73 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -20,6 +20,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Removes dependence on the `distributional` package, replacing the quantiles 
   with `hardhat::quantile_pred()`. Some associated functions are deprecated with
   `lifecycle` messages.
+- Rename `check_enough_train_data()` to `check_enough_data()`, and generalize it
+  enough to use as a check on either training or testing.
+- Add check for enough data to predict in `arx_forecaster()`
 
 ## Improvements
 
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index d1aa292dd..bc8783610 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -222,7 +222,7 @@ arx_class_epi_workflow <- function(
     step_training_window(n_recent = args_list$n_training)
 
   if (!is.null(args_list$check_enough_data_n)) {
-    r <- check_enough_train_data(
+    r <- check_enough_data(
       r,
       recipes::all_predictors(),
       recipes::all_outcomes(),
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 97bd81f08..3a87719eb 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -172,12 +172,12 @@ arx_fcast_epi_workflow <- function(
   r <- r %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training) %>%
-    check_enough_train_data(all_predictors(), n = args_list$check_enough_data_n, skip = FALSE)
+    check_enough_data(all_predictors(), n = 1, skip = FALSE)
 
   if (!is.null(args_list$check_enough_data_n)) {
-    r <- r %>% check_enough_train_data(
+    r <- r %>% check_enough_data(
       all_predictors(),
-      !!outcome,
+      all_outcomes(),
       n = args_list$check_enough_data_n,
       epi_keys = args_list$check_enough_data_epi_keys,
       drop_na = FALSE
diff --git a/R/check_enough_train_data.R b/R/check_enough_data.R
similarity index 71%
rename from R/check_enough_train_data.R
rename to R/check_enough_data.R
index 3b2af6298..e87984e49 100644
--- a/R/check_enough_train_data.R
+++ b/R/check_enough_data.R
@@ -1,13 +1,13 @@
 #' Check the dataset contains enough data points.
 #'
-#' `check_enough_train_data` creates a *specification* of a recipe
+#' `check_enough_data` creates a *specification* of a recipe
 #'  operation that will check if variables contain enough data.
 #'
 #' @param recipe A recipe object. The check will be added to the
 #'  sequence of operations for this recipe.
 #' @param ... One or more selector functions to choose variables for this check.
 #'  See [selections()] for more details. You will usually want to use
-#'  [recipes::all_predictors()] here.
+#'  [recipes::all_predictors()] and/or [recipes::all_outcomes()] here.
 #' @param n The minimum number of data points required for training. If this is
 #'   NULL, the total number of predictors will be used.
 #' @param epi_keys A character vector of column names on which to group the data
@@ -21,24 +21,29 @@
 #' @param columns An internal argument that tracks which columns are evaluated
 #'   for this check. Should not be used by the user.
 #' @param id A character string that is unique to this check to identify it.
-#' @param skip A logical. Should the check be skipped when the
-#'  recipe is baked by [bake()]? While all operations are baked
-#'  when [prep()] is run, some operations may not be able to be
-#'  conducted on new data (e.g. processing the outcome variable(s)).
-#'  Care should be taken when using `skip = TRUE` as it may affect
-#'  the computations for subsequent operations.
+#' @param skip A logical. If `TRUE`, only training data is checked, while if
+#'   `FALSE`, both training and predicting data is checked. Technically, this
+#'   answers the question "should the check be skipped when the recipe is baked
+#'   by [bake()]?" While all operations are baked when [prep()] is run, some
+#'   operations may not be able to be conducted on new data (e.g. processing the
+#'   outcome variable(s)).  Care should be taken when using `skip = TRUE` as it
+#'   may affect the computations for subsequent operations.
 #' @family checks
 #' @export
-#' @details This check will break the `bake` function if any of the checked
-#'  columns have not enough non-NA values. If the check passes, nothing is
-#'  changed to the data.
+#' @details This check will break the `prep` and/or bake function if any of the
+#'   checked columns have not enough non-NA values. If the check passes, nothing
+#'   is changed in the data. It is best used after every other step.
+#'
+#'   For checking training data, it is best to set `...` to be
+#'   `all_predictors(), all_outcomes()`, while for checking prediction data, it
+#'   is best to set `...` to be `all_predictors()` only, with `n = 1`.
 #'
 #'  # tidy() results
 #'
 #'  When you [`tidy()`][tidy.recipe()] this check, a tibble with column
 #'  `terms` (the selectors or variables selected) is returned.
 #'
-check_enough_train_data <-
+check_enough_data <-
   function(recipe,
            ...,
            n = NULL,
@@ -47,11 +52,11 @@ check_enough_train_data <-
            role = NA,
            trained = FALSE,
            columns = NULL,
-           skip = FALSE,
-           id = rand_id("enough_train_data")) {
+           skip = TRUE,
+           id = rand_id("enough_data")) {
     recipes::add_check(
       recipe,
-      check_enough_train_data_new(
+      check_enough_data_new(
         n = n,
         epi_keys = epi_keys,
         drop_na = drop_na,
@@ -65,10 +70,10 @@ check_enough_train_data <-
     )
   }
 
-check_enough_train_data_new <-
+check_enough_data_new <-
   function(n, epi_keys, drop_na, terms, role, trained, columns, skip, id) {
     recipes::check(
-      subclass = "enough_train_data",
+      subclass = "enough_data",
       prefix = "check_",
       n = n,
       epi_keys = epi_keys,
@@ -83,7 +88,7 @@ check_enough_train_data_new <-
   }
 
 #' @export
-prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
+prep.check_enough_data <- function(x, training, info = NULL, ...) {
   col_names <- recipes::recipes_eval_select(x$terms, training, info)
   if (is.null(x$n)) {
     x$n <- length(col_names)
@@ -102,11 +107,11 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
   if (length(cols_not_enough_data) > 0) {
     cli_abort(
       "The following columns don't have enough data to predict: {cols_not_enough_data}.",
-      class = "epipredict__not_enough_train_data"
+      class = "epipredict__not_enough_data"
     )
   }
 
-  check_enough_train_data_new(
+  check_enough_data_new(
     n = x$n,
     epi_keys = x$epi_keys,
     drop_na = x$drop_na,
@@ -120,7 +125,7 @@ prep.check_enough_train_data <- function(x, training, info = NULL, ...) {
 }
 
 #' @export
-bake.check_enough_train_data <- function(object, new_data, ...) {
+bake.check_enough_data <- function(object, new_data, ...) {
   col_names <- object$columns
   if (object$drop_na) {
     non_na_data <- tidyr::drop_na(new_data, any_of(unname(col_names)))
@@ -137,21 +142,21 @@ bake.check_enough_train_data <- function(object, new_data, ...) {
   if (length(cols_not_enough_data) > 0) {
     cli_abort(
       "The following columns don't have enough data to predict: {cols_not_enough_data}.",
-      class = "epipredict__not_enough_train_data"
+      class = "epipredict__not_enough_data"
     )
   }
   new_data
 }
 
 #' @export
-print.check_enough_train_data <- function(x, width = max(20, options()$width - 30), ...) {
+print.check_enough_data <- function(x, width = max(20, options()$width - 30), ...) {
   title <- paste0("Check enough data (n = ", x$n, ") for ")
   recipes::print_step(x$columns, x$terms, x$trained, title, width)
   invisible(x)
 }
 
 #' @export
-tidy.check_enough_train_data <- function(x, ...) {
+tidy.check_enough_data <- function(x, ...) {
   if (recipes::is_trained(x)) {
     res <- tibble(terms = unname(x$columns))
   } else {
diff --git a/_pkgdown.yml b/_pkgdown.yml
index fe34c3b82..814bf6aa4 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -77,7 +77,7 @@ reference:
 
   - title: Epi recipe verification checks
     contents:
-      - check_enough_train_data
+      - check_enough_data
 
   - title: Forecast postprocessing
     desc: Create a series of postprocessing operations
diff --git a/man/check_enough_train_data.Rd b/man/check_enough_data.Rd
similarity index 54%
rename from man/check_enough_train_data.Rd
rename to man/check_enough_data.Rd
index 57a4a9d78..57c321fd1 100644
--- a/man/check_enough_train_data.Rd
+++ b/man/check_enough_data.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/check_enough_train_data.R
-\name{check_enough_train_data}
-\alias{check_enough_train_data}
+% Please edit documentation in R/check_enough_data.R
+\name{check_enough_data}
+\alias{check_enough_data}
 \title{Check the dataset contains enough data points.}
 \usage{
-check_enough_train_data(
+check_enough_data(
   recipe,
   ...,
   n = NULL,
@@ -14,7 +14,7 @@ check_enough_train_data(
   trained = FALSE,
   columns = NULL,
   skip = TRUE,
-  id = rand_id("enough_train_data")
+  id = rand_id("enough_data")
 )
 }
 \arguments{
@@ -23,7 +23,7 @@ sequence of operations for this recipe.}
 
 \item{...}{One or more selector functions to choose variables for this check.
 See \code{\link[=selections]{selections()}} for more details. You will usually want to use
-\code{\link[recipes:has_role]{recipes::all_predictors()}} here.}
+\code{\link[recipes:has_role]{recipes::all_predictors()}} and/or \code{\link[recipes:has_role]{recipes::all_outcomes()}} here.}
 
 \item{n}{The minimum number of data points required for training. If this is
 NULL, the total number of predictors will be used.}
@@ -43,23 +43,28 @@ have been resolved by \code{\link[=prep]{prep()}}.}
 \item{columns}{An internal argument that tracks which columns are evaluated
 for this check. Should not be used by the user.}
 
-\item{skip}{A logical. Should the check be skipped when the
-recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
-when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
-conducted on new data (e.g. processing the outcome variable(s)).
-Care should be taken when using \code{skip = TRUE} as it may affect
-the computations for subsequent operations.}
+\item{skip}{A logical. If \code{TRUE}, only training data is checked, while if
+\code{FALSE}, both training and predicting data is checked. Technically, this
+answers the question "should the check be skipped when the recipe is baked
+by \code{\link[=bake]{bake()}}?" While all operations are baked when \code{\link[=prep]{prep()}} is run, some
+operations may not be able to be conducted on new data (e.g. processing the
+outcome variable(s)).  Care should be taken when using \code{skip = TRUE} as it
+may affect the computations for subsequent operations.}
 
 \item{id}{A character string that is unique to this check to identify it.}
 }
 \description{
-\code{check_enough_train_data} creates a \emph{specification} of a recipe
+\code{check_enough_data} creates a \emph{specification} of a recipe
 operation that will check if variables contain enough data.
 }
 \details{
-This check will break the \code{bake} function if any of the checked
-columns have not enough non-NA values. If the check passes, nothing is
-changed to the data.
+This check will break the \code{prep} and/or bake function if any of the
+checked columns have not enough non-NA values. If the check passes, nothing
+is changed in the data. It is best used after every other step.
+
+For checking training data, it is best to set \code{...} to be
+\verb{all_predictors(), all_outcomes()}, while for checking prediction data, it
+is best to set \code{...} to be \code{all_predictors()} only, with \code{n = 1}.
 }
 \section{tidy() results}{
 When you \code{\link[=tidy.recipe]{tidy()}} this check, a tibble with column
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 75d674472..9e1bafbd5 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -143,7 +143,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 8 x 4
 #>   geo_value time_value     a     b
-#> * <chr>     <date>     <dbl> <dbl>
+#>   <chr>     <date>     <dbl> <dbl>
 #> 1 ca        2015-01-11   100     5
 #> 2 ca        2015-01-12   103    10
 #> 3 ca        2015-01-13   103    10
@@ -179,7 +179,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 21 x 7
 #>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-10    NA    NA      NA      NA       100
 #>  2 ca        2015-01-11   100     5      NA      NA       103
 #>  3 ca        2015-01-12   103    10      NA      NA        NA
@@ -227,7 +227,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 10 x 6
 #>    geo_value time_value     a     b lag_0_a ahead_3_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-08    NA    NA      NA       100
 #>  2 ca        2015-01-09    NA    NA      NA       103
 #>  3 ca        2015-01-11   100     5     100        NA
@@ -267,8 +267,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/tests/testthat/_snaps/check_enough_train_data.md b/tests/testthat/_snaps/check_enough_data.md
similarity index 51%
rename from tests/testthat/_snaps/check_enough_train_data.md
rename to tests/testthat/_snaps/check_enough_data.md
index 8f852092a..37c13b3c3 100644
--- a/tests/testthat/_snaps/check_enough_train_data.md
+++ b/tests/testthat/_snaps/check_enough_data.md
@@ -1,8 +1,8 @@
-# check_enough_train_data works on pooled data
+# check_enough_data works on pooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n + 1,
-      drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
+        prep(toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
       ! The following columns don't have enough data to predict: x and y.
@@ -10,16 +10,16 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 1,
-      drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
+        prep(toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
       ! The following columns don't have enough data to predict: x and y.
 
-# check_enough_train_data works on unpooled data
+# check_enough_data works on unpooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = n + 1, epi_keys = "geo_value",
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = n + 1, epi_keys = "geo_value",
       drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
@@ -28,18 +28,17 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 3,
-      epi_keys = "geo_value", drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value",
+      drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
       ! The following columns don't have enough data to predict: x and y.
 
-# check_enough_train_data works with all_predictors() downstream of constructed terms
+# check_enough_data works with all_predictors() downstream of constructed terms
 
     Code
-      epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>%
-        check_enough_train_data(all_predictors(), y, n = 2 * n - 4) %>% prep(
-        toy_epi_df) %>% bake(new_data = NULL)
+      epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>% check_enough_data(
+        all_predictors(), y, n = 2 * n - 4) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
     Condition
       Error in `prep()`:
       ! The following columns don't have enough data to predict: lag_1_x, lag_2_x, and y.
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index cdc21769d..a72222ddb 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -40,6 +40,6 @@ test_that("warns if there's not enough data to predict", {
 
   expect_error(
     edf %>% arx_forecaster("value"),
-    class = "epipredict__not_enough_train_data"
+    class = "epipredict__not_enough_data"
   )
 })
diff --git a/tests/testthat/test-check_enough_train_data.R b/tests/testthat/test-check_enough_data.R
similarity index 74%
rename from tests/testthat/test-check_enough_train_data.R
rename to tests/testthat/test-check_enough_data.R
index 11e86a915..06526ace7 100644
--- a/tests/testthat/test-check_enough_train_data.R
+++ b/tests/testthat/test-check_enough_data.R
@@ -14,11 +14,11 @@ toy_epi_df <- tibble::tibble(
   y = 1:(2 * n)
 ) %>% epiprocess::as_epi_df()
 
-test_that("check_enough_train_data works on pooled data", {
+test_that("check_enough_data works on pooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = 2 * n, drop_na = FALSE) %>%
+      check_enough_data(x, y, n = 2 * n, drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -26,7 +26,7 @@ test_that("check_enough_train_data works on pooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
+      check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -34,17 +34,17 @@ test_that("check_enough_train_data works on pooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
+      check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
 })
 
-test_that("check_enough_train_data works on unpooled data", {
+test_that("check_enough_data works on unpooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = n, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, n = n, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -52,7 +52,7 @@ test_that("check_enough_train_data works on unpooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, n = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -60,16 +60,16 @@ test_that("check_enough_train_data works on unpooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
+      check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
 })
 
-test_that("check_enough_train_data outputs the correct recipe values", {
+test_that("check_enough_data outputs the correct recipe values", {
   expect_no_error(
     p <- epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = 2 * n - 2) %>%
+      check_enough_data(x, y, n = 2 * n - 2) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -107,13 +107,13 @@ test_that("check_enough_train_data only checks train data", {
   )
 })
 
-test_that("check_enough_train_data works with all_predictors() downstream of constructed terms", {
+test_that("check_enough_data works with all_predictors() downstream of constructed terms", {
   # With a lag of 2, we will get 2 * n - 5 non-NA rows (NA's in x but not in the
   # lags don't count)
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>%
+      check_enough_data(all_predictors(), y, n = 2 * n - 5) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -121,7 +121,7 @@ test_that("check_enough_train_data works with all_predictors() downstream of con
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_train_data(all_predictors(), y, n = 2 * n - 4) %>%
+      check_enough_data(all_predictors(), y, n = 2 * n - 4) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )

From c5b25681b3221253aa11c18c75b4aa7dc164d25a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Mar 2025 11:45:16 -0500
Subject: [PATCH 310/387] fix drop na subtleties, unify prep/bake usage

A couple of simultaneous problems that were making this tricky:
1. drop_na can completely remove states
2. checking each column individually misses cases where combinations of
   the states cause the signal to be left out.
3. checking all columns simultaneously doesn't let the user know which
   columns to check.
---
 R/check_enough_data.R                      | 91 ++++++++++++++--------
 tests/testthat/_snaps/check_enough_data.md | 39 ++++++----
 tests/testthat/test-check_enough_data.R    | 43 ++++++----
 3 files changed, 109 insertions(+), 64 deletions(-)

diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index e87984e49..a43a93c7a 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -94,22 +94,8 @@ prep.check_enough_data <- function(x, training, info = NULL, ...) {
     x$n <- length(col_names)
   }
 
-  if (x$drop_na) {
-    training <- tidyr::drop_na(training, any_of(unname(col_names)))
-  }
-  cols_not_enough_data <- training %>%
-    group_by(across(all_of(.env$x$epi_keys))) %>%
-    summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$x$n), .groups = "drop") %>%
-    summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
-    unlist() %>%
-    names(.)[.]
+  check_enough_data_core(training, x, col_names, "train")
 
-  if (length(cols_not_enough_data) > 0) {
-    cli_abort(
-      "The following columns don't have enough data to predict: {cols_not_enough_data}.",
-      class = "epipredict__not_enough_data"
-    )
-  }
 
   check_enough_data_new(
     n = x$n,
@@ -127,24 +113,7 @@ prep.check_enough_data <- function(x, training, info = NULL, ...) {
 #' @export
 bake.check_enough_data <- function(object, new_data, ...) {
   col_names <- object$columns
-  if (object$drop_na) {
-    non_na_data <- tidyr::drop_na(new_data, any_of(unname(col_names)))
-  } else {
-    non_na_data <- new_data
-  }
-  cols_not_enough_data <- non_na_data %>%
-    group_by(across(all_of(.env$object$epi_keys))) %>%
-    summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$object$n), .groups = "drop") %>%
-    summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
-    unlist() %>%
-    names(.)[.]
-
-  if (length(cols_not_enough_data) > 0) {
-    cli_abort(
-      "The following columns don't have enough data to predict: {cols_not_enough_data}.",
-      class = "epipredict__not_enough_data"
-    )
-  }
+  check_enough_data_core(new_data, object, col_names, "predict")
   new_data
 }
 
@@ -168,3 +137,59 @@ tidy.check_enough_data <- function(x, ...) {
   res$drop_na <- x$drop_na
   res
 }
+
+check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict) {
+  epi_df <- epi_df %>%
+    group_by(across(all_of(.env$step_obj$epi_keys)))
+  if (step_obj$drop_na) {
+    any_missing_data <- epi_df %>%
+      mutate(any_are_na = rowSums(across(any_of(.env$col_names), ~ is.na(.x))) > 0) %>%
+      # count the number of rows where they're all not na
+      summarise(sum(any_are_na == 0) < .env$step_obj$n, .groups = "drop")
+    any_missing_data <- any_missing_data %>%
+      summarize(across(all_of(setdiff(names(any_missing_data), step_obj$epi_keys)), any)) %>%
+      any()
+
+    # figuring out which individual columns (if any) are to blame for this darth
+    # of data
+    cols_not_enough_data <- epi_df %>%
+      summarise(
+        across(
+          all_of(.env$col_names),
+          ~ sum(!is.na(.x)) < .env$step_obj$n
+        ),
+        .groups = "drop"
+      ) %>%
+      summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
+      unlist() %>%
+      names(.)[.]
+
+    if (length(cols_not_enough_data) == 0) {
+      cols_not_enough_data <-
+        glue::glue("no single column, but the combination of {paste0(col_names, collapse = ', ')}")
+    }
+  } else {
+    # if we're not dropping na values, just count
+    cols_not_enough_data <- epi_df %>%
+      summarise(
+        across(
+          all_of(.env$col_names),
+          ~ dplyr::n() < .env$step_obj$n
+        )
+      )
+    any_missing_data <- cols_not_enough_data %>%
+      summarize(across(all_of(.env$col_names), all)) %>%
+      all()
+    cols_not_enough_data <- cols_not_enough_data %>%
+      summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
+      unlist() %>%
+      names(.)[.]
+  }
+
+  if (any_missing_data) {
+    cli_abort(
+      "The following columns don't have enough data to {train_or_predict}: {cols_not_enough_data}.",
+      class = "epipredict__not_enough_data"
+    )
+  }
+}
diff --git a/tests/testthat/_snaps/check_enough_data.md b/tests/testthat/_snaps/check_enough_data.md
index 37c13b3c3..72ce8af99 100644
--- a/tests/testthat/_snaps/check_enough_data.md
+++ b/tests/testthat/_snaps/check_enough_data.md
@@ -2,44 +2,53 @@
 
     Code
       epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
-        prep(toy_epi_df) %>% bake(new_data = NULL)
+        prep(toy_epi_df)
     Condition
-      Error in `prep()`:
-      ! The following columns don't have enough data to predict: x and y.
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to train: x and y.
 
 ---
 
     Code
       epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
-        prep(toy_epi_df) %>% bake(new_data = NULL)
+        prep(toy_epi_df)
     Condition
-      Error in `prep()`:
-      ! The following columns don't have enough data to predict: x and y.
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to train: x.
 
 # check_enough_data works on unpooled data
 
     Code
       epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = n + 1, epi_keys = "geo_value",
-      drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+      drop_na = FALSE) %>% prep(toy_epi_df)
     Condition
-      Error in `prep()`:
-      ! The following columns don't have enough data to predict: x and y.
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to train: x and y.
 
 ---
 
     Code
       epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value",
-      drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+      drop_na = TRUE) %>% prep(toy_epi_df)
     Condition
-      Error in `prep()`:
-      ! The following columns don't have enough data to predict: x and y.
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to train: x and y.
+
+# check_enough_data only checks train data when skip = FALSE
+
+    Code
+      forecaster %>% predict(new_data = toy_test_data %>% filter(time_value >
+        "2020-01-08"))
+    Condition
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to predict: x.
 
 # check_enough_data works with all_predictors() downstream of constructed terms
 
     Code
       epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>% check_enough_data(
-        all_predictors(), y, n = 2 * n - 4) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+        all_predictors(), y, n = 2 * n - 4) %>% prep(toy_epi_df)
     Condition
-      Error in `prep()`:
-      ! The following columns don't have enough data to predict: lag_1_x, lag_2_x, and y.
+      Error in `check_enough_data_core()`:
+      ! The following columns don't have enough data to train: no single column, but the combination of lag_1_x, lag_2_x, y.
 
diff --git a/tests/testthat/test-check_enough_data.R b/tests/testthat/test-check_enough_data.R
index 06526ace7..f31bc47ba 100644
--- a/tests/testthat/test-check_enough_data.R
+++ b/tests/testthat/test-check_enough_data.R
@@ -27,16 +27,14 @@ test_that("check_enough_data works on pooled data", {
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = NULL)
+      prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = NULL)
+      prep(toy_epi_df)
   )
 })
 
@@ -53,16 +51,14 @@ test_that("check_enough_data works on unpooled data", {
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       check_enough_data(x, y, n = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = NULL)
+      prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = NULL)
+      prep(toy_epi_df)
   )
 })
 
@@ -85,7 +81,7 @@ test_that("check_enough_data outputs the correct recipe values", {
   expect_equal(p$geo_value, rep(c("ca", "hi"), each = n))
 })
 
-test_that("check_enough_train_data only checks train data", {
+test_that("check_enough_data only checks train data when skip = FALSE", {
   # Check that the train data has enough data, the test data does not, but
   # the check passes anyway (because it should be applied to training data)
   toy_test_data <- toy_epi_df %>%
@@ -94,16 +90,32 @@ test_that("check_enough_train_data only checks train data", {
     epiprocess::as_epi_df()
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(x, y, n = n - 2, epi_keys = "geo_value", skip = TRUE) %>%
+      check_enough_data(x, y, n = n - 2, epi_keys = "geo_value") %>%
       prep(toy_epi_df) %>%
       bake(new_data = toy_test_data)
   )
-  # Same thing, but skip = FALSE
+  # Making sure `skip = TRUE` is working correctly in `predict`
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_train_data(y, n = n - 2, epi_keys = "geo_value") %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = toy_test_data)
+      add_role(y, new_role = "outcome") %>%
+      check_enough_data(x, n = n - 2, epi_keys = "geo_value") %>%
+      epi_workflow(linear_reg()) %>%
+      fit(toy_epi_df) %>%
+      predict(new_data = toy_test_data %>% filter(time_value > "2020-01-08"))
+  )
+  # making sure it works for skip = FALSE, where there's enough data to train
+  # but not enough to predict
+  expect_no_error(
+    forecaster <- epi_recipe(toy_epi_df) %>%
+      add_role(y, new_role = "outcome") %>%
+      check_enough_data(x, n = 1, epi_keys = "geo_value", skip = FALSE) %>%
+      epi_workflow(linear_reg()) %>%
+      fit(toy_epi_df)
+  )
+  expect_snapshot(
+    error = TRUE,
+    forecaster %>%
+      predict(new_data = toy_test_data %>% filter(time_value > "2020-01-08"))
   )
 })
 
@@ -122,7 +134,6 @@ test_that("check_enough_data works with all_predictors() downstream of construct
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
       check_enough_data(all_predictors(), y, n = 2 * n - 4) %>%
-      prep(toy_epi_df) %>%
-      bake(new_data = NULL)
+      prep(toy_epi_df)
   )
 })

From bba711e5ac26a7e506faf786016d043ae02e20d3 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Mar 2025 13:09:00 -0500
Subject: [PATCH 311/387] note about weird branch

---
 R/check_enough_data.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index a43a93c7a..823485ba6 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -164,6 +164,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
       unlist() %>%
       names(.)[.]
 
+    # if none of the single columns have enough data, that means its the combination of all of them
     if (length(cols_not_enough_data) == 0) {
       cols_not_enough_data <-
         glue::glue("no single column, but the combination of {paste0(col_names, collapse = ', ')}")

From a899188a1d51debe4b08d6371514277a99ec84c7 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Tue, 25 Mar 2025 11:25:57 -0700
Subject: [PATCH 312/387] doc: add comments

---
 R/check_enough_data.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index 823485ba6..f0d594ec2 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -160,11 +160,14 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
         ),
         .groups = "drop"
       ) %>%
+      # Aggregate across keys (if present)
       summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
       unlist() %>%
       names(.)[.]
 
-    # if none of the single columns have enough data, that means its the combination of all of them
+    # Either all columns have enough data, in which case this message won't be
+    # sent later or none of the single columns have enough data, that means its
+    # the combination of all of them.
     if (length(cols_not_enough_data) == 0) {
       cols_not_enough_data <-
         glue::glue("no single column, but the combination of {paste0(col_names, collapse = ', ')}")

From 4e37fe632f69e15c23a2429268b4ef714b133b03 Mon Sep 17 00:00:00 2001
From: Dmitry Shemetov <dshemetov@ucdavis.edu>
Date: Tue, 25 Mar 2025 11:35:57 -0700
Subject: [PATCH 313/387] doc: another comment for a weird R idiom

---
 R/check_enough_data.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index f0d594ec2..ace7e4e25 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -163,6 +163,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
       # Aggregate across keys (if present)
       summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
       unlist() %>%
+      # Select the names of the columns that are TRUE
       names(.)[.]
 
     # Either all columns have enough data, in which case this message won't be
@@ -187,6 +188,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
     cols_not_enough_data <- cols_not_enough_data %>%
       summarise(across(all_of(.env$col_names), any), .groups = "drop") %>%
       unlist() %>%
+      # Select the names of the columns that are TRUE
       names(.)[.]
   }
 

From 51a1c6c713f57c51823b03469ad0f3e20b8c84d5 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 26 Mar 2025 11:15:49 -0500
Subject: [PATCH 314/387] extraneous test_data

---
 R/epi_workflow.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 826ae7f1b..81b443e7b 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -270,6 +270,6 @@ forecast.epi_workflow <- function(object, ..., n_recent = NULL, forecast_date =
     hardhat::extract_preprocessor(object),
     object$original_data
   )
-  test_data
+
   predict(object, new_data = test_data)
 }

From 1b05e99f96d9d4510b3c67c956e1f9ba82813031 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 27 Mar 2025 12:48:53 -0500
Subject: [PATCH 315/387] suggestions from Dan

---
 R/arx_forecaster.R                         |  4 +--
 R/check_enough_data.R                      | 42 +++++++++-------------
 tests/testthat/_snaps/check_enough_data.md | 18 +++++-----
 tests/testthat/test-check_enough_data.R    | 24 ++++++-------
 4 files changed, 40 insertions(+), 48 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 3a87719eb..3cbcdeb4a 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -172,13 +172,13 @@ arx_fcast_epi_workflow <- function(
   r <- r %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training) %>%
-    check_enough_data(all_predictors(), n = 1, skip = FALSE)
+    check_enough_data(all_predictors(), min_data_points = 1, skip = FALSE)
 
   if (!is.null(args_list$check_enough_data_n)) {
     r <- r %>% check_enough_data(
       all_predictors(),
       all_outcomes(),
-      n = args_list$check_enough_data_n,
+      min_data_points = args_list$check_enough_data_n,
       epi_keys = args_list$check_enough_data_epi_keys,
       drop_na = FALSE
     )
diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index ace7e4e25..2a434f6a8 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -8,8 +8,8 @@
 #' @param ... One or more selector functions to choose variables for this check.
 #'  See [selections()] for more details. You will usually want to use
 #'  [recipes::all_predictors()] and/or [recipes::all_outcomes()] here.
-#' @param n The minimum number of data points required for training. If this is
-#'   NULL, the total number of predictors will be used.
+#' @param min_data_points The minimum number of data points required for
+#'   training. If this is NULL, the total number of predictors will be used.
 #' @param epi_keys A character vector of column names on which to group the data
 #'   and check threshold within each group. Useful if your forecaster trains
 #'   per group (for example, per geo_value).
@@ -18,8 +18,6 @@
 #'  created.
 #' @param trained A logical for whether the selectors in `...`
 #' have been resolved by [prep()].
-#' @param columns An internal argument that tracks which columns are evaluated
-#'   for this check. Should not be used by the user.
 #' @param id A character string that is unique to this check to identify it.
 #' @param skip A logical. If `TRUE`, only training data is checked, while if
 #'   `FALSE`, both training and predicting data is checked. Technically, this
@@ -46,24 +44,23 @@
 check_enough_data <-
   function(recipe,
            ...,
-           n = NULL,
+           min_data_points = NULL,
            epi_keys = NULL,
            drop_na = TRUE,
            role = NA,
            trained = FALSE,
-           columns = NULL,
            skip = TRUE,
            id = rand_id("enough_data")) {
     recipes::add_check(
       recipe,
       check_enough_data_new(
-        n = n,
+        min_data_points = min_data_points,
         epi_keys = epi_keys,
         drop_na = drop_na,
         terms = enquos(...),
         role = role,
         trained = trained,
-        columns = columns,
+        columns = NULL,
         skip = skip,
         id = id
       )
@@ -71,11 +68,12 @@ check_enough_data <-
   }
 
 check_enough_data_new <-
-  function(n, epi_keys, drop_na, terms, role, trained, columns, skip, id) {
+  function(min_data_points, epi_keys, drop_na, terms,
+           role, trained, columns, skip, id) {
     recipes::check(
       subclass = "enough_data",
       prefix = "check_",
-      n = n,
+      min_data_points = min_data_points,
       epi_keys = epi_keys,
       drop_na = drop_na,
       terms = terms,
@@ -90,15 +88,14 @@ check_enough_data_new <-
 #' @export
 prep.check_enough_data <- function(x, training, info = NULL, ...) {
   col_names <- recipes::recipes_eval_select(x$terms, training, info)
-  if (is.null(x$n)) {
-    x$n <- length(col_names)
+  if (is.null(x$min_data_points)) {
+    x$min_data_points <- length(col_names)
   }
 
   check_enough_data_core(training, x, col_names, "train")
 
-
   check_enough_data_new(
-    n = x$n,
+    min_data_points = x$min_data_points,
     epi_keys = x$epi_keys,
     drop_na = x$drop_na,
     terms = x$terms,
@@ -119,7 +116,7 @@ bake.check_enough_data <- function(object, new_data, ...) {
 
 #' @export
 print.check_enough_data <- function(x, width = max(20, options()$width - 30), ...) {
-  title <- paste0("Check enough data (n = ", x$n, ") for ")
+  title <- paste0("Check enough data (n = ", x$min_data_points, ") for ")
   recipes::print_step(x$columns, x$terms, x$trained, title, width)
   invisible(x)
 }
@@ -132,7 +129,7 @@ tidy.check_enough_data <- function(x, ...) {
     res <- tibble(terms = recipes::sel2char(x$terms))
   }
   res$id <- x$id
-  res$n <- x$n
+  res$min_data_points <- x$min_data_points
   res$epi_keys <- x$epi_keys
   res$drop_na <- x$drop_na
   res
@@ -145,18 +142,18 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
     any_missing_data <- epi_df %>%
       mutate(any_are_na = rowSums(across(any_of(.env$col_names), ~ is.na(.x))) > 0) %>%
       # count the number of rows where they're all not na
-      summarise(sum(any_are_na == 0) < .env$step_obj$n, .groups = "drop")
+      summarise(sum(any_are_na == 0) < .env$step_obj$min_data_points, .groups = "drop")
     any_missing_data <- any_missing_data %>%
       summarize(across(all_of(setdiff(names(any_missing_data), step_obj$epi_keys)), any)) %>%
       any()
 
-    # figuring out which individual columns (if any) are to blame for this darth
+    # figuring out which individual columns (if any) are to blame for this dearth
     # of data
     cols_not_enough_data <- epi_df %>%
       summarise(
         across(
           all_of(.env$col_names),
-          ~ sum(!is.na(.x)) < .env$step_obj$n
+          ~ sum(!is.na(.x)) < .env$step_obj$min_data_points
         ),
         .groups = "drop"
       ) %>%
@@ -176,12 +173,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
   } else {
     # if we're not dropping na values, just count
     cols_not_enough_data <- epi_df %>%
-      summarise(
-        across(
-          all_of(.env$col_names),
-          ~ dplyr::n() < .env$step_obj$n
-        )
-      )
+      summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$step_obj$min_data_points))
     any_missing_data <- cols_not_enough_data %>%
       summarize(across(all_of(.env$col_names), all)) %>%
       all()
diff --git a/tests/testthat/_snaps/check_enough_data.md b/tests/testthat/_snaps/check_enough_data.md
index 72ce8af99..f98293354 100644
--- a/tests/testthat/_snaps/check_enough_data.md
+++ b/tests/testthat/_snaps/check_enough_data.md
@@ -1,8 +1,8 @@
 # check_enough_data works on pooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
-        prep(toy_epi_df)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n + 1,
+      drop_na = FALSE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: x and y.
@@ -10,8 +10,8 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
-        prep(toy_epi_df)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n - 1,
+      drop_na = TRUE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: x.
@@ -19,8 +19,8 @@
 # check_enough_data works on unpooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = n + 1, epi_keys = "geo_value",
-      drop_na = FALSE) %>% prep(toy_epi_df)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = n + 1,
+      epi_keys = "geo_value", drop_na = FALSE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: x and y.
@@ -28,8 +28,8 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value",
-      drop_na = TRUE) %>% prep(toy_epi_df)
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n - 3,
+      epi_keys = "geo_value", drop_na = TRUE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: x and y.
@@ -47,7 +47,7 @@
 
     Code
       epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>% check_enough_data(
-        all_predictors(), y, n = 2 * n - 4) %>% prep(toy_epi_df)
+        all_predictors(), y, min_data_points = 2 * n - 4) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: no single column, but the combination of lag_1_x, lag_2_x, y.
diff --git a/tests/testthat/test-check_enough_data.R b/tests/testthat/test-check_enough_data.R
index f31bc47ba..e2398e5c6 100644
--- a/tests/testthat/test-check_enough_data.R
+++ b/tests/testthat/test-check_enough_data.R
@@ -18,7 +18,7 @@ test_that("check_enough_data works on pooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = 2 * n, drop_na = FALSE) %>%
+      check_enough_data(x, y, min_data_points = 2 * n, drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -26,14 +26,14 @@ test_that("check_enough_data works on pooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = 2 * n + 1, drop_na = FALSE) %>%
+      check_enough_data(x, y, min_data_points = 2 * n + 1, drop_na = FALSE) %>%
       prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = 2 * n - 1, drop_na = TRUE) %>%
+      check_enough_data(x, y, min_data_points = 2 * n - 1, drop_na = TRUE) %>%
       prep(toy_epi_df)
   )
 })
@@ -42,7 +42,7 @@ test_that("check_enough_data works on unpooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = n, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, min_data_points = n, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -50,14 +50,14 @@ test_that("check_enough_data works on unpooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, min_data_points = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
+      check_enough_data(x, y, min_data_points = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
       prep(toy_epi_df)
   )
 })
@@ -65,7 +65,7 @@ test_that("check_enough_data works on unpooled data", {
 test_that("check_enough_data outputs the correct recipe values", {
   expect_no_error(
     p <- epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = 2 * n - 2) %>%
+      check_enough_data(x, y, min_data_points = 2 * n - 2) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -90,7 +90,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
     epiprocess::as_epi_df()
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, n = n - 2, epi_keys = "geo_value") %>%
+      check_enough_data(x, y, min_data_points = n - 2, epi_keys = "geo_value") %>%
       prep(toy_epi_df) %>%
       bake(new_data = toy_test_data)
   )
@@ -98,7 +98,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       add_role(y, new_role = "outcome") %>%
-      check_enough_data(x, n = n - 2, epi_keys = "geo_value") %>%
+      check_enough_data(x, min_data_points = n - 2, epi_keys = "geo_value") %>%
       epi_workflow(linear_reg()) %>%
       fit(toy_epi_df) %>%
       predict(new_data = toy_test_data %>% filter(time_value > "2020-01-08"))
@@ -108,7 +108,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
   expect_no_error(
     forecaster <- epi_recipe(toy_epi_df) %>%
       add_role(y, new_role = "outcome") %>%
-      check_enough_data(x, n = 1, epi_keys = "geo_value", skip = FALSE) %>%
+      check_enough_data(x, min_data_points = 1, epi_keys = "geo_value", skip = FALSE) %>%
       epi_workflow(linear_reg()) %>%
       fit(toy_epi_df)
   )
@@ -125,7 +125,7 @@ test_that("check_enough_data works with all_predictors() downstream of construct
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_data(all_predictors(), y, n = 2 * n - 5) %>%
+      check_enough_data(all_predictors(), y, min_data_points = 2 * n - 5) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -133,7 +133,7 @@ test_that("check_enough_data works with all_predictors() downstream of construct
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_data(all_predictors(), y, n = 2 * n - 4) %>%
+      check_enough_data(all_predictors(), y, min_data_points = 2 * n - 4) %>%
       prep(toy_epi_df)
   )
 })

From 14992799db2c974151932f9b5f89678aa129a631 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 27 Mar 2025 13:21:45 -0500
Subject: [PATCH 316/387] doc renaming

---
 man/check_enough_data.Rd | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/man/check_enough_data.Rd b/man/check_enough_data.Rd
index 57c321fd1..31a072e62 100644
--- a/man/check_enough_data.Rd
+++ b/man/check_enough_data.Rd
@@ -7,12 +7,11 @@
 check_enough_data(
   recipe,
   ...,
-  n = NULL,
+  min_data_points = NULL,
   epi_keys = NULL,
   drop_na = TRUE,
   role = NA,
   trained = FALSE,
-  columns = NULL,
   skip = TRUE,
   id = rand_id("enough_data")
 )
@@ -25,8 +24,8 @@ sequence of operations for this recipe.}
 See \code{\link[=selections]{selections()}} for more details. You will usually want to use
 \code{\link[recipes:has_role]{recipes::all_predictors()}} and/or \code{\link[recipes:has_role]{recipes::all_outcomes()}} here.}
 
-\item{n}{The minimum number of data points required for training. If this is
-NULL, the total number of predictors will be used.}
+\item{min_data_points}{The minimum number of data points required for
+training. If this is NULL, the total number of predictors will be used.}
 
 \item{epi_keys}{A character vector of column names on which to group the data
 and check threshold within each group. Useful if your forecaster trains
@@ -40,9 +39,6 @@ created.}
 \item{trained}{A logical for whether the selectors in \code{...}
 have been resolved by \code{\link[=prep]{prep()}}.}
 
-\item{columns}{An internal argument that tracks which columns are evaluated
-for this check. Should not be used by the user.}
-
 \item{skip}{A logical. If \code{TRUE}, only training data is checked, while if
 \code{FALSE}, both training and predicting data is checked. Technically, this
 answers the question "should the check be skipped when the recipe is baked

From 84f991d6fc1eed1e420f3f39065a6ea52107876f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 28 Mar 2025 18:06:11 -0500
Subject: [PATCH 317/387] rename, check for `pre` in names, dplyr::between

---
 R/arx_forecaster.R                         |  4 ++--
 R/canned-epipred.R                         |  2 +-
 R/check_enough_data.R                      | 26 +++++++++++-----------
 man/check_enough_data.Rd                   |  4 ++--
 tests/testthat/_snaps/check_enough_data.md | 10 ++++-----
 tests/testthat/test-arx_forecaster.R       |  2 +-
 tests/testthat/test-check_enough_data.R    | 24 ++++++++++----------
 7 files changed, 36 insertions(+), 36 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 3cbcdeb4a..f988490fd 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -172,13 +172,13 @@ arx_fcast_epi_workflow <- function(
   r <- r %>%
     step_epi_naomit() %>%
     step_training_window(n_recent = args_list$n_training) %>%
-    check_enough_data(all_predictors(), min_data_points = 1, skip = FALSE)
+    check_enough_data(all_predictors(), min_observations = 1, skip = FALSE)
 
   if (!is.null(args_list$check_enough_data_n)) {
     r <- r %>% check_enough_data(
       all_predictors(),
       all_outcomes(),
-      min_data_points = args_list$check_enough_data_n,
+      min_observations = args_list$check_enough_data_n,
       epi_keys = args_list$check_enough_data_epi_keys,
       drop_na = FALSE
     )
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 48e984168..7d53862c2 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -112,7 +112,7 @@ print.canned_epipred <- function(x, name, ...) {
     "At forecast date{?s}: {.val {fds}},",
     "For target date{?s}: {.val {tds}},"
   ))
-  if ("actions" %in% names(x$pre) && "recipe" %in% names(x$pre$actions)) {
+  if ("pre" %in% names(x) && "actions" %in% names(x$pre) && "recipe" %in% names(x$pre$actions)) {
     fit_recipe <- extract_recipe(x$epi_workflow)
     if (detect_step(fit_recipe, "adjust_latency")) {
       is_adj_latency <- map_lgl(fit_recipe$steps, function(x) inherits(x, "step_adjust_latency"))
diff --git a/R/check_enough_data.R b/R/check_enough_data.R
index 2a434f6a8..e830d5e54 100644
--- a/R/check_enough_data.R
+++ b/R/check_enough_data.R
@@ -8,7 +8,7 @@
 #' @param ... One or more selector functions to choose variables for this check.
 #'  See [selections()] for more details. You will usually want to use
 #'  [recipes::all_predictors()] and/or [recipes::all_outcomes()] here.
-#' @param min_data_points The minimum number of data points required for
+#' @param min_observations The minimum number of data points required for
 #'   training. If this is NULL, the total number of predictors will be used.
 #' @param epi_keys A character vector of column names on which to group the data
 #'   and check threshold within each group. Useful if your forecaster trains
@@ -44,7 +44,7 @@
 check_enough_data <-
   function(recipe,
            ...,
-           min_data_points = NULL,
+           min_observations = NULL,
            epi_keys = NULL,
            drop_na = TRUE,
            role = NA,
@@ -54,7 +54,7 @@ check_enough_data <-
     recipes::add_check(
       recipe,
       check_enough_data_new(
-        min_data_points = min_data_points,
+        min_observations = min_observations,
         epi_keys = epi_keys,
         drop_na = drop_na,
         terms = enquos(...),
@@ -68,12 +68,12 @@ check_enough_data <-
   }
 
 check_enough_data_new <-
-  function(min_data_points, epi_keys, drop_na, terms,
+  function(min_observations, epi_keys, drop_na, terms,
            role, trained, columns, skip, id) {
     recipes::check(
       subclass = "enough_data",
       prefix = "check_",
-      min_data_points = min_data_points,
+      min_observations = min_observations,
       epi_keys = epi_keys,
       drop_na = drop_na,
       terms = terms,
@@ -88,14 +88,14 @@ check_enough_data_new <-
 #' @export
 prep.check_enough_data <- function(x, training, info = NULL, ...) {
   col_names <- recipes::recipes_eval_select(x$terms, training, info)
-  if (is.null(x$min_data_points)) {
-    x$min_data_points <- length(col_names)
+  if (is.null(x$min_observations)) {
+    x$min_observations <- length(col_names)
   }
 
   check_enough_data_core(training, x, col_names, "train")
 
   check_enough_data_new(
-    min_data_points = x$min_data_points,
+    min_observations = x$min_observations,
     epi_keys = x$epi_keys,
     drop_na = x$drop_na,
     terms = x$terms,
@@ -116,7 +116,7 @@ bake.check_enough_data <- function(object, new_data, ...) {
 
 #' @export
 print.check_enough_data <- function(x, width = max(20, options()$width - 30), ...) {
-  title <- paste0("Check enough data (n = ", x$min_data_points, ") for ")
+  title <- paste0("Check enough data (n = ", x$min_observations, ") for ")
   recipes::print_step(x$columns, x$terms, x$trained, title, width)
   invisible(x)
 }
@@ -129,7 +129,7 @@ tidy.check_enough_data <- function(x, ...) {
     res <- tibble(terms = recipes::sel2char(x$terms))
   }
   res$id <- x$id
-  res$min_data_points <- x$min_data_points
+  res$min_observations <- x$min_observations
   res$epi_keys <- x$epi_keys
   res$drop_na <- x$drop_na
   res
@@ -142,7 +142,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
     any_missing_data <- epi_df %>%
       mutate(any_are_na = rowSums(across(any_of(.env$col_names), ~ is.na(.x))) > 0) %>%
       # count the number of rows where they're all not na
-      summarise(sum(any_are_na == 0) < .env$step_obj$min_data_points, .groups = "drop")
+      summarise(sum(any_are_na == 0) < .env$step_obj$min_observations, .groups = "drop")
     any_missing_data <- any_missing_data %>%
       summarize(across(all_of(setdiff(names(any_missing_data), step_obj$epi_keys)), any)) %>%
       any()
@@ -153,7 +153,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
       summarise(
         across(
           all_of(.env$col_names),
-          ~ sum(!is.na(.x)) < .env$step_obj$min_data_points
+          ~ sum(!is.na(.x)) < .env$step_obj$min_observations
         ),
         .groups = "drop"
       ) %>%
@@ -173,7 +173,7 @@ check_enough_data_core <- function(epi_df, step_obj, col_names, train_or_predict
   } else {
     # if we're not dropping na values, just count
     cols_not_enough_data <- epi_df %>%
-      summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$step_obj$min_data_points))
+      summarise(across(all_of(.env$col_names), ~ dplyr::n() < .env$step_obj$min_observations))
     any_missing_data <- cols_not_enough_data %>%
       summarize(across(all_of(.env$col_names), all)) %>%
       all()
diff --git a/man/check_enough_data.Rd b/man/check_enough_data.Rd
index 31a072e62..969caa1d2 100644
--- a/man/check_enough_data.Rd
+++ b/man/check_enough_data.Rd
@@ -7,7 +7,7 @@
 check_enough_data(
   recipe,
   ...,
-  min_data_points = NULL,
+  min_observations = NULL,
   epi_keys = NULL,
   drop_na = TRUE,
   role = NA,
@@ -24,7 +24,7 @@ sequence of operations for this recipe.}
 See \code{\link[=selections]{selections()}} for more details. You will usually want to use
 \code{\link[recipes:has_role]{recipes::all_predictors()}} and/or \code{\link[recipes:has_role]{recipes::all_outcomes()}} here.}
 
-\item{min_data_points}{The minimum number of data points required for
+\item{min_observations}{The minimum number of data points required for
 training. If this is NULL, the total number of predictors will be used.}
 
 \item{epi_keys}{A character vector of column names on which to group the data
diff --git a/tests/testthat/_snaps/check_enough_data.md b/tests/testthat/_snaps/check_enough_data.md
index f98293354..4a6ff336d 100644
--- a/tests/testthat/_snaps/check_enough_data.md
+++ b/tests/testthat/_snaps/check_enough_data.md
@@ -1,7 +1,7 @@
 # check_enough_data works on pooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n + 1,
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_observations = 2 * n + 1,
       drop_na = FALSE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
@@ -10,7 +10,7 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n - 1,
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_observations = 2 * n - 1,
       drop_na = TRUE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
@@ -19,7 +19,7 @@
 # check_enough_data works on unpooled data
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = n + 1,
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_observations = n + 1,
       epi_keys = "geo_value", drop_na = FALSE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
@@ -28,7 +28,7 @@
 ---
 
     Code
-      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_data_points = 2 * n - 3,
+      epi_recipe(toy_epi_df) %>% check_enough_data(x, y, min_observations = 2 * n - 3,
       epi_keys = "geo_value", drop_na = TRUE) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
@@ -47,7 +47,7 @@
 
     Code
       epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>% check_enough_data(
-        all_predictors(), y, min_data_points = 2 * n - 4) %>% prep(toy_epi_df)
+        all_predictors(), y, min_observations = 2 * n - 4) %>% prep(toy_epi_df)
     Condition
       Error in `check_enough_data_core()`:
       ! The following columns don't have enough data to train: no single column, but the combination of lag_1_x, lag_2_x, y.
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index a72222ddb..d13e6d2ea 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -32,7 +32,7 @@ test_that("warns if there's not enough data to predict", {
   ) %>%
     mutate(value = seq_len(nrow(.)) + rnorm(nrow(.))) %>%
     # Oct to May (flu season, ish) only:
-    filter(!between(as.POSIXlt(time_value)$mon + 1L, 6L, 9L)) %>%
+    filter(!dplyr::between(as.POSIXlt(time_value)$mon + 1L, 6L, 9L)) %>%
     # and actually, pretend we're around mid-October 2022:
     filter(time_value <= as.Date("2022-10-12")) %>%
     as_epi_df(as_of = as.Date("2022-10-12"))
diff --git a/tests/testthat/test-check_enough_data.R b/tests/testthat/test-check_enough_data.R
index e2398e5c6..3ca388afb 100644
--- a/tests/testthat/test-check_enough_data.R
+++ b/tests/testthat/test-check_enough_data.R
@@ -18,7 +18,7 @@ test_that("check_enough_data works on pooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = 2 * n, drop_na = FALSE) %>%
+      check_enough_data(x, y, min_observations = 2 * n, drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -26,14 +26,14 @@ test_that("check_enough_data works on pooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = 2 * n + 1, drop_na = FALSE) %>%
+      check_enough_data(x, y, min_observations = 2 * n + 1, drop_na = FALSE) %>%
       prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = 2 * n - 1, drop_na = TRUE) %>%
+      check_enough_data(x, y, min_observations = 2 * n - 1, drop_na = TRUE) %>%
       prep(toy_epi_df)
   )
 })
@@ -42,7 +42,7 @@ test_that("check_enough_data works on unpooled data", {
   # Check both columns have enough data
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = n, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, min_observations = n, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -50,14 +50,14 @@ test_that("check_enough_data works on unpooled data", {
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
+      check_enough_data(x, y, min_observations = n + 1, epi_keys = "geo_value", drop_na = FALSE) %>%
       prep(toy_epi_df)
   )
   # Check drop_na works
   expect_snapshot(
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
+      check_enough_data(x, y, min_observations = 2 * n - 3, epi_keys = "geo_value", drop_na = TRUE) %>%
       prep(toy_epi_df)
   )
 })
@@ -65,7 +65,7 @@ test_that("check_enough_data works on unpooled data", {
 test_that("check_enough_data outputs the correct recipe values", {
   expect_no_error(
     p <- epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = 2 * n - 2) %>%
+      check_enough_data(x, y, min_observations = 2 * n - 2) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -90,7 +90,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
     epiprocess::as_epi_df()
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
-      check_enough_data(x, y, min_data_points = n - 2, epi_keys = "geo_value") %>%
+      check_enough_data(x, y, min_observations = n - 2, epi_keys = "geo_value") %>%
       prep(toy_epi_df) %>%
       bake(new_data = toy_test_data)
   )
@@ -98,7 +98,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       add_role(y, new_role = "outcome") %>%
-      check_enough_data(x, min_data_points = n - 2, epi_keys = "geo_value") %>%
+      check_enough_data(x, min_observations = n - 2, epi_keys = "geo_value") %>%
       epi_workflow(linear_reg()) %>%
       fit(toy_epi_df) %>%
       predict(new_data = toy_test_data %>% filter(time_value > "2020-01-08"))
@@ -108,7 +108,7 @@ test_that("check_enough_data only checks train data when skip = FALSE", {
   expect_no_error(
     forecaster <- epi_recipe(toy_epi_df) %>%
       add_role(y, new_role = "outcome") %>%
-      check_enough_data(x, min_data_points = 1, epi_keys = "geo_value", skip = FALSE) %>%
+      check_enough_data(x, min_observations = 1, epi_keys = "geo_value", skip = FALSE) %>%
       epi_workflow(linear_reg()) %>%
       fit(toy_epi_df)
   )
@@ -125,7 +125,7 @@ test_that("check_enough_data works with all_predictors() downstream of construct
   expect_no_error(
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_data(all_predictors(), y, min_data_points = 2 * n - 5) %>%
+      check_enough_data(all_predictors(), y, min_observations = 2 * n - 5) %>%
       prep(toy_epi_df) %>%
       bake(new_data = NULL)
   )
@@ -133,7 +133,7 @@ test_that("check_enough_data works with all_predictors() downstream of construct
     error = TRUE,
     epi_recipe(toy_epi_df) %>%
       step_epi_lag(x, lag = c(1, 2)) %>%
-      check_enough_data(all_predictors(), y, min_data_points = 2 * n - 4) %>%
+      check_enough_data(all_predictors(), y, min_observations = 2 * n - 4) %>%
       prep(toy_epi_df)
   )
 })

From e8fa15b10a7b192628c29c7a90ee9e8915581fa9 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 9 Apr 2025 12:04:50 -0700
Subject: [PATCH 318/387] examples seem to work, bump news, version

---
 DESCRIPTION                |  4 ++--
 NAMESPACE                  |  2 ++
 NEWS.md                    |  1 +
 R/autoplot.R               | 41 ++++++++++++++++++++++++++------------
 man/autoplot-epipred.Rd    | 30 +++++++++++++++++++++-------
 man/step_adjust_latency.Rd |  6 +++---
 6 files changed, 59 insertions(+), 25 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d6f37ef63..6a709070b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.13
+Version: 0.1.14
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
@@ -31,7 +31,7 @@ Imports:
     checkmate,
     cli,
     dplyr,
-    epiprocess (>= 0.10.4),
+    epiprocess (>= 0.11.2),
     generics,
     ggplot2,
     glue,
diff --git a/NAMESPACE b/NAMESPACE
index 939a1f01a..70ddcfedc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -47,6 +47,8 @@ S3method(forecast,epi_workflow)
 S3method(key_colnames,epi_workflow)
 S3method(key_colnames,recipe)
 S3method(mean,quantile_pred)
+S3method(plot,canned_epipred)
+S3method(plot,epi_workflow)
 S3method(predict,epi_workflow)
 S3method(predict,flatline)
 S3method(prep,check_enough_train_data)
diff --git a/NEWS.md b/NEWS.md
index e117f3f52..2f2e54bd3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -20,6 +20,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Removes dependence on the `distributional` package, replacing the quantiles 
   with `hardhat::quantile_pred()`. Some associated functions are deprecated with
   `lifecycle` messages.
+- Adds the `.facet_filter` option in `epiprocess::autoplot()` (cmu-delphi/epiprocess#647).
 
 ## Improvements
 
diff --git a/R/autoplot.R b/R/autoplot.R
index 4e52ac3e3..18ccaaca8 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -48,7 +48,7 @@ ggplot2::autoplot
 #'
 #' latest <- jhu %>% filter(time_value >= max(time_value) - 14)
 #' preds <- predict(wf, latest)
-#' autoplot(wf, preds, .max_facets = 4)
+#' autoplot(wf, preds, .facet_filter = geo_value %in% c("ca", "ny", "de", "mt"))
 #'
 #' # ------- Show multiple horizons
 #'
@@ -63,7 +63,7 @@ ggplot2::autoplot
 #' })
 #'
 #' p <- do.call(rbind, p)
-#' autoplot(wf, p, .max_facets = 4)
+#' autoplot(wf, p, .facet_filter = geo_value %in% c("ca", "ny", "de", "mt"))
 #'
 #' # ------- Plotting canned forecaster output
 #'
@@ -75,7 +75,7 @@ ggplot2::autoplot
 #' arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
 #'   args_list = arx_args_list(ahead = 14L)
 #' )
-#' autoplot(arx, .max_facets = 6)
+#' autoplot(arx, .facet_filter = geo_value %in% c("ca", "ny", "de", "mt", "mo", "in"))
 NULL
 
 #' @export
@@ -87,7 +87,8 @@ autoplot.epi_workflow <- function(
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
     .point_pred_color = "orange",
-    .max_facets = Inf) {
+    .facet_filter = NULL,
+    .max_facets = deprecated()) {
   rlang::check_dots_empty()
   arg_is_probabilities(.levels)
   rlang::arg_match(.color_by)
@@ -134,7 +135,7 @@ autoplot.epi_workflow <- function(
     return(autoplot(
       edf, new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
-      .max_facets = .max_facets
+      .facet_filter = {{ .facet_filter }}
     ))
   }
 
@@ -153,25 +154,25 @@ autoplot.epi_workflow <- function(
     return(autoplot(
       edf, !!new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
-      .max_facets = .max_facets
+      .facet_filter = {{ .facet_filter }}
     ))
   }
 
   # First we plot the history, always faceted by everything
   bp <- autoplot(edf, !!new_name_y,
     .color_by = "none", .facet_by = "all_keys",
-    .base_color = "black", .max_facets = .max_facets
+    .base_color = "black", .facet_filter =  {{ .facet_filter }}
   )
 
   # Now, prepare matching facets in the predictions
   ek <- epi_keys_only(edf)
   predictions <- predictions %>%
     mutate(
-      .facets = interaction(!!!rlang::syms(as.list(ek)), sep = "/"),
+      .facets = interaction(!!!rlang::syms(as.list(ek)), sep = " / "),
     )
-  if (.max_facets < Inf) {
-    top_n <- levels(as.factor(bp$data$.facets))[seq_len(.max_facets)]
-    predictions <- filter(predictions, .facets %in% top_n) %>%
+  .facet_filter <- rlang::enquo(.facet_filter)
+  if (!rlang::quo_is_null(.facet_filter) && ".facets" %in% names(bp$data)) {
+    predictions <- filter(predictions, .facets %in% unique(bp$data$.facets)) %>%
       mutate(.facets = droplevels(.facets))
   }
 
@@ -207,7 +208,8 @@ autoplot.canned_epipred <- function(
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
     .point_pred_color = "orange",
-    .max_facets = Inf) {
+    .facet_filter = NULL,
+    .max_facets = deprecated()) {
   rlang::check_dots_empty()
   rlang::arg_match(.color_by)
   rlang::arg_match(.facet_by)
@@ -218,10 +220,23 @@ autoplot.canned_epipred <- function(
 
   autoplot(ewf, predictions,
     .color_by = .color_by, .facet_by = .facet_by,
-    .base_color = .base_color, .max_facets = .max_facets
+    .base_color = .base_color, .facet_filter = {{ .facet_filter }}
   )
 }
 
+#' @export
+#' @rdname autoplot-epipred
+plot.epi_workflow <- function(x, ...) {
+  autoplot(x, ...)
+}
+
+#' @export
+#' @rdname autoplot-epipred
+plot.canned_epipred <- function(x, ...) {
+  autoplot(x, ...)
+}
+
+
 starts_with_impl <- function(x, vars) {
   n <- nchar(x)
   x == substr(vars, 1, n)
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 087f25dba..25dc35572 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -4,6 +4,8 @@
 \alias{autoplot-epipred}
 \alias{autoplot.epi_workflow}
 \alias{autoplot.canned_epipred}
+\alias{plot.epi_workflow}
+\alias{plot.canned_epipred}
 \title{Automatically plot an \code{epi_workflow} or \code{canned_epipred} object}
 \usage{
 \method{autoplot}{epi_workflow}(
@@ -15,7 +17,8 @@
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
   .base_color = "dodgerblue4",
   .point_pred_color = "orange",
-  .max_facets = Inf
+  .facet_filter = NULL,
+  .max_facets = deprecated()
 )
 
 \method{autoplot}{canned_epipred}(
@@ -25,8 +28,13 @@
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
   .base_color = "dodgerblue4",
   .point_pred_color = "orange",
-  .max_facets = Inf
+  .facet_filter = NULL,
+  .max_facets = deprecated()
 )
+
+\method{plot}{epi_workflow}(x, ...)
+
+\method{plot}{canned_epipred}(x, ...)
 }
 \arguments{
 \item{object}{An \code{epi_workflow}}
@@ -60,8 +68,16 @@ color.}
 \item{.point_pred_color}{If available, point forecasts will be shown with
 this color.}
 
-\item{.max_facets}{Cut down of the number of facets displayed. Especially
-useful for testing when there are many \code{geo_value}'s or keys.}
+\item{.facet_filter}{Select which facets will be displayed. Especially
+useful for when there are many \code{geo_value}'s or keys. This is a
+<\code{\link[=args_data_masking]{rlang}}> expression along the lines of \code{\link[dplyr:filter]{dplyr::filter()}}.
+However, it must be a single expression combined with the \code{&} operator. This
+contrasts to the typical use case which allows multiple comma-separated expressions
+which are implicitly combined with \code{&}. When multiple variables are selected
+with \code{...}, their names can be filtered in combination with other factors
+by using \code{.response_name}. See the examples below.}
+
+\item{.max_facets}{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}}
 }
 \description{
 For a fit workflow, the training data will be displayed, the response by
@@ -90,7 +106,7 @@ autoplot(wf)
 
 latest <- jhu \%>\% filter(time_value >= max(time_value) - 14)
 preds <- predict(wf, latest)
-autoplot(wf, preds, .max_facets = 4)
+autoplot(wf, preds, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt"))
 
 # ------- Show multiple horizons
 
@@ -105,7 +121,7 @@ p <- lapply(c(7, 14, 21, 28), function(h) {
 })
 
 p <- do.call(rbind, p)
-autoplot(wf, p, .max_facets = 4)
+autoplot(wf, p, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt"))
 
 # ------- Plotting canned forecaster output
 
@@ -117,5 +133,5 @@ autoplot(flat, .max_facets = 4)
 arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
   args_list = arx_args_list(ahead = 14L)
 )
-autoplot(arx, .max_facets = 6)
+autoplot(arx, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt", "mo", "in"))
 }
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 75d674472..f57005843 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -143,7 +143,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 8 x 4
 #>   geo_value time_value     a     b
-#> * <chr>     <date>     <dbl> <dbl>
+#>   <chr>     <date>     <dbl> <dbl>
 #> 1 ca        2015-01-11   100     5
 #> 2 ca        2015-01-12   103    10
 #> 3 ca        2015-01-13   103    10
@@ -179,7 +179,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 21 x 7
 #>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-10    NA    NA      NA      NA       100
 #>  2 ca        2015-01-11   100     5      NA      NA       103
 #>  3 ca        2015-01-12   103    10      NA      NA        NA
@@ -227,7 +227,7 @@ toy_recipe \%>\%
 #> 
 #> # A tibble: 10 x 6
 #>    geo_value time_value     a     b lag_0_a ahead_3_a
-#>  * <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
 #>  1 ca        2015-01-08    NA    NA      NA       100
 #>  2 ca        2015-01-09    NA    NA      NA       103
 #>  3 ca        2015-01-11   100     5     100        NA

From d8592e08e47f2d9653023481cd831edb26bf9fd9 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 9 Apr 2025 12:06:15 -0700
Subject: [PATCH 319/387] missed one

---
 R/autoplot.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 18ccaaca8..5ebc24151 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -70,7 +70,7 @@ ggplot2::autoplot
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #' flat <- flatline_forecaster(jhu, "death_rate")
-#' autoplot(flat, .max_facets = 4)
+#' autoplot(flat, .facet_filter = geo_value %in% c("ca", "ny", "de", "mt"))
 #'
 #' arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
 #'   args_list = arx_args_list(ahead = 14L)

From 7d40ef85bcdeb7876e71f146021c268103360854 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 9 Apr 2025 12:20:13 -0700
Subject: [PATCH 320/387] missing x documented

---
 R/autoplot.R            | 2 +-
 R/step_climate.R        | 2 +-
 man/autoplot-epipred.Rd | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 5ebc24151..efad56ffe 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -13,7 +13,7 @@ ggplot2::autoplot
 #'
 #'
 #' @inheritParams epiprocess::autoplot.epi_df
-#' @param object An `epi_workflow`
+#' @param object,x An `epi_workflow`
 #' @param predictions A data frame with predictions. If `NULL`, only the
 #'   original data is shown.
 #' @param .levels A numeric vector of levels to plot for any prediction bands.
diff --git a/R/step_climate.R b/R/step_climate.R
index 2ae2c79d2..6e2817faf 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -266,7 +266,7 @@ prep.step_climate <- function(x, training, info = NULL, ...) {
 
   fn <- switch(x$center_method,
     mean = function(x, w) stats::weighted.mean(x, w, na.rm = TRUE),
-    median = function(x, w) median(x, na.rm = TRUE)
+    median = function(x, w) stats::median(x, na.rm = TRUE)
   )
   # suppose it's week 52, and there is no week 53 this year; then
   # as originally written for 1 week ahead this grabs from week 52+1 %% 53
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 25dc35572..f4657967f 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -37,7 +37,7 @@
 \method{plot}{canned_epipred}(x, ...)
 }
 \arguments{
-\item{object}{An \code{epi_workflow}}
+\item{object, x}{An \code{epi_workflow}}
 
 \item{predictions}{A data frame with predictions. If \code{NULL}, only the
 original data is shown.}
@@ -128,7 +128,7 @@ autoplot(wf, p, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt"))
 jhu <- covid_case_death_rates \%>\%
   filter(time_value >= as.Date("2021-11-01"))
 flat <- flatline_forecaster(jhu, "death_rate")
-autoplot(flat, .max_facets = 4)
+autoplot(flat, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt"))
 
 arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
   args_list = arx_args_list(ahead = 14L)

From bd76bf637cf4ceb93a3b2575b4f49bf443141c03 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 9 Apr 2025 12:24:57 -0700
Subject: [PATCH 321/387] bump version past dev

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 6a709070b..81a35b30e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.14
+Version: 0.1.15
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),

From 5c5c87c2d0ee91c44fc76bba19e44c070f69328c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 23 Jan 2025 14:56:20 -0600
Subject: [PATCH 322/387] second readthrough of README.Rmd

---
 README.Rmd | 157 ++++++++++++++++++++++++-------
 README.md  | 265 +++++++++++++++++++++++++++++++++++++++--------------
 2 files changed, 318 insertions(+), 104 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 73cedbeaa..beccae546 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -40,20 +40,46 @@ You can view documentation for the `main` branch at <https://cmu-delphi.github.i
 
 ## Goals for `epipredict`
 
-**We hope to provide:**
-
-1. A set of basic, easy-to-use forecasters that work out of the box. You should be able to do a reasonably limited amount of customization on them. For the basic forecasters, we currently provide:
-    * Baseline flatline forecaster
-    * Autoregressive forecaster
-    * Autoregressive classifier
-    * CDC FluSight flatline forecaster
-2. A framework for creating custom forecasters out of modular components. There are four types of components:
-    * Preprocessor: do things to the data before model training
-    * Trainer: train a model on data, resulting in a fitted model object
-    * Predictor: make predictions, using a fitted model object
-    * Postprocessor: do things to the predictions before returning
+<details>
+<summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
+This dataset can be found in the package as <TODO DOESN'T EXIST>; we demonstrate some of the typically ubiquitous cleaning operations needed to be able to forecast.
+First we pull both jhu-csse cases and deaths from [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
+```{r case_death}
+cases <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "confirmed_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20220101),
+  geo_values = "*") |>
+  select(geo_value, time_value, case_rate = value)
+
+deaths <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "deaths_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20220101),
+  geo_values = "*") |>
+  select(geo_value, time_value, death_rate = value)
+cases_deaths <-
+  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
+plot_locations <- c("ca", "ma", "ny", "tx")
+# plotting the data as it was downloaded
+cases_deaths |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+As with basically any dataset, there is some cleaning that we will need to do to make it actually usable; we'll use some utilities from [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
+First, to eliminate some of the noise coming from daily reporting, we do 7 day averaging over a trailing window[^1]:
 
-**Target audiences:**
+[^1]: This makes it so that any given day of the processed timeseries only depends on the previous week, which means that we avoid leaking future values when making a forecast.
 
 * Basic. Has data, calls forecaster with default arguments.
 * Intermediate. Wants to examine changes to the arguments, take advantage of
@@ -86,6 +112,41 @@ covid_case_death_rates
 
 To create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
 
+After having downloaded and cleaned the data in `cases_deaths`, we plot a subset
+of the states, noting the actual forecast date:
+
+<details>
+<summary> Plot </summary>
+```{r plot_locs}
+forecast_date_label <-
+  tibble(
+    geo_value = rep(plot_locations, 2),
+    source = c(rep("case_rate",4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7*2, 2 * length(plot_locations)),
+    heights = c(rep(150, 4), rep(1.0, 4))
+  )
+processed_data_plot <-
+  cases_deaths |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+</details>
+```{r show-processed-data, warning=FALSE, echo=FALSE}
+processed_data_plot
+```
+
+To make a forecast, we will use a "canned" simple auto-regressive forecaster to predict the death rate four weeks into the future using lagged[^3] deaths and cases
+
+[^3]: lagged by 3 in this context meaning using the value from 3 days ago.
+
 ```{r make-forecasts, warning=FALSE}
 two_week_ahead <- arx_forecaster(
   covid_case_death_rates,
@@ -99,28 +160,56 @@ two_week_ahead <- arx_forecaster(
 two_week_ahead
 ```
 
-In this case, we have used a number of different lags for the case rate, while
-only using 3 weekly lags for the death rate (as predictors). The result is both
-a fitted model object which could be used any time in the future to create
-different forecasts, as well as a set of predicted values (and prediction
-intervals) for each location 14 days after the last available time value in the
-data.
-
-```{r print-model}
-two_week_ahead$epi_workflow
+In this case, we have used 0-3 days, a week, and two week lags for the case
+rate, while using only zero, one and two weekly lags for the death rate (as
+predictors).
+The result `four_week_ahead` is both a fitted model object which could be used
+any time in the future to create different forecasts, as well as a set of
+predicted values (and prediction intervals) for each location 28 days after the
+forecast date.
+Plotting the prediction intervals on our subset above[^2]: 
+
+[^2]: Alternatively, you could call `auto_plot(four_week_ahead)` to get the full collection of forecasts. This is too busy for the space we have for plotting here.
+
+<details>
+<summary> Plot </summary>
+This is the same kind of plot as `processed_data_plot` above, but with the past data narrowed somewhat
+```{r}
+narrow_data_plot <-
+  cases_deaths |>
+  filter(time_value > "2021-04-01") |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-The fitted model here involved preprocessing the data to appropriately generate
-lagged predictors, estimating a linear model with `stats::lm()` and then
-postprocessing the results to be meaningful for epidemiological tasks. We can
-also examine the predictions.
-
-```{r show-preds}
-two_week_ahead$predictions
+Putting that together with a plot of the bands, and a plot of the median prediction.
+```{r plotting_forecast, warning=FALSE}
+epiworkflow <- four_week_ahead$epi_workflow
+restricted_predictions <-
+  four_week_ahead$predictions |>
+  filter(geo_value %in% plot_locations) |>
+  rename(time_value = target_date, value = .pred) |>
+  mutate(source = "death_rate")
+forecast_plot <-
+  narrow_data_plot |>
+  epipredict:::plot_bands(
+    restricted_predictions,
+    levels = 0.9,
+    fill = primary) +
+  geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
 ```
 
-The results above show a distributional forecast produced using data through
-the end of 2021 for the 14th of January 2022. A prediction for the death rate
-per 100K inhabitants is available for every state (`geo_value`) along with a
-90% predictive interval.
-
+```{r show-single-forecast, warning=FALSE, echo=FALSE}
+forecast_plot
+```
+The yellow dot gives the median prediction, while the red interval gives the 5-95%  inter-quantile range.
+For this particular day and these locations, the forecasts are relatively accurate, with the true data being within the 25-75% interval.
+A couple of things to note:
diff --git a/README.md b/README.md
index 1f24bab2b..2a0d58a83 100644
--- a/README.md
+++ b/README.md
@@ -73,9 +73,94 @@ processed using
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
 
 ``` r
-library(epipredict)
-covid_case_death_rates
-#> An `epi_df` object, 20,496 x 4 with metadata:
+forecast_date <- as.Date("2021-08-01")
+```
+
+Below the fold, we construct this dataset as an `epiprocess::epi_df`
+from JHU data.
+
+<details>
+<summary>
+Creating the dataset using `{epidatr}` and `{epiprocess}`
+</summary>
+
+This dataset can be found in the package as \<TODO DOESN’T EXIST\>; we
+demonstrate some of the typically ubiquitous cleaning operations needed
+to be able to forecast. First we pull both jhu-csse cases and deaths
+from [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
+
+``` r
+cases <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "confirmed_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20220101),
+  geo_values = "*") |>
+  select(geo_value, time_value, case_rate = value)
+
+deaths <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "deaths_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20220101),
+  geo_values = "*") |>
+  select(geo_value, time_value, death_rate = value)
+cases_deaths <-
+  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
+plot_locations <- c("ca", "ma", "ny", "tx")
+# plotting the data as it was downloaded
+cases_deaths |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+
+<img src="man/figures/README-case_death-1.png" width="90%" style="display: block; margin: auto;" />
+As with basically any dataset, there is some cleaning that we will need
+to do to make it actually usable; we’ll use some utilities from
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
+First, to eliminate some of the noise coming from daily reporting, we do
+7 day averaging over a trailing window[^1]:
+
+``` r
+cases_deaths <-
+  cases_deaths |> 
+  group_by(geo_value) |>
+  epi_slide(
+    cases_7dav = mean(case_rate, na.rm = TRUE),
+    death_rate_7dav = mean(death_rate, na.rm = TRUE),
+    .window_size = 7
+  ) |>
+  ungroup() |>
+  mutate(case_rate = NULL, death_rate = NULL) |>
+  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
+```
+
+Then trimming outliers, most especially negative values:
+
+``` r
+cases_deaths <-
+  cases_deaths |>
+  group_by(geo_value) |>
+  mutate(
+    outlr_death_rate = detect_outlr_rm(time_value, death_rate, detect_negatives = TRUE),
+    outlr_case_rate = detect_outlr_rm(time_value, case_rate, detect_negatives = TRUE)
+  ) |>
+  unnest(cols = starts_with("outlr"), names_sep = "_") |>
+  ungroup() |>
+  mutate(
+    death_rate = outlr_death_rate_replacement,
+    case_rate = outlr_case_rate_replacement) |>
+  select(geo_value, time_value, case_rate, death_rate)
+cases_deaths
+#> An `epi_df` object, 32,480 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
 #> * as_of     = 2023-03-10
@@ -101,8 +186,38 @@ death rate two weeks into the future using past (lagged) deaths and
 cases, we could use the following function.
 
 ``` r
-two_week_ahead <- arx_forecaster(
-  covid_case_death_rates,
+forecast_date_label <-
+  tibble(
+    geo_value = rep(plot_locations, 2),
+    source = c(rep("case_rate",4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7*2, 2 * length(plot_locations)),
+    heights = c(rep(150, 4), rep(1.0, 4))
+  )
+processed_data_plot <-
+  cases_deaths |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+
+</details>
+
+<img src="man/figures/README-show-processed-data-1.png" width="90%" style="display: block; margin: auto;" />
+
+To make a forecast, we will use a “canned” simple auto-regressive
+forecaster to predict the death rate four weeks into the future using
+lagged[^2] deaths and cases
+
+``` r
+four_week_ahead <- arx_forecaster(
+  cases_deaths |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
@@ -130,53 +245,36 @@ two_week_ahead
 #> 
 ```
 
-In this case, we have used a number of different lags for the case rate,
-while only using 3 weekly lags for the death rate (as predictors). The
-result is both a fitted model object which could be used any time in the
-future to create different forecasts, as well as a set of predicted
-values (and prediction intervals) for each location 14 days after the
-last available time value in the data.
+In this case, we have used 0-3 days, a week, and two week lags for the
+case rate, while using only zero, one and two weekly lags for the death
+rate (as predictors). The result `four_week_ahead` is both a fitted
+model object which could be used any time in the future to create
+different forecasts, as well as a set of predicted values (and
+prediction intervals) for each location 28 days after the forecast date.
+Plotting the prediction intervals on our subset above[^3]:
+
+<details>
+<summary>
+Plot
+</summary>
+
+This is the same kind of plot as `processed_data_plot` above, but with
+the past data narrowed somewhat
 
 ``` r
-two_week_ahead$epi_workflow
-#> 
-#> ══ Epi Workflow [trained] ══════════════════════════════════════════════════
-#> Preprocessor: Recipe
-#> Model: linear_reg()
-#> Postprocessor: Frosting
-#> 
-#> ── Preprocessor ────────────────────────────────────────────────────────────
-#> 
-#> 6 Recipe steps.
-#> 1. step_epi_lag()
-#> 2. step_epi_lag()
-#> 3. step_epi_ahead()
-#> 4. step_naomit()
-#> 5. step_naomit()
-#> 6. step_training_window()
-#> 
-#> ── Model ───────────────────────────────────────────────────────────────────
-#> 
-#> Call:
-#> stats::lm(formula = ..y ~ ., data = data)
-#> 
-#> Coefficients:
-#>       (Intercept)    lag_0_case_rate    lag_1_case_rate    lag_2_case_rate  
-#>        -0.0071026          0.0040340          0.0007863          0.0003699  
-#>   lag_3_case_rate    lag_7_case_rate   lag_14_case_rate   lag_0_death_rate  
-#>         0.0012887          0.0011980          0.0002527          0.1348573  
-#>  lag_7_death_rate  lag_14_death_rate  
-#>         0.1479274          0.1067074
-#> 
-#> ── Postprocessor ───────────────────────────────────────────────────────────
-#> 
-#> 5 Frosting layers.
-#> 1. layer_predict()
-#> 2. layer_residual_quantiles()
-#> 3. layer_add_forecast_date()
-#> 4. layer_add_target_date()
-#> 5. layer_threshold()
-#> 
+narrow_data_plot <-
+  cases_deaths |>
+  filter(time_value > "2021-04-01") |>
+  filter(geo_value %in% plot_locations) |>
+  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
+  ggplot(aes(x = time_value, y = value)) +
+  geom_line() +
+  facet_grid(source ~ geo_value, scale = "free") +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
 The fitted model here involved preprocessing the data to appropriately
@@ -185,21 +283,19 @@ and then postprocessing the results to be meaningful for epidemiological
 tasks. We can also examine the predictions.
 
 ``` r
-two_week_ahead$predictions
-#> # A tibble: 56 × 5
-#>    geo_value .pred        .pred_distn forecast_date target_date
-#>    <chr>     <dbl>             <dist> <date>        <date>     
-#>  1 ak        0.450 quantiles(0.45)[7] 2021-12-31    2022-01-14 
-#>  2 al        0.602  quantiles(0.6)[7] 2021-12-31    2022-01-14 
-#>  3 ar        0.694 quantiles(0.69)[7] 2021-12-31    2022-01-14 
-#>  4 as        0        quantiles(0)[7] 2021-12-31    2022-01-14 
-#>  5 az        0.699  quantiles(0.7)[7] 2021-12-31    2022-01-14 
-#>  6 ca        0.592 quantiles(0.59)[7] 2021-12-31    2022-01-14 
-#>  7 co        1.47  quantiles(1.47)[7] 2021-12-31    2022-01-14 
-#>  8 ct        1.08  quantiles(1.08)[7] 2021-12-31    2022-01-14 
-#>  9 dc        2.14  quantiles(2.14)[7] 2021-12-31    2022-01-14 
-#> 10 de        1.13  quantiles(1.13)[7] 2021-12-31    2022-01-14 
-#> # ℹ 46 more rows
+epiworkflow <- four_week_ahead$epi_workflow
+restricted_predictions <-
+  four_week_ahead$predictions |>
+  filter(geo_value %in% plot_locations) |>
+  rename(time_value = target_date, value = .pred) |>
+  mutate(source = "death_rate")
+forecast_plot <-
+  narrow_data_plot |>
+  epipredict:::plot_bands(
+    restricted_predictions,
+    levels = 0.9,
+    fill = primary) +
+  geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
 ```
 
 The results above show a distributional forecast produced using data
@@ -207,8 +303,37 @@ through the end of 2021 for the 14th of January 2022. A prediction for
 the death rate per 100K inhabitants is available for every state
 (`geo_value`) along with a 90% predictive interval.
 
-[^1]: Other epidemiological signals for non-Covid related illnesses are
-    also available with
-    [`{epidatr}`](https://github.com/cmu-delphi/epidatr) which
-    interfaces directly to Delphi’s [Epidata
-    API](https://cmu-delphi.github.io/delphi-epidata/)
+<img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
+The yellow dot gives the median prediction, while the red interval gives
+the 5-95% inter-quantile range. For this particular day and these
+locations, the forecasts are relatively accurate, with the true data
+being within the 25-75% interval. A couple of things to note:
+
+1.  Our methods are primarily direct forecasters; this means we don’t
+    need to predict 1, 2,…, 27 days ahead to then predict 28 days ahead
+2.  All of our existing engines are geo-pooled, meaning the training
+    data is shared across geographies. This has the advantage of
+    increasing the amount of available training data, with the
+    restriction that the data needs to be on comparable scales, such as
+    rates.
+
+## Getting Help
+
+If you encounter a bug or have a feature request, feel free to file an
+[issue on our github
+page](https://github.com/cmu-delphi/epipredict/issues). For other
+questions, feel free to contact [Daniel](daniel@stat.ubc.ca),
+[David](davidweb@andrew.cmu.edu), [Dmitry](dshemetov@cmu.edu), or
+[Logan](lcbrooks@andrew.cmu.edu), either via email or on the Insightnet
+slack.
+
+[^1]: This makes it so that any given day of the processed timeseries
+    only depends on the previous week, which means that we avoid leaking
+    future values when making a forecast.
+
+[^2]: lagged by 3 in this context meaning using the value from 3 days
+    ago.
+
+[^3]: Alternatively, you could call `auto_plot(four_week_ahead)` to get
+    the full collection of forecasts. This is too busy for the space we
+    have for plotting here.

From 007e2b43f5b619eff45b3c0efc7fdd9e6ab4f59f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 23 Jan 2025 16:26:01 -0600
Subject: [PATCH 323/387] make for manually clearing the caches

---
 DEVELOPMENT.md |  2 ++
 Makefile       | 14 ++++++++++++++
 2 files changed, 16 insertions(+)
 create mode 100644 Makefile

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index f710c2842..27ed2bf9f 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -35,6 +35,8 @@ R -e 'devtools::document()'
 R -e 'pkgdown::build_site()'
 ```
 
+Note that sometimes the caches from either `pkgdown` or `knitr` can cause difficulties. To clear those, run `make`, with either `clean_knitr`, `clean_site`, or `clean` (which does both).
+
 If you work without R Studio and want to iterate on documentation, you might
 find [this
 script](https://gist.github.com/gadenbuie/d22e149e65591b91419e41ea5b2e0621)
diff --git a/Makefile b/Makefile
new file mode 100644
index 000000000..9f5790aca
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,14 @@
+##
+# epipredict docs build
+#
+
+# knitr doesn't actually clean it's own cache properly; this just deletes any of
+# the article knitr caches in vignettes or the base
+clean_knitr:
+	rm -r *_cache; rm -r vignettes/*_cache
+clean_site:
+	Rscript -e "pkgdown::clean_cache(); pkgdown::clean_site()"
+# this combines 
+clean: clean_knitr clean_site
+
+# end

From 2b5908c9abb822271c39d48435edbe65969a9d4b Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 23 Jan 2025 16:26:43 -0600
Subject: [PATCH 324/387] styler

---
 README.Rmd | 108 +++++++++++++++++++++++++++++++++++------------------
 1 file changed, 72 insertions(+), 36 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index beccae546..46bc421ab 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -14,7 +14,35 @@ knitr::opts_chunk$set(
 )
 ```
 
-# epipredict
+```{r coloration, include=FALSE, echo=FALSE}
+base <- "#002676"
+primary <- "#941120"
+secondary <- "#f9c80e"
+tertiary <- "#177245"
+fourth_colour <- "#A393BF"
+fifth_colour <- "#2e8edd"
+colvec <- c(
+  base = base, primary = primary, secondary = secondary,
+  tertiary = tertiary, fourth_colour = fourth_colour,
+  fifth_colour = fifth_colour
+)
+library(epiprocess)
+suppressMessages(library(tidyverse))
+theme_update(legend.position = "bottom", legend.title = element_blank())
+delphi_pal <- function(n) {
+  if (n > 6L) warning("Not enough colors in this palette!")
+  unname(colvec)[1:n]
+}
+scale_fill_delphi <- function(..., aesthetics = "fill") {
+  discrete_scale(aesthetics = aesthetics, palette = delphi_pal, ...)
+}
+scale_color_delphi <- function(..., aesthetics = "color") {
+  discrete_scale(aesthetics = aesthetics, palette = delphi_pal, ...)
+}
+scale_colour_delphi <- scale_color_delphi
+```
+
+# Epipredict
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
@@ -51,7 +79,8 @@ cases <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  geo_values = "*"
+) |>
   select(geo_value, time_value, case_rate = value)
 
 deaths <- pub_covidcast(
@@ -60,7 +89,8 @@ deaths <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  geo_values = "*"
+) |>
   select(geo_value, time_value, death_rate = value)
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
@@ -81,36 +111,39 @@ First, to eliminate some of the noise coming from daily reporting, we do 7 day a
 
 [^1]: This makes it so that any given day of the processed timeseries only depends on the previous week, which means that we avoid leaking future values when making a forecast.
 
-* Basic. Has data, calls forecaster with default arguments.
-* Intermediate. Wants to examine changes to the arguments, take advantage of
-built in flexibility.
-* Advanced. Wants to write their own forecasters. Maybe willing to build up
-from some components.
-
-The Advanced user should find their task to be relatively easy. Examples of
-these tasks are illustrated in the [vignettes and articles](https://cmu-delphi.github.io/epipredict).
-
-See also the (in progress) [Forecasting Book](https://cmu-delphi.github.io/delphi-tooling-book/).
-
-## Intermediate example
-
-The package comes with some built-in historical data for illustration, but
-up-to-date versions of this could be downloaded with the
-[`{epidatr}` package](https://cmu-delphi.github.io/epidatr/)
-and processed using
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
-
-[^1]: Other epidemiological signals for non-Covid related illnesses are also
-available with [`{epidatr}`](https://github.com/cmu-delphi/epidatr) which
-interfaces directly to Delphi's
-[Epidata API](https://cmu-delphi.github.io/delphi-epidata/)
-
-```{r epidf, message=FALSE}
-library(epipredict)
-covid_case_death_rates
+```{r smooth}
+cases_deaths <-
+  cases_deaths |>
+  group_by(geo_value) |>
+  epi_slide(
+    cases_7dav = mean(case_rate, na.rm = TRUE),
+    death_rate_7dav = mean(death_rate, na.rm = TRUE),
+    .window_size = 7
+  ) |>
+  ungroup() |>
+  mutate(case_rate = NULL, death_rate = NULL) |>
+  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
 ```
 
-To create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
+Then trimming outliers, most especially negative values:
+```{r outlier}
+cases_deaths <-
+  cases_deaths |>
+  group_by(geo_value) |>
+  mutate(
+    outlr_death_rate = detect_outlr_rm(time_value, death_rate, detect_negatives = TRUE),
+    outlr_case_rate = detect_outlr_rm(time_value, case_rate, detect_negatives = TRUE)
+  ) |>
+  unnest(cols = starts_with("outlr"), names_sep = "_") |>
+  ungroup() |>
+  mutate(
+    death_rate = outlr_death_rate_replacement,
+    case_rate = outlr_case_rate_replacement
+  ) |>
+  select(geo_value, time_value, case_rate, death_rate)
+cases_deaths
+```
+</details>
 
 After having downloaded and cleaned the data in `cases_deaths`, we plot a subset
 of the states, noting the actual forecast date:
@@ -121,8 +154,8 @@ of the states, noting the actual forecast date:
 forecast_date_label <-
   tibble(
     geo_value = rep(plot_locations, 2),
-    source = c(rep("case_rate",4), rep("death_rate", 4)),
-    dates = rep(forecast_date - 7*2, 2 * length(plot_locations)),
+    source = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(plot_locations)),
     heights = c(rep(150, 4), rep(1.0, 4))
   )
 processed_data_plot <-
@@ -134,7 +167,8 @@ processed_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -185,7 +219,8 @@ narrow_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -203,7 +238,8 @@ forecast_plot <-
   epipredict:::plot_bands(
     restricted_predictions,
     levels = 0.9,
-    fill = primary) +
+    fill = primary
+  ) +
   geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
 ```
 

From e47b5d7aec91bf6a433c398365b1306b979ed83e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 23 Jan 2025 17:52:19 -0600
Subject: [PATCH 325/387] missed an image in the man version

---
 man/figures/README-show-single-forecast-1.png | Bin 0 -> 262795 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 man/figures/README-show-single-forecast-1.png

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HcmV?d00001


From f8b43c4f2b7975239e15f3dd5c27841e89213564 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 12:50:35 -0600
Subject: [PATCH 326/387] getting started page

---
 inst/pkgdown-watch.R     |  67 +++++++++++++++++++++++
 vignettes/epipredict.Rmd | 112 +++++++++++++++++++++++++++++++++++++--
 vignettes/panel-data.Rmd |   4 +-
 3 files changed, 178 insertions(+), 5 deletions(-)
 create mode 100644 inst/pkgdown-watch.R

diff --git a/inst/pkgdown-watch.R b/inst/pkgdown-watch.R
new file mode 100644
index 000000000..4ac977566
--- /dev/null
+++ b/inst/pkgdown-watch.R
@@ -0,0 +1,67 @@
+# Run with: Rscript pkgdown-watch.R
+#
+# Modifying this: https://gist.github.com/gadenbuie/d22e149e65591b91419e41ea5b2e0621
+# - Removed docopts cli interface and various configs/features I didn't need.
+# - Sped up reference building by not running examples.
+#
+# Note that the `pattern` regex is case sensitive, so make sure your Rmd files
+# end in `.Rmd` and not `.rmd`.
+#
+# Also I had issues with `pkgdown::build_reference()` not working, so I just run
+# it manually when I need to.
+
+rlang::check_installed(c("pkgdown", "servr", "devtools", "here", "cli", "fs"))
+library(pkgdown)
+pkg <- pkgdown::as_pkgdown(here::here())
+pkgdown::build_articles(pkg)
+pkgdown::build_site(pkg, lazy = FALSE, examples = FALSE, devel = TRUE, preview = FALSE)
+
+servr::httw(
+  dir = here::here("docs"),
+  watch = here::here(),
+  pattern = "[.](Rm?d|y?ml|s[ac]ss|css|js)$",
+  handler = function(files) {
+    devtools::load_all()
+
+    files_rel <- fs::path_rel(files, start = getwd())
+    cli::cli_inform("{cli::col_yellow('Updated')} {.val {files_rel}}")
+
+    articles <- grep("vignettes.+Rmd$", files, value = TRUE)
+
+    if (length(articles) == 1) {
+      name <- fs::path_ext_remove(fs::path_rel(articles, fs::path(pkg$src_path, "vignettes")))
+      pkgdown::build_article(name, pkg)
+    } else if (length(articles) > 1) {
+      pkgdown::build_articles(pkg, preview = FALSE)
+    }
+
+    refs <- grep("man.+R(m?d)?$", files, value = TRUE)
+    if (length(refs)) {
+      # Doesn't work for me, so I run it manually.
+      # pkgdown::build_reference(pkg, preview = FALSE, examples = FALSE, lazy = FALSE) # nolint: commented_code_linter
+    }
+
+    pkgdown <- grep("pkgdown", files, value = TRUE)
+    if (length(pkgdown) && !pkgdown %in% c(articles, refs)) {
+      pkgdown::init_site(pkg)
+    }
+
+    pkgdown_index <- grep("index[.]Rmd$", files_rel, value = TRUE)
+    if (length(pkgdown_index)) {
+      devtools::build_rmd(pkgdown_index)
+      pkgdown::build_home(pkg)
+    }
+
+    readme <- grep("README[.]rmd$", files, value = TRUE, ignore.case = TRUE)
+    if (length(readme)) {
+      devtools::build_readme()
+      pkgdown::build_home()
+      pkgdown::build_site(pkg, lazy = TRUE, examples = FALSE, devel = TRUE, preview = FALSE)
+
+      devtools::build_readme(".")
+      pkgdown::build_home(pkg)
+    }
+
+    cli::cli_alert("Site rebuild done!")
+  }
+)
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index ce0a7e38e..e1531b1b3 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -11,7 +11,7 @@ vignette: >
 source("_common.R")
 ```
 
-```{r setup, message=FALSE}
+```{r setup, message=FALSE, include = FALSE}
 library(dplyr)
 library(parsnip)
 library(workflows)
@@ -19,6 +19,112 @@ library(recipes)
 library(epipredict)
 ```
 
+At a high level, our goal with `{epipredict}` is to make running simple machine
+learning / statistical forecasters for epidemiology easy.
+To do this, we have extended several [tidymodels](https://www.tidymodels.org/)
+packages to handle the case of panel time-series data.
+Because of this, the package is extremely extensible.
+Our hope is that it is easy for users with epi training and some statistics to
+fit baseline models while still allowing those with more nuanced statistical
+understanding to create complicated specializations using the same framework.
+Towards that end, epipredict provides two main classes of tools:
+
+1. A set of basic, easy-to-use "canned" forecasters that work out of the box.
+   You should be able to do a reasonably limited amount of customization on them.
+   For the basic forecasters, we currently provide:
+    * Baseline flat forecaster: predicts a median that is the previous value
+      with increasingly wide quantiles
+    * Autoregressive forecaster: fits a model (typically linear regression) on
+      lagged data to predict quantiles for continuous values.
+    * Autoregressive classifier: fits a model (typically logistic regression) on
+      lagged data to predict probabilities for discrete values.
+    * CDC FluSight flatline forecaster: a variant of the flatline forecaster as
+      used by the CDC in FluSight.
+2. A framework for creating custom forecasters out of modular components.
+   There are three types of components:
+    * Preprocessor: do things to the data before model training, such as convert
+      counts to rates, create smoothed columns, or [any of the recipes
+      steps](https://recipes.tidymodels.org/reference/index.html)
+    * Trainer: train a model on data, resulting in a fitted model object.
+      Examples include linear regression, quantile regression, or [any parsnip
+      engine](https://parsnip.tidymodels.org/).
+    * Postprocessor: do things to the predictions after the model has been fit,
+      such as generate quantiles from purely point-prediction models, undo
+      operations done in the steps, such as convert back to counts from rates,
+      and generally adapt the format of the prediction to it's eventual use.
+
+The rest of this will focus on using and modifying the canned forecasters. 
+If you need a more complicated model, check out the [Guts
+vignette](preprocessing-and-models) for examples of using the forecaster
+framework.
+
+
+The Advanced user should find their task to be relatively easy. Examples of
+these tasks are illustrated in the [vignettes and articles](https://cmu-delphi.github.io/epipredict).
+
+See also the (in progress) [Forecasting Book](https://cmu-delphi.github.io/delphi-tooling-book/).
+
+# Panel forecasting basics
+## Example data
+
+## Intermediate example
+
+The package comes with some built-in historical data for illustration, but
+up-to-date versions of this could be downloaded with the
+[`{epidatr}` package](https://cmu-delphi.github.io/epidatr/)
+and processed using
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
+
+[^1]: Other epidemiological signals for non-Covid related illnesses are also
+available with [`{epidatr}`](https://github.com/cmu-delphi/epidatr) which
+interfaces directly to Delphi's
+[Epidata API](https://cmu-delphi.github.io/delphi-epidata/)
+
+```{r init_stuff, message=FALSE}
+library(epipredict)
+covid_case_death_rates
+```
+
+To create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
+
+```{r make-forecasts, warning=FALSE}
+two_week_ahead <- arx_forecaster(
+  covid_case_death_rates,
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    ahead = 14
+  )
+)
+two_week_ahead
+```
+
+In this case, we have used a number of different lags for the case rate, while
+only using 3 weekly lags for the death rate (as predictors). The result is both
+a fitted model object which could be used any time in the future to create
+different forecasts, as well as a set of predicted values (and prediction
+intervals) for each location 14 days after the last available time value in the
+data.
+
+```{r print-model}
+two_week_ahead$epi_workflow
+```
+
+The fitted model here involved preprocessing the data to appropriately generate
+lagged predictors, estimating a linear model with `stats::lm()` and then
+postprocessing the results to be meaningful for epidemiological tasks. We can
+also examine the predictions.
+
+```{r show-preds}
+two_week_ahead$predictions
+```
+
+The results above show a distributional forecast produced using data through
+the end of 2021 for the 14th of January 2022. A prediction for the death rate
+per 100K inhabitants is available for every state (`geo_value`) along with a
+90% predictive interval.
+
 
 # Goals for the package
 
@@ -195,9 +301,9 @@ Here, we've used different lags on the `case_rate` and are now predicting 2
 weeks ahead. This example also illustrates a major difficulty with the
 "iterative" versions of AR models. This model doesn't produce forecasts for
 `case_rate`, and so, would not have data to "plug in" for the necessary
-lags.[^1]
+lags.[^3]
 
-[^1]: An obvious fix is to instead use a VAR and predict both, but this would
+[^3]: An obvious fix is to instead use a VAR and predict both, but this would
 likely increase the variance of the model, and therefore, may lead to less
 accurate forecasts for the variable of interest.
 
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index e99057897..668c21e0f 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -234,9 +234,9 @@ summary(extract_fit_engine(wf_linreg))
 
 This output tells us the coefficients of the fitted model; for instance,
 the estimated intercept is $\widehat{\alpha}_0 =$
-`r round(coef(extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for
+`r round(coef(hardhat::extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for
 $y_{tijk}$ is
-$\widehat\alpha_1 =$ `r round(coef(extract_fit_engine(wf_linreg))[2], 3)`.
+$\widehat\alpha_1 =$ `r round(coef(hardhat::extract_fit_engine(wf_linreg))[2], 3)`.
 The summary also tells us that all estimated coefficients are significantly
 different from zero. Extracting the 95% confidence intervals for the
 coefficients also leads us to

From 77cbeb9f046ef332f84612f4d8ec754831b35dce Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 13:08:00 -0600
Subject: [PATCH 327/387] fixing rebase problem

---
 README.Rmd | 87 ++++++++++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 75 insertions(+), 12 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 46bc421ab..528702d92 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -7,12 +7,41 @@ output: github_document
 ```{r, include = FALSE}
 options(width = 76)
 knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
   fig.path = "man/figures/README-",
-  out.width = "100%"
+  digits = 3,
+  comment = "#>",
+  collapse = TRUE,
+  cache = TRUE,
+  dev.args = list(bg = "transparent"),
+  dpi = 300,
+  cache.lazy = FALSE,
+  out.width = "90%",
+  fig.align = "center",
+  fig.width = 9,
+  fig.height = 6
+)
+ggplot2::theme_set(ggplot2::theme_bw())
+options(
+  dplyr.print_min = 6,
+  dplyr.print_max = 6,
+  pillar.max_footer_lines = 2,
+  pillar.min_chars = 15,
+  stringr.view_n = 6,
+  pillar.bold = TRUE,
+  width = 77
 )
 ```
+```{r pkgs, include=FALSE, echo=FALSE}
+library(epipredict)
+library(epidatr)
+library(data.table)
+library(dplyr)
+library(tidyr)
+library(ggplot2)
+library(magrittr)
+library(purrr)
+library(scales)
+```
 
 ```{r coloration, include=FALSE, echo=FALSE}
 base <- "#002676"
@@ -48,11 +77,26 @@ scale_colour_delphi <- scale_color_delphi
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-**Note:** This package is currently in development and may not work as expected. Please file bug reports as issues in this repo, and we will do our best to address them quickly.
+Epipredict is a framework for building transformation and forecasting pipelines
+for epidemiological and other panel time-series datasets. 
+In addition to tools for building forecasting pipelines, it contains a number of
+"canned" forecasters meant to run with little modification as an easy way to get
+started forecasting.
+
+It is designed to work well with
+[`epiprocess`](https://cmu-delphi.github.io/epiprocess/), a utility for handling
+various time series and geographic processing tools in an epidemiological
+context.
+Both of the packages are meant to work well with the panel data provided by
+[`epidatr`](https://cmu-delphi.github.io/epidatr/).
+
+If you are looking for more detail beyond the package documentation, see our
+[forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 ## Installation
 
-To install (unless you're making changes to the package, use the stable version):
+To install (unless you're planning on contributing to package development, we
+suggest using the stable version):
 
 ```r
 # Stable version
@@ -61,12 +105,16 @@ pak::pkg_install("cmu-delphi/epipredict@main")
 # Dev version
 pak::pkg_install("cmu-delphi/epipredict@dev")
 ```
+The documentation for the stable version is at <https://cmu-delphi.github.io/epipredict>, while the development version is at <https://cmu-delphi.github.io/epipredict/dev>.
 
-## Documentation
 
-You can view documentation for the `main` branch at <https://cmu-delphi.github.io/epipredict>.
+## Motivating example
 
-## Goals for `epipredict`
+To demonstrate the kind of forecast epipredict can make, say we're predicting COVID deaths per 100k for each state on
+```{r fc_date}
+forecast_date <- as.Date("2021-08-01")
+```
+Below the fold, we construct this dataset as an `epiprocess::epi_df` from JHU data.
 
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
@@ -182,16 +230,16 @@ To make a forecast, we will use a "canned" simple auto-regressive forecaster to
 [^3]: lagged by 3 in this context meaning using the value from 3 days ago.
 
 ```{r make-forecasts, warning=FALSE}
-two_week_ahead <- arx_forecaster(
-  covid_case_death_rates,
+four_week_ahead <- arx_forecaster(
+  cases_deaths |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 14
+    ahead = 4 * 7
   )
 )
-two_week_ahead
+four_week_ahead
 ```
 
 In this case, we have used 0-3 days, a week, and two week lags for the case
@@ -242,6 +290,7 @@ forecast_plot <-
   ) +
   geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
 ```
+</details>
 
 ```{r show-single-forecast, warning=FALSE, echo=FALSE}
 forecast_plot
@@ -249,3 +298,17 @@ forecast_plot
 The yellow dot gives the median prediction, while the red interval gives the 5-95%  inter-quantile range.
 For this particular day and these locations, the forecasts are relatively accurate, with the true data being within the 25-75% interval.
 A couple of things to note:
+
+1. Our methods are primarily direct forecasters; this means we don't need to
+   predict 1, 2,..., 27 days ahead to then predict 28 days ahead
+2. All of our existing engines are geo-pooled, meaning the training data is
+   shared across geographies. This has the advantage of increasing the amount of
+   available training data, with the restriction that the data needs to be on
+   comparable scales, such as rates.
+
+## Getting Help
+If you encounter a bug or have a feature request, feel free to file an [issue on
+our github page](https://github.com/cmu-delphi/epipredict/issues).
+For other
+questions, feel free to contact [Daniel](daniel@stat.ubc.ca), [David](davidweb@andrew.cmu.edu), [Dmitry](dshemetov@cmu.edu), or
+[Logan](lcbrooks@andrew.cmu.edu), either via email or on the Insightnet slack.

From 1539552362b69d26f500cd9a5a0639d8a3282c4f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 14:28:18 -0600
Subject: [PATCH 328/387] linewrapping

---
 README.Rmd | 85 ++++++++++++++++++++++++++++++++++++++++--------------
 1 file changed, 63 insertions(+), 22 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 528702d92..ddc677872 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -105,28 +105,40 @@ pak::pkg_install("cmu-delphi/epipredict@main")
 # Dev version
 pak::pkg_install("cmu-delphi/epipredict@dev")
 ```
-The documentation for the stable version is at <https://cmu-delphi.github.io/epipredict>, while the development version is at <https://cmu-delphi.github.io/epipredict/dev>.
+
+The documentation for the stable version is at
+<https://cmu-delphi.github.io/epipredict>, while the development version is at
+<https://cmu-delphi.github.io/epipredict/dev>.
 
 
 ## Motivating example
 
-To demonstrate the kind of forecast epipredict can make, say we're predicting COVID deaths per 100k for each state on
+To demonstrate the kind of forecast epipredict can make, say we're predicting
+COVID deaths per 100k for each state on
+
 ```{r fc_date}
 forecast_date <- as.Date("2021-08-01")
 ```
-Below the fold, we construct this dataset as an `epiprocess::epi_df` from JHU data.
+
+Below the fold, we construct this dataset as an `epiprocess::epi_df` from JHU
+data.
 
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
-This dataset can be found in the package as <TODO DOESN'T EXIST>; we demonstrate some of the typically ubiquitous cleaning operations needed to be able to forecast.
-First we pull both jhu-csse cases and deaths from [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
-```{r case_death}
+
+This dataset can be found in the package as <TODO DOESN'T EXIST>; we demonstrate
+some of the typically ubiquitous cleaning operations needed to be able to
+forecast.
+First we pull both jhu-csse cases and deaths from
+[`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
+
+```{r case_death, warning = FALSE}
 cases <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_incidence_prop",
   time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20220101),
+  time_values = epirange(20200601, 20211231),
   geo_values = "*"
 ) |>
   select(geo_value, time_value, case_rate = value)
@@ -136,7 +148,7 @@ deaths <- pub_covidcast(
   signals = "deaths_incidence_prop",
   time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20220101),
+  time_values = epirange(20200601, 20211231),
   geo_values = "*"
 ) |>
   select(geo_value, time_value, death_rate = value)
@@ -154,10 +166,16 @@ cases_deaths |>
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
-As with basically any dataset, there is some cleaning that we will need to do to make it actually usable; we'll use some utilities from [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
-First, to eliminate some of the noise coming from daily reporting, we do 7 day averaging over a trailing window[^1]:
 
-[^1]: This makes it so that any given day of the processed timeseries only depends on the previous week, which means that we avoid leaking future values when making a forecast.
+As with basically any dataset, there is some cleaning that we will need to do to
+make it actually usable; we'll use some utilities from
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.  First, to
+eliminate some of the noise coming from daily reporting, we do 7 day averaging
+over a trailing window[^1]:
+
+[^1]: This makes it so that any given day of the processed timeseries only
+    depends on the previous week, which means that we avoid leaking future
+    values when making a forecast.
 
 ```{r smooth}
 cases_deaths <-
@@ -174,13 +192,18 @@ cases_deaths <-
 ```
 
 Then trimming outliers, most especially negative values:
+
 ```{r outlier}
 cases_deaths <-
   cases_deaths |>
   group_by(geo_value) |>
   mutate(
-    outlr_death_rate = detect_outlr_rm(time_value, death_rate, detect_negatives = TRUE),
-    outlr_case_rate = detect_outlr_rm(time_value, case_rate, detect_negatives = TRUE)
+    outlr_death_rate = detect_outlr_rm(
+      time_value, death_rate, detect_negatives = TRUE
+    ),
+    outlr_case_rate = detect_outlr_rm(
+      time_value, case_rate, detect_negatives = TRUE
+    )
   ) |>
   unnest(cols = starts_with("outlr"), names_sep = "_") |>
   ungroup() |>
@@ -215,7 +238,9 @@ processed_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+    data = forecast_date_label,
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
@@ -225,7 +250,9 @@ processed_data_plot <-
 processed_data_plot
 ```
 
-To make a forecast, we will use a "canned" simple auto-regressive forecaster to predict the death rate four weeks into the future using lagged[^3] deaths and cases
+To make a forecast, we will use a "canned" simple auto-regressive forecaster to
+predict the death rate four weeks into the future using lagged[^3] deaths and
+cases
 
 [^3]: lagged by 3 in this context meaning using the value from 3 days ago.
 
@@ -251,11 +278,16 @@ predicted values (and prediction intervals) for each location 28 days after the
 forecast date.
 Plotting the prediction intervals on our subset above[^2]: 
 
-[^2]: Alternatively, you could call `auto_plot(four_week_ahead)` to get the full collection of forecasts. This is too busy for the space we have for plotting here.
+[^2]: Alternatively, you could call `auto_plot(four_week_ahead)` to get the full
+    collection of forecasts. This is too busy for the space we have for plotting
+    here.
 
 <details>
 <summary> Plot </summary>
-This is the same kind of plot as `processed_data_plot` above, but with the past data narrowed somewhat
+
+This is the same kind of plot as `processed_data_plot` above, but with the past
+data narrowed somewhat
+
 ```{r}
 narrow_data_plot <-
   cases_deaths |>
@@ -267,13 +299,17 @@ narrow_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+    data = forecast_date_label,
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-Putting that together with a plot of the bands, and a plot of the median prediction.
+Putting that together with a plot of the bands, and a plot of the median
+prediction.
+
 ```{r plotting_forecast, warning=FALSE}
 epiworkflow <- four_week_ahead$epi_workflow
 restricted_predictions <-
@@ -288,15 +324,20 @@ forecast_plot <-
     levels = 0.9,
     fill = primary
   ) +
-  geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
+  geom_point(data = restricted_predictions,
+             aes(y = .data$value),
+             color = secondary)
 ```
 </details>
 
 ```{r show-single-forecast, warning=FALSE, echo=FALSE}
 forecast_plot
 ```
-The yellow dot gives the median prediction, while the red interval gives the 5-95%  inter-quantile range.
-For this particular day and these locations, the forecasts are relatively accurate, with the true data being within the 25-75% interval.
+
+The yellow dot gives the median prediction, while the red interval gives the
+5-95%  inter-quantile range.
+For this particular day and these locations, the forecasts are relatively
+accurate, with the true data being within the 25-75% interval.
 A couple of things to note:
 
 1. Our methods are primarily direct forecasters; this means we don't need to

From 91a980be0cfa05e68804586acc341197cfd78876 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 14:42:34 -0600
Subject: [PATCH 329/387] pushing only the dev docs

---
 README.md | 190 ++++++++++++++++++++++++++----------------------------
 1 file changed, 90 insertions(+), 100 deletions(-)

diff --git a/README.md b/README.md
index 2a0d58a83..7b5dc87f2 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,34 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# epipredict
+# Epipredict
 
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-**Note:** This package is currently in development and may not work as
-expected. Please file bug reports as issues in this repo, and we will do
-our best to address them quickly.
+Epipredict is a framework for building transformation and forecasting
+pipelines for epidemiological and other panel time-series datasets. In
+addition to tools for building forecasting pipelines, it contains a
+number of “canned” forecasters meant to run with little modification as
+an easy way to get started forecasting.
+
+It is designed to work well with
+[`epiprocess`](https://cmu-delphi.github.io/epiprocess/), a utility for
+handling various time series and geographic processing tools in an
+epidemiological context. Both of the packages are meant to work well
+with the panel data provided by
+[`epidatr`](https://cmu-delphi.github.io/epidatr/).
+
+If you are looking for more detail beyond the package documentation, see
+our [forecasting
+book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 ## Installation
 
-To install (unless you’re making changes to the package, use the stable
-version):
+To install (unless you’re planning on contributing to package
+development, we suggest using the stable version):
 
 ``` r
 # Stable version
@@ -25,52 +38,14 @@ pak::pkg_install("cmu-delphi/epipredict@main")
 pak::pkg_install("cmu-delphi/epipredict@dev")
 ```
 
-## Documentation
-
-You can view documentation for the `main` branch at
-<https://cmu-delphi.github.io/epipredict>.
-
-## Goals for `epipredict`
-
-**We hope to provide:**
-
-1.  A set of basic, easy-to-use forecasters that work out of the box.
-    You should be able to do a reasonably limited amount of
-    customization on them. For the basic forecasters, we currently
-    provide:
-    - Baseline flatline forecaster
-    - Autoregressive forecaster
-    - Autoregressive classifier
-    - CDC FluSight flatline forecaster
-2.  A framework for creating custom forecasters out of modular
-    components. There are four types of components:
-    - Preprocessor: do things to the data before model training
-    - Trainer: train a model on data, resulting in a fitted model object
-    - Predictor: make predictions, using a fitted model object
-    - Postprocessor: do things to the predictions before returning
+The documentation for the stable version is at
+<https://cmu-delphi.github.io/epipredict>, while the development version
+is at <https://cmu-delphi.github.io/epipredict/dev>.
 
-**Target audiences:**
+## Motivating example
 
-- Basic. Has data, calls forecaster with default arguments.
-- Intermediate. Wants to examine changes to the arguments, take
-  advantage of built in flexibility.
-- Advanced. Wants to write their own forecasters. Maybe willing to build
-  up from some components.
-
-The Advanced user should find their task to be relatively easy. Examples
-of these tasks are illustrated in the [vignettes and
-articles](https://cmu-delphi.github.io/epipredict).
-
-See also the (in progress) [Forecasting
-Book](https://cmu-delphi.github.io/delphi-tooling-book/).
-
-## Intermediate example
-
-The package comes with some built-in historical data for illustration,
-but up-to-date versions of this could be downloaded with the
-[`{epidatr}` package](https://cmu-delphi.github.io/epidatr/) and
-processed using
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
+To demonstrate the kind of forecast epipredict can make, say we’re
+predicting COVID deaths per 100k for each state on
 
 ``` r
 forecast_date <- as.Date("2021-08-01")
@@ -95,8 +70,9 @@ cases <- pub_covidcast(
   signals = "confirmed_incidence_prop",
   time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  time_values = epirange(20200601, 20211231),
+  geo_values = "*"
+) |>
   select(geo_value, time_value, case_rate = value)
 
 deaths <- pub_covidcast(
@@ -104,8 +80,9 @@ deaths <- pub_covidcast(
   signals = "deaths_incidence_prop",
   time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  time_values = epirange(20200601, 20211231),
+  geo_values = "*"
+) |>
   select(geo_value, time_value, death_rate = value)
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
@@ -123,6 +100,7 @@ cases_deaths |>
 ```
 
 <img src="man/figures/README-case_death-1.png" width="90%" style="display: block; margin: auto;" />
+
 As with basically any dataset, there is some cleaning that we will need
 to do to make it actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
@@ -131,7 +109,7 @@ First, to eliminate some of the noise coming from daily reporting, we do
 
 ``` r
 cases_deaths <-
-  cases_deaths |> 
+  cases_deaths |>
   group_by(geo_value) |>
   epi_slide(
     cases_7dav = mean(case_rate, na.rm = TRUE),
@@ -150,47 +128,54 @@ cases_deaths <-
   cases_deaths |>
   group_by(geo_value) |>
   mutate(
-    outlr_death_rate = detect_outlr_rm(time_value, death_rate, detect_negatives = TRUE),
-    outlr_case_rate = detect_outlr_rm(time_value, case_rate, detect_negatives = TRUE)
+    outlr_death_rate = detect_outlr_rm(
+      time_value, death_rate, detect_negatives = TRUE
+    ),
+    outlr_case_rate = detect_outlr_rm(
+      time_value, case_rate, detect_negatives = TRUE
+    )
   ) |>
   unnest(cols = starts_with("outlr"), names_sep = "_") |>
   ungroup() |>
   mutate(
     death_rate = outlr_death_rate_replacement,
-    case_rate = outlr_case_rate_replacement) |>
+    case_rate = outlr_case_rate_replacement
+  ) |>
   select(geo_value, time_value, case_rate, death_rate)
 cases_deaths
-#> An `epi_df` object, 32,480 x 4 with metadata:
+#> An `epi_df` object, 32,424 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
-#> * as_of     = 2023-03-10
+#> * as_of     = 2022-01-01
 #> 
-#> # A tibble: 20,496 × 4
-#>    geo_value time_value case_rate death_rate
-#>  * <chr>     <date>         <dbl>      <dbl>
-#>  1 ak        2020-12-31      35.9      0.158
-#>  2 al        2020-12-31      65.1      0.438
-#>  3 ar        2020-12-31      66.0      1.27 
-#>  4 as        2020-12-31       0        0    
-#>  5 az        2020-12-31      76.8      1.10 
-#>  6 ca        2020-12-31      95.9      0.755
-#>  7 co        2020-12-31      37.8      0.376
-#>  8 ct        2020-12-31      52.1      0.819
-#>  9 dc        2020-12-31      31.0      0.601
-#> 10 de        2020-12-31      64.3      0.912
-#> # ℹ 20,486 more rows
+#> # A tibble: 32,424 × 4
+#>   geo_value time_value case_rate death_rate
+#> * <chr>     <date>         <dbl>      <dbl>
+#> 1 ak        2020-06-01      2.31          0
+#> 2 ak        2020-06-02      1.94          0
+#> 3 ak        2020-06-03      2.63          0
+#> 4 ak        2020-06-04      2.59          0
+#> 5 ak        2020-06-05      2.43          0
+#> 6 ak        2020-06-06      2.35          0
+#> # ℹ 32,418 more rows
 ```
 
-To create and train a simple auto-regressive forecaster to predict the
-death rate two weeks into the future using past (lagged) deaths and
-cases, we could use the following function.
+</details>
+
+After having downloaded and cleaned the data in `cases_deaths`, we plot
+a subset of the states, noting the actual forecast date:
+
+<details>
+<summary>
+Plot
+</summary>
 
 ``` r
 forecast_date_label <-
   tibble(
     geo_value = rep(plot_locations, 2),
-    source = c(rep("case_rate",4), rep("death_rate", 4)),
-    dates = rep(forecast_date - 7*2, 2 * length(plot_locations)),
+    source = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(plot_locations)),
     heights = c(rep(150, 4), rep(1.0, 4))
   )
 processed_data_plot <-
@@ -202,7 +187,10 @@ processed_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label,
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -222,26 +210,26 @@ four_week_ahead <- arx_forecaster(
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 14
+    ahead = 4 * 7
   )
 )
-two_week_ahead
-#> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
+four_week_ahead
+#> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-02-11 12:32:56.
+#> This forecaster was fit on 2025-01-24 14:47:38.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
-#> • Using data up-to-date as of: 2023-03-10.
-#> • With the last data available on 2021-12-31
+#> • Using data up-to-date as of: 2022-01-01.
+#> • With the last data available on 2021-08-01
 #> 
-#> ── Predictions ─────────────────────────────────────────────────────────────
+#> ── Predictions ──────────────────────────────────────────────────────────────
 #> 
 #> A total of 56 predictions are available for
 #> • 56 unique geographic regions,
-#> • At forecast date: 2021-12-31,
-#> • For target date: 2022-01-14,
+#> • At forecast date: 2021-08-01,
+#> • For target date: 2021-08-29,
 #> 
 ```
 
@@ -272,15 +260,16 @@ narrow_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label,
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-The fitted model here involved preprocessing the data to appropriately
-generate lagged predictors, estimating a linear model with `stats::lm()`
-and then postprocessing the results to be meaningful for epidemiological
-tasks. We can also examine the predictions.
+Putting that together with a plot of the bands, and a plot of the median
+prediction.
 
 ``` r
 epiworkflow <- four_week_ahead$epi_workflow
@@ -294,16 +283,17 @@ forecast_plot <-
   epipredict:::plot_bands(
     restricted_predictions,
     levels = 0.9,
-    fill = primary) +
-  geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
+    fill = primary
+  ) +
+  geom_point(data = restricted_predictions,
+             aes(y = .data$value),
+             color = secondary)
 ```
 
-The results above show a distributional forecast produced using data
-through the end of 2021 for the 14th of January 2022. A prediction for
-the death rate per 100K inhabitants is available for every state
-(`geo_value`) along with a 90% predictive interval.
+</details>
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
+
 The yellow dot gives the median prediction, while the red interval gives
 the 5-95% inter-quantile range. For this particular day and these
 locations, the forecasts are relatively accurate, with the true data

From a52934a7d91fc5ff2515c7521b4a042a66795828 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 15:04:32 -0600
Subject: [PATCH 330/387] isn't building the readme

---
 inst/pkgdown-watch.R                          |   3 ---
 man/figures/README-case_death-1.png           | Bin 0 -> 248400 bytes
 man/figures/README-show-processed-data-1.png  | Bin 0 -> 303166 bytes
 man/figures/README-show-single-forecast-1.png | Bin 262795 -> 262694 bytes
 4 files changed, 3 deletions(-)
 create mode 100644 man/figures/README-case_death-1.png
 create mode 100644 man/figures/README-show-processed-data-1.png

diff --git a/inst/pkgdown-watch.R b/inst/pkgdown-watch.R
index 4ac977566..89d4bd91e 100644
--- a/inst/pkgdown-watch.R
+++ b/inst/pkgdown-watch.R
@@ -57,9 +57,6 @@ servr::httw(
       devtools::build_readme()
       pkgdown::build_home()
       pkgdown::build_site(pkg, lazy = TRUE, examples = FALSE, devel = TRUE, preview = FALSE)
-
-      devtools::build_readme(".")
-      pkgdown::build_home(pkg)
     }
 
     cli::cli_alert("Site rebuild done!")
diff --git a/man/figures/README-case_death-1.png b/man/figures/README-case_death-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..f1b4ed98e0d22644d53fbd5780ede72058dd8cce
GIT binary patch
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UrhMBR?3a(lK1d4}3hDU#4<qBscmMzZ


From 06d55812620d8cfce5f586a7a6ea0b2901fffdef Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 24 Jan 2025 15:34:32 -0600
Subject: [PATCH 331/387] readme.rmd red/yellow -> blue/black

---
 README.Rmd                                    |  10 +++++-----
 README.md                                     |  12 ++++++------
 man/figures/README-show-single-forecast-1.png | Bin 262694 -> 262984 bytes
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index ddc677872..d3e4fb3e1 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -321,12 +321,12 @@ forecast_plot <-
   narrow_data_plot |>
   epipredict:::plot_bands(
     restricted_predictions,
-    levels = 0.9,
-    fill = primary
+    levels = 0.9
   ) +
-  geom_point(data = restricted_predictions,
-             aes(y = .data$value),
-             color = secondary)
+  geom_point(
+    data = restricted_predictions,
+    aes(y = .data$value)
+  )
 ```
 </details>
 
diff --git a/README.md b/README.md
index 7b5dc87f2..041662947 100644
--- a/README.md
+++ b/README.md
@@ -216,7 +216,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-01-24 14:47:38.
+#> This forecaster was fit on 2025-01-24 15:31:46.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -282,12 +282,12 @@ forecast_plot <-
   narrow_data_plot |>
   epipredict:::plot_bands(
     restricted_predictions,
-    levels = 0.9,
-    fill = primary
+    levels = 0.9
   ) +
-  geom_point(data = restricted_predictions,
-             aes(y = .data$value),
-             color = secondary)
+  geom_point(
+    data = restricted_predictions,
+    aes(y = .data$value)
+  )
 ```
 
 </details>
diff --git a/man/figures/README-show-single-forecast-1.png b/man/figures/README-show-single-forecast-1.png
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From eed91fad7096cbec8e9f65b52bdcf0154f09968e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 27 Jan 2025 14:47:21 -0600
Subject: [PATCH 332/387] training on only the shown subset

---
 README.Rmd | 72 +++++++++++++++++++++++++++++++-----------------------
 1 file changed, 41 insertions(+), 31 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index d3e4fb3e1..2d0ee2aa7 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -126,9 +126,9 @@ data.
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
-This dataset can be found in the package as <TODO DOESN'T EXIST>; we demonstrate
-some of the typically ubiquitous cleaning operations needed to be able to
-forecast.
+This dataset can be found in the package as `covid_case_death_rates`; we
+demonstrate some of the typically ubiquitous cleaning operations needed to be
+able to forecast.
 First we pull both jhu-csse cases and deaths from
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
@@ -152,26 +152,34 @@ deaths <- pub_covidcast(
   geo_values = "*"
 ) |>
   select(geo_value, time_value, death_rate = value)
+```
+
+Since visualizing the results on every geography is somewhat overwhelming,
+we'll only train on a subset of 5.
+```{r date, warning = FALSE}
+used_locations <- c("ca", "ma", "ny", "tx")
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  filter(geo_value %in% used_locations) |>
   as_epi_df(as_of = as.Date("2022-01-01"))
-plot_locations <- c("ca", "ma", "ny", "tx")
 # plotting the data as it was downloaded
 cases_deaths |>
-  filter(geo_value %in% plot_locations) |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+  autoplot(
+    case_rate,
+    death_rate,
+    .color_by = "none"
+  ) +
+  facet_grid(.response_name ~ geo_value, scale = "free") +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
 As with basically any dataset, there is some cleaning that we will need to do to
 make it actually usable; we'll use some utilities from
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.  First, to
-eliminate some of the noise coming from daily reporting, we do 7 day averaging
-over a trailing window[^1]:
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
+
+First, to eliminate some of the noise coming from daily reporting, we do 7 day
+averaging over a trailing window[^1]:
 
 [^1]: This makes it so that any given day of the processed timeseries only
     depends on the previous week, which means that we avoid leaking future
@@ -199,10 +207,12 @@ cases_deaths <-
   group_by(geo_value) |>
   mutate(
     outlr_death_rate = detect_outlr_rm(
-      time_value, death_rate, detect_negatives = TRUE
+      time_value, death_rate,
+      detect_negatives = TRUE
     ),
     outlr_case_rate = detect_outlr_rm(
-      time_value, case_rate, detect_negatives = TRUE
+      time_value, case_rate,
+      detect_negatives = TRUE
     )
   ) |>
   unnest(cols = starts_with("outlr"), names_sep = "_") |>
@@ -212,7 +222,6 @@ cases_deaths <-
     case_rate = outlr_case_rate_replacement
   ) |>
   select(geo_value, time_value, case_rate, death_rate)
-cases_deaths
 ```
 </details>
 
@@ -224,14 +233,13 @@ of the states, noting the actual forecast date:
 ```{r plot_locs}
 forecast_date_label <-
   tibble(
-    geo_value = rep(plot_locations, 2),
-    source = c(rep("case_rate", 4), rep("death_rate", 4)),
-    dates = rep(forecast_date - 7 * 2, 2 * length(plot_locations)),
+    geo_value = rep(used_locations, 2),
+    .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
     heights = c(rep(150, 4), rep(1.0, 4))
   )
 processed_data_plot <-
   cases_deaths |>
-  filter(geo_value %in% plot_locations) |>
   pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
   ggplot(aes(x = time_value, y = value)) +
   geom_line() +
@@ -292,11 +300,12 @@ data narrowed somewhat
 narrow_data_plot <-
   cases_deaths |>
   filter(time_value > "2021-04-01") |>
-  filter(geo_value %in% plot_locations) |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+  autoplot(
+    case_rate,
+    death_rate,
+    .color_by = "none"
+  ) +
+  facet_grid(.response_name ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -304,7 +313,8 @@ narrow_data_plot <-
     size = 3, hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
-  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
+  ylim(0, NA)
 ```
 
 Putting that together with a plot of the bands, and a plot of the median
@@ -312,16 +322,15 @@ prediction.
 
 ```{r plotting_forecast, warning=FALSE}
 epiworkflow <- four_week_ahead$epi_workflow
+
 restricted_predictions <-
   four_week_ahead$predictions |>
-  filter(geo_value %in% plot_locations) |>
   rename(time_value = target_date, value = .pred) |>
-  mutate(source = "death_rate")
+  mutate(.response_name = "death_rate")
 forecast_plot <-
   narrow_data_plot |>
   epipredict:::plot_bands(
-    restricted_predictions,
-    levels = 0.9
+    restricted_predictions
   ) +
   geom_point(
     data = restricted_predictions,
@@ -351,5 +360,6 @@ A couple of things to note:
 If you encounter a bug or have a feature request, feel free to file an [issue on
 our github page](https://github.com/cmu-delphi/epipredict/issues).
 For other
-questions, feel free to contact [Daniel](daniel@stat.ubc.ca), [David](davidweb@andrew.cmu.edu), [Dmitry](dshemetov@cmu.edu), or
-[Logan](lcbrooks@andrew.cmu.edu), either via email or on the Insightnet slack.
+questions, feel free to reach out to the authors, either via this [contact
+form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
+email or the Insightnet slack.

From 39e203ce4976d31457db2bbf5106448c47ea56d0 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 27 Jan 2025 16:05:15 -0600
Subject: [PATCH 333/387] autoplot new data

---
 R/autoplot.R | 66 +++++++++++++++++++++++++++++-----------------------
 1 file changed, 37 insertions(+), 29 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index efad56ffe..07fe56466 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -16,6 +16,8 @@ ggplot2::autoplot
 #' @param object,x An `epi_workflow`
 #' @param predictions A data frame with predictions. If `NULL`, only the
 #'   original data is shown.
+#' @param plot_data An epi_df of the data to plot against. This is for the case
+#'   where you have the actual results to compare the forecast against.
 #' @param .levels A numeric vector of levels to plot for any prediction bands.
 #'   More than 3 levels begins to be difficult to see.
 #' @param ... Ignored
@@ -81,7 +83,9 @@ NULL
 #' @export
 #' @rdname autoplot-epipred
 autoplot.epi_workflow <- function(
-    object, predictions = NULL,
+    object,
+    predictions = NULL,
+    plot_data = NULL,
     .levels = c(.5, .8, .9), ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
@@ -109,31 +113,35 @@ autoplot.epi_workflow <- function(
   }
   keys <- c("geo_value", "time_value", "key")
   mold_roles <- names(mold$extras$roles)
-  edf <- bind_cols(mold$extras$roles[mold_roles %in% keys], y)
-  if (starts_with_impl("ahead_", names(y))) {
-    old_name_y <- unlist(strsplit(names(y), "_"))
-    shift <- as.numeric(old_name_y[2])
-    new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
-    edf <- rename(edf, !!new_name_y := !!names(y))
-  } else if (starts_with_impl("lag_", names(y))) {
-    old_name_y <- unlist(strsplit(names(y), "_"))
-    shift <- -as.numeric(old_name_y[2])
-    new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
-    edf <- rename(edf, !!new_name_y := !!names(y))
-  } else {
-    new_name_y <- names(y)
-    shift <- 0
+  # extract the relevant column names for plotting
+  old_name_y <- unlist(strsplit(names(y), "_"))
+  new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
+  if (is.null(plot_data)) {
+    # the outcome has shifted, so we need to shift it forward (or back)
+    # by the corresponding amount
+    plot_data <- bind_cols(mold$extras$roles[mold_roles %in% keys], y)
+    if (starts_with_impl("ahead_", names(y))) {
+      shift <- as.numeric(old_name_y[2])
+    } else if (starts_with_impl("lag_", names(y))) {
+      old_name_y <- unlist(strsplit(names(y), "_"))
+      shift <- -as.numeric(old_name_y[2])
+    } else {
+      new_name_y <- names(y)
+      shift <- 0
+    }
+    plot_data <- rename(plot_data, !!new_name_y := !!names(y))
+    if (!is.null(shift)) {
+      plot_data <- mutate(plot_data, time_value = time_value + shift)
+    }
+    other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
+    plot_data <- as_epi_df(plot_data,
+      as_of = object$fit$meta$as_of,
+      other_keys = other_keys
+    )
   }
-
-  edf <- mutate(edf, time_value = time_value + shift)
-  other_keys <- key_colnames(object, exclude = c("geo_value", "time_value"))
-  edf <- as_epi_df(edf,
-    as_of = object$fit$meta$as_of,
-    other_keys = other_keys
-  )
   if (is.null(predictions)) {
     return(autoplot(
-      edf, new_name_y,
+      plot_data, new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
       .facet_filter = {{ .facet_filter }}
     ))
@@ -145,27 +153,27 @@ autoplot.epi_workflow <- function(
     }
     predictions <- rename(predictions, time_value = target_date)
   }
-  pred_cols_ok <- hardhat::check_column_names(predictions, key_colnames(edf))
+  pred_cols_ok <- hardhat::check_column_names(predictions, key_colnames(plot_data))
   if (!pred_cols_ok$ok) {
     cli_warn(c(
       "`predictions` is missing required variables: {.var {pred_cols_ok$missing_names}}.",
       i = "Plotting the original data."
     ))
     return(autoplot(
-      edf, !!new_name_y,
+      plot_data, !!new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
       .facet_filter = {{ .facet_filter }}
     ))
   }
 
   # First we plot the history, always faceted by everything
-  bp <- autoplot(edf, !!new_name_y,
+  bp <- autoplot(plot_data, !!new_name_y,
     .color_by = "none", .facet_by = "all_keys",
     .base_color = "black", .facet_filter =  {{ .facet_filter }}
   )
 
   # Now, prepare matching facets in the predictions
-  ek <- epi_keys_only(edf)
+  ek <- epi_keys_only(plot_data)
   predictions <- predictions %>%
     mutate(
       .facets = interaction(!!!rlang::syms(as.list(ek)), sep = " / "),
@@ -203,7 +211,7 @@ autoplot.epi_workflow <- function(
 #' @export
 #' @rdname autoplot-epipred
 autoplot.canned_epipred <- function(
-    object, ...,
+    object, plot_data = NULL, ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
@@ -218,7 +226,7 @@ autoplot.canned_epipred <- function(
   predictions <- object$predictions %>%
     rename(time_value = target_date)
 
-  autoplot(ewf, predictions,
+  autoplot(ewf, predictions, plot_data, ...,
     .color_by = .color_by, .facet_by = .facet_by,
     .base_color = .base_color, .facet_filter = {{ .facet_filter }}
   )

From 0be48a0527a78b6e96465fd46355863d738e1a53 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 27 Jan 2025 16:47:43 -0600
Subject: [PATCH 334/387] using new autoplot

---
 README.Rmd                                    |  63 +++------
 README.md                                     | 130 +++++++-----------
 man/figures/README-show-processed-data-1.png  | Bin 303166 -> 316271 bytes
 man/figures/README-show-single-forecast-1.png | Bin 262984 -> 290530 bytes
 4 files changed, 70 insertions(+), 123 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 2d0ee2aa7..d68a2aefb 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -240,10 +240,12 @@ forecast_date_label <-
   )
 processed_data_plot <-
   cases_deaths |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+  autoplot(
+    case_rate,
+    death_rate,
+    .color_by = "none"
+  ) +
+  facet_grid(.response_name ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -292,50 +294,23 @@ Plotting the prediction intervals on our subset above[^2]:
 
 <details>
 <summary> Plot </summary>
-
-This is the same kind of plot as `processed_data_plot` above, but with the past
-data narrowed somewhat
-
-```{r}
-narrow_data_plot <-
-  cases_deaths |>
-  filter(time_value > "2021-04-01") |>
-  autoplot(
-    case_rate,
-    death_rate,
-    .color_by = "none"
-  ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
-  geom_vline(aes(xintercept = forecast_date)) +
-  geom_text(
-    data = forecast_date_label,
-    aes(x = dates, label = "forecast\ndate", y = heights),
-    size = 3, hjust = "right"
-  ) +
-  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
-  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
-  ylim(0, NA)
-```
-
-Putting that together with a plot of the bands, and a plot of the median
-prediction.
-
 ```{r plotting_forecast, warning=FALSE}
 epiworkflow <- four_week_ahead$epi_workflow
-
 restricted_predictions <-
   four_week_ahead$predictions |>
   rename(time_value = target_date, value = .pred) |>
   mutate(.response_name = "death_rate")
 forecast_plot <-
-  narrow_data_plot |>
-  epipredict:::plot_bands(
-    restricted_predictions
+  four_week_ahead |>
+  autoplot(plot_data = cases_deaths) +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label %>% filter(.response_name == "death_rate"),
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
   ) +
-  geom_point(
-    data = restricted_predictions,
-    aes(y = .data$value)
-  )
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 </details>
 
@@ -343,10 +318,10 @@ forecast_plot <-
 forecast_plot
 ```
 
-The yellow dot gives the median prediction, while the red interval gives the
-5-95%  inter-quantile range.
+The black dot gives the median prediction, while the blue intervals give the
+25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges.
 For this particular day and these locations, the forecasts are relatively
-accurate, with the true data being within the 25-75% interval.
+accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
 
 1. Our methods are primarily direct forecasters; this means we don't need to
@@ -362,4 +337,4 @@ our github page](https://github.com/cmu-delphi/epipredict/issues).
 For other
 questions, feel free to reach out to the authors, either via this [contact
 form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
-email or the Insightnet slack.
+email, or the Insightnet slack.
diff --git a/README.md b/README.md
index 041662947..289355a57 100644
--- a/README.md
+++ b/README.md
@@ -59,7 +59,7 @@ from JHU data.
 Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
-This dataset can be found in the package as \<TODO DOESN’T EXIST\>; we
+This dataset can be found in the package as `covid_case_death_rates`; we
 demonstrate some of the typically ubiquitous cleaning operations needed
 to be able to forecast. First we pull both jhu-csse cases and deaths
 from [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
@@ -84,26 +84,35 @@ deaths <- pub_covidcast(
   geo_values = "*"
 ) |>
   select(geo_value, time_value, death_rate = value)
+```
+
+Since visualizing the results on every geography is somewhat
+overwhelming, we’ll only train on a subset of 5.
+
+``` r
+used_locations <- c("ca", "ma", "ny", "tx")
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  filter(geo_value %in% used_locations) |>
   as_epi_df(as_of = as.Date("2022-01-01"))
-plot_locations <- c("ca", "ma", "ny", "tx")
 # plotting the data as it was downloaded
 cases_deaths |>
-  filter(geo_value %in% plot_locations) |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+  autoplot(
+    case_rate,
+    death_rate,
+    .color_by = "none"
+  ) +
+  facet_grid(.response_name ~ geo_value, scale = "free") +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-<img src="man/figures/README-case_death-1.png" width="90%" style="display: block; margin: auto;" />
+<img src="man/figures/README-date-1.png" width="90%" style="display: block; margin: auto;" />
 
 As with basically any dataset, there is some cleaning that we will need
 to do to make it actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
+
 First, to eliminate some of the noise coming from daily reporting, we do
 7 day averaging over a trailing window[^1]:
 
@@ -129,10 +138,12 @@ cases_deaths <-
   group_by(geo_value) |>
   mutate(
     outlr_death_rate = detect_outlr_rm(
-      time_value, death_rate, detect_negatives = TRUE
+      time_value, death_rate,
+      detect_negatives = TRUE
     ),
     outlr_case_rate = detect_outlr_rm(
-      time_value, case_rate, detect_negatives = TRUE
+      time_value, case_rate,
+      detect_negatives = TRUE
     )
   ) |>
   unnest(cols = starts_with("outlr"), names_sep = "_") |>
@@ -142,22 +153,6 @@ cases_deaths <-
     case_rate = outlr_case_rate_replacement
   ) |>
   select(geo_value, time_value, case_rate, death_rate)
-cases_deaths
-#> An `epi_df` object, 32,424 x 4 with metadata:
-#> * geo_type  = state
-#> * time_type = day
-#> * as_of     = 2022-01-01
-#> 
-#> # A tibble: 32,424 × 4
-#>   geo_value time_value case_rate death_rate
-#> * <chr>     <date>         <dbl>      <dbl>
-#> 1 ak        2020-06-01      2.31          0
-#> 2 ak        2020-06-02      1.94          0
-#> 3 ak        2020-06-03      2.63          0
-#> 4 ak        2020-06-04      2.59          0
-#> 5 ak        2020-06-05      2.43          0
-#> 6 ak        2020-06-06      2.35          0
-#> # ℹ 32,418 more rows
 ```
 
 </details>
@@ -173,18 +168,19 @@ Plot
 ``` r
 forecast_date_label <-
   tibble(
-    geo_value = rep(plot_locations, 2),
-    source = c(rep("case_rate", 4), rep("death_rate", 4)),
-    dates = rep(forecast_date - 7 * 2, 2 * length(plot_locations)),
+    geo_value = rep(used_locations, 2),
+    .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
     heights = c(rep(150, 4), rep(1.0, 4))
   )
 processed_data_plot <-
   cases_deaths |>
-  filter(geo_value %in% plot_locations) |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+  autoplot(
+    case_rate,
+    death_rate,
+    .color_by = "none"
+  ) +
+  facet_grid(.response_name ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -216,7 +212,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-01-24 15:31:46.
+#> This forecaster was fit on 2025-01-27 16:36:10.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -226,8 +222,8 @@ four_week_ahead
 #> 
 #> ── Predictions ──────────────────────────────────────────────────────────────
 #> 
-#> A total of 56 predictions are available for
-#> • 56 unique geographic regions,
+#> A total of 4 predictions are available for
+#> • 4 unique geographic regions,
 #> • At forecast date: 2021-08-01,
 #> • For target date: 2021-08-29,
 #> 
@@ -246,21 +242,18 @@ Plotting the prediction intervals on our subset above[^3]:
 Plot
 </summary>
 
-This is the same kind of plot as `processed_data_plot` above, but with
-the past data narrowed somewhat
-
 ``` r
-narrow_data_plot <-
-  cases_deaths |>
-  filter(time_value > "2021-04-01") |>
-  filter(geo_value %in% plot_locations) |>
-  pivot_longer(cols = c("case_rate", "death_rate"), names_to = "source") |>
-  ggplot(aes(x = time_value, y = value)) +
-  geom_line() +
-  facet_grid(source ~ geo_value, scale = "free") +
+epiworkflow <- four_week_ahead$epi_workflow
+restricted_predictions <-
+  four_week_ahead$predictions |>
+  rename(time_value = target_date, value = .pred) |>
+  mutate(.response_name = "death_rate")
+forecast_plot <-
+  four_week_ahead |>
+  autoplot(plot_data = cases_deaths) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label,
+    data = forecast_date_label %>% filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
     size = 3, hjust = "right"
   ) +
@@ -268,36 +261,15 @@ narrow_data_plot <-
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-Putting that together with a plot of the bands, and a plot of the median
-prediction.
-
-``` r
-epiworkflow <- four_week_ahead$epi_workflow
-restricted_predictions <-
-  four_week_ahead$predictions |>
-  filter(geo_value %in% plot_locations) |>
-  rename(time_value = target_date, value = .pred) |>
-  mutate(source = "death_rate")
-forecast_plot <-
-  narrow_data_plot |>
-  epipredict:::plot_bands(
-    restricted_predictions,
-    levels = 0.9
-  ) +
-  geom_point(
-    data = restricted_predictions,
-    aes(y = .data$value)
-  )
-```
-
 </details>
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
 
-The yellow dot gives the median prediction, while the red interval gives
-the 5-95% inter-quantile range. For this particular day and these
-locations, the forecasts are relatively accurate, with the true data
-being within the 25-75% interval. A couple of things to note:
+The black dot gives the median prediction, while the blue intervals give
+the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges. For this
+particular day and these locations, the forecasts are relatively
+accurate, with the true data being at least within the 10-90% interval.
+A couple of things to note:
 
 1.  Our methods are primarily direct forecasters; this means we don’t
     need to predict 1, 2,…, 27 days ahead to then predict 28 days ahead
@@ -312,10 +284,10 @@ being within the 25-75% interval. A couple of things to note:
 If you encounter a bug or have a feature request, feel free to file an
 [issue on our github
 page](https://github.com/cmu-delphi/epipredict/issues). For other
-questions, feel free to contact [Daniel](daniel@stat.ubc.ca),
-[David](davidweb@andrew.cmu.edu), [Dmitry](dshemetov@cmu.edu), or
-[Logan](lcbrooks@andrew.cmu.edu), either via email or on the Insightnet
-slack.
+questions, feel free to reach out to the authors, either via this
+[contact
+form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
+email, or the Insightnet slack.
 
 [^1]: This makes it so that any given day of the processed timeseries
     only depends on the previous week, which means that we avoid leaking
diff --git a/man/figures/README-show-processed-data-1.png b/man/figures/README-show-processed-data-1.png
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From 6e3d2ff022f8c6896d66fd664a5a8879163460df Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 31 Jan 2025 17:50:08 -0600
Subject: [PATCH 335/387] getting started first draft

---
 README.Rmd                                    |  40 +-
 README.md                                     |  50 +-
 _pkgdown.yml                                  |   2 +
 inst/pkgdown-watch.R                          |   2 +-
 man/figures/README-date-1.png                 | Bin 0 -> 266688 bytes
 man/figures/README-show-processed-data-1.png  | Bin 316271 -> 317105 bytes
 man/figures/README-show-single-forecast-1.png | Bin 290530 -> 290340 bytes
 tests/testthat/test-step_adjust_latency.R     |   2 +-
 vignettes/epipredict.Rmd                      | 771 +++++++-----------
 vignettes/guts.Rmd                            | 270 ++++++
 10 files changed, 655 insertions(+), 482 deletions(-)
 create mode 100644 man/figures/README-date-1.png
 create mode 100644 vignettes/guts.Rmd

diff --git a/README.Rmd b/README.Rmd
index d68a2aefb..9f27ce668 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -169,7 +169,11 @@ cases_deaths |>
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -181,7 +185,7 @@ make it actually usable; we'll use some utilities from
 First, to eliminate some of the noise coming from daily reporting, we do 7 day
 averaging over a trailing window[^1]:
 
-[^1]: This makes it so that any given day of the processed timeseries only
+[^1]: This makes it so that any given day of the processed time-series only
     depends on the previous week, which means that we avoid leaking future
     values when making a forecast.
 
@@ -236,16 +240,21 @@ forecast_date_label <-
     geo_value = rep(used_locations, 2),
     .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
-    heights = c(rep(150, 4), rep(1.0, 4))
+    heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  cases_deaths |>
+  covid_case_death_rates |>
+  filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free"
+  ) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -273,7 +282,8 @@ four_week_ahead <- arx_forecaster(
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 4 * 7
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
   )
 )
 four_week_ahead
@@ -288,7 +298,7 @@ predicted values (and prediction intervals) for each location 28 days after the
 forecast date.
 Plotting the prediction intervals on our subset above[^2]: 
 
-[^2]: Alternatively, you could call `auto_plot(four_week_ahead)` to get the full
+[^2]: Alternatively, you could call `autoplot(four_week_ahead)` to get the full
     collection of forecasts. This is too busy for the space we have for plotting
     here.
 
@@ -318,8 +328,15 @@ forecast_plot <-
 forecast_plot
 ```
 
+And as a tibble of quantile level-value pairs:
+```{r pivot_wider}
+four_week_ahead$predictions |>
+  select(-.pred) |>
+  pivot_quantiles_longer(.pred_distn)
+```
+
 The black dot gives the median prediction, while the blue intervals give the
-25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges.
+25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges[^4].
 For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
@@ -334,7 +351,10 @@ A couple of things to note:
 ## Getting Help
 If you encounter a bug or have a feature request, feel free to file an [issue on
 our github page](https://github.com/cmu-delphi/epipredict/issues).
-For other
-questions, feel free to reach out to the authors, either via this [contact
+For other questions, feel free to reach out to the authors, either via this
+[contact
 form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
 email, or the Insightnet slack.
+
+[^4]: Note that these are not the same quantiles that we fit when creating
+    `four_week_ahead`. They are extrapolated from those quantiles using `extrapolate_quantiles()` (which assumes an exponential decay in the tails).
diff --git a/README.md b/README.md
index 289355a57..fdfa37c72 100644
--- a/README.md
+++ b/README.md
@@ -102,7 +102,10 @@ cases_deaths |>
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free") +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -171,16 +174,19 @@ forecast_date_label <-
     geo_value = rep(used_locations, 2),
     .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
-    heights = c(rep(150, 4), rep(1.0, 4))
+    heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  cases_deaths |>
+  covid_case_death_rates |> filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
     death_rate,
     .color_by = "none"
   ) +
-  facet_grid(.response_name ~ geo_value, scale = "free") +
+  facet_grid(
+    rows = vars(.response_name),
+    cols = vars(geo_value),
+    scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -206,13 +212,14 @@ four_week_ahead <- arx_forecaster(
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
-    ahead = 4 * 7
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
   )
 )
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-01-27 16:36:10.
+#> This forecaster was fit on 2025-01-31 10:46:32.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -265,9 +272,27 @@ forecast_plot <-
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
 
+And as a tibble of quantile level-value pairs:
+
+``` r
+four_week_ahead$predictions |>
+  select(-.pred) |>
+  pivot_quantiles_longer(.pred_distn)
+#> # A tibble: 20 × 5
+#>   geo_value values quantile_levels forecast_date target_date
+#>   <chr>      <dbl>           <dbl> <date>        <date>     
+#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
+#> # ℹ 14 more rows
+```
+
 The black dot gives the median prediction, while the blue intervals give
-the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges. For this
-particular day and these locations, the forecasts are relatively
+the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^4]. For
+this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
 
@@ -289,13 +314,18 @@ questions, feel free to reach out to the authors, either via this
 form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
 email, or the Insightnet slack.
 
-[^1]: This makes it so that any given day of the processed timeseries
+[^1]: This makes it so that any given day of the processed time-series
     only depends on the previous week, which means that we avoid leaking
     future values when making a forecast.
 
 [^2]: lagged by 3 in this context meaning using the value from 3 days
     ago.
 
-[^3]: Alternatively, you could call `auto_plot(four_week_ahead)` to get
+[^3]: Alternatively, you could call `autoplot(four_week_ahead)` to get
     the full collection of forecasts. This is too busy for the space we
     have for plotting here.
+
+[^4]: Note that these are not the same quantiles that we fit when
+    creating `four_week_ahead`. They are extrapolated from those
+    quantiles using `extrapolate_quantiles()` (which assumes an
+    exponential decay in the tails).
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 814bf6aa4..feb2bec75 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -15,6 +15,7 @@ articles:
       - backtesting
       - arx-classifier
       - update
+      - guts
   - title: Advanced methods
     contents:
       - articles/smooth-qr
@@ -47,6 +48,7 @@ reference:
     contents:
       - contains("args_list")
       - contains("_epi_workflow")
+
   - title: Helper functions for Hub submission
     contents:
       - flusight_hub_formatter
diff --git a/inst/pkgdown-watch.R b/inst/pkgdown-watch.R
index 89d4bd91e..6541a1365 100644
--- a/inst/pkgdown-watch.R
+++ b/inst/pkgdown-watch.R
@@ -13,6 +13,7 @@
 rlang::check_installed(c("pkgdown", "servr", "devtools", "here", "cli", "fs"))
 library(pkgdown)
 pkg <- pkgdown::as_pkgdown(here::here())
+devtools::build_readme()
 pkgdown::build_articles(pkg)
 pkgdown::build_site(pkg, lazy = FALSE, examples = FALSE, devel = TRUE, preview = FALSE)
 
@@ -55,7 +56,6 @@ servr::httw(
     readme <- grep("README[.]rmd$", files, value = TRUE, ignore.case = TRUE)
     if (length(readme)) {
       devtools::build_readme()
-      pkgdown::build_home()
       pkgdown::build_site(pkg, lazy = TRUE, examples = FALSE, devel = TRUE, preview = FALSE)
     }
 
diff --git a/man/figures/README-date-1.png b/man/figures/README-date-1.png
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diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 80e31dc17..99709ecaf 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -1,6 +1,6 @@
 library(dplyr)
 # Test ideas that were dropped:
-# - "epi_adjust_latency works correctly when there's gaps in the timeseries"
+# - "epi_adjust_latency works correctly when there's gaps in the time-series"
 # - "epi_adjust_latency extend_ahead uses the same adjustment when predicting on new data after being baked"
 # - "`step_adjust_latency` only allows one instance of itself"
 # - "data with epi_df shorn off works"
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index e1531b1b3..d68b7d63e 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -16,561 +16,412 @@ library(dplyr)
 library(parsnip)
 library(workflows)
 library(recipes)
+library(epidatasets)
 library(epipredict)
+library(ggplot2)
+forecast_date <- as.Date("2021-08-01")
+used_locations <- c("ca", "ma", "ny", "tx")
+library(epidatr)
 ```
 
-At a high level, our goal with `{epipredict}` is to make running simple machine
+At a high level, the goal of `{epipredict}` is to make running simple machine
 learning / statistical forecasters for epidemiology easy.
 To do this, we have extended several [tidymodels](https://www.tidymodels.org/)
 packages to handle the case of panel time-series data.
-Because of this, the package is extremely extensible.
 Our hope is that it is easy for users with epi training and some statistics to
 fit baseline models while still allowing those with more nuanced statistical
 understanding to create complicated specializations using the same framework.
 Towards that end, epipredict provides two main classes of tools:
 
 1. A set of basic, easy-to-use "canned" forecasters that work out of the box.
-   You should be able to do a reasonably limited amount of customization on them.
-   For the basic forecasters, we currently provide:
-    * Baseline flat forecaster: predicts a median that is the previous value
-      with increasingly wide quantiles
-    * Autoregressive forecaster: fits a model (typically linear regression) on
+    For the basic forecasters, we currently provide:
+    * Flatline forecaster: predicts a median that is the last value
+      with increasingly wide quantiles.
+    * Autoregressive forecaster: fits a model (e.g. linear regression) on
       lagged data to predict quantiles for continuous values.
-    * Autoregressive classifier: fits a model (typically logistic regression) on
-      lagged data to predict probabilities for discrete values.
+    * Autoregressive classifier: fits a model (e.g. logistic regression) on
+      lagged data to predict a binned version of the growth rate.
     * CDC FluSight flatline forecaster: a variant of the flatline forecaster as
       used by the CDC in FluSight.
-2. A framework for creating custom forecasters out of modular components.
-   There are three types of components:
+2. A framework for creating custom forecasters out of modular components, from
+   which the canned forecasters were created.  There are three types of
+   components:
     * Preprocessor: do things to the data before model training, such as convert
       counts to rates, create smoothed columns, or [any of the recipes
       steps](https://recipes.tidymodels.org/reference/index.html)
     * Trainer: train a model on data, resulting in a fitted model object.
       Examples include linear regression, quantile regression, or [any parsnip
       engine](https://parsnip.tidymodels.org/).
-    * Postprocessor: do things to the predictions after the model has been fit,
-      such as generate quantiles from purely point-prediction models, undo
-      operations done in the steps, such as convert back to counts from rates,
-      and generally adapt the format of the prediction to it's eventual use.
-
-The rest of this will focus on using and modifying the canned forecasters. 
+    * Postprocessor: unique to this package, and used to do things to the
+      predictions after the model has been fit, such as 
+      - generate quantiles from purely point-prediction models,
+      - undo operations done in the steps, such as convert back to counts from
+      rates
+      - generally adapt the format of the prediction to it's eventual use.
+
+The rest of the getting started will focus on using and modifying the canned forecasters. 
 If you need a more complicated model, check out the [Guts
 vignette](preprocessing-and-models) for examples of using the forecaster
 framework.
 
+If you are interested in time series in a non-panel data context, you may also
+want to look at `{timetk}` and `{modeltime}` for some related techniques.
 
-The Advanced user should find their task to be relatively easy. Examples of
-these tasks are illustrated in the [vignettes and articles](https://cmu-delphi.github.io/epipredict).
-
-See also the (in progress) [Forecasting Book](https://cmu-delphi.github.io/delphi-tooling-book/).
+For a more in-depth treatment with some practical applications, see also the
+[Forecasting Book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 # Panel forecasting basics
 ## Example data
 
-## Intermediate example
+The forecasting methods in this package are designed to work with panel time
+series data, specifically in the form of an `epi_df` from the `{epiprocess}`
+package.
+This is a collection of one or more time-series indexed by one or more
+categorical variables.
+For example, on the landing page:
 
-The package comes with some built-in historical data for illustration, but
-up-to-date versions of this could be downloaded with the
-[`{epidatr}` package](https://cmu-delphi.github.io/epidatr/)
-and processed using
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
+```{r data_ex}
+covid_case_death_rates
+```
 
-[^1]: Other epidemiological signals for non-Covid related illnesses are also
-available with [`{epidatr}`](https://github.com/cmu-delphi/epidatr) which
-interfaces directly to Delphi's
-[Epidata API](https://cmu-delphi.github.io/delphi-epidata/)
+`geo_value` is the only key for this dataset, while there are two separate
+time-series, `case_rate` and `death_rate`.
+The keys are represented in "long" format (so separate columns for the key and
+the value), while separate time series are represented in "wide" format (so each
+time-series has a separate column).
 
-```{r init_stuff, message=FALSE}
-library(epipredict)
-covid_case_death_rates
+`{epiprocess}` is designed to handle data that always has a geographic key, and
+potentially other key values, such as age, ethnicity or other demographic
+information.
+For example, `grad_employ_subset` from `{epidatasets}` also has both `age_group`
+and `edu_qual` as additional keys:
+
+```{r extra_keys}
+grad_employ_subset
 ```
 
-To create and train a simple auto-regressive forecaster to predict the death rate two weeks into the future using past (lagged) deaths and cases, we could use the following function.
+See `{epiprocess}` for more details
+on the format.
+
+Panel time series are ubiquitous in epidemiology, but are also common in
+economics, psychology, sociology, and many other areas.
+While this package was designed with epidemiology in mind, many of the
+techniques are more broadly applicable.
+
+## Customizing `arx_forecaster()`
+Moving on from the example on the [landing
+page](../index.html#motivating-example), let's adjust some parameters for
+`arx_forecaster()`.
+`trainer` allows us to set a different fitting engines, either one of the
+included ones, such as `quantile_reg()`, or one of the relevant [parsnip
+models](https://www.tidymodels.org/find/parsnip/):
 
 ```{r make-forecasts, warning=FALSE}
 two_week_ahead <- arx_forecaster(
-  covid_case_death_rates,
+  covid_case_death_rates |> filter(time_value <= forecast_date),
   outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
+  trainer = quantile_reg(),
+  predictors = c("death_rate"),
   args_list = arx_args_list(
-    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    lags = list(c(0, 7, 14)),
     ahead = 14
   )
 )
-two_week_ahead
-```
-
-In this case, we have used a number of different lags for the case rate, while
-only using 3 weekly lags for the death rate (as predictors). The result is both
-a fitted model object which could be used any time in the future to create
-different forecasts, as well as a set of predicted values (and prediction
-intervals) for each location 14 days after the last available time value in the
-data.
-
-```{r print-model}
-two_week_ahead$epi_workflow
+hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 ```
 
-The fitted model here involved preprocessing the data to appropriately generate
-lagged predictors, estimating a linear model with `stats::lm()` and then
-postprocessing the results to be meaningful for epidemiological tasks. We can
-also examine the predictions.
-
-```{r show-preds}
-two_week_ahead$predictions
-```
-
-The results above show a distributional forecast produced using data through
-the end of 2021 for the 14th of January 2022. A prediction for the death rate
-per 100K inhabitants is available for every state (`geo_value`) along with a
-90% predictive interval.
-
-
-# Goals for the package
-
-At a high level, our goal with `{epipredict}` is to make running simple Machine
-Learning / Statistical forecasters for epidemiology easy. However, this package
-is extremely extensible, and that is part of its utility. Our hope is that it is
-easy for users with epi training and some statistics to fit baseline models
-while still allowing those with more nuanced statistical understanding to create
-complicated specializations using the same framework.
-
-Serving both populations is the main motivation for our efforts, but at the same
-time, we have tried hard to make it useful.
-
-
-## Baseline models
-
-We provide a set of basic, easy-to-use forecasters that work out of the box. You
-should be able to do a reasonably limited amount of customization on them. Any
-serious customization happens with the framework discussed below).
-
-For the basic forecasters, we provide:
-
-* Baseline flat-line forecaster
-* Autoregressive forecaster
-* Autoregressive classifier
-
-All the forcasters we provide are built on our framework. So we will use these
-basic models to illustrate its flexibility.
-
-## Forecasting framework
-
-Our framework for creating custom forecasters views the prediction task as a set
-of modular components. There are four types of components:
-
-1. Preprocessor: make transformations to the data before model training
-2. Trainer: train a model on data, resulting in a fitted model object
-3. Predictor: make predictions, using a fitted model object and processed test data
-4. Postprocessor: manipulate or transform the predictions before returning
-
-Users familiar with [`{tidymodels}`](https://www.tidymodels.org) and especially
-the [`{workflows}`](https://workflows.tidymodels.org) package will notice a lot
-of overlap. This is by design, and is in fact a feature. The truth is that
-`{epipredict}` is a wrapper around much that is contained in these packages.
-Therefore, if you want something from this -verse, it should "just work" (we
-hope).
-
-The reason for the overlap is that `{workflows}` *already implements* the first
-three steps. And it does this very well. However, it is missing the
-postprocessing stage and currently has no plans for such an implementation. And
-this feature is important. The baseline forecaster we provide *requires*
-postprocessing. Anything more complicated needs this as well.
-
-The second omission from `{tidymodels}` is support for panel data. Besides
-epidemiological data, economics, psychology, sociology, and many other areas
-frequently deal with data of this type. So the framework of behind
-`{epipredict}` implements this. In principle, this has nothing to do with
-epidemiology, and one could simply use this package as a solution for the
-missing functionality in `{tidymodels}`. Again, this should "just work".
-
-All of the *panel data* functionality is implemented through the `epi_df` data
-type in the companion [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/)
-package. There is much more to see there, but for the moment, it's enough to
-look at a simple one:
-
-```{r epidf}
-jhu <- covid_case_death_rates
-jhu
-```
-
-This data is built into the package and contains the measured variables
-`case_rate` and `death_rate` for COVID-19 at the daily level for each US state
-for the year 2021. The "panel" part is because we have repeated measurements
-across a number of locations.
-
-The `epi_df` encodes the time stamp as `time_value` and the `key` as
-`geo_value`. While these 2 names are required, the values don't need to actually
-represent such objects. Additional `key`'s are also supported (like age group,
-ethnicity, taxonomy, etc.).
-
-The `epi_df` also contains some metadata that describes the keys as well as the
-vintage of the data. It's possible that data collected at different times for
-the *same set* of `geo_value`'s and `time_value`'s could actually be different.
-For more details, see
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/articles/epiprocess.html).
-
-## Why doesn't this package already exist?
-
-As described above:
-
-* Parts actually DO exist. There's a universe called `{tidymodels}`. It handles
-preprocessing, training, and prediction, bound together, through a package called
-`{workflows}`. We built `{epipredict}` on top of that setup. In this way, you CAN
-use almost everything they provide.
-
-* However, `{workflows}` doesn't do postprocessing. And nothing in the -verse
-handles _panel data_.
-
-* The tidy-team doesn't have plans to do either of these things. (We checked).
-
-* There are two packages that do _time series_ built on `{tidymodels}`, but it's
-"basic" time series: 1-step AR models, exponential smoothing, STL decomposition,
-etc.[^2] Our group has not prioritized these sorts of models for epidemic
-forecasting, but one could also integrate these methods into our framework.
-
-[^2]: These are [`{timetk}`](https://business-science.github.io/timetk/index.html)
-and [`{modeltime}`](https://business-science.github.io/timetk/index.html). There
-are *lots* of useful methods there than can be used to do fairly complex machine
-learning methodology, though not directly for panel data and not for direct
-prediction of future targets.
-
-# Show me the basics
-
-We start with the `jhu` data displayed above. One of the "canned" forecasters we
-provide is an autoregressive forecaster with (or without) covariates that
-*directly* trains on the response. This is in contrast to a typical "iterative"
-AR model that trains to predict one-step-ahead, and then plugs in the
-predictions to "leverage up" to longer horizons.
-
-We'll estimate the model jointly across all locations using only the most
-recent 30 days.
-
-```{r demo-workflow}
-jhu <- jhu %>% filter(time_value >= max(time_value) - 30)
-out <- arx_forecaster(
-  jhu,
-  outcome = "death_rate",
-  predictors = c("case_rate", "death_rate")
-)
-```
+The default trainer is `parsnip::linear_reg()`, which generates quantiles after
+the fact in the post-processing layers, rather than as part of the model.
+`quantile_reg()` on the other hand directly estimates different linear models
+for each quantile, reflected in the 3 different columns for `tau` above.
 
-The `out` object has two components:
-
-  1. The predictions which is just another `epi_df`. It contains the predictions for
-each location along with additional columns. By default, these are a 90%
-predictive interval, the `forecast_date` (the date on which the forecast was
-putatively made) and the `target_date` (the date for which the forecast is being
-made).
-  ```{r}
-out$predictions
-  ```
-  2. A list object of class `epi_workflow`. This object encapsulates all the
-instructions necessary to create the prediction. More details on this below.
-  ```{r}
-out$epi_workflow
-  ```
-
-By default, the forecaster predicts the outcome (`death_rate`) 1-week ahead,
-using 3 lags of each predictor (`case_rate` and `death_rate`) at 0 (today), 1
-week back and 2 weeks back. The predictors and outcome can be changed directly.
-The rest of the defaults are encapsulated into a list of arguments. This list is
-produced by `arx_args_list()`.
-
-## Simple adjustments
-
-Basic adjustments can be made through the `args_list`.
-
-```{r kill-warnings, echo=FALSE}
-knitr::opts_chunk$set(warning = FALSE, message = FALSE)
-```
+Because of the flexibility of `{parsnip}`, there are a whole host of models
+available to us[^5]; as an example, we could have just as easily substituted a
+non-linear random forest model from `{ranger}`:
 
-```{r differential-lags}
-out2week <- arx_forecaster(
-  jhu,
+```{r rand_forest_ex, warning=FALSE}
+two_week_ahead <- arx_forecaster(
+  covid_case_death_rates |> filter(time_value <= forecast_date),
   outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
+  trainer = rand_forest(mode = "regression"),
+  predictors = c("death_rate"),
   args_list = arx_args_list(
-    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    lags = list(c(0, 7, 14)),
     ahead = 14
   )
 )
 ```
 
-Here, we've used different lags on the `case_rate` and are now predicting 2
-weeks ahead. This example also illustrates a major difficulty with the
-"iterative" versions of AR models. This model doesn't produce forecasts for
-`case_rate`, and so, would not have data to "plug in" for the necessary
-lags.[^3]
+Any other customization is routed through `arx_args_list()`; for example, if we
+wanted to increase the number of quantiles fit:
 
-[^3]: An obvious fix is to instead use a VAR and predict both, but this would
-likely increase the variance of the model, and therefore, may lead to less
-accurate forecasts for the variable of interest.
-
-Another property of the basic model is the predictive interval. We describe this
-in more detail in a different vignette, but it is easy to request multiple
-quantiles.
-
-```{r differential-levels}
-out_q <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
+```{r make-quantile-levels-forecasts, warning=FALSE}
+two_week_ahead <- arx_forecaster(
+  covid_case_death_rates |>
+    filter(time_value <= forecast_date, geo_value %in% used_locations),
+  outcome = "death_rate",
+  trainer = quantile_reg(),
+  predictors = c("death_rate"),
   args_list = arx_args_list(
-    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
+    lags = list(c(0, 7, 14)),
+    ahead = 14,
+    ############ changing quantile_levels ############
+    quantile_levels = c(0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95)
+    ##################################################
   )
 )
+hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 ```
 
-The column `.pred_dstn` in the `predictions` object is actually a "distribution"
-here parameterized by its quantiles. For this default forecaster, these are
-created using the quantiles of the residuals of the predictive model (possibly
-symmetrized). Here, we used 23 quantiles, but one can grab a particular
-quantile,
-
-```{r q1}
-round(head(quantile(out_q$predictions$.pred_distn, p = .4)), 3)
-```
-
-or extract the entire distribution into a "long" `epi_df` with `quantile_levels`
-being the probability and `values` being the value associated to that quantile.
-
-```{r q2}
-out_q$predictions %>%
-  pivot_quantiles_longer(.pred_distn)
+See the function documentation of `arx_args_list()` for more examples of the modifications available.
+If you are looking for even further modifications, you will want a custom
+workflow, for which you should see the [guts
+vignette](preprocessing-and-models).
+
+## Generating multiple aheads
+Frequently, one doesn't want just a forecast for a single day, but a trajectory
+of forecasts for several weeks.
+The way to do this using `arx_forecaster()` is by looping over aheads; for
+example, to predict every day over 4 weeks:
+
+```{r temp-thing}
+all_canned_results <- lapply(
+  seq(0, 28),
+  \(days_ahead) {
+    arx_forecaster(
+      covid_case_death_rates |>
+        filter(time_value <= forecast_date, geo_value %in% used_locations),
+      outcome = "death_rate",
+      predictors = c("case_rate", "death_rate"),
+      trainer = quantile_reg(),
+      args_list = arx_args_list(
+        lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+        ahead = days_ahead
+      )
+    )
+  }
+)
+# pull out the workflow and the predictions to be able to
+#  effectively use autoplot
+workflow <- all_canned_results[[1]]$epi_workflow
+results <- purrr::map_df(all_canned_results, ~ `$`(., "predictions"))
+autoplot(
+  object = workflow,
+  predictions = results,
+  plot_data = covid_case_death_rates |>
+    filter(geo_value %in% used_locations, time_value > "2021-07-01")
+)
 ```
 
-Additional simple adjustments to the basic forecaster can be made using the
-function:
-
-```{r, eval = FALSE}
-arx_args_list(
-  lags = c(0L, 7L, 14L), ahead = 7L, n_training = Inf,
-  forecast_date = NULL, target_date = NULL,
-  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
-  symmetrize = TRUE, nonneg = TRUE, quantile_by_key = character(0L),
-  nafill_buffer = Inf
+## Other canned forecasters
+### `flatline_forecaster()`
+The simplest model we provide is the `flatline_forecaster()`, which predicts a
+flat line (with quantiles generated from the residuals using
+`layer_residual_quantiles()`).
+For example, on the same dataset as above:
+```{r make-flatline-forecast, warning=FALSE}
+all_flatlines <- lapply(
+  seq(0, 28),
+  \(days_ahead) {
+    flatline_forecaster(
+      covid_case_death_rates |>
+        filter(time_value <= forecast_date, geo_value %in% used_locations),
+      outcome = "death_rate",
+      args_list = flatline_args_list(ahead = days_ahead)
+    )
+  }
+)
+# same plotting code as in the arx multi-ahead case
+workflow <- all_flatlines[[1]]$epi_workflow
+results <- purrr::map_df(all_flatlines, ~ `$`(., "predictions"))
+autoplot(
+  object = workflow,
+  predictions = results,
+  plot_data = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
+_**the problem is only occurring in the plot, not the underlying prediction**_
 
-## Changing the engine
+Note that the `cdc_baseline_forecaster` is a slight modification of this method
+for use in [the CDC COVID19 Forecasting Hub](https://covid19forecasthub.org/).
 
-So far, our forecasts have been produced using simple linear regression. But
-this is not the only way to estimate such a model. The `trainer` argument
-determines the type of model we want. This takes a
-[`{parsnip}`](https://parsnip.tidymodels.org) model. The default is linear
-regression, but we could instead use a random forest with the `{ranger}`
-package:
+### `arx_classifier()`
 
-```{r ranger, warning = FALSE}
-out_rf <- arx_forecaster(
-  jhu,
+The most complicated of the canned forecasters, `arx_classifier` first
+translates the outcome into a growth rate, and then classifies that growth rate
+into bins.
+For example, on the same dataset and `forecast_date` as above, we get:
+
+```{r discrete-rt}
+classifier <- arx_classifier(
+  covid_case_death_rates |>
+    filter(geo_value %in% used_locations, time_value < forecast_date),
   outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
-  trainer = rand_forest(mode = "regression")
+  predictors = c("death_rate", "case_rate"),
+  trainer = multinom_reg(),
+  args_list = arx_class_args_list(
+    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    ahead = 2 * 7,
+    breaks = c(-0.01, 0.01, 0.1)
+  )
 )
+classifier$predictions
 ```
 
-Or boosted regression trees with `{xgboost}`:
-
-```{r xgboost, warning = FALSE}
-out_gb <- arx_forecaster(
-  jhu,
-  outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
-  trainer = boost_tree(mode = "regression", trees = 20)
-)
+The prediction splits into 4 cases: `(-∞, -0.01)`, `(-0.01, 0.01)`,  `(0.01,
+0.1)`, and `(0.1, ∞)`.
+In this case, the classifier put all 4 of the states in the same category,
+`(0.01, 0.1)`. **TODO** _effected by the old data._
+The number and size of the categories is controlled by `breaks`, which gives the
+boundary values.
+
+For comparison, the growth rates for the `target_date`, as computed using
+`{epiprocess}`:
+
+```{r growth_rate_results}
+growth_rates <- covid_case_death_rates |>
+  filter(geo_value %in% used_locations) |>
+  group_by(geo_value) |>
+  mutate(
+    deaths_gr = growth_rate(x = time_value, y = death_rate)
+  ) |>
+  ungroup()
+growth_rates |> filter(time_value == "2021-08-14")
 ```
 
-Or quantile regression, using our custom forecasting engine `quantile_reg()`:
+Unfortunately, this forecast was not particularly accurate, since for example
+`-1.39` is not remotely in the interval `(-0.01, 0.01]`.
 
-```{r quantreg, warning = FALSE}
-out_qr <- arx_forecaster(
-  jhu,
-  outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
-  trainer = quantile_reg()
-)
-```
 
-FWIW, this last case (using quantile regression), is not far from what the
-Delphi production forecast team used for its Covid forecasts over the past few
-years.
-
-## Inner workings
-
-Underneath the hood, this forecaster creates (and returns) an `epi_workflow`.
-Essentially, this is a big S3 object that wraps up the 4 modular steps
-(preprocessing - postprocessing) described above.
-
-### Preprocessing
-
-Preprocessing is accomplished through a `recipe` (imagine baking a cake) as
-provided in the [`{recipes}`](https://recipes.tidymodels.org) package.
-We've made a few modifications (to handle
-panel data) as well as added some additional options. The recipe gives a
-specification of how to handle training data. Think of it like a fancified
-`formula` that you would pass to `lm()`: `y ~ x1 + log(x2)`. In general,
-there are 2 extensions to the `formula` that `{recipes}` handles:
-
-  1. Doing transformations of both training and test data that can always be
-  applied. These are things like taking the log of a variable, leading or
-  lagging, filtering out rows, handling dummy variables, etc.
-  2. Using statistics from the training data to eventually process test data.
-    This is a major benefit of `{recipes}`. It prevents what the tidy team calls
-    "data leakage". A simple example is centering a predictor by its mean. We
-    need to store the mean of the predictor from the training data and use that
-    value on the test data rather than accidentally calculating the mean of
-    the test predictor for centering.
-
-A recipe is processed in 2 steps, first it is "prepped". This calculates and
-stores any intermediate statistics necessary for use on the test data.
-Then it is "baked"
-resulting in training data ready for passing into a statistical model (like `lm`).
-
-We have introduced an `epi_recipe`. It's just a `recipe` that knows how to handle
-the `time_value`, `geo_value`, and any additional keys so that these are available
-when necessary.
-
-The `epi_recipe` from `out_gb` can be extracted from the result:
-
-```{r}
-extract_recipe(out_gb$epi_workflow)
-```
+## Fitting multi-key panel data
 
-The "Inputs" are the original `epi_df` and the "roles" that these are assigned.
-None of these are predictors or outcomes. Those will be created
-by the recipe when it is prepped. The "Operations" are the sequence of
-instructions to create the cake (baked training data).
-Here we create lagged predictors, lead the outcome, and then remove `NA`s.
-Some models like `lm` internally handle `NA`s, but not everything does, so we
-deal with them explicitly. The code to do this (inside the forecaster) is
-
-```{r}
-er <- epi_recipe(jhu) %>%
-  step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%
-  step_epi_ahead(death_rate, ahead = 7) %>%
-  step_epi_naomit()
-```
+If you have multiple keys that are set in the `epi_df` as `other_keys`,
+`arx_forecaster` will automatically group by those as well.
+For example, predicting the number of graduates in each of the categories in `grad_employ` from above:
 
-While `{recipes}` provides a function `step_lag()`, it assumes that the data
-have no breaks in the sequence of `time_values`. This is a bit dangerous, so
-we avoid that behaviour. Our `lag/ahead` functions also appropriately adjust the
-amount of data to avoid accidentally dropping recent predictors from the test
-data.
-
-### The model specification
-
-Users with familiarity with the `{parsnip}` package will have no trouble here.
-Basically, `{parsnip}` unifies the function signature across statistical models.
-For example, `lm()` "likes" to work with formulas, but `glmnet::glmnet()` uses
-`x` and `y` for predictors and response. `{parsnip}` is agnostic. Both of these
-do "linear regression". Above we switched from `lm()` to `xgboost()` without
-any issue despite the fact that these functions couldn't be more different.
-
-```{r, eval = FALSE}
-lm(formula, data, subset, weights, na.action,
-  method = "qr",
-  model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
-  contrasts = NULL, offset, ...
+```{r multi_key_forecast, warning=FALSE}
+# only fitting a subset, otherwise there are ~550 distinct pairs, which is bad for plotting
+edu_quals <- c("Undergraduate degree", "Professional degree")
+geo_values <- c("Quebec", "British Columbia")
+grad_forecast <- arx_forecaster(
+  grad_employ_subset |>
+    filter(time_value < 2017) |>
+    filter(edu_qual %in% edu_quals, geo_value %in% geo_values),
+  outcome = "num_graduates",
+  predictors = c("num_graduates"),
+  args_list = arx_args_list(
+    lags = list(c(0, 1, 2)),
+    ahead = 1
+  )
 )
-
-xgboost(
-  data = NULL, label = NULL, missing = NA, weight = NULL,
-  params = list(), nrounds, verbose = 1, print_every_n = 1L,
-  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
-  ...
+# and plotting
+autoplot(
+  grad_forecast$epi_workflow,
+  grad_forecast$predictions,
+  grad_employ_subset |>
+    filter(edu_qual %in% edu_quals, geo_value %in% geo_values),
 )
 ```
 
-`{epipredict}` provides a few engines/modules (the flatline forecaster and
-quantile regression), but you should be able to use any available models
-listed [here](https://www.tidymodels.org/find/parsnip/).
+The 8 graphs are all pairs of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
-To estimate (fit) a preprocessed model, one calls `fit()` on the `epi_workflow`.
+# Anatomy of a canned forecaster
+## Code object
+Let's dissect the forecaster we trained back on the [landing
+page](../index.html#motivating-example):
 
-```{r}
-ewf <- epi_workflow(er, linear_reg()) %>% fit(jhu)
+```{r make-four-forecasts, warning=FALSE}
+four_week_ahead <- arx_forecaster(
+  covid_case_death_rates |> filter(time_value <= forecast_date),
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
+  )
+)
 ```
 
-### Postprocessing
-
-To stretch the metaphor of preparing a cake to its natural limits, we have
-created postprocessing functionality called "frosting". Much like the recipe,
-each postprocessing operation is a "layer" and we "slather" these onto our
-baked cake. To fix ideas, below is the postprocessing `frosting` for
-`arx_forecaster()`
+`four_week_ahead` has two components: an `epi_workflow`, and a table of
+`predictions`.
+The table of predictions is simply a tibble of the predictions,
 
-```{r}
-extract_frosting(out_q$epi_workflow)
+```{r show_predictions}
+four_week_ahead$predictions
 ```
 
-Here we have 5 layers of frosting. The first generates the forecasts from the test data.
-The second uses quantiles of the residuals to create distributional
-forecasts. The next two add columns for the date the forecast was made and the
-date for which it is intended to occur. Because we are predicting rates, they
-should be non-negative, so the last layer thresholds both predicted values and
-intervals at 0. The code to do this (inside the forecaster) is
-
-```{r}
-f <- frosting() %>%
-  layer_predict() %>%
-  layer_residual_quantiles(
-    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
-    symmetrize = TRUE
-  ) %>%
-  layer_add_forecast_date() %>%
-  layer_add_target_date() %>%
-  layer_threshold(starts_with(".pred"))
-```
+`.pred` gives the point/median prediction, while `.pred_distn` is a
+`dist_quantiles()` object representing a distribution through various quantile
+levels.
+The `[6]` in the name refers to the number of quantiles that have been
+explicitly created[^4]; by default, this covers a 90% prediction interval, or 5%
+and 95%.
 
-At predict time, we add this object onto the `epi_workflow` and call `forecast()`
+The `epi_workflow` is a significantly more complicated object, extending a
+`workflows::workflow()`  to include post-processing:
 
-```{r, warning=FALSE}
-ewf %>%
-  add_frosting(f) %>%
-  forecast()
+```{r show_workflow}
+four_week_ahead$epi_workflow
 ```
 
-The above `get_test_data()` function examines the recipe and ensures that enough
-test data is available to create the necessary lags and produce a prediction
-for the desired future time point (after the end of the training data). This mimics
-what would happen if `jhu` contained the most recent available historical data and
-we wanted to actually predict the future. We could have instead used any test data
-that contained the necessary predictors.
-
-
-## Conclusion
-
-Internally, we provide some simple functions to create reasonable forecasts.
-But ideally, a user could create their own forecasters by building up the
-components we provide. In other vignettes, we try to walk through some of these
-customizations.
-
-To illustrate everything above, here is (roughly) the code for the
-`flatline_forecaster()` applied to the `case_rate`.
+An `epi_workflow()` consists of 3 parts:
+
+- `Preprocessor`: a collection of steps that transform the data to be ready for
+      modelling. They come from this package or [any of the recipes
+      steps](https://recipes.tidymodels.org/reference/index.html);
+      `four_week_ahead` has 5 of these, and you can inspect them more closely by
+      running `hardhat::extract_recipe(four_week_ahead$epi_workflow)`.
+- `Model`: the actual model that does the fitting, given by a
+  `parsnip::model_spec`; `four_week_ahead` has the default of
+  `parsnip::linear_reg()`, which is a wrapper from `{parsnip}` for
+  `stats::lm()`. You can inspect the model more closely by running
+  `hardhat::extract_fit_recipe(four_week_ahead$epi_workflow)`.
+- `Postprocessor`: a collection of layers to be applied to the resulting
+  forecast, internal to this package. `four_week_ahead` just so happens to have
+  5 of as these well. You can inspect the layers more closely by running
+  `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
+
+See the [Guts vignette](preprocessing-and-models) for recreating and then
+extending `four_week_ahead` using the custom forecaster framework.
+
+## Mathematical description
+
+Let's describe in more detail the actual fit model for a more minimal version of
+`four_week_ahead`:
+
+```{r, four_week_again}
+four_week_small <- arx_forecaster(
+  covid_case_death_rates |> filter(time_value <= forecast_date),
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    lags = list(c(0, 7, 14), c(0, 7, 14)),
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
+  )
+)
+hardhat::extract_fit_engine(four_week_small$epi_workflow)
+```
 
-```{r}
-r <- epi_recipe(jhu) %>%
-  step_epi_ahead(case_rate, ahead = 7, skip = TRUE) %>%
-  update_role(case_rate, new_role = "predictor") %>%
-  add_role(all_of(key_colnames(jhu)), new_role = "predictor")
+If $d_t$ is the death rate on day $t$ and $c_t$ is the case rate, then the model
+we're fitting is:
 
-f <- frosting() %>%
-  layer_predict() %>%
-  layer_residual_quantiles() %>%
-  layer_add_forecast_date() %>%
-  layer_add_target_date() %>%
-  layer_threshold(starts_with(".pred"))
+$$
+d_{t+28} = a_0 + a_1 d_t + a_2 d_{t-7} + a_3 d_{t-14} + a_4 c_t + a_5 c_{t-7} + a_6 c_{t-14}.
+$$
 
-eng <- linear_reg() %>% set_engine("flatline")
-wf <- epi_workflow(r, eng, f) %>% fit(jhu)
-preds <- forecast(wf)
-```
+For example, $a_1$ is `lag_0_death_rate` above, with a value of `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"] `,
+while $a_5$ is `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"] `.
 
-All that really differs from the `arx_forecaster()` is the `recipe`, the
-test data, and the engine. The `frosting` is identical, as is the fitting
-and predicting procedure.
+The training data for fitting this linear model is created by creating a series
+of columns shifted by the appropriate amount; this makes it so that each row
+without `NA` values is a training point to fit the coefficients $a_0,\ldots, a_6$.
 
-```{r}
-preds
-```
+[^4]: in the case of a `{parsnip}` engine which doesn't explicitly predict
+    quantiles, these quantiles are created using `layer_residual_quantiles()`,
+    which infers the quantiles from the residuals of the fit.
 
+[^5]: in the case of `arx_forecaster`, this is any model with
+    `mode="regression"` from [this
+    list](https://www.tidymodels.org/find/parsnip/).
diff --git a/vignettes/guts.Rmd b/vignettes/guts.Rmd
new file mode 100644
index 000000000..f17065384
--- /dev/null
+++ b/vignettes/guts.Rmd
@@ -0,0 +1,270 @@
+---
+title: "Guts"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Guts}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+source("_common.R")
+```
+
+```{r setup, message=FALSE, include = FALSE}
+library(dplyr)
+library(parsnip)
+library(workflows)
+library(recipes)
+library(epipredict)
+forecast_date <- as.Date("2021-08-01")
+used_locations <- c("ca", "ma", "ny", "tx")
+library(epidatr)
+cases <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "confirmed_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20211231),
+  geo_values = "*"
+) |>
+  select(geo_value, time_value, case_rate = value)
+
+deaths <- pub_covidcast(
+  source = "jhu-csse",
+  signals = "deaths_incidence_prop",
+  time_type = "day",
+  geo_type = "state",
+  time_values = epirange(20200601, 20211231),
+  geo_values = "*"
+) |>
+  select(geo_value, time_value, death_rate = value)
+cases_deaths <-
+  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  filter(geo_value %in% used_locations) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
+cases_deaths <-
+  cases_deaths |>
+  group_by(geo_value) |>
+  epi_slide(
+    cases_7dav = mean(case_rate, na.rm = TRUE),
+    death_rate_7dav = mean(death_rate, na.rm = TRUE),
+    .window_size = 7
+  ) |>
+  ungroup() |>
+  mutate(case_rate = NULL, death_rate = NULL) |>
+  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
+cases_deaths <-
+  cases_deaths |>
+  group_by(geo_value) |>
+  mutate(
+    outlr_death_rate = detect_outlr_rm(
+      time_value, death_rate,
+      detect_negatives = TRUE
+    ),
+    outlr_case_rate = detect_outlr_rm(
+      time_value, case_rate,
+      detect_negatives = TRUE
+    )
+  ) |>
+  unnest(cols = starts_with("outlr"), names_sep = "_") |>
+  ungroup() |>
+  mutate(
+    death_rate = outlr_death_rate_replacement,
+    case_rate = outlr_case_rate_replacement
+  ) |>
+  select(geo_value, time_value, case_rate, death_rate)
+jhu <- covid_case_death_rates
+out_gb <- arx_forecaster(
+  jhu,
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  trainer = boost_tree(mode = "regression", trees = 20)
+)
+out_q <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99)
+  )
+)
+```
+# Document Title
+## Inner workings
+
+Underneath the hood, this forecaster creates (and returns) an `epi_workflow`.
+Essentially, this is a big S3 object that wraps up the 4 modular steps
+(preprocessing - postprocessing) described above.
+
+### Preprocessing
+
+Preprocessing is accomplished through a `recipe` (imagine baking a cake) as
+provided in the [`{recipes}`](https://recipes.tidymodels.org) package.
+We've made a few modifications (to handle
+panel data) as well as added some additional options. The recipe gives a
+specification of how to handle training data. Think of it like a fancified
+`formula` that you would pass to `lm()`: `y ~ x1 + log(x2)`. In general,
+there are 2 extensions to the `formula` that `{recipes}` handles:
+
+  1. Doing transformations of both training and test data that can always be
+  applied. These are things like taking the log of a variable, leading or
+  lagging, filtering out rows, handling dummy variables, etc.
+  2. Using statistics from the training data to eventually process test data.
+    This is a major benefit of `{recipes}`. It prevents what the tidy team calls
+    "data leakage". A simple example is centering a predictor by its mean. We
+    need to store the mean of the predictor from the training data and use that
+    value on the test data rather than accidentally calculating the mean of
+    the test predictor for centering.
+
+A recipe is processed in 2 steps, first it is "prepped". This calculates and
+stores any intermediate statistics necessary for use on the test data.
+Then it is "baked"
+resulting in training data ready for passing into a statistical model (like `lm`).
+
+We have introduced an `epi_recipe`. It's just a `recipe` that knows how to handle
+the `time_value`, `geo_value`, and any additional keys so that these are available
+when necessary.
+
+The `epi_recipe` from `out_gb` can be extracted from the result:
+
+```{r}
+extract_recipe(out_gb$epi_workflow)
+```
+
+The "Inputs" are the original `epi_df` and the "roles" that these are assigned.
+None of these are predictors or outcomes. Those will be created
+by the recipe when it is prepped. The "Operations" are the sequence of
+instructions to create the cake (baked training data).
+Here we create lagged predictors, lead the outcome, and then remove `NA`s.
+Some models like `lm` internally handle `NA`s, but not everything does, so we
+deal with them explicitly. The code to do this (inside the forecaster) is
+
+```{r}
+er <- epi_recipe(jhu) %>%
+  step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%
+  step_epi_ahead(death_rate, ahead = 7) %>%
+  step_epi_naomit()
+```
+
+While `{recipes}` provides a function `step_lag()`, it assumes that the data
+have no breaks in the sequence of `time_values`. This is a bit dangerous, so
+we avoid that behaviour. Our `lag/ahead` functions also appropriately adjust the
+amount of data to avoid accidentally dropping recent predictors from the test
+data.
+
+### The model specification
+
+Users with familiarity with the `{parsnip}` package will have no trouble here.
+Basically, `{parsnip}` unifies the function signature across statistical models.
+For example, `lm()` "likes" to work with formulas, but `glmnet::glmnet()` uses
+`x` and `y` for predictors and response. `{parsnip}` is agnostic. Both of these
+do "linear regression". Above we switched from `lm()` to `xgboost()` without
+any issue despite the fact that these functions couldn't be more different.
+
+```{r, eval = FALSE}
+lm(formula, data, subset, weights, na.action,
+  method = "qr",
+  model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
+  contrasts = NULL, offset, ...
+)
+
+xgboost(
+  data = NULL, label = NULL, missing = NA, weight = NULL,
+  params = list(), nrounds, verbose = 1, print_every_n = 1L,
+  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
+  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
+  ...
+)
+```
+
+`{epipredict}` provides a few engines/modules (the flatline forecaster and
+quantile regression), but you should be able to use any available models
+listed [here](https://www.tidymodels.org/find/parsnip/).
+
+To estimate (fit) a preprocessed model, one calls `fit()` on the `epi_workflow`.
+
+```{r}
+ewf <- epi_workflow(er, linear_reg()) %>% fit(jhu)
+```
+
+### Postprocessing
+
+To stretch the metaphor of preparing a cake to its natural limits, we have
+created postprocessing functionality called "frosting". Much like the recipe,
+each postprocessing operation is a "layer" and we "slather" these onto our
+baked cake. To fix ideas, below is the postprocessing `frosting` for
+`arx_forecaster()`
+
+```{r}
+extract_frosting(out_q$epi_workflow)
+```
+
+Here we have 5 layers of frosting. The first generates the forecasts from the test data.
+The second uses quantiles of the residuals to create distributional
+forecasts. The next two add columns for the date the forecast was made and the
+date for which it is intended to occur. Because we are predicting rates, they
+should be non-negative, so the last layer thresholds both predicted values and
+intervals at 0. The code to do this (inside the forecaster) is
+
+```{r}
+f <- frosting() %>%
+  layer_predict() %>%
+  layer_residual_quantiles(
+    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99),
+    symmetrize = TRUE
+  ) %>%
+  layer_add_forecast_date() %>%
+  layer_add_target_date() %>%
+  layer_threshold(starts_with(".pred"))
+```
+
+At predict time, we add this object onto the `epi_workflow` and call `forecast()`
+
+```{r, warning=FALSE}
+ewf %>%
+  add_frosting(f) %>%
+  forecast()
+```
+
+The above `get_test_data()` function examines the recipe and ensures that enough
+test data is available to create the necessary lags and produce a prediction
+for the desired future time point (after the end of the training data). This mimics
+what would happen if `jhu` contained the most recent available historical data and
+we wanted to actually predict the future. We could have instead used any test data
+that contained the necessary predictors.
+
+
+## Conclusion
+
+Internally, we provide some simple functions to create reasonable forecasts.
+But ideally, a user could create their own forecasters by building up the
+components we provide. In other vignettes, we try to walk through some of these
+customizations.
+
+To illustrate everything above, here is (roughly) the code for the
+`flatline_forecaster()` applied to the `case_rate`.
+
+```{r}
+r <- epi_recipe(jhu) %>%
+  step_epi_ahead(case_rate, ahead = 7, skip = TRUE) %>%
+  update_role(case_rate, new_role = "predictor") %>%
+  add_role(all_of(key_colnames(jhu)), new_role = "predictor")
+
+f <- frosting() %>%
+  layer_predict() %>%
+  layer_residual_quantiles() %>%
+  layer_add_forecast_date() %>%
+  layer_add_target_date() %>%
+  layer_threshold(starts_with(".pred"))
+
+eng <- linear_reg() %>% set_engine("flatline")
+wf <- epi_workflow(r, eng, f) %>% fit(jhu)
+preds <- forecast(wf)
+```
+
+All that really differs from the `arx_forecaster()` is the `recipe`, the
+test data, and the engine. The `frosting` is identical, as is the fitting
+and predicting procedure.
+
+```{r}
+preds
+```
+

From c7f5d3b2e299f9d7e26156562ace014417890a86 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 12:09:27 -0600
Subject: [PATCH 336/387] much more complete guts example, branching flatline
 fixes

---
 R/step_epi_shift.R                           |   6 +
 README.md                                    |  11 +-
 _pkgdown.yml                                 |   1 +
 man/figures/README-show-processed-data-1.png | Bin 317105 -> 308662 bytes
 vignettes/arx-classifier.Rmd                 |   2 +-
 vignettes/backtesting.Rmd                    |   2 +-
 vignettes/custom_epiworkflows.Rmd            | 356 +++++++++++++++++++
 vignettes/epipredict.Rmd                     |  13 +-
 vignettes/guts.Rmd                           | 270 --------------
 vignettes/panel-data.Rmd                     |   2 +-
 vignettes/preprocessing-and-models.Rmd       |   2 +-
 vignettes/update.Rmd                         |   2 +-
 12 files changed, 385 insertions(+), 282 deletions(-)
 create mode 100644 vignettes/custom_epiworkflows.Rmd
 delete mode 100644 vignettes/guts.Rmd

diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index ae4bd3f31..8d68a2ef9 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -33,6 +33,12 @@
 #' @details The step assumes that the data are already _in the proper sequential
 #'  order_ for shifting.
 #'
+#'  While `{recipes}` provides a function `step_lag()`, it assumes that the data
+#'  have no breaks in the sequence of `time_values`, and has no ability to group by
+#'  `geo_value` or other keys. Our `lag/ahead` functions also appropriately adjust
+#'  the amount of data to avoid accidentally dropping recent predictors from the
+#'  test data.
+#'
 #' The `prefix` and `id` arguments are unchangeable to ensure that the code runs
 #' properly and to avoid inconsistency with naming. For `step_epi_ahead`, they
 #' are always set to `"ahead_"` and `"epi_ahead"` respectively, while for
diff --git a/README.md b/README.md
index fdfa37c72..5d9a36b92 100644
--- a/README.md
+++ b/README.md
@@ -105,7 +105,8 @@ cases_deaths |>
   facet_grid(
     rows = vars(.response_name),
     cols = vars(geo_value),
-    scale = "free") +
+    scale = "free"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -177,7 +178,8 @@ forecast_date_label <-
     heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  covid_case_death_rates |> filter(geo_value %in% used_locations) |>
+  covid_case_death_rates |>
+  filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
     death_rate,
@@ -186,7 +188,8 @@ processed_data_plot <-
   facet_grid(
     rows = vars(.response_name),
     cols = vars(geo_value),
-    scale = "free") +
+    scale = "free"
+  ) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label,
@@ -219,7 +222,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-01-31 10:46:32.
+#> This forecaster was fit on 2025-02-05 17:01:43.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
diff --git a/_pkgdown.yml b/_pkgdown.yml
index feb2bec75..658ba442f 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -11,6 +11,7 @@ articles:
     navbar: ~
     contents:
       - epipredict
+      - custom_epiworkflows
       - preprocessing-and-models
       - backtesting
       - arx-classifier
diff --git a/man/figures/README-show-processed-data-1.png b/man/figures/README-show-processed-data-1.png
index 04583bd241e20300ea95abcd4f49de600cc4d7a7..92c106bc20860ffa53d473a2267a944c5db9df99 100644
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diff --git a/vignettes/arx-classifier.Rmd b/vignettes/arx-classifier.Rmd
index 1e2a6949a..efc82ebaf 100644
--- a/vignettes/arx-classifier.Rmd
+++ b/vignettes/arx-classifier.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ## Load required packages
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 70d18fa30..7b7237d82 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ```{r pkgs, message=FALSE}
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
new file mode 100644
index 000000000..949ba86c9
--- /dev/null
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -0,0 +1,356 @@
+---
+title: "Custom Epiworkflows"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Custom Epiworkflows}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+source(here::here("vignettes/_common.R"))
+```
+
+```{r setup, message=FALSE, include = FALSE}
+library(dplyr)
+library(parsnip)
+library(workflows)
+library(recipes)
+library(epipredict)
+library(ggplot2)
+forecast_date <- as.Date("2021-08-01")
+used_locations <- c("ca", "ma", "ny", "tx")
+library(epidatr)
+```
+
+To get a better handle on custom `epi_workflow()`s, lets recreate and then
+modify the example of `four_week_ahead` from the [landing
+page](../index.html#motivating-example), 
+
+```{r make-four-forecasts, warning=FALSE}
+training_data <- covid_case_death_rates |>
+  filter(time_value <= forecast_date, geo_value %in% used_locations)
+four_week_ahead <- arx_forecaster(
+  training_data,
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
+  )
+)
+four_week_ahead$epi_workflow
+```
+# Anatomy of an `epi_workflow`
+An `epi_workflow` is an extension of a `workflows::workflow()` to handle panel
+data and post-processing.
+It consists of 3 components, shown above:
+
+1. Preprocessor: transform the data before model training and prediction, such
+  as convert counts to rates, create smoothed columns, or [any of the recipes
+  steps](https://recipes.tidymodels.org/reference/index.html).
+  Think of it as a more flexible `formula` that you would pass to `lm()`: `y ~
+  x1 + log(x2) + lag(x1, 5)`.
+  In general, there are 2 broad classes of transformation that `{recipes}`
+  handles:
+    - Transforms of both training and test data that are always applied. 
+      Examples include taking the log of a variable, leading or lagging,
+      filtering out rows, handling dummy variables, etc.
+    - Operations that fit parameters during training to apply during prediction,
+      such as centering by the mean.
+      This is a major benefit of `{recipes}`, since it prevents data leakage,
+      where information about the test/predict time data "leaks" into the
+      parameters. <!-- TODO unsure if worth even keeping, as we effectively
+      can't have data leakage. -->
+      For the case of centering, we need to store the mean of the predictor from
+      the training data and use that value on the prediction data rather than
+      accidentally calculating the mean of the test predictor for centering.
+2. Trainer: use a `{parsnip}` model to train a model on data, resulting in a
+   fitted model object.
+  Examples include linear regression, quantile regression, or [any parsnip
+  engine](https://www.tidymodels.org/find/parsnip/). 
+  Parsnip serves as a front-end that abstracts away the differences in interface
+  between a wide collection of statistical models.
+3. Postprocessor: unique to this package, and used to format and modify the
+   prediction after the model has been fit.
+   Each operation is a layer, and the stack of layers is known as frosting,
+   continuing the metaphor of baking a cake established in the recipe.
+   Some example operations include:
+  - generate quantiles from purely point-prediction models,
+  - undo operations done in the steps, such as convert back to counts from rates
+  - threshold so the forecast doesn't include negative values
+  - generally adapt the format of the prediction to it's eventual use.
+
+# Recreating `four_week_ahead` in an `epi_workflow()`
+To be able to extend this beyond what `arx_forecaster()` itself will let us do,
+we first need to understand how to recreate it using a custom `epi_workflow()`.
+
+To use a custom workflow, there are a couple of steps:
+1. define the `epi_recipe()`, which contains the preprocessing steps
+2. define the `frosting()` which contains the post-processing layers
+3. Combine these with a trainer such as `quantile_reg()` into an `epi_workflow`, which we can then fit on the training data
+4. fit the workflow on some data
+5. grab the right prediction data using `get_test_data()` and apply the fit data to generate a prediction
+
+## Define the `epi_recipe()`
+To do this, we'll first take a look at the steps as they're found in
+`four_week_ahead`:
+
+```{r inspect_fwa_steps, warning=FALSE}
+hardhat::extract_recipe(four_week_ahead$epi_workflow)
+```
+
+So there are 6 steps we will need to recreate.
+One thing to note about the extracted recipe is that it has already been
+trained; for steps such as `recipes::step_BoxCox()`, this means that their
+parameters have been calculated.
+Recreating this:
+```{r make_recipe}
+four_week_recipe <- epi_recipe(
+  covid_case_death_rates |>
+    filter(time_value <= forecast_date, geo_value %in% used_locations)
+)
+```
+
+The data here, `covid_case_death_rates` doesn't strictly need to be the actual
+dataset on which you are going to train. 
+However, it should have the same columns and the same metadata, such as `as_of`
+or `other_keys`; it is typically easiest to just use the training data itself.
+
+
+Then we add each step via pipes; in principle the order matters, though for this
+recipe only `step_epi_naomit` and `step_training_window` depend on the steps
+before them:
+
+```{r make_steps}
+four_week_recipe <- four_week_recipe |>
+  step_epi_lag(case_rate, lag = c(0, 1, 2, 3, 7, 14)) |>
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) |>
+  step_epi_ahead(death_rate, ahead = 4 * 7) |>
+  step_epi_naomit() |>
+  step_training_window()
+```
+
+one thing to note: we only added 5 steps here because `step_epi_naomit()` is
+actually a wrapper around adding 2 base `step_naomit()`s, on for
+`all_predictors()` and one for `all_outcomes()`, differing in their treatment at
+predict time.
+
+`step_epi_lag` and `step_epi_ahead` both accept tidy syntax, so if we
+wanted the same lags for both `case_rate` and `death_rate`, we could have done
+`step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
+
+In general for the `{recipes}` package, steps assign roles, such as `predictor`
+and `outcome` to columns, either by adding new ones or adjusting existing ones. 
+`step_epi_lag()` for example, creates a new column per lag with the name
+`lag_x_column_name` and assigns them as predictors, while `step_epi_ahead()`
+creates `ahead_column_name` columns and assigns them as outcomes.[^2]
+
+One way to inspect the roles assigned is to use `prep()`:
+
+```{r prep_recipe}
+prepped <- four_week_recipe |> prep(training_data)
+prepped$term_info |> print(n=14)
+```
+
+The way to inspect the columns created is by using `bake()` on the resulting
+recipe:
+
+```{r bake_recipe}
+four_week_recipe |>
+  prep(training_data) |>
+  bake(training_data)
+```
+
+## Define the `frosting()`
+Since the post-processing frosting[^1] layers are unique to this package, to
+inspect them we use `extract_frosting` from `{epipredict}`:
+
+```{r inspect_fwa_layers, warning=FALSE}
+epipredict::extract_frosting(four_week_ahead$epi_workflow)
+```
+
+The above gives us detailed descriptions of the arguments to the functions named
+above in the postprocessor of `four_week_ahead$epiworkflow`.
+Creating the layers is a similar process, except with frosting instead of a
+`recipe`:
+
+```{r make_frosting}
+four_week_layers <- frosting() |>
+  layer_predict() |>
+  layer_residual_quantiles(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)) |>
+  layer_add_forecast_date() |>
+  layer_add_target_date() |>
+  layer_threshold()
+```
+
+Most layers will work for any engine or steps; `layer_predict()` you will want
+to call in every case.
+There are a couple of layers, however, which depend on whether the engine used predicts quantiles or point estimates.
+
+The layers that are only supported by point estimate engines (such as `linear_reg()`):
+
+- `layer_residual_quantiles()`: the preferred method of generating quantiles, it
+  uses the error residuals of the engine. This will work for most parsnip engines.
+- `layer_predictive_distn()`: alternate method of generating quantiles, it uses
+  an approximate parametric distribution. This will work for linear regression
+  specifically.
+  
+TODO check this
+  
+On the other hand, the layers that are only supported by quantile estimating
+engines (such as `quantile_reg()`):
+
+- `layer_quantile_distn()`
+- `layer_point_from_distn()`: this adds the median quantile as an point
+  estimate[^3]. 
+
+## Fitting an `epi_workflow()`
+
+Given that we now have a recipe and some layers, we need to assemble the workflow:
+```{r workflow_building}
+four_week_workflow <- epi_workflow(
+  four_week_recipe,
+  linear_reg(),
+  four_week_layers
+)
+```
+
+And fit it[^4]:
+```{r workflow_fitting}
+fit_workflow <- four_week_workflow |> fit(training_data)
+```
+
+This has recreated `four_week_ahead$epi_workflow`
+
+## Predicting
+
+To do a prediction, we need to first narrow the dataset down to the relevant
+datapoints:
+
+```{r grab_data}
+relevant_data <- get_test_data(
+  extract_preprocessor(fit_workflow),
+  training_data
+)
+```
+
+Note the use of `extract_preprocessor()` rather than `extract_recipe()`; this
+gets the unfit version of the `epi_recipe`, which is necessary.
+
+With a fit workflow and test data in hand, we can actually make our predictions:
+
+```{r workflow_pred}
+fit_workflow |> predict(relevant_data)
+```
+
+Note that if we had simply plugged `training_data` into `predict()` we still get
+predictions:
+
+```{r workflow_pred_training}
+fit_workflow |> predict(training_data)
+```
+
+However, this produces forecasts for not just the actual `forecast_date`, but
+for every day in the dataset it has enough data to actually make a prediction.
+
+# Extending `four_week_ahead`
+
+There are many ways we could modify `four_week_ahead`; one simple modification
+would be to include a growth rate estimate as part of the model.
+Another would be to include a time component to the prediction if we expect
+there to be a strong seasonal variation.
+Another would be to convert from rates to counts, for example if that were the
+preferred prediction format.
+
+## Growth rate
+
+One feature that may potentially improve our forecast is looking at the growth
+rate 
+
+```{r growth_rate_recipe}
+growth_rate_recipe <- epi_recipe(
+  covid_case_death_rates |>
+    filter(time_value <= forecast_date, geo_value %in% used_locations)
+) |>
+  step_epi_lag(case_rate, lag = c(0, 1, 2, 3, 7, 14)) |>
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) |>
+  step_epi_ahead(death_rate, ahead = 4 * 7) |>
+  step_growth_rate(death_rate) |>
+  step_epi_naomit() |>
+  step_training_window()
+```
+Inspecting the newly added column:
+```{r growth_rate_print}
+growth_rate_recipe |>
+  prep(training_data) |>
+  bake(training_data) |>
+  select(geo_value, time_value, case_rate, death_rate, gr_7_rel_change_death_rate)
+```
+And the role:
+```{r growth_rate_roles}
+prepped <- growth_rate_recipe |>
+  prep(training_data) 
+prepped$term_info |> filter(grepl("gr", variable))
+```
+
+To demonstrate the changes in the layers that come along with it, we will use
+`quantile_reg()` as the model:
+```{r layer_and_fit}
+growth_rate_layers <- frosting() |>
+  layer_predict() |>
+  layer_quantile_distn(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)) |>
+  layer_point_from_distn() |>
+  layer_add_forecast_date() |>
+  layer_add_target_date() |>
+  layer_threshold()
+
+growth_rate_workflow <- epi_workflow(
+  growth_rate_recipe,
+  quantile_reg(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)),
+  growth_rate_layers
+)
+
+relevant_data <- get_test_data(
+  extract_preprocessor(fit_workflow),
+  training_data
+)
+gr_fit_workflow <- growth_rate_workflow |> fit(training_data)
+gr_predictions <- fit_workflow |>
+  predict(relevant_data) |>
+  filter(time_value == forecast_date)
+```
+<details>
+<summary> Plot </summary>
+```{r plotting}
+# plotting the result
+forecast_date_label <-
+  tibble(
+    geo_value = rep(used_locations, 2),
+    .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
+    heights = c(rep(150, 4), rep(0.40, 4))
+  )
+
+result_plot <- autoplot(
+  object = gr_fit_workflow,
+  predictions = gr_predictions,
+  plot_data = covid_case_death_rates |>
+    filter(geo_value %in% used_locations, time_value > "2021-07-01")
+) +
+  geom_vline(aes(xintercept = forecast_date)) +
+  geom_text(
+    data = forecast_date_label %>% filter(.response_name == "death_rate"),
+    aes(x = dates, label = "forecast\ndate", y = heights),
+    size = 3, hjust = "right"
+  ) +
+  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+</details>
+```{r, echo=FALSE}
+result_plot
+```
+
+TODO `get_test_data` isn't actually working here...
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index d68b7d63e..79909357a 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ```{r setup, message=FALSE, include = FALSE}
@@ -227,13 +227,17 @@ all_flatlines <- lapply(
       covid_case_death_rates |>
         filter(time_value <= forecast_date, geo_value %in% used_locations),
       outcome = "death_rate",
-      args_list = flatline_args_list(ahead = days_ahead)
+      args_list = flatline_args_list(
+        ahead = days_ahead,
+        quantile_levels = c(0.05, 0.95)
+      )
     )
   }
 )
 # same plotting code as in the arx multi-ahead case
 workflow <- all_flatlines[[1]]$epi_workflow
 results <- purrr::map_df(all_flatlines, ~ `$`(., "predictions"))
+results %>% filter(target_date == max(target_date))
 autoplot(
   object = workflow,
   predictions = results,
@@ -371,7 +375,7 @@ An `epi_workflow()` consists of 3 parts:
       modelling. They come from this package or [any of the recipes
       steps](https://recipes.tidymodels.org/reference/index.html);
       `four_week_ahead` has 5 of these, and you can inspect them more closely by
-      running `hardhat::extract_recipe(four_week_ahead$epi_workflow)`.
+      running `hardhat::extract_recipe(four_week_ahead$epi_workflow)`.[^6]
 - `Model`: the actual model that does the fitting, given by a
   `parsnip::model_spec`; `four_week_ahead` has the default of
   `parsnip::linear_reg()`, which is a wrapper from `{parsnip}` for
@@ -425,3 +429,6 @@ without `NA` values is a training point to fit the coefficients $a_0,\ldots, a_6
 [^5]: in the case of `arx_forecaster`, this is any model with
     `mode="regression"` from [this
     list](https://www.tidymodels.org/find/parsnip/).
+
+[^6]: alternatively, for an unfit version of the preprocessor, you can call
+    `hardhat::extract_preprocessor(four_week_ahead$epi_workflow)`
diff --git a/vignettes/guts.Rmd b/vignettes/guts.Rmd
deleted file mode 100644
index f17065384..000000000
--- a/vignettes/guts.Rmd
+++ /dev/null
@@ -1,270 +0,0 @@
----
-title: "Guts"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Guts}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-source("_common.R")
-```
-
-```{r setup, message=FALSE, include = FALSE}
-library(dplyr)
-library(parsnip)
-library(workflows)
-library(recipes)
-library(epipredict)
-forecast_date <- as.Date("2021-08-01")
-used_locations <- c("ca", "ma", "ny", "tx")
-library(epidatr)
-cases <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "confirmed_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20200601, 20211231),
-  geo_values = "*"
-) |>
-  select(geo_value, time_value, case_rate = value)
-
-deaths <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "deaths_incidence_prop",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20200601, 20211231),
-  geo_values = "*"
-) |>
-  select(geo_value, time_value, death_rate = value)
-cases_deaths <-
-  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
-  filter(geo_value %in% used_locations) |>
-  as_epi_df(as_of = as.Date("2022-01-01"))
-cases_deaths <-
-  cases_deaths |>
-  group_by(geo_value) |>
-  epi_slide(
-    cases_7dav = mean(case_rate, na.rm = TRUE),
-    death_rate_7dav = mean(death_rate, na.rm = TRUE),
-    .window_size = 7
-  ) |>
-  ungroup() |>
-  mutate(case_rate = NULL, death_rate = NULL) |>
-  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
-cases_deaths <-
-  cases_deaths |>
-  group_by(geo_value) |>
-  mutate(
-    outlr_death_rate = detect_outlr_rm(
-      time_value, death_rate,
-      detect_negatives = TRUE
-    ),
-    outlr_case_rate = detect_outlr_rm(
-      time_value, case_rate,
-      detect_negatives = TRUE
-    )
-  ) |>
-  unnest(cols = starts_with("outlr"), names_sep = "_") |>
-  ungroup() |>
-  mutate(
-    death_rate = outlr_death_rate_replacement,
-    case_rate = outlr_case_rate_replacement
-  ) |>
-  select(geo_value, time_value, case_rate, death_rate)
-jhu <- covid_case_death_rates
-out_gb <- arx_forecaster(
-  jhu,
-  outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
-  trainer = boost_tree(mode = "regression", trees = 20)
-)
-out_q <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
-  args_list = arx_args_list(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99)
-  )
-)
-```
-# Document Title
-## Inner workings
-
-Underneath the hood, this forecaster creates (and returns) an `epi_workflow`.
-Essentially, this is a big S3 object that wraps up the 4 modular steps
-(preprocessing - postprocessing) described above.
-
-### Preprocessing
-
-Preprocessing is accomplished through a `recipe` (imagine baking a cake) as
-provided in the [`{recipes}`](https://recipes.tidymodels.org) package.
-We've made a few modifications (to handle
-panel data) as well as added some additional options. The recipe gives a
-specification of how to handle training data. Think of it like a fancified
-`formula` that you would pass to `lm()`: `y ~ x1 + log(x2)`. In general,
-there are 2 extensions to the `formula` that `{recipes}` handles:
-
-  1. Doing transformations of both training and test data that can always be
-  applied. These are things like taking the log of a variable, leading or
-  lagging, filtering out rows, handling dummy variables, etc.
-  2. Using statistics from the training data to eventually process test data.
-    This is a major benefit of `{recipes}`. It prevents what the tidy team calls
-    "data leakage". A simple example is centering a predictor by its mean. We
-    need to store the mean of the predictor from the training data and use that
-    value on the test data rather than accidentally calculating the mean of
-    the test predictor for centering.
-
-A recipe is processed in 2 steps, first it is "prepped". This calculates and
-stores any intermediate statistics necessary for use on the test data.
-Then it is "baked"
-resulting in training data ready for passing into a statistical model (like `lm`).
-
-We have introduced an `epi_recipe`. It's just a `recipe` that knows how to handle
-the `time_value`, `geo_value`, and any additional keys so that these are available
-when necessary.
-
-The `epi_recipe` from `out_gb` can be extracted from the result:
-
-```{r}
-extract_recipe(out_gb$epi_workflow)
-```
-
-The "Inputs" are the original `epi_df` and the "roles" that these are assigned.
-None of these are predictors or outcomes. Those will be created
-by the recipe when it is prepped. The "Operations" are the sequence of
-instructions to create the cake (baked training data).
-Here we create lagged predictors, lead the outcome, and then remove `NA`s.
-Some models like `lm` internally handle `NA`s, but not everything does, so we
-deal with them explicitly. The code to do this (inside the forecaster) is
-
-```{r}
-er <- epi_recipe(jhu) %>%
-  step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%
-  step_epi_ahead(death_rate, ahead = 7) %>%
-  step_epi_naomit()
-```
-
-While `{recipes}` provides a function `step_lag()`, it assumes that the data
-have no breaks in the sequence of `time_values`. This is a bit dangerous, so
-we avoid that behaviour. Our `lag/ahead` functions also appropriately adjust the
-amount of data to avoid accidentally dropping recent predictors from the test
-data.
-
-### The model specification
-
-Users with familiarity with the `{parsnip}` package will have no trouble here.
-Basically, `{parsnip}` unifies the function signature across statistical models.
-For example, `lm()` "likes" to work with formulas, but `glmnet::glmnet()` uses
-`x` and `y` for predictors and response. `{parsnip}` is agnostic. Both of these
-do "linear regression". Above we switched from `lm()` to `xgboost()` without
-any issue despite the fact that these functions couldn't be more different.
-
-```{r, eval = FALSE}
-lm(formula, data, subset, weights, na.action,
-  method = "qr",
-  model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
-  contrasts = NULL, offset, ...
-)
-
-xgboost(
-  data = NULL, label = NULL, missing = NA, weight = NULL,
-  params = list(), nrounds, verbose = 1, print_every_n = 1L,
-  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
-  ...
-)
-```
-
-`{epipredict}` provides a few engines/modules (the flatline forecaster and
-quantile regression), but you should be able to use any available models
-listed [here](https://www.tidymodels.org/find/parsnip/).
-
-To estimate (fit) a preprocessed model, one calls `fit()` on the `epi_workflow`.
-
-```{r}
-ewf <- epi_workflow(er, linear_reg()) %>% fit(jhu)
-```
-
-### Postprocessing
-
-To stretch the metaphor of preparing a cake to its natural limits, we have
-created postprocessing functionality called "frosting". Much like the recipe,
-each postprocessing operation is a "layer" and we "slather" these onto our
-baked cake. To fix ideas, below is the postprocessing `frosting` for
-`arx_forecaster()`
-
-```{r}
-extract_frosting(out_q$epi_workflow)
-```
-
-Here we have 5 layers of frosting. The first generates the forecasts from the test data.
-The second uses quantiles of the residuals to create distributional
-forecasts. The next two add columns for the date the forecast was made and the
-date for which it is intended to occur. Because we are predicting rates, they
-should be non-negative, so the last layer thresholds both predicted values and
-intervals at 0. The code to do this (inside the forecaster) is
-
-```{r}
-f <- frosting() %>%
-  layer_predict() %>%
-  layer_residual_quantiles(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99),
-    symmetrize = TRUE
-  ) %>%
-  layer_add_forecast_date() %>%
-  layer_add_target_date() %>%
-  layer_threshold(starts_with(".pred"))
-```
-
-At predict time, we add this object onto the `epi_workflow` and call `forecast()`
-
-```{r, warning=FALSE}
-ewf %>%
-  add_frosting(f) %>%
-  forecast()
-```
-
-The above `get_test_data()` function examines the recipe and ensures that enough
-test data is available to create the necessary lags and produce a prediction
-for the desired future time point (after the end of the training data). This mimics
-what would happen if `jhu` contained the most recent available historical data and
-we wanted to actually predict the future. We could have instead used any test data
-that contained the necessary predictors.
-
-
-## Conclusion
-
-Internally, we provide some simple functions to create reasonable forecasts.
-But ideally, a user could create their own forecasters by building up the
-components we provide. In other vignettes, we try to walk through some of these
-customizations.
-
-To illustrate everything above, here is (roughly) the code for the
-`flatline_forecaster()` applied to the `case_rate`.
-
-```{r}
-r <- epi_recipe(jhu) %>%
-  step_epi_ahead(case_rate, ahead = 7, skip = TRUE) %>%
-  update_role(case_rate, new_role = "predictor") %>%
-  add_role(all_of(key_colnames(jhu)), new_role = "predictor")
-
-f <- frosting() %>%
-  layer_predict() %>%
-  layer_residual_quantiles() %>%
-  layer_add_forecast_date() %>%
-  layer_add_target_date() %>%
-  layer_threshold(starts_with(".pred"))
-
-eng <- linear_reg() %>% set_engine("flatline")
-wf <- epi_workflow(r, eng, f) %>% fit(jhu)
-preds <- forecast(wf)
-```
-
-All that really differs from the `arx_forecaster()` is the `recipe`, the
-test data, and the engine. The `frosting` is identical, as is the fitting
-and predicting procedure.
-
-```{r}
-preds
-```
-
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 668c21e0f..eeb0fb7ad 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ```{r libraries, warning=FALSE, message=FALSE}
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 6bff45611..a546f5ac9 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ## Introduction
diff --git a/vignettes/update.Rmd b/vignettes/update.Rmd
index 02fe71be7..fbda6be37 100644
--- a/vignettes/update.Rmd
+++ b/vignettes/update.Rmd
@@ -8,7 +8,7 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-source("_common.R")
+source(here::here("vignettes/_common.R"))
 ```
 
 ```{r setup, message=FALSE}

From 7d1273e840897d0a7d23b0ab129232710767caa7 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 7 Feb 2025 12:20:49 -0600
Subject: [PATCH 337/387] fix for flatline discovered, rename guts

---
 _pkgdown.yml             | 1 -
 vignettes/epipredict.Rmd | 3 +--
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 658ba442f..643b354b1 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -16,7 +16,6 @@ articles:
       - backtesting
       - arx-classifier
       - update
-      - guts
   - title: Advanced methods
     contents:
       - articles/smooth-qr
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 79909357a..f64955813 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -229,7 +229,7 @@ all_flatlines <- lapply(
       outcome = "death_rate",
       args_list = flatline_args_list(
         ahead = days_ahead,
-        quantile_levels = c(0.05, 0.95)
+        quantile_levels = c(0.05, 0.5, 0.95)
       )
     )
   }
@@ -244,7 +244,6 @@ autoplot(
   plot_data = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
-_**the problem is only occurring in the plot, not the underlying prediction**_
 
 Note that the `cdc_baseline_forecaster` is a slight modification of this method
 for use in [the CDC COVID19 Forecasting Hub](https://covid19forecasthub.org/).

From 2054f9cd42137afa280c3eb34ba88e25c8bab894 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 10 Feb 2025 11:11:12 -0600
Subject: [PATCH 338/387] docs, styler

---
 man/autoplot-epipred.Rd           | 5 +++++
 man/step_epi_shift.Rd             | 6 ++++++
 vignettes/custom_epiworkflows.Rmd | 4 ++--
 3 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index f4657967f..e92864664 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -11,6 +11,7 @@
 \method{autoplot}{epi_workflow}(
   object,
   predictions = NULL,
+  plot_data = NULL,
   .levels = c(0.5, 0.8, 0.9),
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
@@ -23,6 +24,7 @@
 
 \method{autoplot}{canned_epipred}(
   object,
+  plot_data = NULL,
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
@@ -42,6 +44,9 @@
 \item{predictions}{A data frame with predictions. If \code{NULL}, only the
 original data is shown.}
 
+\item{plot_data}{An epi_df of the data to plot against. This is for the case
+where you have the actual results to compare the forecast against.}
+
 \item{.levels}{A numeric vector of levels to plot for any prediction bands.
 More than 3 levels begins to be difficult to see.}
 
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 867410360..e2140248d 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -72,6 +72,12 @@ argument.
 The step assumes that the data are already \emph{in the proper sequential
 order} for shifting.
 
+While \code{{recipes}} provides a function \code{step_lag()}, it assumes that the data
+have no breaks in the sequence of \code{time_values}, and has no ability to group by
+\code{geo_value} or other keys. Our \code{lag/ahead} functions also appropriately adjust
+the amount of data to avoid accidentally dropping recent predictors from the
+test data.
+
 The \code{prefix} and \code{id} arguments are unchangeable to ensure that the code runs
 properly and to avoid inconsistency with naming. For \code{step_epi_ahead}, they
 are always set to \code{"ahead_"} and \code{"epi_ahead"} respectively, while for
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 949ba86c9..1afc7fd07 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -151,7 +151,7 @@ One way to inspect the roles assigned is to use `prep()`:
 
 ```{r prep_recipe}
 prepped <- four_week_recipe |> prep(training_data)
-prepped$term_info |> print(n=14)
+prepped$term_info |> print(n = 14)
 ```
 
 The way to inspect the columns created is by using `bake()` on the resulting
@@ -291,7 +291,7 @@ growth_rate_recipe |>
 And the role:
 ```{r growth_rate_roles}
 prepped <- growth_rate_recipe |>
-  prep(training_data) 
+  prep(training_data)
 prepped$term_info |> filter(grepl("gr", variable))
 ```
 

From 763885ef04b247086c9117b95c5c1ed6e606bbe4 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 10 Feb 2025 11:39:42 -0600
Subject: [PATCH 339/387] passing check & news

---
 DESCRIPTION | 1 +
 NEWS.md     | 1 +
 2 files changed, 2 insertions(+)

diff --git a/DESCRIPTION b/DESCRIPTION
index 81a35b30e..3ec5fc38b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -53,6 +53,7 @@ Suggests:
     epidatr (>= 1.0.0),
     fs,
     grf,
+    here,
     knitr,
     poissonreg,
     purrr,
diff --git a/NEWS.md b/NEWS.md
index de698ee96..bbcdb2eb4 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -38,6 +38,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Replace `dist_quantiles()` with `hardhat::quantile_pred()`
 - Allow `quantile()` to threshold to an interval if desired (#434)
 - `arx_forecaster()` detects if there's enough data to predict
+- Add `plot_data` to `autoplot` so that forecasts can be plotted against the values they're predicting
 
 ## Bug fixes
 

From ad8bf9302e0cc5888344d4aa07dac9c7bf550c96 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 10 Feb 2025 17:10:41 -0600
Subject: [PATCH 340/387] revising custom_epiworkflows

---
 man/figures/README-show-single-forecast-1.png | Bin 290340 -> 290496 bytes
 vignettes/custom_epiworkflows.Rmd             | 163 +++++++++++++-----
 vignettes/epipredict.Rmd                      |   2 +
 3 files changed, 119 insertions(+), 46 deletions(-)

diff --git a/man/figures/README-show-single-forecast-1.png b/man/figures/README-show-single-forecast-1.png
index 0acd24d182f460b631a8f1ef2e9cffd8a101c17e..0e58a52f5f69d33231cd1742eef37129444cddf8 100644
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diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 1afc7fd07..ce397a998 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -25,7 +25,7 @@ library(epidatr)
 
 To get a better handle on custom `epi_workflow()`s, lets recreate and then
 modify the example of `four_week_ahead` from the [landing
-page](../index.html#motivating-example), 
+page](../index.html#motivating-example)
 
 ```{r make-four-forecasts, warning=FALSE}
 training_data <- covid_case_death_rates |>
@@ -43,7 +43,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead$epi_workflow
 ```
 # Anatomy of an `epi_workflow`
-An `epi_workflow` is an extension of a `workflows::workflow()` to handle panel
+An `epi_workflow()` is an extension of a `workflows::workflow()` to handle panel
 data and post-processing.
 It consists of 3 components, shown above:
 
@@ -52,17 +52,21 @@ It consists of 3 components, shown above:
   steps](https://recipes.tidymodels.org/reference/index.html).
   Think of it as a more flexible `formula` that you would pass to `lm()`: `y ~
   x1 + log(x2) + lag(x1, 5)`.
+  The above model has 6 of these steps.
   In general, there are 2 broad classes of transformation that `{recipes}`
   handles:
-    - Transforms of both training and test data that are always applied. 
+    - Transforms of both training and test data that are always applied.
       Examples include taking the log of a variable, leading or lagging,
-      filtering out rows, handling dummy variables, etc.
+      filtering out rows, handling dummy variables, calculating growth rates,
+      etc.
     - Operations that fit parameters during training to apply during prediction,
       such as centering by the mean.
       This is a major benefit of `{recipes}`, since it prevents data leakage,
       where information about the test/predict time data "leaks" into the
       parameters. <!-- TODO unsure if worth even keeping, as we effectively
       can't have data leakage. -->
+      However, the main mechanism we rely on to prevent data leakage is proper
+      [backtesting](backtesting.html).
       For the case of centering, we need to store the mean of the predictor from
       the training data and use that value on the prediction data rather than
       accidentally calculating the mean of the test predictor for centering.
@@ -74,24 +78,27 @@ It consists of 3 components, shown above:
   between a wide collection of statistical models.
 3. Postprocessor: unique to this package, and used to format and modify the
    prediction after the model has been fit.
-   Each operation is a layer, and the stack of layers is known as frosting,
+   Each operation is a `layer_`, and the stack of layers is known as `frosting()`,
    continuing the metaphor of baking a cake established in the recipe.
    Some example operations include:
-  - generate quantiles from purely point-prediction models,
-  - undo operations done in the steps, such as convert back to counts from rates
-  - threshold so the forecast doesn't include negative values
-  - generally adapt the format of the prediction to it's eventual use.
+    - generate quantiles from purely point-prediction models,
+    - undo operations done in the steps, such as convert back to counts from rates
+    - threshold so the forecast doesn't include negative values
+    - generally adapt the format of the prediction to it's eventual use.
 
 # Recreating `four_week_ahead` in an `epi_workflow()`
 To be able to extend this beyond what `arx_forecaster()` itself will let us do,
 we first need to understand how to recreate it using a custom `epi_workflow()`.
 
 To use a custom workflow, there are a couple of steps:
+
 1. define the `epi_recipe()`, which contains the preprocessing steps
 2. define the `frosting()` which contains the post-processing layers
-3. Combine these with a trainer such as `quantile_reg()` into an `epi_workflow`, which we can then fit on the training data
-4. fit the workflow on some data
-5. grab the right prediction data using `get_test_data()` and apply the fit data to generate a prediction
+3. Combine these with a trainer such as `quantile_reg()` into an
+   `epi_workflow()`, which we can then fit on the training data
+4. `fit()` the workflow on some data
+5. grab the right prediction data using `get_test_data()` and apply the fit data
+   to generate a prediction
 
 ## Define the `epi_recipe()`
 To do this, we'll first take a look at the steps as they're found in
@@ -103,9 +110,9 @@ hardhat::extract_recipe(four_week_ahead$epi_workflow)
 
 So there are 6 steps we will need to recreate.
 One thing to note about the extracted recipe is that it has already been
-trained; for steps such as `recipes::step_BoxCox()`, this means that their
+trained; for steps such as `recipes::step_BoxCox()` which have parameters, this means that their
 parameters have been calculated.
-Recreating this:
+Before defining steps, we need to create an `epi_recipe()` to hold them
 ```{r make_recipe}
 four_week_recipe <- epi_recipe(
   covid_case_death_rates |>
@@ -120,7 +127,7 @@ or `other_keys`; it is typically easiest to just use the training data itself.
 
 
 Then we add each step via pipes; in principle the order matters, though for this
-recipe only `step_epi_naomit` and `step_training_window` depend on the steps
+recipe only `step_epi_naomit()` and `step_training_window()` depend on the steps
 before them:
 
 ```{r make_steps}
@@ -132,20 +139,21 @@ four_week_recipe <- four_week_recipe |>
   step_training_window()
 ```
 
-one thing to note: we only added 5 steps here because `step_epi_naomit()` is
-actually a wrapper around adding 2 base `step_naomit()`s, on for
+One thing to note: we only added 5 steps here because `step_epi_naomit()` is
+actually a wrapper around adding 2 base `step_naomit()`s, one for
 `all_predictors()` and one for `all_outcomes()`, differing in their treatment at
 predict time.
 
-`step_epi_lag` and `step_epi_ahead` both accept tidy syntax, so if we
-wanted the same lags for both `case_rate` and `death_rate`, we could have done
-`step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
+`step_epi_lag()` and `step_epi_ahead()` both accept tidy syntax, so if for
+example we wanted the same lags for both `case_rate` and `death_rate`, we could
+have done `step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
 
 In general for the `{recipes}` package, steps assign roles, such as `predictor`
-and `outcome` to columns, either by adding new ones or adjusting existing ones. 
-`step_epi_lag()` for example, creates a new column per lag with the name
+and `outcome` to columns, either by adding new columns or adjusting existing
+ones. 
+`step_epi_lag()` for example, creates a new column for each lag with the name
 `lag_x_column_name` and assigns them as predictors, while `step_epi_ahead()`
-creates `ahead_column_name` columns and assigns them as outcomes.[^2]
+creates `ahead_x_column_name` columns and assigns them as outcomes.
 
 One way to inspect the roles assigned is to use `prep()`:
 
@@ -163,9 +171,14 @@ four_week_recipe |>
   bake(training_data)
 ```
 
+This is also useful for debugging malfunctioning pipelines; if you define
+`four_week_recipe` only up to the step that is misbehaving, you can get a
+partial evaluation to see the exact data the step is being applied to.
+It also allows you to see the exact data that the parsnip model is fitting on.
+
 ## Define the `frosting()`
-Since the post-processing frosting[^1] layers are unique to this package, to
-inspect them we use `extract_frosting` from `{epipredict}`:
+Since the post-processing frosting layers[^1] are unique to this package, to
+inspect them we use `extract_frosting()` from `{epipredict}`:
 
 ```{r inspect_fwa_layers, warning=FALSE}
 epipredict::extract_frosting(four_week_ahead$epi_workflow)
@@ -174,7 +187,7 @@ epipredict::extract_frosting(four_week_ahead$epi_workflow)
 The above gives us detailed descriptions of the arguments to the functions named
 above in the postprocessor of `four_week_ahead$epiworkflow`.
 Creating the layers is a similar process, except with frosting instead of a
-`recipe`:
+`recipe`[^2]:
 
 ```{r make_frosting}
 four_week_layers <- frosting() |>
@@ -185,14 +198,17 @@ four_week_layers <- frosting() |>
   layer_threshold()
 ```
 
+
 Most layers will work for any engine or steps; `layer_predict()` you will want
 to call in every case.
 There are a couple of layers, however, which depend on whether the engine used predicts quantiles or point estimates.
 
-The layers that are only supported by point estimate engines (such as `linear_reg()`):
+The layers that are only supported by point estimate engines (such as
+`linear_reg()`) are
 
-- `layer_residual_quantiles()`: the preferred method of generating quantiles, it
-  uses the error residuals of the engine. This will work for most parsnip engines.
+- `layer_residual_quantiles()`: the preferred method of generating quantiles for
+  non-quantile models, it uses the error residuals of the engine. 
+  This will work for most parsnip engines.
 - `layer_predictive_distn()`: alternate method of generating quantiles, it uses
   an approximate parametric distribution. This will work for linear regression
   specifically.
@@ -200,11 +216,13 @@ The layers that are only supported by point estimate engines (such as `linear_re
 TODO check this
   
 On the other hand, the layers that are only supported by quantile estimating
-engines (such as `quantile_reg()`):
+engines (such as `quantile_reg()`) are
 
-- `layer_quantile_distn()`
-- `layer_point_from_distn()`: this adds the median quantile as an point
-  estimate[^3]. 
+- `layer_quantile_distn()`: adds the specified quantiles.
+  If they differ from the ones actually fit, they will be interpolated and/or
+  extrapolated.
+- `layer_point_from_distn()`: this adds the median quantile as a point estimate,
+  and should be called after `layer_quantile_distn()` if called at all. 
 
 ## Fitting an `epi_workflow()`
 
@@ -217,13 +235,11 @@ four_week_workflow <- epi_workflow(
 )
 ```
 
-And fit it[^4]:
+And fit it to recreate `four_week_ahead$epi_workflow`
 ```{r workflow_fitting}
 fit_workflow <- four_week_workflow |> fit(training_data)
 ```
 
-This has recreated `four_week_ahead$epi_workflow`
-
 ## Predicting
 
 To do a prediction, we need to first narrow the dataset down to the relevant
@@ -231,14 +247,11 @@ datapoints:
 
 ```{r grab_data}
 relevant_data <- get_test_data(
-  extract_preprocessor(fit_workflow),
+  four_week_recipe,
   training_data
 )
 ```
 
-Note the use of `extract_preprocessor()` rather than `extract_recipe()`; this
-gets the unfit version of the `epi_recipe`, which is necessary.
-
 With a fit workflow and test data in hand, we can actually make our predictions:
 
 ```{r workflow_pred}
@@ -252,15 +265,26 @@ predictions:
 fit_workflow |> predict(training_data)
 ```
 
-However, this produces forecasts for not just the actual `forecast_date`, but
-for every day in the dataset it has enough data to actually make a prediction.
+The resulting tibble is 800 rows long, however.
+This produces forecasts for not just the actual `forecast_date`, but for every
+day in the dataset it has enough data to actually make a prediction.
+To narrow this down, we could filter to rows where the `time_value` matches the `forecast_date`:
+
+```{r workflow_pred_training_filter}
+fit_workflow |>
+  predict(training_data) |>
+  filter(time_value == forecast_date)
+```
+
+This can be useful for cases where `get_test_data()` doesn't pull sufficient
+data.
 
 # Extending `four_week_ahead`
 
 There are many ways we could modify `four_week_ahead`; one simple modification
 would be to include a growth rate estimate as part of the model.
-Another would be to include a time component to the prediction if we expect
-there to be a strong seasonal variation.
+Another would be to include a time component to the prediction (useful if we
+expect there to be a strong seasonal component).
 Another would be to convert from rates to counts, for example if that were the
 preferred prediction format.
 
@@ -313,11 +337,11 @@ growth_rate_workflow <- epi_workflow(
 )
 
 relevant_data <- get_test_data(
-  extract_preprocessor(fit_workflow),
+  growth_rate_recipe,
   training_data
 )
 gr_fit_workflow <- growth_rate_workflow |> fit(training_data)
-gr_predictions <- fit_workflow |>
+gr_predictions <- gr_fit_workflow |>
   predict(relevant_data) |>
   filter(time_value == forecast_date)
 ```
@@ -354,3 +378,50 @@ result_plot
 ```
 
 TODO `get_test_data` isn't actually working here...
+
+[^1]: Think of baking a cake, where adding the frosting is the last step in the process of actually baking.
+
+[^2]: Note that the frosting doesn't require any information about the training
+    data, since the output of the model only depends on the model used.
+
+## Population scaling
+
+Suppose we're sending our predictions to someone who is looking to understand
+counts, rather than rates.
+Then we can adjust just the frosting to get a forecast for the counts from our
+rates forecaster:
+
+```{r rate_scale}
+count_layers <-
+  frosting() |>
+  layer_predict() |>
+  layer_residual_quantiles(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)) |>
+  layer_population_scaling(
+    .pred,
+    .pred_distn,
+    df = epidatasets::state_census,
+    df_pop_col = "pop",
+    create_new = FALSE,
+    rate_rescaling = 1e5,
+    by = c("geo_value" = "abbr")
+  ) |>
+  layer_add_forecast_date() |>
+  layer_add_target_date() |>
+  layer_threshold()
+# building the new workflow
+count_workflow <- epi_workflow(
+  four_week_recipe,
+  linear_reg(),
+  count_layers
+)
+count_pred_data <- get_test_data(four_week_recipe, training_data)
+count_predictions <- count_workflow |>
+  fit(training_data) |>
+  predict(count_pred_data)
+count_predictions
+```
+
+which are 2-3 orders of magnitude larger than the corresponding rates above.
+`df` represents the scaling value; in this case it is the state populations,
+while `rate_rescaling` gives the denominator of the rate (our fit values were
+per 100,000).
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index f64955813..cdfbe07ef 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -132,6 +132,8 @@ hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 
 The default trainer is `parsnip::linear_reg()`, which generates quantiles after
 the fact in the post-processing layers, rather than as part of the model.
+While this does work, it is generally preferable to use `quantile_reg()`, as the
+quantiles generated in this manner can be poorly behaved.
 `quantile_reg()` on the other hand directly estimates different linear models
 for each quantile, reflected in the 3 different columns for `tau` above.
 

From 5177e681da6a302ae127f6bf9d689e736f5583b5 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 11 Feb 2025 13:39:11 -0600
Subject: [PATCH 341/387] some more editing

---
 README.md                         |  2 +-
 vignettes/custom_epiworkflows.Rmd | 32 +++++++++++++++++++------------
 2 files changed, 21 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index 5d9a36b92..d41d9a1b3 100644
--- a/README.md
+++ b/README.md
@@ -222,7 +222,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-02-05 17:01:43.
+#> This forecaster was fit on 2025-02-10 12:09:58.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index ce397a998..c13858152 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -283,15 +283,15 @@ data.
 
 There are many ways we could modify `four_week_ahead`; one simple modification
 would be to include a growth rate estimate as part of the model.
-Another would be to include a time component to the prediction (useful if we
-expect there to be a strong seasonal component).
 Another would be to convert from rates to counts, for example if that were the
 preferred prediction format.
+Another would be to include a time component to the prediction (useful if we
+expect there to be a strong seasonal component).
 
 ## Growth rate
 
 One feature that may potentially improve our forecast is looking at the growth
-rate 
+rate
 
 ```{r growth_rate_recipe}
 growth_rate_recipe <- epi_recipe(
@@ -305,12 +305,17 @@ growth_rate_recipe <- epi_recipe(
   step_epi_naomit() |>
   step_training_window()
 ```
+
 Inspecting the newly added column:
+
 ```{r growth_rate_print}
 growth_rate_recipe |>
   prep(training_data) |>
   bake(training_data) |>
-  select(geo_value, time_value, case_rate, death_rate, gr_7_rel_change_death_rate)
+  select(
+    geo_value, time_value, case_rate,
+    death_rate, gr_7_rel_change_death_rate
+  )
 ```
 And the role:
 ```{r growth_rate_roles}
@@ -320,7 +325,7 @@ prepped$term_info |> filter(grepl("gr", variable))
 ```
 
 To demonstrate the changes in the layers that come along with it, we will use
-`quantile_reg()` as the model:
+`quantile_reg()` as the model, which requires changing from `layer_residual_quantiles()` to `layer_quantile_distn()` and `layer_point_from_distn()`:
 ```{r layer_and_fit}
 growth_rate_layers <- frosting() |>
   layer_predict() |>
@@ -347,14 +352,17 @@ gr_predictions <- gr_fit_workflow |>
 ```
 <details>
 <summary> Plot </summary>
+
+Plotting the result; this is reusing some code from the landing page to print
+the forecast date.
+
 ```{r plotting}
-# plotting the result
 forecast_date_label <-
   tibble(
     geo_value = rep(used_locations, 2),
     .response_name = c(rep("case_rate", 4), rep("death_rate", 4)),
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
-    heights = c(rep(150, 4), rep(0.40, 4))
+    heights = c(rep(150, 4), rep(0.30, 4))
   )
 
 result_plot <- autoplot(
@@ -379,11 +387,6 @@ result_plot
 
 TODO `get_test_data` isn't actually working here...
 
-[^1]: Think of baking a cake, where adding the frosting is the last step in the process of actually baking.
-
-[^2]: Note that the frosting doesn't require any information about the training
-    data, since the output of the model only depends on the model used.
-
 ## Population scaling
 
 Suppose we're sending our predictions to someone who is looking to understand
@@ -425,3 +428,8 @@ which are 2-3 orders of magnitude larger than the corresponding rates above.
 `df` represents the scaling value; in this case it is the state populations,
 while `rate_rescaling` gives the denominator of the rate (our fit values were
 per 100,000).
+
+[^1]: Think of baking a cake, where adding the frosting is the last step in the process of actually baking.
+
+[^2]: Note that the frosting doesn't require any information about the training
+    data, since the output of the model only depends on the model used.

From a5270ccc1d508f33170606bd76ba43ea88727d13 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 11 Feb 2025 17:53:55 -0600
Subject: [PATCH 342/387] finished custom_workflows, reviewing backtesting

---
 _pkgdown.yml                           |   3 +-
 vignettes/backtesting.Rmd              |  83 +---
 vignettes/custom_epiworkflows.Rmd      | 124 ++++-
 vignettes/epipredict.Rmd               |   3 +
 vignettes/preprocessing-and-models.Rmd | 622 -------------------------
 5 files changed, 148 insertions(+), 687 deletions(-)
 delete mode 100644 vignettes/preprocessing-and-models.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 643b354b1..a9f6bbe8a 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -12,12 +12,11 @@ articles:
     contents:
       - epipredict
       - custom_epiworkflows
-      - preprocessing-and-models
       - backtesting
-      - arx-classifier
       - update
   - title: Advanced methods
     contents:
+      - arx-classifier
       - articles/smooth-qr
       - panel-data
 
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 7b7237d82..a1fc5ff51 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -23,8 +23,6 @@ library(magrittr)
 library(purrr)
 ```
 
-# Accurately backtesting forecasters
-
 Backtesting is a crucial step in the development of forecasting models. It
 involves testing the model on historical data to see how well it performs. This
 is important because it allows us to see how well the model generalizes to new
@@ -46,22 +44,17 @@ therein).
 
 In the `{epiprocess}` package, we provide `epix_slide()`, a function that allows
 a convenient way to perform version-aware forecasting by only using the data as
-it would have been available at forecast reference time. In
-`vignette("epi_archive", package = "epiprocess")`, we introduced the concept of
-an `epi_archive` and we demonstrated how to use `epix_slide()` to forecast the
-future using a simple quantile regression model. In this vignette, we will
-demonstrate how to use `epix_slide()` to backtest an auto-regressive forecaster
-on historical COVID-19 case data from the US and Canada. Instead of building a
-forecaster from scratch as we did in the previous vignette, we will use the
-`arx_forecaster()` function from the `{epipredict}` package.
+it would have been available at forecast reference time.
+In this vignette, we will demonstrate how to use `epix_slide()` to backtest an
+auto-regressive forecaster constructed using `arx_forecaster()` on historical
+COVID-19 case data from the US and Canada. 
 
 ## Getting case data from US states into an `epi_archive`
 
-First, we download the version history (ie. archive) of the percentage of
-doctor's visits with CLI (COVID-like illness) computed from medical insurance
-claims and the number of new confirmed COVID-19 cases per 100,000 population
-(daily) for 6 states from the COVIDcast API (as used in the `epiprocess`
-vignette mentioned above).
+First, we create an `epi_archive()` to store the version history of the
+percentage of doctor's visits with CLI (COVID-like illness) computed from
+medical insurance claims and the number of new confirmed COVID-19 cases per
+100,000 population (daily) for 4 states 
 
 ```{r grab-epi-data}
 # Select the `percent_cli` column from the data archive
@@ -89,59 +82,25 @@ doctor_visits <- pub_covidcast(
   as_epi_archive(compactify = TRUE)
 ```
 
-## Backtesting a simple autoregressive forecaster
+`issues` is the name for `version` in the Epidata API.
 
-One of the most common use cases of `epiprocess::epi_archive()` object
-is for accurate model backtesting.
+__Note__: In the interest of computational speed, we only use the 4 state
+dataset here, but the full archive can be used in the same way and has performed
+well in the past.
 
-In this section we will:
+## Backtesting a simple autoregressive forecaster
 
-- develop a simple autoregressive forecaster that predicts the next value of the
-signal based on the current and past values of the signal itself, and
-- demonstrate how to slide this forecaster over the `epi_archive` object to
-produce forecasts at a few dates date, using version-unaware and -aware
-computations,
-- compare the two approaches.
+One of the most common use cases of `epiprocess::epi_archive()` object is for
+accurate model backtesting.
 
 To start, let's use a simple autoregressive forecaster to predict the percentage
 of doctor's hospital visits with CLI (COVID-like illness) (`percent_cli`) in the
-future (we choose this target because of the dataset's pattern of substantial
-revisions; forecasting doctor's visits is an unusual forecasting target
-otherwise). While some AR models output single point forecasts, we will use
-quantile regression to produce a point prediction along with an 90\% uncertainty
-band, represented by a predictive quantiles at the 5\% and 95\% levels (lower
-and upper endpoints of the uncertainty band).
+future[^1]. 
+For increased accuracy we will use quantile regression.
 
-The `arx_forecaster()` function wraps the autoregressive forecaster we need and
-comes with sensible defaults:
 
-- we specify the predicted outcome to be the percentage of doctor's visits with
-  CLI (`percent_cli`),
-- we use a linear regression model as the engine,
-- the autoregressive features assume lags of 0, 7, and 14 days,
-- we forecast 7 days ahead.
-
-All these default settings and more can be seen by calling `arx_args_list()`:
-
-```{r}
-arx_args_list()
-```
-
-These can be modified as needed, by sending your desired arguments into
-`arx_forecaster(args_list = arx_args_list())`. For now we will use the defaults.
-
-__Note__: We will use a __geo-pooled approach__, where we train the model on
-data from all states and territories combined. This is because the data is quite
-similar across states, and pooling the data can help improve the accuracy of the
-forecasts, while also reducing the susceptibility of the model to noise. In the
-interest of computational speed, we only use the 6 state dataset here, but the
-full archive can be used in the same way and has performed well in the past.
-Implementation-wise, geo-pooling is achieved by not using `group_by(geo_value)`
-prior to `epix_slide()`. In other cases, grouping may be preferrable, so we
-leave it to the user to decide, but flag this modeling decision here.
-
-Let's use the `epix_as_of()` method to generate a snapshot of the archive at the
-last date, and then run the forecaster.
+As truth data, we'll use `epix_as_of()` to generate a snapshot of the archive at
+the last date, and then run the forecaster.
 
 ```{r}
 # Let's forecast 14 days prior to the last date in the archive, to compare.
@@ -465,3 +424,7 @@ ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
 ```
 
 </details>
+
+[^1]: (we choose this target because of the dataset's pattern of substantial
+revisions; forecasting doctor's visits is an unusual forecasting target
+otherwise)
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index c13858152..e5c2102f3 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -301,8 +301,8 @@ growth_rate_recipe <- epi_recipe(
   step_epi_lag(case_rate, lag = c(0, 1, 2, 3, 7, 14)) |>
   step_epi_lag(death_rate, lag = c(0, 7, 14)) |>
   step_epi_ahead(death_rate, ahead = 4 * 7) |>
-  step_growth_rate(death_rate) |>
   step_epi_naomit() |>
+  step_growth_rate(death_rate) |>
   step_training_window()
 ```
 
@@ -317,7 +317,9 @@ growth_rate_recipe |>
     death_rate, gr_7_rel_change_death_rate
   )
 ```
+
 And the role:
+
 ```{r growth_rate_roles}
 prepped <- growth_rate_recipe |>
   prep(training_data)
@@ -329,7 +331,9 @@ To demonstrate the changes in the layers that come along with it, we will use
 ```{r layer_and_fit}
 growth_rate_layers <- frosting() |>
   layer_predict() |>
-  layer_quantile_distn(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)) |>
+  layer_quantile_distn(
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
+  ) |>
   layer_point_from_distn() |>
   layer_add_forecast_date() |>
   layer_add_target_date() |>
@@ -429,7 +433,121 @@ which are 2-3 orders of magnitude larger than the corresponding rates above.
 while `rate_rescaling` gives the denominator of the rate (our fit values were
 per 100,000).
 
-[^1]: Think of baking a cake, where adding the frosting is the last step in the process of actually baking.
+# Custom classifier workflow
+
+As a more complicated example of the kind of pipeline that you can build using
+this framework, here is an example of a hotspot prediction model, which predicts
+whether the case rates are increasing (`up`), decreasing (`down`) or flat
+(`flat`).
+This comes from a paper by McDonald, Bien, Green, Hu et al[^3], and roughly
+serves as an extension of `arx_classifier()`.
+
+First, we need to add a factor version of the `geo_value`, so that it can
+actually be used as a feature.
+
+```{r training_factor}
+training_data <-
+  training_data %>%
+  mutate(geo_value_factor = as.factor(geo_value))
+```
+
+Then we put together the recipe, using a combination of base `{recipe}`
+functions such as `add_role()` and `step_dummy()`, and `{epipreict}` functions
+such as `step_growth_rate()`.
+
+```{r class_recipe}
+classifier_recipe <- epi_recipe(training_data) %>%
+  add_role(time_value, new_role = "predictor") %>%
+  step_dummy(geo_value_factor) %>%
+  step_growth_rate(case_rate, role = "none", prefix = "gr_") %>%
+  step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) %>%
+  step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") %>%
+  # note recipes::step_cut() has a bug in it, or we could use that here
+  step_mutate(
+    response = cut(
+      ahead_7_gr_7_rel_change_case_rate,
+      breaks = c(-Inf, -0.2, 0.25, Inf) / 7, # division gives weekly not daily
+      labels = c("down", "flat", "up")
+    ),
+    role = "outcome"
+  ) %>%
+  step_rm(has_role("none"), has_role("raw")) %>%
+  step_epi_naomit()
+```
+
+
+Roughly, this adds as predictors:
+
+1. the time value (via `add_role()`)
+2. the `geo_value` (via `step_dummy()` and the `as.factor()` above)
+3. the growth rate, both at prediction time and lagged by one and two weeks
+
+The outcome is created by composing several steps together: `step_epi_ahead()`
+creates a column with the growth rate one week into the future, while
+`step_mutate()` creates a factor with the 3 values:
+
+$$
+ Z_{\ell, t}=
+    \begin{cases}
+      \text{up}, & \text{if}\ Y^{\Delta}_{\ell, t} > 0.25 \\
+      \text{down}, & \text{if}\  Y^{\Delta}_{\ell, t} < -0.20\\
+      \text{flat}, & \text{otherwise}
+    \end{cases}
+$$
+
+where $Y^{\Delta}_{\ell, t}$ is the growth rate at location $\ell$ and time $t$.
+`up` means that the `case_rate` is has increased by at least 25%, while `down`
+means it has decreased by at least 20%.
+
+Note that both `step_growth_rate()` and `step_epi_ahead()` assign the role
+`none` explicitly; this is because they're used as intermediate steps to create
+both predictors and the outcome.
+`step_rm()` drops them after they're done, along with the original `raw` columns
+`death_rate` and `case_rate` (both `geo_value` and `time_value` are retained
+because their roles have been reassigned).
+
+
+To fit a classification model like this, we will need to use a parsnip model
+with mode classification; the simplest example is `multinomial_reg()`.
+We don't actually need to apply any layers, so we can skip adding one to the `epiworkflow()`:
+
+```{r, warning=FALSE}
+wf <- epi_workflow(
+  classifier_recipe,
+  multinom_reg()
+) %>%
+  fit(training_data)
+
+forecast(wf) %>% filter(!is.na(.pred_class))
+```
+
+And comparing the result with the actual growth rates at that point:
+```{r growth_rate_results}
+growth_rates <- covid_case_death_rates |>
+  filter(geo_value %in% used_locations) |>
+  group_by(geo_value) |>
+  mutate(
+    # multiply by 7 to get to weekly equivalents
+    case_gr = growth_rate(x = time_value, y = case_rate) * 7
+  ) |>
+  ungroup()
+
+growth_rates |> filter(time_value == "2021-08-01")
+```
+
+So they're all increasing at significantly higher than 25% per week (36%-62%),
+which matches the classification.
+
+
+See the [tooling book](https://cmu-delphi.github.io/delphi-tooling-book/preprocessing-and-models.html) for a more in depth discussion of this example.
+
+
+[^1]: Think of baking a cake, where adding the frosting is the last step in the
+    process of actually baking.
 
 [^2]: Note that the frosting doesn't require any information about the training
     data, since the output of the model only depends on the model used.
+
+[^3]: McDonald, Bien, Green, Hu, et al. “Can auxiliary indicators improve
+    COVID-19 forecasting and hotspot prediction?.” Proceedings of the National
+    Academy of Sciences 118.51 (2021): e2111453118. doi:10.1073/pnas.2111453118
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index cdfbe07ef..a169b85ba 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -330,6 +330,9 @@ autoplot(
 
 The 8 graphs are all pairs of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
+## Fitting a non-geo-pooled model
+The primary difference to avoid geo-pooling is to first `group_by(geo_value)`
+before forecasting
 # Anatomy of a canned forecaster
 ## Code object
 Let's dissect the forecaster we trained back on the [landing
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
deleted file mode 100644
index a546f5ac9..000000000
--- a/vignettes/preprocessing-and-models.Rmd
+++ /dev/null
@@ -1,622 +0,0 @@
----
-title: Examples of Preprocessing and Models
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Examples of Preprocessing and Models}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-source(here::here("vignettes/_common.R"))
-```
-
-## Introduction
-
-The `epipredict` package utilizes the `tidymodels` framework, namely
-[`{recipes}`](https://recipes.tidymodels.org/) for
-[dplyr](https://dplyr.tidyverse.org/)-like pipeable sequences
-of feature engineering and [`{parsnip}`](https://parsnip.tidymodels.org/) for a
-unified interface to a range of models.
-
-`epipredict` has additional customized feature engineering and preprocessing
-steps, such as `step_epi_lag()`, `step_population_scaling()`,
-`step_epi_naomit()`. They can be used along with
-steps from the `{recipes}` package for more feature engineering.
-
-In this vignette, we will illustrate some examples of how to use `epipredict`
-with `recipes` and `parsnip` for different purposes of epidemiological forecasting.
-We will focus on basic autoregressive models, in which COVID cases and
-deaths in the near future are predicted using a linear combination of cases and
-deaths in the near past.
-
-The remaining vignette will be split into three sections. The first section, we
-will use a Poisson regression to predict death counts. In the second section,
-we will use a linear regression to predict death rates. Last but not least, we
-will create a classification model for hotspot predictions.
-
-```{r, warning=FALSE, message=FALSE}
-library(tidyr)
-library(dplyr)
-library(epipredict)
-library(recipes)
-library(workflows)
-library(poissonreg)
-```
-
-## Poisson Regression
-
-During COVID-19, the US Center for Disease Control and Prevention (CDC) collected
-models
-and forecasts to characterize the state of an outbreak and its course. They use
-it to inform public health decision makers on potential consequences of
-deploying control measures.
-
-One of the outcomes that the CDC forecasts is
-[death counts from COVID-19](https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html).
-Although there are many state-of-the-art models, we choose to use Poisson
-regression, the textbook example for modeling count data, as an illustration
-for using the `epipredict` package with other existing tidymodels packages.
-
-The `counts_subset` dataset is available in the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
-and contains the number of confirmed cases and deaths from June 4, 2021 to Dec
-31, 2021 in some U.S. states. It can be loaded with:
-
-```{r poisson-reg-data}
-x <- epidatasets::counts_subset
-```
-
-The data can also be fetched from the Delphi API with the following query:
-```{r, eval = FALSE}
-library(epidatr)
-
-d <- as.Date("2024-03-20")
-
-x <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "confirmed_incidence_num",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj",
-  as_of = d
-) %>%
-  select(geo_value, time_value, cases = value)
-
-y <- pub_covidcast(
-  source = "jhu-csse",
-  signals = "deaths_incidence_num",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj",
-  as_of = d
-) %>%
-  select(geo_value, time_value, deaths = value)
-
-x <- full_join(x, y, by = c("geo_value", "time_value")) %>%
-  as_epi_df(as_of = d)
-```
-
-We wish to predict the 7-day ahead death counts with lagged cases and deaths.
-Furthermore, we will let each state be a dummy variable. Using differential
-intercept coefficients, we can allow for an intercept shift between states.
-
-The model takes the form
-\begin{aligned}
-\log\left( \mu_{t+7} \right) &= \beta_0 + \delta_1 s_{\text{state}_1} +
-\delta_2 s_{\text{state}_2} + \cdots + \nonumber \\
-&\quad\beta_1 \text{deaths}_{t} +
-\beta_2 \text{deaths}_{t-7} + \beta_3 \text{cases}_{t} +
-\beta_4 \text{cases}_{t-7},
-\end{aligned}
-where $\mu_{t+7} = \mathbb{E}(y_{t+7})$, and $y_{t+7}$ is assumed to follow a
-Poisson distribution with mean $\mu_{t+7}$; $s_{\text{state}}$ are dummy
-variables for each state and take values of either 0 or 1.
-
-Preprocessing steps will be performed to prepare the
-data for model fitting. But before diving into them, it will be helpful to
-understand what `roles` are in the `recipes` framework.
-
----
-
-#### Aside on `recipes`
-
-`recipes` can assign one or more roles to each column in the data. The roles
-are not restricted to a predefined set; they can be anything.
-For most conventional situations, they are typically “predictor” and/or
-"outcome". Additional roles enable targeted `step_*()` operations on specific
-variables or groups of variables.
-
-In our case, the role `predictor` is given to explanatory variables on the
-right-hand side of the model (in the equation above).
-The role `outcome` is the response variable
-that we wish to predict. `geo_value` and `time_value` are predefined roles
-that are unique to the `epipredict` package. Since we work with `epi_df`
-objects, all datasets should have `geo_value` and `time_value` passed through
-automatically with these two roles assigned to the appropriate columns in the data.
-
-The `recipes` package also allows [manual alterations of roles](https://recipes.tidymodels.org/reference/roles.html)
-in bulk. There are a few handy functions that can be used together to help us
-manipulate variable roles easily.
-
-> `update_role()` alters an existing role in the recipe or assigns an initial role
-> to variables that do not yet have a declared role.
->
-> `add_role()` adds an additional role to variables that already have a role in
-> the recipe, without overwriting old roles.
->
-> `remove_role()` eliminates a single existing role in the recipe.
-
-#### End aside
-
----
-
-Notice in the following preprocessing steps, we used `add_role()` on
-`geo_value_factor` since, currently, the default role for it is `raw`, but
-we would like to reuse this variable as `predictor`s.
-
-```{r}
-counts_subset <- counts_subset %>%
-  mutate(geo_value_factor = as.factor(geo_value)) %>%
-  as_epi_df()
-
-epi_recipe(counts_subset)
-
-r <- epi_recipe(counts_subset) %>%
-  add_role(geo_value_factor, new_role = "predictor") %>%
-  step_dummy(geo_value_factor) %>%
-  ## Occasionally, data reporting errors / corrections result in negative
-  ## cases / deaths
-  step_mutate(cases = pmax(cases, 0), deaths = pmax(deaths, 0)) %>%
-  step_epi_lag(cases, deaths, lag = c(0, 7)) %>%
-  step_epi_ahead(deaths, ahead = 7, role = "outcome") %>%
-  step_epi_naomit()
-```
-
-After specifying the preprocessing steps, we will use the `parsnip` package for
-modeling and producing the prediction for death count, 7 days after the
-latest available date in the dataset.
-
-```{r}
-latest <- get_test_data(r, counts_subset)
-
-wf <- epi_workflow(r, parsnip::poisson_reg()) %>%
-  fit(counts_subset)
-
-predict(wf, latest) %>% filter(!is.na(.pred))
-```
-
-Note that the `time_value` corresponds to the last available date in the
-training set, **NOT** to the target date of the forecast
-(`r max(latest$time_value) + 7`).
-
-Let's take a look at the fit:
-
-```{r}
-extract_fit_engine(wf)
-```
-
-Up to now, we've used the Poisson regression to model count data. Poisson
-regression can also be used to model rate data, such as case rates or death
-rates, by incorporating offset terms in the model.
-
-To model death rates, the Poisson regression would be expressed as:
-\begin{aligned}
-\log\left( \mu_{t+7} \right) &= \log(\text{population}) +
-\beta_0 + \delta_1 s_{\text{state}_1} +
-\delta_2 s_{\text{state}_2} + \cdots + \nonumber \\
-&\quad\beta_1 \text{deaths}_{t} +
-\beta_2 \text{deaths}_{t-7} + \beta_3 \text{cases}_{t} +
-\beta_4 \text{cases}_{t-7}
-\end{aligned}
-where $\log(\text{population})$ is the log of the state population that was
-used to scale the count data on the left-hand side of the equation. This offset
-is simply a predictor with coefficient fixed at 1 rather than estimated.
-
-There are several ways to model rate data given count and population data.
-First, in the `parsnip` framework, we could specify the formula in `fit()`.
-However, by doing so we lose the ability to use the `recipes` framework to
-create new variables since variables that do not exist in the
-original dataset (such as, here, the lags and leads) cannot be called directly in `fit()`.
-
-Alternatively, `step_population_scaling()` and `layer_population_scaling()`
-in the `epipredict` package can perform the population scaling if we provide the
-population data, which we will illustrate in the next section.
-
-## Linear Regression
-
-For COVID-19, the CDC required submission of case and death count predictions.
-However, the Delphi Group preferred to train on rate data instead, because it
-puts different locations on a similar scale (eliminating the need for location-specific intercepts).
-We can use a liner regression to predict the death
-rates and use state population data to scale the rates to counts.[^pois] We will do so
-using `layer_population_scaling()` from the `epipredict` package.
-
-[^pois]: We could continue with the Poisson model, but we'll switch to the Gaussian likelihood just for simplicity.
-
-Additionally, when forecasts are submitted, prediction intervals should be
-provided along with the point estimates. This can be obtained via postprocessing
-using
-`layer_residual_quantiles()`. It is worth pointing out, however, that
-`layer_residual_quantiles()` should be used before population scaling or else
-the transformation will make the results uninterpretable.
-
-We wish, now, to predict the 7-day ahead death counts with lagged case rates and
-death rates, along with some extra behaviourial predictors. Namely, we will use
-survey data from
-[COVID-19 Trends and Impact Survey](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html#behavior-indicators).
-
-The survey data provides the estimated percentage of people who wore a mask for
-most or all of the time while in public in the past 7 days and the estimated
-percentage of respondents who reported that all or most people they encountered
-in public in the past 7 days maintained a distance of at least 6 feet.
-
-State-wise population data from the 2019 U.S. Census will be used in 
-`layer_population_scaling()`.
-
-Both datasets are available in the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)), 
-and can be loaded with:
-
-```{r}
-behav_ind <- epidatasets::ctis_covid_behaviours
-pop_dat <- epidatasets::state_census %>% select(abbr, pop)
-```
-
-The data can also be fetched from the Delphi API with the following query:
-```{r, eval = FALSE}
-library(epidatr)
-
-d <- as.Date("2024-03-20")
-
-behav_ind_mask <- pub_covidcast(
-  source = "fb-survey",
-  signals = "smoothed_wwearing_mask_7d",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj",
-  as_of = d
-) %>%
-  select(geo_value, time_value, masking = value)
-
-behav_ind_distancing <- pub_covidcast(
-  source = "fb-survey",
-  signals = "smoothed_wothers_distanced_public",
-  time_type = "day",
-  geo_type = "state",
-  time_values = epirange(20210604, 20211231),
-  geo_values = "ca,fl,tx,ny,nj",
-  as_of = d
-) %>%
-  select(geo_value, time_value, distancing = value)
-
-behav_ind <- behav_ind_mask %>%
-  full_join(behav_ind_distancing, by = c("geo_value", "time_value")) %>%
-  as_epi_df(as_of = d)
-
-pop_dat <- state_census %>% select(abbr, pop)
-```
-
-Rather than using raw mask-wearing / social-distancing metrics, for the sake
-of illustration, we'll convert both into categorical predictors.
-
-```{r, echo=FALSE, message=FALSE,fig.align='center', fig.width=6, fig.height=4}
-library(ggplot2)
-behav_ind %>%
-  pivot_longer(masking:distancing) %>%
-  ggplot(aes(value, fill = geo_value)) +
-  geom_density(alpha = 0.5) +
-  scale_fill_brewer(palette = "Set1", name = "") +
-  theme_bw() +
-  scale_x_continuous(expand = c(0, 0)) +
-  scale_y_continuous(expand = expansion(c(0, .05))) +
-  facet_wrap(~name, scales = "free") +
-  theme(legend.position = "bottom")
-```
-
-We will take a subset of death rate and case rate data from the built-in dataset
-`covid_case_death_rates`.
-
-```{r}
-jhu <- filter(
-  covid_case_death_rates,
-  time_value >= "2021-06-04",
-  time_value <= "2021-12-31",
-  geo_value %in% c("ca", "fl", "tx", "ny", "nj")
-)
-```
-
-Preprocessing steps will again rely on functions from the `epipredict` package as well
-as the `recipes` package.
-There are also many functions in the `recipes` package that allow for
-[scalar transformations](https://recipes.tidymodels.org/reference/#step-functions-individual-transformations),
-such as log transformations and data centering. In our case, we will
-center the numerical predictors to allow for a more meaningful interpretation of the
-intercept.
-
-```{r}
-jhu <- jhu %>%
-  mutate(geo_value_factor = as.factor(geo_value)) %>%
-  left_join(behav_ind, by = c("geo_value", "time_value")) %>%
-  as_epi_df()
-
-r <- epi_recipe(jhu) %>%
-  add_role(geo_value_factor, new_role = "predictor") %>%
-  step_dummy(geo_value_factor) %>%
-  step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%
-  step_mutate(
-    masking = cut_number(masking, 5),
-    distancing = cut_number(distancing, 5)
-  ) %>%
-  step_epi_ahead(death_rate, ahead = 7, role = "outcome") %>%
-  step_center(contains("lag"), role = "predictor") %>%
-  step_epi_naomit()
-```
-
-As a sanity check we can examine the structure of the training data:
-
-```{r, warning = FALSE}
-glimpse(slice_sample(bake(prep(r, jhu), jhu), n = 6))
-```
-
-Before directly predicting the results, we need to add postprocessing layers to
-obtain the death counts instead of death rates. Note that the rates used so
-far are "per 100K people" rather than "per person". We'll also use quantile
-regression with the `quantile_reg` engine rather than ordinary least squares
-to create median predictions and a 90% prediction interval.
-
-```{r, warning=FALSE}
-f <- frosting() %>%
-  layer_predict() %>%
-  layer_add_target_date("2022-01-07") %>%
-  layer_threshold(.pred, lower = 0) %>%
-  layer_quantile_distn() %>%
-  layer_naomit(.pred) %>%
-  layer_population_scaling(
-    .pred, .pred_distn,
-    df = pop_dat,
-    rate_rescaling = 1e5,
-    by = c("geo_value" = "abbr"),
-    df_pop_col = "pop"
-  )
-
-wf <- epi_workflow(r, quantile_reg()) %>%
-  fit(jhu) %>%
-  add_frosting(f)
-
-p <- forecast(wf)
-p
-```
-
-The columns marked `*_scaled` have been rescaled to the correct units, in this
-case `deaths` rather than deaths per 100K people (these remain in `.pred`).
-
-To look at the prediction intervals:
-
-```{r}
-p %>%
-  select(geo_value, target_date, .pred_scaled, .pred_distn_scaled) %>%
-  pivot_quantiles_wider(.pred_distn_scaled)
-```
-
-Last but not least, let's take a look at the regression fit and check the
-coefficients:
-
-```{r, echo =FALSE}
-extract_fit_engine(wf)
-```
-
-## Classification
-
-Sometimes it is preferable to create a predictive model for surges or upswings
-rather than for raw values. In this case,
-the target is to predict if the future will have increased case rates (denoted `up`),
-decreased case rates (`down`), or flat case rates (`flat`) relative to the current
-level. Such models may be
-referred to as "hotspot prediction models". We will follow the analysis
-in [McDonald, Bien, Green, Hu, et al.](#references) but extend the application
-to predict three categories instead of two.
-
-Hotspot prediction uses a categorical outcome variable defined in terms of the
-relative change of $Y_{\ell, t+a}$ compared to $Y_{\ell, t}$.
-Where $Y_{\ell, t}$ denotes the case rates in location $\ell$ at time $t$.
-We define the response variables as follows:
-
-$$
- Z_{\ell, t}=
-    \begin{cases}
-      \text{up}, & \text{if}\ Y^{\Delta}_{\ell, t} > 0.25 \\
-      \text{down}, & \text{if}\  Y^{\Delta}_{\ell, t} < -0.20\\
-      \text{flat}, & \text{otherwise}
-    \end{cases}
-$$
-
-where $Y^{\Delta}_{\ell, t} = (Y_{\ell, t}- Y_{\ell, t-7})\ /\ (Y_{\ell, t-7})$.
-We say location $\ell$ is a hotspot at time $t$ when $Z_{\ell,t}$ is
-`up`, meaning the number of newly reported cases over the past 7 days has
-increased by at least 25% compared to the preceding week. When $Z_{\ell,t}$
-is categorized as `down`, it suggests that there has been at least a 20%
-decrease in newly reported cases over the past 7 days (a 20% decrease is the
-inverse of a 25% increase). Otherwise, we will
-consider the trend to be `flat`.
-
-The expression of the multinomial regression we will use is as follows:
-
-$$
-\pi_{j}(x) = \text{Pr}(Z_{\ell,t} = j|x) = \frac{e^{g_j(x)}}{1 + \sum_{k=1}^{2}e^{g_k(x)} }
-$$
-
-where $j$ is either down, flat, or up
-
-\begin{aligned}
-g_{\text{down}}(x) &= 0.\\
-g_{\text{flat}}(x) &= \log\left(\frac{Pr(Z_{\ell,t}=\text{flat}\mid x)}{Pr(Z_{\ell,t}=\text{down}\mid x)}\right) =
-\beta_{10} + \beta_{11} t + \delta_{10} s_{\text{state_1}} +
-\delta_{11} s_{\text{state_2}} + \cdots \nonumber \\
-&\quad + \beta_{12} Y^{\Delta}_{\ell, t} +
-\beta_{13} Y^{\Delta}_{\ell, t-7} + \beta_{14} Y^{\Delta}_{\ell, t-14}\\
-g_{\text{up}}(x) &= \log\left(\frac{Pr(Z_{\ell,t}=\text{up}\mid x)}{Pr(Z_{\ell,t}=\text{down} \mid x)}\right) =
-\beta_{20} + \beta_{21}t + \delta_{20} s_{\text{state_1}} +
-\delta_{21} s_{\text{state}\_2} + \cdots \nonumber \\
-&\quad + \beta_{22} Y^{\Delta}_{\ell, t} +
-\beta_{23} Y^{\Delta}_{\ell, t-7} + \beta_{24} Y^{\Delta}_{\ell, t-14}
-\end{aligned}
-
-Preprocessing steps are similar to the previous models with an additional step
-of categorizing the response variables. Again, we will use a subset of death
-rate and case rate data from our built-in dataset
-`covid_case_death_rates`.
-
-```{r}
-jhu <- covid_case_death_rates %>%
-  dplyr::filter(
-    time_value >= "2021-06-04",
-    time_value <= "2021-12-31",
-    geo_value %in% c("ca", "fl", "tx", "ny", "nj")
-  ) %>%
-  mutate(geo_value_factor = as.factor(geo_value))
-
-r <- epi_recipe(jhu) %>%
-  add_role(time_value, new_role = "predictor") %>%
-  step_dummy(geo_value_factor) %>%
-  step_growth_rate(case_rate, role = "none", prefix = "gr_") %>%
-  step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) %>%
-  step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") %>%
-  # note recipes::step_cut() has a bug in it, or we could use that here
-  step_mutate(
-    response = cut(
-      ahead_7_gr_7_rel_change_case_rate,
-      breaks = c(-Inf, -0.2, 0.25, Inf) / 7, # division gives weekly not daily
-      labels = c("down", "flat", "up")
-    ),
-    role = "outcome"
-  ) %>%
-  step_rm(has_role("none"), has_role("raw")) %>%
-  step_epi_naomit()
-```
-
-We will fit the multinomial regression and examine the predictions:
-
-```{r, warning=FALSE}
-wf <- epi_workflow(r, multinom_reg()) %>%
-  fit(jhu)
-
-forecast(wf) %>% filter(!is.na(.pred_class))
-```
-
-We can also look at the estimated coefficients and model summary information:
-
-```{r}
-extract_fit_engine(wf)
-```
-
-One could also use a formula in `epi_recipe()` to achieve the same results as
-above. However, only one of `add_formula()`, `add_recipe()`, or
-`workflow_variables()` can be specified. For the purpose of demonstrating
-`add_formula` rather than `add_recipe`, we will `prep` and `bake` our recipe to
-return a `data.frame` that could be used for model fitting.
-
-```{r}
-b <- bake(prep(r, jhu), jhu)
-
-epi_workflow() %>%
-  add_formula(
-    response ~ geo_value + time_value + lag_0_gr_7_rel_change_case_rate +
-      lag_7_gr_7_rel_change_case_rate + lag_14_gr_7_rel_change_case_rate
-  ) %>%
-  add_model(parsnip::multinom_reg()) %>%
-  fit(data = b)
-```
-
-## Benefits of Lagging and Leading in `epipredict`
-
-The `step_epi_ahead` and `step_epi_lag` functions in the `epipredict` package
-is handy for creating correct lags and leads for future predictions.
-
-Let's start with a simple dataset and preprocessing:
-
-```{r}
-ex <- filter(
-  covid_case_death_rates,
-  time_value >= "2021-12-01",
-  time_value <= "2021-12-31",
-  geo_value == "ca"
-)
-
-dim(ex)
-```
-
-We want to predict death rates on `r max(ex$time_value) + 7`, which is 7 days ahead of the
-latest available date in our dataset.
-
-We will compare two methods of trying to create lags and leads:
-
-```{r}
-p1 <- epi_recipe(ex) %>%
-  step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-  step_epi_ahead(death_rate, ahead = 7, role = "outcome") %>%
-  step_epi_naomit() %>%
-  prep(ex)
-
-b1 <- bake(p1, ex)
-b1
-
-
-p2 <- epi_recipe(ex) %>%
-  step_mutate(
-    lag0case_rate = lag(case_rate, 0),
-    lag7case_rate = lag(case_rate, 7),
-    lag14case_rate = lag(case_rate, 14),
-    lag0death_rate = lag(death_rate, 0),
-    lag7death_rate = lag(death_rate, 7),
-    lag14death_rate = lag(death_rate, 14),
-    ahead7death_rate = lead(death_rate, 7)
-  ) %>%
-  step_epi_naomit() %>%
-  prep(ex)
-
-b2 <- bake(p2, ex)
-b2
-```
-
-Notice the difference in number of rows `b1` and `b2` returns. This is because
-the second version, the one that doesn't use `step_epi_ahead` and `step_epi_lag`,
-has omitted dates compared to the one that used the `epipredict` functions.
-
-```{r}
-dates_used_in_training1 <- b1 %>%
-  select(-ahead_7_death_rate) %>%
-  na.omit() %>%
-  pull(time_value)
-dates_used_in_training1
-
-dates_used_in_training2 <- b2 %>%
-  select(-ahead7death_rate) %>%
-  na.omit() %>%
-  pull(time_value)
-dates_used_in_training2
-```
-
-The model that is trained based on the `{recipes}` functions will predict 7 days ahead from
-`r max(dates_used_in_training2)`
-instead of 7 days ahead from `r max(dates_used_in_training1)`.
-
-## References
-
-McDonald, Bien, Green, Hu, et al. "Can auxiliary indicators improve COVID-19
-forecasting and hotspot prediction?." Proceedings of the National Academy of
-Sciences 118.51 (2021): e2111453118. [doi:10.1073/pnas.2111453118](https://doi.org/10.1073/pnas.2111453118)
-
-## Attribution
-
-This object contains a modified part of the
-[COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19)
-as [republished in the COVIDcast Epidata API.](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
-
-This data set is licensed under the terms of the
-[Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/)
-by the Johns Hopkins
-University on behalf of its Center for Systems Science in Engineering. Copyright
-Johns Hopkins University 2020.

From 563f185fdec08084ba874b00a5f8b71b89969cf1 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Feb 2025 13:29:42 -0600
Subject: [PATCH 343/387] backtesting rmd rewrite

---
 R/step_adjust_latency.R    |  15 +-
 man/step_adjust_latency.Rd |  15 +-
 vignettes/backtesting.Rmd  | 497 ++++++++++++++++++++++---------------
 3 files changed, 316 insertions(+), 211 deletions(-)

diff --git a/R/step_adjust_latency.R b/R/step_adjust_latency.R
index ae9db6ef2..5b9db2995 100644
--- a/R/step_adjust_latency.R
+++ b/R/step_adjust_latency.R
@@ -26,15 +26,16 @@
 #' ) %>%
 #'    as_epi_df(as_of = as.Date("2015-01-14"))
 #' ```
-#' If we're looking to predict the value on the 15th, forecasting from the 14th (the `as_of` date above),
-#'   there are two issues we will need to address:
+#' If we're looking to predict the value on the 15th, forecasting from the 14th
+#'   (the `as_of` date above), there are two issues we will need to address:
 #' 1. `"ca"` is latent by 2 days, whereas `"ma"` is latent by 1
-#' 2. if we want to use `b` as an exogenous variable, for `"ma"` it is latent by 3 days instead of just 1.
+#' 2. if we want to use `b` as an exogenous variable, for `"ma"` it is latent by
+#'   3 days instead of just 1.
 #'
-#' Regardless of `method`, `epi_keys_checked="geo_value"` guarantees that the
-#'   difference between `"ma"` and `"ca"` is accounted for by making  the
-#'   latency adjustment at least 2. For some comparison, here's what the various
-#'   methods will do:
+#' Regardless of `method`, `epi_keys_checked="geo_value"` guarantees tha the
+#'   difference between `"ma"` and `"ca"` is accounted for by making the latency
+#'   adjustment at least 2. For some comparison, here's what the various methods
+#'   will do:
 #'
 #' ## `locf`
 #' Short for "last observation carried forward", `locf` assumes that every day
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 9e1bafbd5..437811b39 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -107,17 +107,18 @@ demonstrate some of the subtleties, let's consider a toy dataset:
    as_epi_df(as_of = as.Date("2015-01-14"))
 }\if{html}{\out{</div>}}
 
-If we're looking to predict the value on the 15th, forecasting from the 14th (the \code{as_of} date above),
-there are two issues we will need to address:
+If we're looking to predict the value on the 15th, forecasting from the 14th
+(the \code{as_of} date above), there are two issues we will need to address:
 \enumerate{
 \item \code{"ca"} is latent by 2 days, whereas \code{"ma"} is latent by 1
-\item if we want to use \code{b} as an exogenous variable, for \code{"ma"} it is latent by 3 days instead of just 1.
+\item if we want to use \code{b} as an exogenous variable, for \code{"ma"} it is latent by
+3 days instead of just 1.
 }
 
-Regardless of \code{method}, \code{epi_keys_checked="geo_value"} guarantees that the
-difference between \code{"ma"} and \code{"ca"} is accounted for by making  the
-latency adjustment at least 2. For some comparison, here's what the various
-methods will do:
+Regardless of \code{method}, \code{epi_keys_checked="geo_value"} guarantees tha the
+difference between \code{"ma"} and \code{"ca"} is accounted for by making the latency
+adjustment at least 2. For some comparison, here's what the various methods
+will do:
 \subsection{\code{locf}}{
 
 Short for "last observation carried forward", \code{locf} assumes that every day
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index a1fc5ff51..200b4fcc8 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -33,7 +33,7 @@ different from the data available at the time of the backtest. This is because
 new data is constantly being collected and added to the dataset, which can
 affect the accuracy of the forecast.
 
-For this reason, it is important to use version-aware forecasting, where the
+For this reason, it is important to use version-faithful forecasting, where the
 model is trained on data that would have been available at the time of the
 forecast. This ensures that the model is tested on data that is as close as
 possible to what would have been available in real-time; training and making
@@ -43,13 +43,13 @@ predictions on finalized data can lead to an overly optimistic sense of accuracy
 therein).
 
 In the `{epiprocess}` package, we provide `epix_slide()`, a function that allows
-a convenient way to perform version-aware forecasting by only using the data as
+a convenient way to perform version-faithful forecasting by only using the data as
 it would have been available at forecast reference time.
 In this vignette, we will demonstrate how to use `epix_slide()` to backtest an
 auto-regressive forecaster constructed using `arx_forecaster()` on historical
 COVID-19 case data from the US and Canada. 
 
-## Getting case data from US states into an `epi_archive`
+# Getting case data from US states into an `epi_archive`
 
 First, we create an `epi_archive()` to store the version history of the
 percentage of doctor's visits with CLI (COVID-like illness) computed from
@@ -83,30 +83,108 @@ doctor_visits <- pub_covidcast(
 ```
 
 `issues` is the name for `version` in the Epidata API.
-
-__Note__: In the interest of computational speed, we only use the 4 state
-dataset here, but the full archive can be used in the same way and has performed
+In the interest of computational speed, we only use the 4 state dataset limited
+to 2020-2021, but the full archive can be used in the same way and has performed
 well in the past.
 
-## Backtesting a simple autoregressive forecaster
+We choose this dataset in particular partly because it is revision heavy; for
+example, here is a plot that compares monthly snapshots of the data.
+
+<details>
+<summary>Code for plotting</summary>
+```{r plot_revision_example, warn = FALSE}
+geo_choose <- "ca"
+forecast_dates <- seq(from = as.Date("2020-08-01"), to = as.Date("2021-11-01"), by = "1 month")
+percent_cli_data <- bind_rows(
+  # Snapshotted data for the version-faithful forecasts
+  map(
+    forecast_dates,
+    ~ doctor_visits %>%
+      epix_as_of(.x) %>%
+      mutate(version = .x)
+  ) %>%
+    bind_rows() %>%
+    mutate(version_faithful = TRUE),
+  # Latest data for the version-faithless forecasts
+  doctor_visits %>%
+    epix_as_of(doctor_visits$versions_end) %>%
+    mutate(version_faithful = FALSE)
+)
+
+p0 <-
+  ggplot(data = percent_cli_data %>% filter(geo_value == geo_choose)) +
+  geom_vline(aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_line(
+    aes(x = time_value, y = percent_cli, color = factor(version)),
+    inherit.aes = FALSE, na.rm = TRUE
+  ) +
+  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
+  scale_y_continuous(expand = expansion(c(0, 0.05))) +
+  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  theme(legend.position = "none")
+```
+</details>
+
+```{r plot_just_revisioning, warn = FALSE}
+p0
+```
+
+The snapshots are taken on the first of each month, with the vertical dashed
+line representing the sample date for the time series of the corresponding
+color.
+For example, the snapshot on March 1st, 2021 is aquamarine, and increases to
+slightly over 10.
+Every series is necessarily to the left of the snapshot date (since all known
+values must happen before the snapshot is taken[^4]).
+The grey line at the center of all the various snapshots represents the "final
+value", which is just the snapshot at the last version in the archive (the
+`versions_end`).
+
+Comparing with the grey line tells us how much the value at the time of the
+snapshot differs with what was eventually reported.
+The drop in January 2021 in the snapshot on `2021-02-01` was initially reported
+as much steeper than it eventually turned out to be, while in the period after
+that the values were initially reported as higher than they actually were.
+
+A feature that is common in both real-time forecasting and retrospective
+forecasting is data latency.
+Looking at the very first snapshot, `2020-08-01` (the red dotted
+vertical line), there is a noticeable gap between the forecast date and the end
+of the red time-series to its left.
+In fact, if we take a snapshot and get the last `time_value`:
+
+```{r}
+doctor_visits %>%
+  epix_as_of(as.Date("2020-08-01")) %>%
+  pull(time_value) %>%
+  max()
+```
+
+The last day of data is the 25th, a entire week before `2020-08-01`.
+This can require some effort to work around, especially if the latency is
+variable; see `step_adjust_latency()` for some methods included in this package.
+Much of that functionality is built into `arx_forecaster()` using the parameter
+`adjust_ahead`, which we will use below.
+
+
+# Backtesting a simple autoregressive forecaster
 
 One of the most common use cases of `epiprocess::epi_archive()` object is for
-accurate model backtesting.
+accurate model back-testing.
 
 To start, let's use a simple autoregressive forecaster to predict the percentage
-of doctor's hospital visits with CLI (COVID-like illness) (`percent_cli`) in the
-future[^1]. 
+of doctor's hospital visits with CLI (COVID-like illness) (`percent_cli`) 14
+days in the future. 
 For increased accuracy we will use quantile regression.
 
+## Comparing a single day and ahead
 
-As truth data, we'll use `epix_as_of()` to generate a snapshot of the archive at
-the last date, and then run the forecaster.
+As a sanity check before we backtest the entire dataset, let's look at
+forecasting a single day in the middle of the dataset. 
+One way to do this is by setting the `.version` argument for `epix_slide()`:
 
-```{r}
-# Let's forecast 14 days prior to the last date in the archive, to compare.
-forecast_date <- doctor_visits$versions_end - 14
-# The .versions argument selects only the last version in the archive and
-# produces a forecast only on that date.
+```{r single_version, warn = FALSE}
+forecast_date <- as.Date("2021-04-06")
 forecasts <- doctor_visits %>%
   epix_slide(
     ~ arx_forecaster(
@@ -118,32 +196,35 @@ forecasts <- doctor_visits %>%
       pivot_quantiles_wider(.pred_distn),
     .versions = forecast_date
   )
-# Join the forecasts with with the latest data at the time of the forecast to
-# compare. Since `percent_cli` data has a few days of lag, we use `tidyr::fill` to
-# fill the missing values with the last observed value.
+```
+
+As truth data, we'll compare with the `epix_as_of()` to generate a snapshot of
+the archive at the last date[^1].
+
+```{r compare_single_with_result}
 forecasts %>%
   inner_join(
     doctor_visits %>%
-      epix_as_of(doctor_visits$versions_end) %>%
-      group_by(geo_value) %>%
-      fill(percent_cli),
+      epix_as_of(doctor_visits$versions_end),
     by = c("geo_value", "target_date" = "time_value")
   ) %>%
   select(geo_value, forecast_date, .pred, `0.05`, `0.95`, percent_cli)
 ```
 
-The resulting epi_df now contains two new columns: `.pred` and `.pred_distn`,
-corresponding to the point forecast (median) and the quantile distribution
-containing our requested quantile forecasts (in this case, 0.05 and 0.95)
-respectively. The forecasts fall within the prediction interval, so our
+`.pred` corresponds to the point forecast (median) and the `0.05`, `0.95`
+correspond to those respective quantiles.
+The forecasts fall within the prediction interval, so our
 implementation passes a simple validation.
 
-Now let's go ahead and slide this forecaster in a version unaware way and a
-version aware way. For the version unaware way, we need to snapshot the latest
-version of the data, and then make a faux archive by setting `version =
-time_value`. This has the effect of simulating a data set that receives the
-final version updates every day. For the version aware way, we will simply use
-the true `epi_archive` object.
+## Comparing version faithful and version faithless forecasts
+
+Now let's go ahead and slide this forecaster in a version faithless way and a
+version faithful way.
+For the version faithless way, to still use `epix_slide` to do the backtesting
+we need to snapshot the latest version of the data, and then make a faux archive
+by setting `version = time_value`[^2].
+This has the effect of simulating a data set that receives the final version
+updates every day.
 
 ```{r}
 archive_cases_dv_subset_faux <- doctor_visits %>%
@@ -152,196 +233,197 @@ archive_cases_dv_subset_faux <- doctor_visits %>%
   as_epi_archive()
 ```
 
-To reduce typing, we create the wrapper function `forecast_k_week_ahead()`.
+For the version faithful way, we will simply use the true `epi_archive` object.
+To reduce typing, we'll create `forecast_wrapper()` to cover mapping across
+aheads and some variations we will use later.
 
 ```{r arx-kweek-preliminaries, warning = FALSE}
-# Latest snapshot of data, and forecast dates
-forecast_dates <- seq(from = as.Date("2020-08-01"), to = as.Date("2021-11-01"), by = "1 month")
-aheads <- c(7, 14, 21, 28)
-
-# @param epi_archive The epi_archive object to forecast from
-# @param ahead The number of days ahead to forecast
-# @param outcome The outcome variable to forecast
-# @param predictors The predictors to use in the model
-# @param forecast_dates The dates to forecast on
-# @param process_data A function to process the data before forecasting
-forecast_k_week_ahead <- function(
-    epi_archive,
-    ahead = 7,
-    outcome = NULL, predictors = NULL, forecast_dates = NULL, process_data = identity) {
-  if (is.null(forecast_dates)) {
-    forecast_dates <- epi_archive$versions_end
-  }
-  if (is.null(outcome) || is.null(predictors)) {
-    stop("Please specify the outcome and predictors.")
-  }
-  epi_archive %>%
-    epix_slide(
-      ~ arx_forecaster(
-        process_data(.x), outcome, predictors,
-        args_list = arx_args_list(ahead = ahead)
-      )$predictions %>%
-        pivot_quantiles_wider(.pred_distn),
-      .before = 120,
-      .versions = forecast_dates
-    )
+forecast_wrapper <- function(
+    epi_data, aheads, outcome, predictors,
+    process_data = identity
+    ) {
+  map(aheads,
+      \(ahead) {
+        arx_forecaster(
+          process_data(epi_data), outcome, predictors,
+          args_list = arx_args_list(
+            ahead = ahead,
+            lags = c(0:7, 14, 21),
+            adjust_latency = "extend_ahead")
+        )$predictions %>%
+          pivot_quantiles_wider(.pred_distn)}) %>%
+    bind_rows()
 }
 ```
 
-```{r}
-# Generate the forecasts and bind them together
-forecasts <- bind_rows(
-  map(aheads, ~ forecast_k_week_ahead(
-    archive_cases_dv_subset_faux,
-    ahead = .x,
-    outcome = "percent_cli",
-    predictors = "percent_cli",
-    forecast_dates = forecast_dates
-  ) %>% mutate(version_aware = FALSE)),
-  map(aheads, ~ forecast_k_week_ahead(
-    doctor_visits,
-    ahead = .x,
-    outcome = "percent_cli",
-    predictors = "percent_cli",
-    forecast_dates = forecast_dates
-  ) %>% mutate(version_aware = TRUE))
-)
+Note that we have used the parameter`adjust_latency` mentioned above, which
+comes up because on any given forecast date, such as `2020-08-01` the latest
+data to be released may be several days old.
+`adjust_latency` will modify the forecaster to compensate[^5].
+See the function `step_adjust_latency()` for more details and examples.
+
+And now we generate the forecasts for both the version faithful and faithless
+archives, and bind the results together.
+
+```{r generate_forecasts, warning = FALSE}
+forecast_dates <- seq(
+  from = as.Date("2020-09-01"),
+  to = as.Date("2021-11-01"),
+  by = "1 month")
+aheads <- c(1, 7, 14, 21, 28)
+
+version_faithless <- archive_cases_dv_subset_faux %>%
+      epix_slide(
+        ~forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
+        .before = 120,
+        .versions = forecast_dates
+      ) %>% 
+      mutate(version_faithful = FALSE)
+
+version_faithful <- doctor_visits %>%
+      epix_slide(
+        ~forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
+        .before = 120,
+        .versions = forecast_dates
+      ) %>% 
+      mutate(version_faithful = TRUE)
+
+forecasts <-
+  bind_rows(
+    version_faithless,
+    version_faithful
+  )
 ```
 
-Here, `arx_forecaster()` does all the heavy lifting. It creates leads of the
-target (respecting time stamps and locations) along with lags of the features
-(here, the response and doctors visits), estimates a forecasting model using the
-specified engine, creates predictions, and non-parametric confidence bands.
+Here, `arx_forecaster()` does all the heavy lifting.
+It creates leads of the target (respecting time stamps and locations) along with
+lags of the features (here, the response and doctors visits), estimates a
+forecasting model using the specified engine, creates predictions, and
+non-parametric confidence bands.
 
 To see how the predictions compare, we plot them on top of the latest case
-rates. Note that even though we've fitted the model on all states, we'll just
-display the results for two states, California (CA) and Florida (FL), to get a
-sense of the model performance while keeping the graphic simple.
+rates, using the same versioning plotting method as above.
+Note that even though we've fitted the model on four states (ca, fl, tx, and
+ny), we'll just display the results for two states, California (CA) and Florida
+(FL), to get a sense of the model performance while keeping the graphic simple.
 
 <details>
 <summary>Code for plotting</summary>
 
-```{r}
+```{r plot_ca_forecasts, warning = FALSE}
 geo_choose <- "ca"
 forecasts_filtered <- forecasts %>%
   filter(geo_value == geo_choose) %>%
   mutate(time_value = version)
-percent_cli_data <- bind_rows(
-  # Snapshotted data for the version-aware forecasts
-  map(
-    forecast_dates,
-    ~ doctor_visits %>%
-      epix_as_of(.x) %>%
-      mutate(version = .x)
-  ) %>%
-    bind_rows() %>%
-    mutate(version_aware = TRUE),
-  # Latest data for the version-unaware forecasts
-  doctor_visits %>%
-    epix_as_of(doctor_visits$versions_end) %>%
-    mutate(version_aware = FALSE)
-) %>%
-  filter(geo_value == geo_choose)
 
-p1 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+p1 <- # first plotting the forecasts as bands, lines and points
+  ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
   geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
-  geom_vline(data = percent_cli_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  # the forecast date
+  geom_vline(data = percent_cli_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
+  # the underlying data
   geom_line(
-    data = percent_cli_data,
+    data = percent_cli_data %>% filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
-  facet_grid(version_aware ~ geo_value, scales = "free") +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  facet_grid(version_faithful ~ geo_value, scales = "free") +
+  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
   theme(legend.position = "none")
 ```
 
-```{r}
+```{r plot_fl_forecasts, warning = FALSE}
 geo_choose <- "fl"
 forecasts_filtered <- forecasts %>%
   filter(geo_value == geo_choose) %>%
   mutate(time_value = version)
-percent_cli_data <- bind_rows(
-  # Snapshotted data for the version-aware forecasts
-  map(
-    forecast_dates,
-    ~ doctor_visits %>%
-      epix_as_of(.x) %>%
-      mutate(version = .x)
-  ) %>%
-    bind_rows() %>%
-    mutate(version_aware = TRUE),
-  # Latest data for the version-unaware forecasts
-  doctor_visits %>%
-    epix_as_of(doctor_visits$versions_end) %>%
-    mutate(version_aware = FALSE)
-) %>%
-  filter(geo_value == geo_choose)
 
-p2 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+p2 <-
+  ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
   geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
-  geom_vline(data = percent_cli_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_vline(
+    data = percent_cli_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful),
+    aes(color = factor(version), xintercept = version), lty = 2) +
   geom_line(
-    data = percent_cli_data,
+    data = percent_cli_data %>% filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
-  facet_grid(version_aware ~ geo_value, scales = "free") +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  facet_grid(version_faithful ~ geo_value, scales = "free") +
+  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
   theme(legend.position = "none")
 ```
 </details>
 
-```{r show-plot1, echo=FALSE}
+```{r show-plot1, warning = FALSE, echo=FALSE}
 p1
-p2
 ```
 
-For the two states of interest, neither approach produces amazingly accurate
-forecasts. However, the extent to which using versioned data can affect
-backtesting, scoring, and therefore model choice for production can be inferred
-from these plots.
+For California above and Florida below, neither approach produces amazingly
+accurate forecasts. 
+The difference between the forecasts produced is quite striking however,
+especially in the single day ahead forecasts.
 
-### Example using case data from Canada
+In the version faithful case for California above, the December 2020 forecast
+starts at nearly 30, whereas the equivalent version faithless forecast at least
+starts at the correct value, even if it is overly pessimistic about the eventual
+peak that will occur.
 
-<details>
+In the version faithful case for Florida below, the late 2021 forecasts, such as
+September are wildly off base, routinely estimating zero cases, thanks to the
+versioned data systematically under-reporting.
+There is a similar if somewhat less extreme effect on December 2020 as in
+California.
 
-<summary>Data and forecasts. Similar to the above.</summary>
+Without using the data as you would have seen it at the time as in the version
+faithful case, you potentially have no insight into what kind of performance you
+can expect in practice.
+
+```{r show-plot2, warning = FALSE, echo=FALSE}
+p2
+```
+
+## Comparing version faithful and faithless in Canada
 
-By leveraging the flexibility of `epiprocess`, we can apply the same techniques
-to data from other sources. Since some collaborators are in British Columbia,
-Canada, we'll do essentially the same thing for Canada as we did above.
+
+By leveraging the flexibility of `{epipredict}`, we can apply the same
+techniques to data from other sources.
+Since some collaborators are in British Columbia, Canada, we'll do essentially
+the same thing for Canadian Provincial data as we did above.
 
 The [COVID-19 Canada Open Data Working Group](https://opencovid.ca/) collects
 daily time series data on COVID-19 cases, deaths, recoveries, testing and
-vaccinations at the health region and province levels. Data are collected from
-publicly available sources such as government datasets and news releases.
+vaccinations at the health region and province levels.
+Data are collected from publicly available sources such as government datasets
+and news releases.
 Unfortunately, there is no simple versioned source, so we have created our own
-from the Github commit history.
+from the Github commit history and stored it in `epidatasets::can_prov_cases`.
 
-First, we load versioned case rates at the provincial level. After converting
-these to 7-day averages (due to highly variable provincial reporting
-mismatches), we then convert the data to an `epi_archive` object, and extract
-the latest version from it. Finally, we run the same forcasting exercise as for
-the American data, but here we compare the forecasts produced from using simple
-linear regression with those from using boosted regression trees.
+First, we load versioned case rates at the provincial level.
 
 ```{r get-can-fc, warning = FALSE}
 aheads <- c(7, 14, 21, 28)
-canada_archive <- can_prov_cases
+canada_archive <- epidatasets::can_prov_cases
+revis_can <- epidatasets::can_prov_cases %>% revision_summary()
+revis_can %>% group_by(geo_value) %>% summarize(n_revisions = mean(n_revisions)) %>% print(n=13)
 canada_archive_faux <- epix_as_of(canada_archive, canada_archive$versions_end) %>%
   mutate(version = time_value) %>%
   as_epi_archive()
-# This function will add the 7-day average of the case rate to the data
-# before forecasting.
+```
+
+We run more or less the same forecasting method as above, but with the addition
+of 7-day averaging for each snapshot before forecasting (due to highly variable
+provincial reporting mismatches).
+
+```{r smoothing, warning = FALSE}
 smooth_cases <- function(epi_df) {
   epi_df %>%
     group_by(geo_value) %>%
@@ -353,42 +435,39 @@ forecast_dates <- seq.Date(
   by = "1 month"
 )
 
-# Generate the forecasts, and bind them together
+canada_version_faithless <-
+  canada_archive_faux %>%
+      epix_slide(
+        ~forecast_wrapper(.x, aheads, "case_rate_7dav", "case_rate_7dav", smooth_cases),
+        .before = 120,
+        .versions = forecast_dates
+      ) %>% 
+      mutate(version_faithful = FALSE)
+canada_version_faithful <-
+  canada_archive %>%
+      epix_slide(
+        ~forecast_wrapper(.x, aheads, "case_rate_7dav", "case_rate_7dav", smooth_cases),
+        .before = 120,
+        .versions = forecast_dates
+      ) %>%
+      mutate(version_faithful = TRUE)
 canada_forecasts <- bind_rows(
-  map(
-    aheads,
-    ~ forecast_k_week_ahead(
-      canada_archive_faux,
-      ahead = .x,
-      outcome = "case_rate_7dav",
-      predictors = "case_rate_7dav",
-      forecast_dates = forecast_dates,
-      process_data = smooth_cases
-    ) %>% mutate(version_aware = FALSE)
-  ),
-  map(
-    aheads,
-    ~ forecast_k_week_ahead(
-      canada_archive,
-      ahead = .x,
-      outcome = "case_rate_7dav",
-      predictors = "case_rate_7dav",
-      forecast_dates = forecast_dates,
-      process_data = smooth_cases
-    ) %>% mutate(version_aware = TRUE)
-  )
+  canada_version_faithless,
+  canada_version_faithful
 )
 ```
 
 The figures below shows the results for a single province.
-
-```{r plot-can-fc-lr, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
-geo_choose <- "Alberta"
+<details>
+<summary> Plotting </summary>
+First prepping some data to make plotting more informative.
+```{r plot-can-fc-lr-data, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
+geo_choose <- "Saskatchewan"
 forecasts_filtered <- canada_forecasts %>%
   filter(geo_value == geo_choose) %>%
   mutate(time_value = version)
 case_rate_data <- bind_rows(
-  # Snapshotted data for the version-aware forecasts
+  # Snapshotted data for the version-faithful forecasts
   map(
     forecast_dates,
     ~ canada_archive %>%
@@ -397,34 +476,58 @@ case_rate_data <- bind_rows(
       mutate(case_rate = case_rate_7dav, version = .x)
   ) %>%
     bind_rows() %>%
-    mutate(version_aware = TRUE),
-  # Latest data for the version-unaware forecasts
+    mutate(version_faithful = TRUE),
+  # Latest data for the version-faithless forecasts
   canada_archive %>%
     epix_as_of(doctor_visits$versions_end) %>%
     smooth_cases() %>%
-    mutate(case_rate = case_rate_7dav, version_aware = FALSE)
+    mutate(case_rate = case_rate_7dav, version_faithful = FALSE)
 ) %>%
   filter(geo_value == geo_choose)
-
-ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+case_rate_data %>% filter(time_value == "2021-01-13") %>% print(n=12)
+canada_archive %>% revision_summary()
+```
+And actually generating the plot
+```{r plot-can-fc-lr, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
+p3 <-
+  ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
   geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
-  geom_vline(data = case_rate_data, aes(color = factor(version), xintercept = version), lty = 2) +
+  # the forecast date
+  geom_vline(data = case_rate_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
+  # the underlying data
   geom_line(
-    data = case_rate_data,
+    data = case_rate_data %>% filter(geo_value == geo_choose),
     aes(x = time_value, y = case_rate, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
-  facet_grid(version_aware ~ geo_value, scales = "free") +
-  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
+  facet_grid(version_faithful ~ geo_value, scales = "free") +
+  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
   theme(legend.position = "none")
 ```
-
 </details>
 
-[^1]: (we choose this target because of the dataset's pattern of substantial
-revisions; forecasting doctor's visits is an unusual forecasting target
-otherwise)
+```{r show-can-plot, warning = FALSE, echo = FALSE}
+p3
+```
+
+
+[^1]: For forecasting a single day like this, we could have actually just used
+    `doctor_visits %>% epix_as_of(forecast_date)` to get the relevant snapshot, and then fed that into `arx_forecaster()` as we did in the [landing
+page](../index.html#motivating-example).
+
+
+[^2]: Generally we advise against this; the only time to consider faking
+ versioning like this are if you're back-testing data with no versions available
+ at all, or if you're doing an explicit comparison like this. If you have no
+ versions you should assume performance is worse than what the test would
+ otherwise suggest.
+    
+[^4]: Until we have a time machine
+
+[^5]: In this case by adjusting the length of the ahead so that it is actually
+ forecasting from the last day of data (e.g. for 2 day latent data and a true
+ ahead of 5, the `extended_ahead` would actually be 7)

From 9ac8c82a7396c0b5d56927ddf52f6d6db0ca1ac3 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Feb 2025 13:37:00 -0600
Subject: [PATCH 344/387] dropping CAN backtesting example b/c ~no revisions

---
 vignettes/backtesting.Rmd | 123 --------------------------------------
 1 file changed, 123 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 200b4fcc8..78d8abfa9 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -391,129 +391,6 @@ can expect in practice.
 p2
 ```
 
-## Comparing version faithful and faithless in Canada
-
-
-By leveraging the flexibility of `{epipredict}`, we can apply the same
-techniques to data from other sources.
-Since some collaborators are in British Columbia, Canada, we'll do essentially
-the same thing for Canadian Provincial data as we did above.
-
-The [COVID-19 Canada Open Data Working Group](https://opencovid.ca/) collects
-daily time series data on COVID-19 cases, deaths, recoveries, testing and
-vaccinations at the health region and province levels.
-Data are collected from publicly available sources such as government datasets
-and news releases.
-Unfortunately, there is no simple versioned source, so we have created our own
-from the Github commit history and stored it in `epidatasets::can_prov_cases`.
-
-First, we load versioned case rates at the provincial level.
-
-```{r get-can-fc, warning = FALSE}
-aheads <- c(7, 14, 21, 28)
-canada_archive <- epidatasets::can_prov_cases
-revis_can <- epidatasets::can_prov_cases %>% revision_summary()
-revis_can %>% group_by(geo_value) %>% summarize(n_revisions = mean(n_revisions)) %>% print(n=13)
-canada_archive_faux <- epix_as_of(canada_archive, canada_archive$versions_end) %>%
-  mutate(version = time_value) %>%
-  as_epi_archive()
-```
-
-We run more or less the same forecasting method as above, but with the addition
-of 7-day averaging for each snapshot before forecasting (due to highly variable
-provincial reporting mismatches).
-
-```{r smoothing, warning = FALSE}
-smooth_cases <- function(epi_df) {
-  epi_df %>%
-    group_by(geo_value) %>%
-    epi_slide_mean("case_rate", .window_size = 7, na.rm = TRUE, .suffix = "_{.n}dav")
-}
-forecast_dates <- seq.Date(
-  from = min(canada_archive$DT$version),
-  to = max(canada_archive$DT$version),
-  by = "1 month"
-)
-
-canada_version_faithless <-
-  canada_archive_faux %>%
-      epix_slide(
-        ~forecast_wrapper(.x, aheads, "case_rate_7dav", "case_rate_7dav", smooth_cases),
-        .before = 120,
-        .versions = forecast_dates
-      ) %>% 
-      mutate(version_faithful = FALSE)
-canada_version_faithful <-
-  canada_archive %>%
-      epix_slide(
-        ~forecast_wrapper(.x, aheads, "case_rate_7dav", "case_rate_7dav", smooth_cases),
-        .before = 120,
-        .versions = forecast_dates
-      ) %>%
-      mutate(version_faithful = TRUE)
-canada_forecasts <- bind_rows(
-  canada_version_faithless,
-  canada_version_faithful
-)
-```
-
-The figures below shows the results for a single province.
-<details>
-<summary> Plotting </summary>
-First prepping some data to make plotting more informative.
-```{r plot-can-fc-lr-data, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
-geo_choose <- "Saskatchewan"
-forecasts_filtered <- canada_forecasts %>%
-  filter(geo_value == geo_choose) %>%
-  mutate(time_value = version)
-case_rate_data <- bind_rows(
-  # Snapshotted data for the version-faithful forecasts
-  map(
-    forecast_dates,
-    ~ canada_archive %>%
-      epix_as_of(.x) %>%
-      smooth_cases() %>%
-      mutate(case_rate = case_rate_7dav, version = .x)
-  ) %>%
-    bind_rows() %>%
-    mutate(version_faithful = TRUE),
-  # Latest data for the version-faithless forecasts
-  canada_archive %>%
-    epix_as_of(doctor_visits$versions_end) %>%
-    smooth_cases() %>%
-    mutate(case_rate = case_rate_7dav, version_faithful = FALSE)
-) %>%
-  filter(geo_value == geo_choose)
-case_rate_data %>% filter(time_value == "2021-01-13") %>% print(n=12)
-canada_archive %>% revision_summary()
-```
-And actually generating the plot
-```{r plot-can-fc-lr, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 12}
-p3 <-
-  ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
-  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
-  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
-  # the forecast date
-  geom_vline(data = case_rate_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
-  # the underlying data
-  geom_line(
-    data = case_rate_data %>% filter(geo_value == geo_choose),
-    aes(x = time_value, y = case_rate, color = factor(version)),
-    inherit.aes = FALSE, na.rm = TRUE
-  ) +
-  facet_grid(version_faithful ~ geo_value, scales = "free") +
-  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
-  scale_y_continuous(expand = expansion(c(0, 0.05))) +
-  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
-  theme(legend.position = "none")
-```
-</details>
-
-```{r show-can-plot, warning = FALSE, echo = FALSE}
-p3
-```
-
 
 [^1]: For forecasting a single day like this, we could have actually just used
     `doctor_visits %>% epix_as_of(forecast_date)` to get the relevant snapshot, and then fed that into `arx_forecaster()` as we did in the [landing

From 4f0c7e6aac64e0582b9f3b1b67ead4e2b991d11f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Feb 2025 14:24:02 -0600
Subject: [PATCH 345/387] formatting

---
 vignettes/backtesting.Rmd | 57 +++++++++++++++++++++------------------
 1 file changed, 31 insertions(+), 26 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 78d8abfa9..9c127df8e 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -240,18 +240,21 @@ aheads and some variations we will use later.
 ```{r arx-kweek-preliminaries, warning = FALSE}
 forecast_wrapper <- function(
     epi_data, aheads, outcome, predictors,
-    process_data = identity
-    ) {
-  map(aheads,
-      \(ahead) {
-        arx_forecaster(
-          process_data(epi_data), outcome, predictors,
-          args_list = arx_args_list(
-            ahead = ahead,
-            lags = c(0:7, 14, 21),
-            adjust_latency = "extend_ahead")
-        )$predictions %>%
-          pivot_quantiles_wider(.pred_distn)}) %>%
+    process_data = identity) {
+  map(
+    aheads,
+    \(ahead) {
+      arx_forecaster(
+        process_data(epi_data), outcome, predictors,
+        args_list = arx_args_list(
+          ahead = ahead,
+          lags = c(0:7, 14, 21),
+          adjust_latency = "extend_ahead"
+        )
+      )$predictions %>%
+        pivot_quantiles_wider(.pred_distn)
+    }
+  ) %>%
     bind_rows()
 }
 ```
@@ -269,24 +272,25 @@ archives, and bind the results together.
 forecast_dates <- seq(
   from = as.Date("2020-09-01"),
   to = as.Date("2021-11-01"),
-  by = "1 month")
+  by = "1 month"
+)
 aheads <- c(1, 7, 14, 21, 28)
 
 version_faithless <- archive_cases_dv_subset_faux %>%
-      epix_slide(
-        ~forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
-        .before = 120,
-        .versions = forecast_dates
-      ) %>% 
-      mutate(version_faithful = FALSE)
+  epix_slide(
+    ~ forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
+    .before = 120,
+    .versions = forecast_dates
+  ) %>%
+  mutate(version_faithful = FALSE)
 
 version_faithful <- doctor_visits %>%
-      epix_slide(
-        ~forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
-        .before = 120,
-        .versions = forecast_dates
-      ) %>% 
-      mutate(version_faithful = TRUE)
+  epix_slide(
+    ~ forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
+    .before = 120,
+    .versions = forecast_dates
+  ) %>%
+  mutate(version_faithful = TRUE)
 
 forecasts <-
   bind_rows(
@@ -349,7 +353,8 @@ p2 <-
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
   geom_vline(
     data = percent_cli_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful),
-    aes(color = factor(version), xintercept = version), lty = 2) +
+    aes(color = factor(version), xintercept = version), lty = 2
+  ) +
   geom_line(
     data = percent_cli_data %>% filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),

From a552e14b905177243b285776f46a6b9b11e53088 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 25 Feb 2025 17:07:30 -0600
Subject: [PATCH 346/387] |> in backtesting, dropped a section in get started

---
 vignettes/backtesting.Rmd | 76 +++++++++++++++++++--------------------
 vignettes/epipredict.Rmd  | 42 ++++++++++++++++++++--
 2 files changed, 77 insertions(+), 41 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 9c127df8e..581bb31ae 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -58,9 +58,9 @@ medical insurance claims and the number of new confirmed COVID-19 cases per
 
 ```{r grab-epi-data}
 # Select the `percent_cli` column from the data archive
-doctor_visits <- archive_cases_dv_subset$DT %>%
-  select(geo_value, time_value, version, percent_cli) %>%
-  drop_na(percent_cli) %>%
+doctor_visits <- archive_cases_dv_subset$DT |>
+  select(geo_value, time_value, version, percent_cli) |>
+  tidyr::drop_na(percent_cli) |>
   as_epi_archive(compactify = TRUE)
 ```
 
@@ -77,8 +77,8 @@ doctor_visits <- pub_covidcast(
   geo_values = "ca,fl,ny,tx",
   time_values = epirange(20200601, 20211201),
   issues = epirange(20200601, 20211201)
-) %>%
-  rename(version = issue, percent_cli = value) %>%
+) |>
+  rename(version = issue, percent_cli = value) |>
   as_epi_archive(compactify = TRUE)
 ```
 
@@ -99,20 +99,20 @@ percent_cli_data <- bind_rows(
   # Snapshotted data for the version-faithful forecasts
   map(
     forecast_dates,
-    ~ doctor_visits %>%
-      epix_as_of(.x) %>%
+    ~ doctor_visits |>
+      epix_as_of(.x) |>
       mutate(version = .x)
-  ) %>%
-    bind_rows() %>%
+  ) |>
+    bind_rows() |>
     mutate(version_faithful = TRUE),
   # Latest data for the version-faithless forecasts
-  doctor_visits %>%
-    epix_as_of(doctor_visits$versions_end) %>%
+  doctor_visits |>
+    epix_as_of(doctor_visits$versions_end) |>
     mutate(version_faithful = FALSE)
 )
 
 p0 <-
-  ggplot(data = percent_cli_data %>% filter(geo_value == geo_choose)) +
+  ggplot(data = percent_cli_data |> filter(geo_value == geo_choose)) +
   geom_vline(aes(color = factor(version), xintercept = version), lty = 2) +
   geom_line(
     aes(x = time_value, y = percent_cli, color = factor(version)),
@@ -154,9 +154,9 @@ of the red time-series to its left.
 In fact, if we take a snapshot and get the last `time_value`:
 
 ```{r}
-doctor_visits %>%
-  epix_as_of(as.Date("2020-08-01")) %>%
-  pull(time_value) %>%
+doctor_visits |>
+  epix_as_of(as.Date("2020-08-01")) |>
+  pull(time_value) |>
   max()
 ```
 
@@ -185,14 +185,14 @@ One way to do this is by setting the `.version` argument for `epix_slide()`:
 
 ```{r single_version, warn = FALSE}
 forecast_date <- as.Date("2021-04-06")
-forecasts <- doctor_visits %>%
+forecasts <- doctor_visits |>
   epix_slide(
     ~ arx_forecaster(
       .x,
       outcome = "percent_cli",
       predictors = "percent_cli",
       args_list = arx_args_list()
-    )$predictions %>%
+    )$predictions |>
       pivot_quantiles_wider(.pred_distn),
     .versions = forecast_date
   )
@@ -202,12 +202,12 @@ As truth data, we'll compare with the `epix_as_of()` to generate a snapshot of
 the archive at the last date[^1].
 
 ```{r compare_single_with_result}
-forecasts %>%
+forecasts |>
   inner_join(
-    doctor_visits %>%
+    doctor_visits |>
       epix_as_of(doctor_visits$versions_end),
     by = c("geo_value", "target_date" = "time_value")
-  ) %>%
+  ) |>
   select(geo_value, forecast_date, .pred, `0.05`, `0.95`, percent_cli)
 ```
 
@@ -227,9 +227,9 @@ This has the effect of simulating a data set that receives the final version
 updates every day.
 
 ```{r}
-archive_cases_dv_subset_faux <- doctor_visits %>%
-  epix_as_of(doctor_visits$versions_end) %>%
-  mutate(version = time_value) %>%
+archive_cases_dv_subset_faux <- doctor_visits |>
+  epix_as_of(doctor_visits$versions_end) |>
+  mutate(version = time_value) |>
   as_epi_archive()
 ```
 
@@ -251,10 +251,10 @@ forecast_wrapper <- function(
           lags = c(0:7, 14, 21),
           adjust_latency = "extend_ahead"
         )
-      )$predictions %>%
+      )$predictions |>
         pivot_quantiles_wider(.pred_distn)
     }
-  ) %>%
+  ) |>
     bind_rows()
 }
 ```
@@ -276,20 +276,20 @@ forecast_dates <- seq(
 )
 aheads <- c(1, 7, 14, 21, 28)
 
-version_faithless <- archive_cases_dv_subset_faux %>%
+version_faithless <- archive_cases_dv_subset_faux |>
   epix_slide(
     ~ forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
     .before = 120,
     .versions = forecast_dates
-  ) %>%
+  ) |>
   mutate(version_faithful = FALSE)
 
-version_faithful <- doctor_visits %>%
+version_faithful <- doctor_visits |>
   epix_slide(
     ~ forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
     .before = 120,
     .versions = forecast_dates
-  ) %>%
+  ) |>
   mutate(version_faithful = TRUE)
 
 forecasts <-
@@ -316,8 +316,8 @@ ny), we'll just display the results for two states, California (CA) and Florida
 
 ```{r plot_ca_forecasts, warning = FALSE}
 geo_choose <- "ca"
-forecasts_filtered <- forecasts %>%
-  filter(geo_value == geo_choose) %>%
+forecasts_filtered <- forecasts |>
+  filter(geo_value == geo_choose) |>
   mutate(time_value = version)
 
 p1 <- # first plotting the forecasts as bands, lines and points
@@ -326,10 +326,10 @@ p1 <- # first plotting the forecasts as bands, lines and points
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
   # the forecast date
-  geom_vline(data = percent_cli_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_vline(data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
   # the underlying data
   geom_line(
-    data = percent_cli_data %>% filter(geo_value == geo_choose),
+    data = percent_cli_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
@@ -342,8 +342,8 @@ p1 <- # first plotting the forecasts as bands, lines and points
 
 ```{r plot_fl_forecasts, warning = FALSE}
 geo_choose <- "fl"
-forecasts_filtered <- forecasts %>%
-  filter(geo_value == geo_choose) %>%
+forecasts_filtered <- forecasts |>
+  filter(geo_value == geo_choose) |>
   mutate(time_value = version)
 
 p2 <-
@@ -352,11 +352,11 @@ p2 <-
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
   geom_vline(
-    data = percent_cli_data %>% filter(geo_value == geo_choose) %>% select(-version_faithful),
+    data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
     aes(color = factor(version), xintercept = version), lty = 2
   ) +
   geom_line(
-    data = percent_cli_data %>% filter(geo_value == geo_choose),
+    data = percent_cli_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
@@ -398,7 +398,7 @@ p2
 
 
 [^1]: For forecasting a single day like this, we could have actually just used
-    `doctor_visits %>% epix_as_of(forecast_date)` to get the relevant snapshot, and then fed that into `arx_forecaster()` as we did in the [landing
+    `doctor_visits |> epix_as_of(forecast_date)` to get the relevant snapshot, and then fed that into `arx_forecaster()` as we did in the [landing
 page](../index.html#motivating-example).
 
 
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index a169b85ba..54f4dfcd7 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -19,6 +19,7 @@ library(recipes)
 library(epidatasets)
 library(epipredict)
 library(ggplot2)
+library(purrr)
 forecast_date <- as.Date("2021-08-01")
 used_locations <- c("ca", "ma", "ny", "tx")
 library(epidatr)
@@ -331,8 +332,41 @@ autoplot(
 The 8 graphs are all pairs of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
 ## Fitting a non-geo-pooled model
-The primary difference to avoid geo-pooling is to first `group_by(geo_value)`
-before forecasting
+
+Because our internal methods fit a single model, to fit a non-geo-pooled model
+that has a different fit for each geography, one either needs a multi-level
+engine (which at the moment parsnip doesn't support), or one needs to map over
+geographies.
+
+```{r fit_non_geo_pooled, warning=FALSE}
+geo_values <- covid_case_death_rates |> pull(geo_value) |> unique()
+
+all_fits <-
+  purrr::map(geo_values, \(geo) {
+  covid_case_death_rates |>
+    filter(
+      geo_value == geo,
+      time_value <= forecast_date) |>
+    arx_forecaster(
+      outcome = "death_rate",
+      trainer = linear_reg(),
+      predictors = c("death_rate"),
+      args_list = arx_args_list(
+        lags = list(c(0, 7, 14)),
+        ahead = 14
+      )
+    )
+})
+map_df(all_fits, ~ pluck(., "predictions"))
+```
+
+This is both 56 times slower[^7], and uses far less data to fit each model.
+If the geographies are at all comparable, for example by normalization, we would
+ get much better results by pooling.
+
+If we wanted to build a geo-aware model, such as one that sets the constant in a
+ linear regression fit to be different for each geography, we would need to build a [Custom workflow](custom_epiworkflows) with geography as a factor.
+
 # Anatomy of a canned forecaster
 ## Code object
 Let's dissect the forecaster we trained back on the [landing
@@ -390,7 +424,7 @@ An `epi_workflow()` consists of 3 parts:
   5 of as these well. You can inspect the layers more closely by running
   `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
 
-See the [Guts vignette](preprocessing-and-models) for recreating and then
+See the [Guts vignette](custom_epiworkflows) for recreating and then
 extending `four_week_ahead` using the custom forecaster framework.
 
 ## Mathematical description
@@ -436,3 +470,5 @@ without `NA` values is a training point to fit the coefficients $a_0,\ldots, a_6
 
 [^6]: alternatively, for an unfit version of the preprocessor, you can call
     `hardhat::extract_preprocessor(four_week_ahead$epi_workflow)`
+
+[^7]: the number of geographies

From decc9638943f4b64bc174919c9c5e0e4bc8b2f49 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 28 Feb 2025 16:25:02 -0500
Subject: [PATCH 347/387] landing page wording and get code running

---
 README.md | 114 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 61 insertions(+), 53 deletions(-)

diff --git a/README.md b/README.md
index d41d9a1b3..bc9e01c7c 100644
--- a/README.md
+++ b/README.md
@@ -8,27 +8,28 @@
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-Epipredict is a framework for building transformation and forecasting
+`{epipredict}` is a framework for building transformation and forecasting
 pipelines for epidemiological and other panel time-series datasets. In
 addition to tools for building forecasting pipelines, it contains a
 number of “canned” forecasters meant to run with little modification as
 an easy way to get started forecasting.
 
 It is designed to work well with
-[`epiprocess`](https://cmu-delphi.github.io/epiprocess/), a utility for
-handling various time series and geographic processing tools in an
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/), a utility for
+time series handling and geographic processing in an
 epidemiological context. Both of the packages are meant to work well
 with the panel data provided by
-[`epidatr`](https://cmu-delphi.github.io/epidatr/).
+[`{epidatr}`](https://cmu-delphi.github.io/epidatr/).
+Pre-compiled example datasets are also availalbe in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
 
-If you are looking for more detail beyond the package documentation, see
-our [forecasting
-book](https://cmu-delphi.github.io/delphi-tooling-book/).
+If you are looking for detail beyond the package documentation, see
+our [forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 ## Installation
 
-To install (unless you’re planning on contributing to package
-development, we suggest using the stable version):
+Unless you’re planning on contributing to package
+development, we suggest using the stable version.
+To install, run:
 
 ``` r
 # Stable version
@@ -44,25 +45,32 @@ is at <https://cmu-delphi.github.io/epipredict/dev>.
 
 ## Motivating example
 
-To demonstrate the kind of forecast epipredict can make, say we’re
-predicting COVID deaths per 100k for each state on
+To demonstrate the kind of forecast `{epipredict}` can make, say we want to
+predict COVID-19 deaths per 100k people for each state on 2021-08-01.
 
 ``` r
+library(epipredict)
+library(epidatr)
+library(epiprocess)
+library(dplyr)
+library(ggplot2)
+
 forecast_date <- as.Date("2021-08-01")
 ```
 
 Below the fold, we construct this dataset as an `epiprocess::epi_df`
-from JHU data.
+from [Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
 
 <details>
 <summary>
 Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
-This dataset can be found in the package as `covid_case_death_rates`; we
-demonstrate some of the typically ubiquitous cleaning operations needed
-to be able to forecast. First we pull both jhu-csse cases and deaths
-from [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
+This section is intended to demonstrate some of the ubiquitous cleaning operations needed
+to be able to forecast.
+The dataset prepared here is also included ready-to-go in `{epipredict}` as `covid_case_death_rates`.
+
+First we pull both `jhu-csse` cases and deaths data from the [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
 ``` r
 cases <- pub_covidcast(
@@ -87,7 +95,7 @@ deaths <- pub_covidcast(
 ```
 
 Since visualizing the results on every geography is somewhat
-overwhelming, we’ll only train on a subset of 5.
+overwhelming, we’ll only train on a subset of locations.
 
 ``` r
 used_locations <- c("ca", "ma", "ny", "tx")
@@ -113,12 +121,11 @@ cases_deaths |>
 
 <img src="man/figures/README-date-1.png" width="90%" style="display: block; margin: auto;" />
 
-As with basically any dataset, there is some cleaning that we will need
-to do to make it actually usable; we’ll use some utilities from
+As with the typical dataset, we will need to do some cleaning to make it actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
 
-First, to eliminate some of the noise coming from daily reporting, we do
-7 day averaging over a trailing window[^1]:
+First, to reduce the noise from daily reporting, we will compute a
+7 day average over a trailing window[^1]:
 
 ``` r
 cases_deaths <-
@@ -134,7 +141,7 @@ cases_deaths <-
   rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
 ```
 
-Then trimming outliers, most especially negative values:
+Then we'll trim outliers, especially negative values:
 
 ``` r
 cases_deaths <-
@@ -161,8 +168,8 @@ cases_deaths <-
 
 </details>
 
-After having downloaded and cleaned the data in `cases_deaths`, we plot
-a subset of the states, noting the actual forecast date:
+After downloading and cleaning the cases and deaths data, we can plot
+a subset of the states, marking the desired forecast date:
 
 <details>
 <summary>
@@ -170,6 +177,7 @@ Plot
 </summary>
 
 ``` r
+used_locations <- c("ca", "ma", "ny", "tx")
 forecast_date_label <-
   tibble(
     geo_value = rep(used_locations, 2),
@@ -177,8 +185,8 @@ forecast_date_label <-
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
     heights = c(rep(150, 4), rep(0.75, 4))
   )
-processed_data_plot <-
-  covid_case_death_rates |>
+
+covid_case_death_rates |>
   filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
@@ -204,13 +212,13 @@ processed_data_plot <-
 
 <img src="man/figures/README-show-processed-data-1.png" width="90%" style="display: block; margin: auto;" />
 
-To make a forecast, we will use a “canned” simple auto-regressive
+To make a forecast, we will use a simple “canned” auto-regressive
 forecaster to predict the death rate four weeks into the future using
-lagged[^2] deaths and cases
+lagged[^2] deaths and cases.
 
 ``` r
 four_week_ahead <- arx_forecaster(
-  cases_deaths |> filter(time_value <= forecast_date),
+  covid_case_death_rates |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
@@ -221,31 +229,31 @@ four_week_ahead <- arx_forecaster(
 )
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
-#> 
+#>
 #> This forecaster was fit on 2025-02-10 12:09:58.
-#> 
+#>
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
 #> • Using data up-to-date as of: 2022-01-01.
 #> • With the last data available on 2021-08-01
-#> 
+#>
 #> ── Predictions ──────────────────────────────────────────────────────────────
-#> 
+#>
 #> A total of 4 predictions are available for
 #> • 4 unique geographic regions,
 #> • At forecast date: 2021-08-01,
 #> • For target date: 2021-08-29,
-#> 
+#>
 ```
 
-In this case, we have used 0-3 days, a week, and two week lags for the
-case rate, while using only zero, one and two weekly lags for the death
-rate (as predictors). The result `four_week_ahead` is both a fitted
+In our model setup, we are defining as our predictors case rate lagged 0-3 days, one week, and two weeks, and death rate lagged 0-2 weeks.
+The result `four_week_ahead` is both a fitted
 model object which could be used any time in the future to create
-different forecasts, as well as a set of predicted values (and
+different forecasts, and a set of predicted values (and
 prediction intervals) for each location 28 days after the forecast date.
-Plotting the prediction intervals on our subset above[^3]:
+
+Plotting the prediction intervals on the true values for our location subset[^3]:
 
 <details>
 <summary>
@@ -275,7 +283,7 @@ forecast_plot <-
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
 
-And as a tibble of quantile level-value pairs:
+And as a tibble of quantile-value pairs:
 
 ``` r
 four_week_ahead$predictions |>
@@ -283,20 +291,21 @@ four_week_ahead$predictions |>
   pivot_quantiles_longer(.pred_distn)
 #> # A tibble: 20 × 5
 #>   geo_value values quantile_levels forecast_date target_date
-#>   <chr>      <dbl>           <dbl> <date>        <date>     
-#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
-#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
-#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
-#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
-#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
-#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
+#>   <chr>      <dbl>           <dbl> <date>        <date>
+#> 1 ca        0.199             0.1  2021-08-01    2021-08-29
+#> 2 ca        0.285             0.25 2021-08-01    2021-08-29
+#> 3 ca        0.345             0.5  2021-08-01    2021-08-29
+#> 4 ca        0.405             0.75 2021-08-01    2021-08-29
+#> 5 ca        0.491             0.9  2021-08-01    2021-08-29
+#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29
 #> # ℹ 14 more rows
 ```
 
-The black dot gives the median prediction, while the blue intervals give
+The orange dot gives the predicted median, and the blue intervals give
 the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^4]. For
 this particular day and these locations, the forecasts are relatively
-accurate, with the true data being at least within the 10-90% interval.
+accurate, with the true data being at worst within the 10-90% interval.
+
 A couple of things to note:
 
 1.  Our methods are primarily direct forecasters; this means we don’t
@@ -310,12 +319,11 @@ A couple of things to note:
 ## Getting Help
 
 If you encounter a bug or have a feature request, feel free to file an
-[issue on our github
+[issue on our GitHub
 page](https://github.com/cmu-delphi/epipredict/issues). For other
 questions, feel free to reach out to the authors, either via this
-[contact
-form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
-email, or the Insightnet slack.
+[contact form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
+email, or the InsightNet Slack.
 
 [^1]: This makes it so that any given day of the processed time-series
     only depends on the previous week, which means that we avoid leaking

From ad236b6d224aadc2269a5170daebade74c8fe5b3 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 28 Feb 2025 19:08:20 -0500
Subject: [PATCH 348/387] landing page again but in Rmd

---
 README.Rmd                                   | 104 +++++++++++--------
 man/figures/README-date-1.png                | Bin 266688 -> 232965 bytes
 man/figures/README-show-processed-data-1.png | Bin 308662 -> 251187 bytes
 vignettes/epipredict.Rmd                     |   8 +-
 4 files changed, 65 insertions(+), 47 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 9f27ce668..1b24ff022 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -77,26 +77,24 @@ scale_colour_delphi <- scale_color_delphi
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-Epipredict is a framework for building transformation and forecasting pipelines
-for epidemiological and other panel time-series datasets. 
-In addition to tools for building forecasting pipelines, it contains a number of
-"canned" forecasters meant to run with little modification as an easy way to get
-started forecasting.
+`{epipredict}` is a framework for building transformation and forecasting pipelines for epidemiological and other panel time-series datasets.
+In addition to tools for building forecasting pipelines, it contains a number of “canned” forecasters meant to run with little modification as an easy way to get started forecasting.
 
 It is designed to work well with
-[`epiprocess`](https://cmu-delphi.github.io/epiprocess/), a utility for handling
-various time series and geographic processing tools in an epidemiological
-context.
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/), a utility for time series handling and geographic processing in an epidemiological context.
 Both of the packages are meant to work well with the panel data provided by
-[`epidatr`](https://cmu-delphi.github.io/epidatr/).
+[`{epidatr}`](https://cmu-delphi.github.io/epidatr/).
+Pre-compiled example datasets are also available in
+[`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
 
-If you are looking for more detail beyond the package documentation, see our
+If you are looking for detail beyond the package documentation, see our
 [forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
+
 ## Installation
 
-To install (unless you're planning on contributing to package development, we
-suggest using the stable version):
+Unless you’re planning on contributing to package development, we suggest using the stable version.
+To install, run:
 
 ```r
 # Stable version
@@ -113,23 +111,43 @@ The documentation for the stable version is at
 
 ## Motivating example
 
-To demonstrate the kind of forecast epipredict can make, say we're predicting
-COVID deaths per 100k for each state on
+To demonstrate using `{epipredict}` for forecasting, say we want to
+predict COVID-19 deaths per 100k people for each of a subset of states
+
+```{r subset_geos}
+used_locations <- c("ca", "ma", "ny", "tx")
+```
+
+on
 
 ```{r fc_date}
 forecast_date <- as.Date("2021-08-01")
 ```
 
-Below the fold, we construct this dataset as an `epiprocess::epi_df` from JHU
-data.
+<details>
+<summary> Required packages </summary>
+
+```{r install, run = FALSE}
+library(epipredict)
+library(epidatr)
+library(epiprocess)
+library(dplyr)
+library(ggplot2)
+```
+</details>
+
+
+Below the fold, we construct this dataset as an `epiprocess::epi_df` from
+[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
 
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
-This dataset can be found in the package as `covid_case_death_rates`; we
-demonstrate some of the typically ubiquitous cleaning operations needed to be
-able to forecast.
-First we pull both jhu-csse cases and deaths from
+This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
+The dataset prepared here is also included ready-to-go in `{epipredict}` as `covid_case_death_rates`.
+
+First we pull both `jhu-csse` cases and deaths data from the
+[Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
 ```{r case_death, warning = FALSE}
@@ -155,9 +173,9 @@ deaths <- pub_covidcast(
 ```
 
 Since visualizing the results on every geography is somewhat overwhelming,
-we'll only train on a subset of 5.
+we’ll only train on a subset of locations.
+
 ```{r date, warning = FALSE}
-used_locations <- c("ca", "ma", "ny", "tx")
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
   filter(geo_value %in% used_locations) |>
@@ -178,12 +196,12 @@ cases_deaths |>
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-As with basically any dataset, there is some cleaning that we will need to do to
-make it actually usable; we'll use some utilities from
+As with the typical dataset, we will need to do some cleaning to
+make it actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
 
-First, to eliminate some of the noise coming from daily reporting, we do 7 day
-averaging over a trailing window[^1]:
+First, to reduce noise from daily reporting, we will compute a 7 day
+average over a trailing window[^1]:
 
 [^1]: This makes it so that any given day of the processed time-series only
     depends on the previous week, which means that we avoid leaking future
@@ -203,7 +221,7 @@ cases_deaths <-
   rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
 ```
 
-Then trimming outliers, most especially negative values:
+Then we'll trim outliers, especially negative values:
 
 ```{r outlier}
 cases_deaths <-
@@ -229,11 +247,12 @@ cases_deaths <-
 ```
 </details>
 
-After having downloaded and cleaned the data in `cases_deaths`, we plot a subset
-of the states, noting the actual forecast date:
+After downloading and cleaning the cases and deaths data, we can plot
+a subset of the states, marking the desired forecast date:
 
 <details>
 <summary> Plot </summary>
+
 ```{r plot_locs}
 forecast_date_label <-
   tibble(
@@ -269,15 +288,15 @@ processed_data_plot <-
 processed_data_plot
 ```
 
-To make a forecast, we will use a "canned" simple auto-regressive forecaster to
+To make a forecast, we will use a simple “canned” auto-regressive forecaster to
 predict the death rate four weeks into the future using lagged[^3] deaths and
-cases
+cases.
 
 [^3]: lagged by 3 in this context meaning using the value from 3 days ago.
 
 ```{r make-forecasts, warning=FALSE}
 four_week_ahead <- arx_forecaster(
-  cases_deaths |> filter(time_value <= forecast_date),
+  covid_case_death_rates |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
@@ -289,14 +308,14 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 ```
 
-In this case, we have used 0-3 days, a week, and two week lags for the case
-rate, while using only zero, one and two weekly lags for the death rate (as
-predictors).
+In our model setup, we are defining as our predictors case rate lagged 0-3
+days, one week, and two weeks, and death rate lagged 0-2 weeks.
 The result `four_week_ahead` is both a fitted model object which could be used
-any time in the future to create different forecasts, as well as a set of
-predicted values (and prediction intervals) for each location 28 days after the
-forecast date.
-Plotting the prediction intervals on our subset above[^2]: 
+any time in the future to create different forecasts, and a set of predicted
+values (and prediction intervals) for each location 28 days after the forecast
+date.
+
+Plotting the prediction intervals on the true values for our location subset[^2]:
 
 [^2]: Alternatively, you could call `autoplot(four_week_ahead)` to get the full
     collection of forecasts. This is too busy for the space we have for plotting
@@ -350,11 +369,10 @@ A couple of things to note:
 
 ## Getting Help
 If you encounter a bug or have a feature request, feel free to file an [issue on
-our github page](https://github.com/cmu-delphi/epipredict/issues).
+our GitHub page](https://github.com/cmu-delphi/epipredict/issues).
 For other questions, feel free to reach out to the authors, either via this
-[contact
-form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
-email, or the Insightnet slack.
+[contact form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
+email, or the InsightNet Slack.
 
 [^4]: Note that these are not the same quantiles that we fit when creating
     `four_week_ahead`. They are extrapolated from those quantiles using `extrapolate_quantiles()` (which assumes an exponential decay in the tails).
diff --git a/man/figures/README-date-1.png b/man/figures/README-date-1.png
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zG4N6voz4mRdk|}9D2ArD0W2>2cJNBd_`@G=v59l(SWJ1`HPT*q+vFoQoV;pRtF1JX
z*UC_>GfQ@(tvKqdr`22ke7AYcl&s6m{OEbZDu2EgaWM=Qdz_851ahhYs@#{etBmZo
zjyJ(0>J3}u)-Az}-k-m>BQO2#M1yrm(^sDS`SyQESk57=12e0Yt{i_%(Wqmq*Zynz
h;{Qg~{J*GG0RdsVXUq#F*OuS8i=)SxD*J0m{{~YDA=dx^

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 54f4dfcd7..d390352b6 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "Get started with epipredict"
+title: "Get started with `epipredict`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Get started with epipredict}
+  %\VignetteIndexEntry{Get started with `epipredict`}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -54,13 +54,13 @@ Towards that end, epipredict provides two main classes of tools:
       Examples include linear regression, quantile regression, or [any parsnip
       engine](https://parsnip.tidymodels.org/).
     * Postprocessor: unique to this package, and used to do things to the
-      predictions after the model has been fit, such as 
+      predictions after the model has been fit, such as
       - generate quantiles from purely point-prediction models,
       - undo operations done in the steps, such as convert back to counts from
       rates
       - generally adapt the format of the prediction to it's eventual use.
 
-The rest of the getting started will focus on using and modifying the canned forecasters. 
+The rest of the getting started will focus on using and modifying the canned forecasters.
 If you need a more complicated model, check out the [Guts
 vignette](preprocessing-and-models) for examples of using the forecaster
 framework.

From 9a8fc7cc80b84e2cc63f90e000ba7d4cb4cf24d8 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 3 Mar 2025 16:53:13 -0600
Subject: [PATCH 349/387] consistent naming, 7dav pull instead of manually

---
 README.Rmd                                    |  69 +++---
 README.md                                     | 202 ++++++++++--------
 man/figures/README-case_death-1.png           | Bin 248400 -> 0 bytes
 man/figures/README-date-1.png                 | Bin 232965 -> 265890 bytes
 man/figures/README-show-processed-data-1.png  | Bin 251187 -> 299152 bytes
 man/figures/README-show-single-forecast-1.png | Bin 290496 -> 274790 bytes
 vignettes/epipredict.Rmd                      |  31 +--
 7 files changed, 152 insertions(+), 150 deletions(-)
 delete mode 100644 man/figures/README-case_death-1.png

diff --git a/README.Rmd b/README.Rmd
index 1b24ff022..24615206b 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -77,7 +77,7 @@ scale_colour_delphi <- scale_color_delphi
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-`{epipredict}` is a framework for building transformation and forecasting pipelines for epidemiological and other panel time-series datasets.
+[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) is a framework for building transformation and forecasting pipelines for epidemiological and other panel time-series datasets.
 In addition to tools for building forecasting pipelines, it contains a number of “canned” forecasters meant to run with little modification as an easy way to get started forecasting.
 
 It is designed to work well with
@@ -111,7 +111,7 @@ The documentation for the stable version is at
 
 ## Motivating example
 
-To demonstrate using `{epipredict}` for forecasting, say we want to
+To demonstrate using [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for forecasting, say we want to
 predict COVID-19 deaths per 100k people for each of a subset of states
 
 ```{r subset_geos}
@@ -136,24 +136,33 @@ library(ggplot2)
 ```
 </details>
 
+```{r setting_cases_deaths}
+cases_deaths <- covid_case_death_rates |>
+  filter(
+    geo_value %in% used_locations,
+    time_value <= "2021-12-31"
+  )
+attr(cases_deaths, "metadata")$as_of <- as.Date("2022-01-01")
+```
 
-Below the fold, we construct this dataset as an `epiprocess::epi_df` from
-[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
+`covid_case_death_rates` is a subset of
+[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html) stored in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
+Below the fold, we clean this dataset and demonstrate pulling it from the epidata API.
 
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
-The dataset prepared here is also included ready-to-go in `{epipredict}` as `covid_case_death_rates`.
+The dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
 
 First we pull both `jhu-csse` cases and deaths data from the
 [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
-```{r case_death, warning = FALSE}
+```{r case_death, warning = FALSE, eval = FALSE}
 cases <- pub_covidcast(
   source = "jhu-csse",
-  signals = "confirmed_incidence_prop",
+  signals = "confirmed_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20211231),
@@ -163,23 +172,23 @@ cases <- pub_covidcast(
 
 deaths <- pub_covidcast(
   source = "jhu-csse",
-  signals = "deaths_incidence_prop",
+  signals = "deaths_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20211231),
   geo_values = "*"
 ) |>
   select(geo_value, time_value, death_rate = value)
+cases_deaths <-
+  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  filter(geo_value %in% used_locations) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
 ```
 
 Since visualizing the results on every geography is somewhat overwhelming,
 we’ll only train on a subset of locations.
 
 ```{r date, warning = FALSE}
-cases_deaths <-
-  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
-  filter(geo_value %in% used_locations) |>
-  as_epi_df(as_of = as.Date("2022-01-01"))
 # plotting the data as it was downloaded
 cases_deaths |>
   autoplot(
@@ -199,29 +208,7 @@ cases_deaths |>
 As with the typical dataset, we will need to do some cleaning to
 make it actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
-
-First, to reduce noise from daily reporting, we will compute a 7 day
-average over a trailing window[^1]:
-
-[^1]: This makes it so that any given day of the processed time-series only
-    depends on the previous week, which means that we avoid leaking future
-    values when making a forecast.
-
-```{r smooth}
-cases_deaths <-
-  cases_deaths |>
-  group_by(geo_value) |>
-  epi_slide(
-    cases_7dav = mean(case_rate, na.rm = TRUE),
-    death_rate_7dav = mean(death_rate, na.rm = TRUE),
-    .window_size = 7
-  ) |>
-  ungroup() |>
-  mutate(case_rate = NULL, death_rate = NULL) |>
-  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
-```
-
-Then we'll trim outliers, especially negative values:
+Specifically we'll trim outliers, especially negative values:
 
 ```{r outlier}
 cases_deaths <-
@@ -262,7 +249,7 @@ forecast_date_label <-
     heights = c(rep(150, 4), rep(0.75, 4))
   )
 processed_data_plot <-
-  covid_case_death_rates |>
+  cases_deaths |>
   filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
@@ -296,7 +283,7 @@ cases.
 
 ```{r make-forecasts, warning=FALSE}
 four_week_ahead <- arx_forecaster(
-  covid_case_death_rates |> filter(time_value <= forecast_date),
+  cases_deaths |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
@@ -317,9 +304,9 @@ date.
 
 Plotting the prediction intervals on the true values for our location subset[^2]:
 
-[^2]: Alternatively, you could call `autoplot(four_week_ahead)` to get the full
-    collection of forecasts. This is too busy for the space we have for plotting
-    here.
+[^2]: Alternatively, you could call `autoplot(four_week_ahead, plot_data =
+    cases_deaths)` to get the full collection of forecasts. This is too busy for
+    the space we have for plotting here.
 
 <details>
 <summary> Plot </summary>
@@ -354,7 +341,7 @@ four_week_ahead$predictions |>
   pivot_quantiles_longer(.pred_distn)
 ```
 
-The black dot gives the median prediction, while the blue intervals give the
+The yellow dot gives the median prediction, while the blue intervals give the
 25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges[^4].
 For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
diff --git a/README.md b/README.md
index bc9e01c7c..03dbc5622 100644
--- a/README.md
+++ b/README.md
@@ -8,28 +8,28 @@
 [![R-CMD-check](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cmu-delphi/epipredict/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-`{epipredict}` is a framework for building transformation and forecasting
-pipelines for epidemiological and other panel time-series datasets. In
-addition to tools for building forecasting pipelines, it contains a
-number of “canned” forecasters meant to run with little modification as
-an easy way to get started forecasting.
+[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) is a
+framework for building transformation and forecasting pipelines for
+epidemiological and other panel time-series datasets. In addition to
+tools for building forecasting pipelines, it contains a number of
+“canned” forecasters meant to run with little modification as an easy
+way to get started forecasting.
 
 It is designed to work well with
-[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/), a utility for
-time series handling and geographic processing in an
-epidemiological context. Both of the packages are meant to work well
-with the panel data provided by
-[`{epidatr}`](https://cmu-delphi.github.io/epidatr/).
-Pre-compiled example datasets are also availalbe in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
+[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/), a utility
+for time series handling and geographic processing in an epidemiological
+context. Both of the packages are meant to work well with the panel data
+provided by [`{epidatr}`](https://cmu-delphi.github.io/epidatr/).
+Pre-compiled example datasets are also available in
+[`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
 
-If you are looking for detail beyond the package documentation, see
-our [forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
+If you are looking for detail beyond the package documentation, see our
+[forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 ## Installation
 
-Unless you’re planning on contributing to package
-development, we suggest using the stable version.
-To install, run:
+Unless you’re planning on contributing to package development, we
+suggest using the stable version. To install, run:
 
 ``` r
 # Stable version
@@ -45,8 +45,25 @@ is at <https://cmu-delphi.github.io/epipredict/dev>.
 
 ## Motivating example
 
-To demonstrate the kind of forecast `{epipredict}` can make, say we want to
-predict COVID-19 deaths per 100k people for each state on 2021-08-01.
+To demonstrate using
+[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for
+forecasting, say we want to predict COVID-19 deaths per 100k people for
+each of a subset of states
+
+``` r
+used_locations <- c("ca", "ma", "ny", "tx")
+```
+
+on
+
+``` r
+forecast_date <- as.Date("2021-08-01")
+```
+
+<details>
+<summary>
+Required packages
+</summary>
 
 ``` r
 library(epipredict)
@@ -54,28 +71,43 @@ library(epidatr)
 library(epiprocess)
 library(dplyr)
 library(ggplot2)
+```
 
-forecast_date <- as.Date("2021-08-01")
+</details>
+
+``` r
+cases_deaths <- covid_case_death_rates |>
+  filter(geo_value %in% used_locations,
+         time_value <= "2021-12-31")
+attr(cases_deaths, "metadata")$as_of <- as.Date("2022-01-01")
 ```
 
-Below the fold, we construct this dataset as an `epiprocess::epi_df`
-from [Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
+`covid_case_death_rates` is a subset of [Johns Hopkins Center for
+Systems Science and Engineering deaths
+data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
+stored in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
+Below the fold, we clean this dataset and demonstrate pulling it from
+the epidata API.
 
 <details>
 <summary>
 Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
-This section is intended to demonstrate some of the ubiquitous cleaning operations needed
-to be able to forecast.
-The dataset prepared here is also included ready-to-go in `{epipredict}` as `covid_case_death_rates`.
+This section is intended to demonstrate some of the ubiquitous cleaning
+operations needed to be able to forecast. The dataset prepared here is
+also included ready-to-go in
+[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as
+`covid_case_death_rates`.
 
-First we pull both `jhu-csse` cases and deaths data from the [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
+First we pull both `jhu-csse` cases and deaths data from the [Delphi
+API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html)
+using the [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
 ``` r
 cases <- pub_covidcast(
   source = "jhu-csse",
-  signals = "confirmed_incidence_prop",
+  signals = "confirmed_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20211231),
@@ -85,24 +117,23 @@ cases <- pub_covidcast(
 
 deaths <- pub_covidcast(
   source = "jhu-csse",
-  signals = "deaths_incidence_prop",
+  signals = "deaths_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20211231),
   geo_values = "*"
 ) |>
   select(geo_value, time_value, death_rate = value)
+cases_deaths <-
+  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
+  filter(geo_value %in% used_locations) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
 ```
 
 Since visualizing the results on every geography is somewhat
 overwhelming, we’ll only train on a subset of locations.
 
 ``` r
-used_locations <- c("ca", "ma", "ny", "tx")
-cases_deaths <-
-  full_join(cases, deaths, by = c("time_value", "geo_value")) |>
-  filter(geo_value %in% used_locations) |>
-  as_epi_df(as_of = as.Date("2022-01-01"))
 # plotting the data as it was downloaded
 cases_deaths |>
   autoplot(
@@ -121,27 +152,10 @@ cases_deaths |>
 
 <img src="man/figures/README-date-1.png" width="90%" style="display: block; margin: auto;" />
 
-As with the typical dataset, we will need to do some cleaning to make it actually usable; we’ll use some utilities from
+As with the typical dataset, we will need to do some cleaning to make it
+actually usable; we’ll use some utilities from
 [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) for this.
-
-First, to reduce the noise from daily reporting, we will compute a
-7 day average over a trailing window[^1]:
-
-``` r
-cases_deaths <-
-  cases_deaths |>
-  group_by(geo_value) |>
-  epi_slide(
-    cases_7dav = mean(case_rate, na.rm = TRUE),
-    death_rate_7dav = mean(death_rate, na.rm = TRUE),
-    .window_size = 7
-  ) |>
-  ungroup() |>
-  mutate(case_rate = NULL, death_rate = NULL) |>
-  rename(case_rate = cases_7dav, death_rate = death_rate_7dav)
-```
-
-Then we'll trim outliers, especially negative values:
+Specifically we’ll trim outliers, especially negative values:
 
 ``` r
 cases_deaths <-
@@ -168,8 +182,8 @@ cases_deaths <-
 
 </details>
 
-After downloading and cleaning the cases and deaths data, we can plot
-a subset of the states, marking the desired forecast date:
+After downloading and cleaning the cases and deaths data, we can plot a
+subset of the states, marking the desired forecast date:
 
 <details>
 <summary>
@@ -177,7 +191,6 @@ Plot
 </summary>
 
 ``` r
-used_locations <- c("ca", "ma", "ny", "tx")
 forecast_date_label <-
   tibble(
     geo_value = rep(used_locations, 2),
@@ -185,8 +198,8 @@ forecast_date_label <-
     dates = rep(forecast_date - 7 * 2, 2 * length(used_locations)),
     heights = c(rep(150, 4), rep(0.75, 4))
   )
-
-covid_case_death_rates |>
+processed_data_plot <-
+  cases_deaths |>
   filter(geo_value %in% used_locations) |>
   autoplot(
     case_rate,
@@ -214,11 +227,11 @@ covid_case_death_rates |>
 
 To make a forecast, we will use a simple “canned” auto-regressive
 forecaster to predict the death rate four weeks into the future using
-lagged[^2] deaths and cases.
+lagged[^1] deaths and cases.
 
 ``` r
 four_week_ahead <- arx_forecaster(
-  covid_case_death_rates |> filter(time_value <= forecast_date),
+  cases_deaths |> filter(time_value <= forecast_date),
   outcome = "death_rate",
   predictors = c("case_rate", "death_rate"),
   args_list = arx_args_list(
@@ -229,31 +242,33 @@ four_week_ahead <- arx_forecaster(
 )
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
-#>
-#> This forecaster was fit on 2025-02-10 12:09:58.
-#>
+#> 
+#> This forecaster was fit on 2025-03-03 14:43:07.
+#> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
 #> • Using data up-to-date as of: 2022-01-01.
 #> • With the last data available on 2021-08-01
-#>
+#> 
 #> ── Predictions ──────────────────────────────────────────────────────────────
-#>
+#> 
 #> A total of 4 predictions are available for
 #> • 4 unique geographic regions,
 #> • At forecast date: 2021-08-01,
 #> • For target date: 2021-08-29,
-#>
+#> 
 ```
 
-In our model setup, we are defining as our predictors case rate lagged 0-3 days, one week, and two weeks, and death rate lagged 0-2 weeks.
-The result `four_week_ahead` is both a fitted
-model object which could be used any time in the future to create
-different forecasts, and a set of predicted values (and
-prediction intervals) for each location 28 days after the forecast date.
+In our model setup, we are defining as our predictors case rate lagged
+0-3 days, one week, and two weeks, and death rate lagged 0-2 weeks. The
+result `four_week_ahead` is both a fitted model object which could be
+used any time in the future to create different forecasts, and a set of
+predicted values (and prediction intervals) for each location 28 days
+after the forecast date.
 
-Plotting the prediction intervals on the true values for our location subset[^3]:
+Plotting the prediction intervals on the true values for our location
+subset[^2]:
 
 <details>
 <summary>
@@ -283,7 +298,7 @@ forecast_plot <-
 
 <img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
 
-And as a tibble of quantile-value pairs:
+And as a tibble of quantile level-value pairs:
 
 ``` r
 four_week_ahead$predictions |>
@@ -291,22 +306,21 @@ four_week_ahead$predictions |>
   pivot_quantiles_longer(.pred_distn)
 #> # A tibble: 20 × 5
 #>   geo_value values quantile_levels forecast_date target_date
-#>   <chr>      <dbl>           <dbl> <date>        <date>
-#> 1 ca        0.199             0.1  2021-08-01    2021-08-29
-#> 2 ca        0.285             0.25 2021-08-01    2021-08-29
-#> 3 ca        0.345             0.5  2021-08-01    2021-08-29
-#> 4 ca        0.405             0.75 2021-08-01    2021-08-29
-#> 5 ca        0.491             0.9  2021-08-01    2021-08-29
-#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29
+#>   <chr>      <dbl>           <dbl> <date>        <date>     
+#> 1 ca        0.0425            0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.0803            0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.115             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.150             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.187             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0                 0.1  2021-08-01    2021-08-29 
 #> # ℹ 14 more rows
 ```
 
-The orange dot gives the predicted median, and the blue intervals give
-the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^4]. For
-this particular day and these locations, the forecasts are relatively
-accurate, with the true data being at worst within the 10-90% interval.
-
-A couple of things to note:
+The yellow dot gives the median prediction, while the blue intervals
+give the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^3].
+For this particular day and these locations, the forecasts are
+relatively accurate, with the true data being at least within the 10-90%
+interval. A couple of things to note:
 
 1.  Our methods are primarily direct forecasters; this means we don’t
     need to predict 1, 2,…, 27 days ahead to then predict 28 days ahead
@@ -322,21 +336,19 @@ If you encounter a bug or have a feature request, feel free to file an
 [issue on our GitHub
 page](https://github.com/cmu-delphi/epipredict/issues). For other
 questions, feel free to reach out to the authors, either via this
-[contact form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
+[contact
+form](https://docs.google.com/forms/d/e/1FAIpQLScqgT1fKZr5VWBfsaSp-DNaN03aV6EoZU4YljIzHJ1Wl_zmtg/viewform),
 email, or the InsightNet Slack.
 
-[^1]: This makes it so that any given day of the processed time-series
-    only depends on the previous week, which means that we avoid leaking
-    future values when making a forecast.
-
-[^2]: lagged by 3 in this context meaning using the value from 3 days
+[^1]: lagged by 3 in this context meaning using the value from 3 days
     ago.
 
-[^3]: Alternatively, you could call `autoplot(four_week_ahead)` to get
-    the full collection of forecasts. This is too busy for the space we
-    have for plotting here.
+[^2]: Alternatively, you could call
+    `autoplot(four_week_ahead, plot_data = cases_deaths)` to get the
+    full collection of forecasts. This is too busy for the space we have
+    for plotting here.
 
-[^4]: Note that these are not the same quantiles that we fit when
+[^3]: Note that these are not the same quantiles that we fit when
     creating `four_week_ahead`. They are extrapolated from those
     quantiles using `extrapolate_quantiles()` (which assumes an
     exponential decay in the tails).
diff --git a/man/figures/README-case_death-1.png b/man/figures/README-case_death-1.png
deleted file mode 100644
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diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index d390352b6..8c304756f 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -339,24 +339,27 @@ engine (which at the moment parsnip doesn't support), or one needs to map over
 geographies.
 
 ```{r fit_non_geo_pooled, warning=FALSE}
-geo_values <- covid_case_death_rates |> pull(geo_value) |> unique()
+geo_values <- covid_case_death_rates |>
+  pull(geo_value) |>
+  unique()
 
 all_fits <-
   purrr::map(geo_values, \(geo) {
-  covid_case_death_rates |>
-    filter(
-      geo_value == geo,
-      time_value <= forecast_date) |>
-    arx_forecaster(
-      outcome = "death_rate",
-      trainer = linear_reg(),
-      predictors = c("death_rate"),
-      args_list = arx_args_list(
-        lags = list(c(0, 7, 14)),
-        ahead = 14
+    covid_case_death_rates |>
+      filter(
+        geo_value == geo,
+        time_value <= forecast_date
+      ) |>
+      arx_forecaster(
+        outcome = "death_rate",
+        trainer = linear_reg(),
+        predictors = c("death_rate"),
+        args_list = arx_args_list(
+          lags = list(c(0, 7, 14)),
+          ahead = 14
+        )
       )
-    )
-})
+  })
 map_df(all_fits, ~ pluck(., "predictions"))
 ```
 

From 97ded60fe7a1a68bafd82dd774c99a646745349d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 4 Mar 2025 11:07:56 -0600
Subject: [PATCH 350/387] going back to just using the API call

---
 README.Rmd                        | 38 +++++++++++---------------
 README.md                         | 45 +++++++++++++------------------
 vignettes/custom_epiworkflows.Rmd |  1 +
 3 files changed, 34 insertions(+), 50 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 24615206b..23a8f6bc1 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -111,18 +111,6 @@ The documentation for the stable version is at
 
 ## Motivating example
 
-To demonstrate using [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for forecasting, say we want to
-predict COVID-19 deaths per 100k people for each of a subset of states
-
-```{r subset_geos}
-used_locations <- c("ca", "ma", "ny", "tx")
-```
-
-on
-
-```{r fc_date}
-forecast_date <- as.Date("2021-08-01")
-```
 
 <details>
 <summary> Required packages </summary>
@@ -136,24 +124,28 @@ library(ggplot2)
 ```
 </details>
 
-```{r setting_cases_deaths}
-cases_deaths <- covid_case_death_rates |>
-  filter(
-    geo_value %in% used_locations,
-    time_value <= "2021-12-31"
-  )
-attr(cases_deaths, "metadata")$as_of <- as.Date("2022-01-01")
+To demonstrate using [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for forecasting, say we want to
+predict COVID-19 deaths per 100k people for each of a subset of states
+
+```{r subset_geos}
+used_locations <- c("ca", "ma", "ny", "tx")
+```
+
+on
+
+```{r fc_date}
+forecast_date <- as.Date("2021-08-01")
 ```
 
-`covid_case_death_rates` is a subset of
-[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html) stored in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
-Below the fold, we clean this dataset and demonstrate pulling it from the epidata API.
+We will be using a subset of
+[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
+Below the fold, we pull the dataset from the epidata API and clean it.
 
 <details>
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
-The dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
+A subset of this dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
 
 First we pull both `jhu-csse` cases and deaths data from the
 [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
diff --git a/README.md b/README.md
index 03dbc5622..9932b5f5a 100644
--- a/README.md
+++ b/README.md
@@ -45,21 +45,6 @@ is at <https://cmu-delphi.github.io/epipredict/dev>.
 
 ## Motivating example
 
-To demonstrate using
-[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for
-forecasting, say we want to predict COVID-19 deaths per 100k people for
-each of a subset of states
-
-``` r
-used_locations <- c("ca", "ma", "ny", "tx")
-```
-
-on
-
-``` r
-forecast_date <- as.Date("2021-08-01")
-```
-
 <details>
 <summary>
 Required packages
@@ -75,19 +60,25 @@ library(ggplot2)
 
 </details>
 
+To demonstrate using
+[`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for
+forecasting, say we want to predict COVID-19 deaths per 100k people for
+each of a subset of states
+
 ``` r
-cases_deaths <- covid_case_death_rates |>
-  filter(geo_value %in% used_locations,
-         time_value <= "2021-12-31")
-attr(cases_deaths, "metadata")$as_of <- as.Date("2022-01-01")
+used_locations <- c("ca", "ma", "ny", "tx")
+```
+
+on
+
+``` r
+forecast_date <- as.Date("2021-08-01")
 ```
 
-`covid_case_death_rates` is a subset of [Johns Hopkins Center for
-Systems Science and Engineering deaths
-data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
-stored in [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
-Below the fold, we clean this dataset and demonstrate pulling it from
-the epidata API.
+We will be using a subset of [Johns Hopkins Center for Systems Science
+and Engineering deaths
+data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
+Below the fold, we pull the dataset from the epidata API and clean it.
 
 <details>
 <summary>
@@ -95,8 +86,8 @@ Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning
-operations needed to be able to forecast. The dataset prepared here is
-also included ready-to-go in
+operations needed to be able to forecast. A subset of this dataset
+prepared here is also included ready-to-go in
 [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as
 `covid_case_death_rates`.
 
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index e5c2102f3..400fce26c 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -17,6 +17,7 @@ library(parsnip)
 library(workflows)
 library(recipes)
 library(epipredict)
+library(epiprocess)
 library(ggplot2)
 forecast_date <- as.Date("2021-08-01")
 used_locations <- c("ca", "ma", "ny", "tx")

From 2a29a6bc499f7884128f8195f6ca550c74afc097 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 4 Mar 2025 11:19:02 -0600
Subject: [PATCH 351/387] recipes version, include epiprocess in the rmds

---
 DESCRIPTION              | 2 +-
 vignettes/epipredict.Rmd | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 3ec5fc38b..0fcae7990 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -39,7 +39,7 @@ Imports:
     lifecycle,
     lubridate,
     magrittr,
-    recipes (>= 1.0.4),
+    recipes (>= 1.1.1),
     rlang (>= 1.1.0),
     stats,
     tibble,
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 8c304756f..f9ba9dabc 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -18,6 +18,7 @@ library(workflows)
 library(recipes)
 library(epidatasets)
 library(epipredict)
+library(epiprocess)
 library(ggplot2)
 library(purrr)
 forecast_date <- as.Date("2021-08-01")

From bd72da58c82f2efbc1f8ac2824f099853e864876 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 4 Mar 2025 12:27:47 -0500
Subject: [PATCH 352/387] rebuild landing page

---
 README.Rmd                                    |  10 +++----
 README.md                                     |  25 +++++++++---------
 man/figures/README-date-1.png                 | Bin 265890 -> 239191 bytes
 man/figures/README-show-single-forecast-1.png | Bin 274790 -> 261019 bytes
 4 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 23a8f6bc1..e52112ba2 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -145,13 +145,13 @@ Below the fold, we pull the dataset from the epidata API and clean it.
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
-A subset of this dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
+A subset of the dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
 
 First we pull both `jhu-csse` cases and deaths data from the
 [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
-```{r case_death, warning = FALSE, eval = FALSE}
+```{r case_death, warning = FALSE, eval = TRUE}
 cases <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
@@ -287,7 +287,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 ```
 
-In our model setup, we are defining as our predictors case rate lagged 0-3
+In our model setup, we are defining as predictors case rate lagged 0-3
 days, one week, and two weeks, and death rate lagged 0-2 weeks.
 The result `four_week_ahead` is both a fitted model object which could be used
 any time in the future to create different forecasts, and a set of predicted
@@ -339,8 +339,8 @@ For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
 
-1. Our methods are primarily direct forecasters; this means we don't need to
-   predict 1, 2,..., 27 days ahead to then predict 28 days ahead
+1. `epipredict` methods are primarily direct forecasters; this means we don't need to
+   predict 1, 2,..., 27 days ahead to then predict 28 days ahead.
 2. All of our existing engines are geo-pooled, meaning the training data is
    shared across geographies. This has the advantage of increasing the amount of
    available training data, with the restriction that the data needs to be on
diff --git a/README.md b/README.md
index 9932b5f5a..868c105fb 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning
-operations needed to be able to forecast. A subset of this dataset
+operations needed to be able to forecast. A subset of the dataset
 prepared here is also included ready-to-go in
 [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as
 `covid_case_death_rates`.
@@ -234,7 +234,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-03-03 14:43:07.
+#> This forecaster was fit on 2025-03-04 12:12:54.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -251,8 +251,8 @@ four_week_ahead
 #> 
 ```
 
-In our model setup, we are defining as our predictors case rate lagged
-0-3 days, one week, and two weeks, and death rate lagged 0-2 weeks. The
+In our model setup, we are defining as predictors case rate lagged 0-3
+days, one week, and two weeks, and death rate lagged 0-2 weeks. The
 result `four_week_ahead` is both a fitted model object which could be
 used any time in the future to create different forecasts, and a set of
 predicted values (and prediction intervals) for each location 28 days
@@ -298,12 +298,12 @@ four_week_ahead$predictions |>
 #> # A tibble: 20 × 5
 #>   geo_value values quantile_levels forecast_date target_date
 #>   <chr>      <dbl>           <dbl> <date>        <date>     
-#> 1 ca        0.0425            0.1  2021-08-01    2021-08-29 
-#> 2 ca        0.0803            0.25 2021-08-01    2021-08-29 
-#> 3 ca        0.115             0.5  2021-08-01    2021-08-29 
-#> 4 ca        0.150             0.75 2021-08-01    2021-08-29 
-#> 5 ca        0.187             0.9  2021-08-01    2021-08-29 
-#> 6 ma        0                 0.1  2021-08-01    2021-08-29 
+#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
 #> # ℹ 14 more rows
 ```
 
@@ -313,8 +313,9 @@ For this particular day and these locations, the forecasts are
 relatively accurate, with the true data being at least within the 10-90%
 interval. A couple of things to note:
 
-1.  Our methods are primarily direct forecasters; this means we don’t
-    need to predict 1, 2,…, 27 days ahead to then predict 28 days ahead
+1.  `epipredict` methods are primarily direct forecasters; this means we
+    don’t need to predict 1, 2,…, 27 days ahead to then predict 28 days
+    ahead.
 2.  All of our existing engines are geo-pooled, meaning the training
     data is shared across geographies. This has the advantage of
     increasing the amount of available training data, with the
diff --git a/man/figures/README-date-1.png b/man/figures/README-date-1.png
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From e638288d4802f7072c09369b942a54a2a7cef38a Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Tue, 4 Mar 2025 17:56:51 -0500
Subject: [PATCH 353/387] first half epipredict.Rmd

---
 vignettes/epipredict.Rmd | 140 +++++++++++++++++++++------------------
 1 file changed, 74 insertions(+), 66 deletions(-)

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index f9ba9dabc..338187c16 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "Get started with `epipredict`"
+title: "Get started with `{epipredict}`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Get started with `epipredict`}
+  %\VignetteIndexEntry{Get started with `{epipredict}`}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -26,45 +26,52 @@ used_locations <- c("ca", "ma", "ny", "tx")
 library(epidatr)
 ```
 
-At a high level, the goal of `{epipredict}` is to make running simple machine
-learning / statistical forecasters for epidemiology easy.
-To do this, we have extended several [tidymodels](https://www.tidymodels.org/)
-packages to handle the case of panel time-series data.
-Our hope is that it is easy for users with epi training and some statistics to
-fit baseline models while still allowing those with more nuanced statistical
-understanding to create complicated specializations using the same framework.
-Towards that end, epipredict provides two main classes of tools:
-
-1. A set of basic, easy-to-use "canned" forecasters that work out of the box.
-    For the basic forecasters, we currently provide:
-    * Flatline forecaster: predicts a median that is the last value
-      with increasingly wide quantiles.
-    * Autoregressive forecaster: fits a model (e.g. linear regression) on
-      lagged data to predict quantiles for continuous values.
-    * Autoregressive classifier: fits a model (e.g. logistic regression) on
-      lagged data to predict a binned version of the growth rate.
-    * CDC FluSight flatline forecaster: a variant of the flatline forecaster as
-      used by the CDC in FluSight.
-2. A framework for creating custom forecasters out of modular components, from
-   which the canned forecasters were created.  There are three types of
-   components:
-    * Preprocessor: do things to the data before model training, such as convert
-      counts to rates, create smoothed columns, or [any of the recipes
-      steps](https://recipes.tidymodels.org/reference/index.html)
-    * Trainer: train a model on data, resulting in a fitted model object.
-      Examples include linear regression, quantile regression, or [any parsnip
-      engine](https://parsnip.tidymodels.org/).
-    * Postprocessor: unique to this package, and used to do things to the
-      predictions after the model has been fit, such as
-      - generate quantiles from purely point-prediction models,
-      - undo operations done in the steps, such as convert back to counts from
-      rates
-      - generally adapt the format of the prediction to it's eventual use.
-
-The rest of the getting started will focus on using and modifying the canned forecasters.
-If you need a more complicated model, check out the [Guts
-vignette](preprocessing-and-models) for examples of using the forecaster
-framework.
+At a high level, the goal of `{epipredict}` is to make it easy to run simple machine
+learning and statistical forecasters for epidemiological data.
+To do this, we have extended the [tidymodels](https://www.tidymodels.org/)
+framework to handle the case of panel time-series data.
+
+Our hope is that it is easy for users with epidemiological training and some statistical knowledge to
+fit baseline models, while also allowing those with more nuanced statistical
+understanding to create complex custom models using the same framework.
+Towards that end, `{epipredict}` provides two main classes of tools:
+
+## Canned forecasters
+
+A set of basic, easy-to-use "canned" forecasters that work out of the box.
+We currently provide the following basic forecasters:
+  
+  * _Flatline forecaster_: predicts as the median the most recently seen value 
+    with increasingly wide quantiles.
+  * _Autoregressive forecaster_: fits a model (e.g. linear regression) on
+    lagged data to predict quantiles for continuous values.
+  * _Autoregressive classifier_: fits a model (e.g. logistic regression) on
+    lagged data to predict a binned version of the growth rate.
+  * _CDC FluSight flatline forecaster_: a variant of the flatline forecaster that is
+    used as a baseline in the CDC's [FluSight forecasting competition](https://www.cdc.gov/flu-forecasting/about/index.html).
+
+## Forecasting framework
+
+A framework for creating custom forecasters out of modular components, from
+which the canned forecasters were created.  There are three types of
+components:
+ 
+  * _Preprocessor_: transform the data before model training, such as converting
+    counts to rates, creating smoothed columns, or [any `{recipes}`
+    `step`](https://recipes.tidymodels.org/reference/index.html)
+  * _Trainer_: train a model on data, resulting in a fitted model object.
+    Examples include linear regression, quantile regression, or [any `{parsnip}`
+    engine](https://parsnip.tidymodels.org/reference/index.html).
+  * _Postprocessor_: unique to `{epipredict}`; used to transform the
+    predictions after the model has been fit, such as
+    - generating quantiles from purely point-prediction models,
+    - reverting operations done in the `step`s, such as converting from
+    rates back to counts
+    - generally adapting the format of the prediction to its eventual use.
+
+The rest of the "getting started" vignette will focus on using and modifying the canned forecasters.
+Check out the ["guts" vignette](preprocessing-and-models) for examples of using the forecaster
+framework to make more complex, custom forecasters.
 
 If you are interested in time series in a non-panel data context, you may also
 want to look at `{timetk}` and `{modeltime}` for some related techniques.
@@ -76,24 +83,26 @@ For a more in-depth treatment with some practical applications, see also the
 ## Example data
 
 The forecasting methods in this package are designed to work with panel time
-series data, specifically in the form of an `epi_df` from the `{epiprocess}`
+series data in `epi_df` format as made available in the `{epiprocess}`
 package.
-This is a collection of one or more time-series indexed by one or more
+An `epi_df` is a collection of one or more time-series indexed by one or more
 categorical variables.
-For example, on the landing page:
+The [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/) package makes several
+pre-compiled example datasets available.
+Let's look at an example `epi_df`:
 
 ```{r data_ex}
 covid_case_death_rates
 ```
 
-`geo_value` is the only key for this dataset, while there are two separate
-time-series, `case_rate` and `death_rate`.
-The keys are represented in "long" format (so separate columns for the key and
-the value), while separate time series are represented in "wide" format (so each
-time-series has a separate column).
+This dataset uses a single key, `geo_value`, and two separate
+time series, `case_rate` and `death_rate`.
+The keys are represented in "long" format, with separate columns for the key and
+the value, while separate time series are represented in "wide" format with each
+time series stored in a separate column.
 
 `{epiprocess}` is designed to handle data that always has a geographic key, and
-potentially other key values, such as age, ethnicity or other demographic
+potentially other key values, such as age, ethnicity, or other demographic
 information.
 For example, `grad_employ_subset` from `{epidatasets}` also has both `age_group`
 and `edu_qual` as additional keys:
@@ -102,8 +111,7 @@ and `edu_qual` as additional keys:
 grad_employ_subset
 ```
 
-See `{epiprocess}` for more details
-on the format.
+See `{epiprocess}` for [more details on the `epi_df` format](https://cmu-delphi.github.io/epiprocess/articles/epi_df.html).
 
 Panel time series are ubiquitous in epidemiology, but are also common in
 economics, psychology, sociology, and many other areas.
@@ -111,11 +119,12 @@ While this package was designed with epidemiology in mind, many of the
 techniques are more broadly applicable.
 
 ## Customizing `arx_forecaster()`
-Moving on from the example on the [landing
-page](../index.html#motivating-example), let's adjust some parameters for
+Let's expand on the basic example presented on the [landing
+page](../index.html#motivating-example), starting with adjusting some parameters in
 `arx_forecaster()`.
-`trainer` allows us to set a different fitting engines, either one of the
-included ones, such as `quantile_reg()`, or one of the relevant [parsnip
+
+The `trainer` argument allows us to set the fitting engine. We can use either one of the
+included engines, such as `quantile_reg()`, or one of the relevant [parsnip
 models](https://www.tidymodels.org/find/parsnip/):
 
 ```{r make-forecasts, warning=FALSE}
@@ -135,9 +144,9 @@ hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 The default trainer is `parsnip::linear_reg()`, which generates quantiles after
 the fact in the post-processing layers, rather than as part of the model.
 While this does work, it is generally preferable to use `quantile_reg()`, as the
-quantiles generated in this manner can be poorly behaved.
-`quantile_reg()` on the other hand directly estimates different linear models
-for each quantile, reflected in the 3 different columns for `tau` above.
+quantiles generated in post-processing can be poorly behaved.
+`quantile_reg()` on the other hand directly estimates a different linear model
+for each quantile, reflected in the several different columns for `tau` above.
 
 Because of the flexibility of `{parsnip}`, there are a whole host of models
 available to us[^5]; as an example, we could have just as easily substituted a
@@ -177,16 +186,15 @@ two_week_ahead <- arx_forecaster(
 hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 ```
 
-See the function documentation of `arx_args_list()` for more examples of the modifications available.
-If you are looking for even further modifications, you will want a custom
-workflow, for which you should see the [guts
-vignette](preprocessing-and-models).
+See the function documentation for `arx_args_list()` for more examples of the modifications available.
+If you want to make further modifications, you will need a custom
+workflow; see the ["guts" vignette](custom_epiworkflows) for details.
 
 ## Generating multiple aheads
 Frequently, one doesn't want just a forecast for a single day, but a trajectory
 of forecasts for several weeks.
-The way to do this using `arx_forecaster()` is by looping over aheads; for
-example, to predict every day over 4 weeks:
+We can do this with `arx_forecaster()` by looping over aheads; for
+example, to predict every day over a 4-week time period:
 
 ```{r temp-thing}
 all_canned_results <- lapply(
@@ -428,7 +436,7 @@ An `epi_workflow()` consists of 3 parts:
   5 of as these well. You can inspect the layers more closely by running
   `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
 
-See the [Guts vignette](custom_epiworkflows) for recreating and then
+See the ["guts" vignette](custom_epiworkflows) for recreating and then
 extending `four_week_ahead` using the custom forecaster framework.
 
 ## Mathematical description

From 93a9e8c2d6a816d5d6ceaae2615a4ecb66d40944 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 4 Mar 2025 17:30:14 -0600
Subject: [PATCH 354/387] custom header, dropping arx_classifier smooth-qr

---
 R/arx_classifier.R               | 104 +++++-
 README.md                        |  14 +-
 _pkgdown.yml                     |  32 +-
 man/arx_classifier.Rd            | 118 ++++++-
 vignettes/articles/smooth-qr.Rmd | 544 -------------------------------
 vignettes/arx-classifier.Rmd     | 274 ----------------
 6 files changed, 242 insertions(+), 844 deletions(-)
 delete mode 100644 vignettes/articles/smooth-qr.Rmd
 delete mode 100644 vignettes/arx-classifier.Rmd

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index bc8783610..456796790 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -1,8 +1,106 @@
 #' Direct autoregressive classifier with covariates
 #'
-#' This is an autoregressive classification model for
-#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
-#' that it estimates a class at a particular target horizon.
+#'
+#' @description
+#' This is an autoregressive classification model for continuous data. It does
+#'   "direct" forecasting, meaning that it estimates a class at a particular
+#'   target horizon.
+#'
+#' @details
+#' The `arx_classifier()` is an autoregressive classification model for `epi_df`
+#'   data that is used to predict a discrete class for each case under
+#'   consideration.  It is a direct forecaster in that it estimates the classes
+#'   at a specific horizon or ahead value.
+#'
+#' To get a sense of how the `arx_classifier()` works, let's consider a simple
+#'   example with minimal inputs. For this, we will use the built-in
+#'   `covid_case_death_rates` that contains confirmed COVID-19 cases and deaths
+#'   from JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this,
+#'   we'll take a subset of data for five states over June 4, 2021 to December
+#'   31, 2021. Our objective is to predict whether the case rates are increasing
+#'   when considering the 0, 7 and 14 day case rates:
+#'
+#' ```{r}
+#' jhu <- covid_case_death_rates %>%
+#'   filter(
+#'     time_value >= "2021-06-04",
+#'     time_value <= "2021-12-31",
+#'     geo_value %in% c("ca", "fl", "tx", "ny", "nj")
+#'   )
+#'
+#' out <- arx_classifier(jhu, outcome = "case_rate", predictors = "case_rate")
+#'
+#' out$predictions
+#' ```
+#'
+#' The key takeaway from the predictions is that there are two prediction
+#'   classes: (-Inf, 0.25] and (0.25, Inf). This is because for our goal of
+#'   classification the classes must be discrete. The discretization of the
+#'   real-valued outcome is controlled by the `breaks` argument, which defaults
+#'   to 0.25. Such breaks will be automatically extended to cover the entire
+#'   real line. For example, the default break of 0.25 is silently extended to
+#'   breaks = c(-Inf, .25, Inf) and, therefore, results in two classes: [-Inf,
+#'   0.25] and (0.25, Inf). These two classes are used to discretize the
+#'   outcome. The conversion of the outcome to such classes is handled
+#'   internally. So if discrete classes already exist for the outcome in the
+#'   `epi_df`, then we recommend to code a classifier from scratch using the
+#'   `epi_workflow` framework for more control.
+#'
+#' The `trainer` is a `parsnip` model describing the type of estimation such
+#'   that `mode = "classification"` is enforced. The two typical trainers that
+#'   are used are `parsnip::logistic_reg()` for two classes or
+#'   `parsnip::multinom_reg()` for more than two classes.
+#'
+#' ```{r}
+#' workflows::extract_spec_parsnip(out$epi_workflow)
+#' ```
+#'
+#' From the parsnip model specification, we can see that the trainer used is
+#'   logistic regression, which is expected for our binary outcome. More
+#'   complicated trainers like `parsnip::naive_Bayes()` or
+#'   `parsnip::rand_forest()` may also be used (however, we will stick to the
+#'   basics in this gentle introduction to the classifier).
+#'
+#' If you use the default trainer of logistic regression for binary
+#'   classification and you decide against using the default break of 0.25, then
+#'   you should only input one break so that there are two classification bins
+#'   to properly dichotomize the outcome. For example, let's set a break of 0.5
+#'   instead of relying on the default of 0.25. We can do this by passing 0.5 to
+#'   the `breaks` argument in `arx_class_args_list()` as follows:
+#'
+#' ```{r}
+#' out_break_0.5 <- arx_classifier(
+#'   jhu,
+#'   outcome = "case_rate",
+#'   predictors = "case_rate",
+#'   args_list = arx_class_args_list(
+#'     breaks = 0.5
+#'   )
+#' )
+#'
+#' out_break_0.5$predictions
+#' ```
+#' Indeed, we can observe that the two `.pred_class` are now (-Inf, 0.5] and
+#'   (0.5, Inf). See `help(arx_class_args_list)` for other available
+#'   modifications.
+#'
+#' Additional arguments that may be supplied to `arx_class_args_list()` include
+#'   the expected `lags` and `ahead` arguments for an autoregressive-type model.
+#'   These have default values of 0, 7, and 14 days for the lags of the
+#'   predictors and 7 days ahead of the forecast date for predicting the
+#'   outcome. There is also `n_training` to indicate the upper bound for the
+#'   number of training rows per key. If you would like some practice with using
+#'   this, then remove the filtering command to obtain data within "2021-06-04"
+#'   and "2021-12-31" and instead set `n_training` to be the number of days
+#'   between these two dates, inclusive of the end points. The end results
+#'   should be the same. In addition to `n_training`, there are `forecast_date`
+#'   and `target_date` to specify the date that the forecast is created and
+#'   intended, respectively. We will not dwell on such arguments here as they
+#'   are not unique to this classifier or absolutely essential to understanding
+#'   how it operates. The remaining arguments will be discussed organically, as
+#'   they are needed to serve our purposes. For information on any remaining
+#'   arguments that are not discussed here, please see the function
+#'   documentation for a complete list and their definitions.
 #'
 #' @inheritParams arx_forecaster
 #' @param outcome A character (scalar) specifying the outcome (in the
diff --git a/README.md b/README.md
index 868c105fb..25024c4d1 100644
--- a/README.md
+++ b/README.md
@@ -234,7 +234,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-03-04 12:12:54.
+#> This forecaster was fit on 2025-03-03 14:43:07.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -298,12 +298,12 @@ four_week_ahead$predictions |>
 #> # A tibble: 20 × 5
 #>   geo_value values quantile_levels forecast_date target_date
 #>   <chr>      <dbl>           <dbl> <date>        <date>     
-#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
-#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
-#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
-#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
-#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
-#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
+#> 1 ca        0.0425            0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.0803            0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.115             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.150             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.187             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0                 0.1  2021-08-01    2021-08-29 
 #> # ℹ 14 more rows
 ```
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index a9f6bbe8a..7bcaa007b 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -4,21 +4,27 @@ development:
   mode: devel
 
 template:
+  light-switch: true
   package: delphidocs
 
-articles:
-  - title: Get started
-    navbar: ~
-    contents:
-      - epipredict
-      - custom_epiworkflows
-      - backtesting
-      - update
-  - title: Advanced methods
-    contents:
-      - arx-classifier
-      - articles/smooth-qr
-      - panel-data
+navbar:
+  structure:
+    left:  [intro, workflows, backtesting, reference, articles, news]
+    right: [search, github, lightswitch]
+  components:
+    workflows:
+      text: Epiworkflows
+      href: articles/custom_epiworkflows.html
+    backtesting:
+      text: Backtesting
+      href: articles/backtesting.html
+    articles:
+      text: Articles
+      menu:
+        - text: Using the add/update/remove/adjust functions
+          href: articles/update.html
+        - text: Using epipredict on non-epidemic panel data
+          href: articles/panel-data.html
 
 home:
   links:
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index d78700df3..d9d632d90 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -43,9 +43,121 @@ and (2) \code{epi_workflow}, a list that encapsulates the entire estimation
 workflow
 }
 \description{
-This is an autoregressive classification model for
-\link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
-that it estimates a class at a particular target horizon.
+This is an autoregressive classification model for continuous data. It does
+"direct" forecasting, meaning that it estimates a class at a particular
+target horizon.
+}
+\details{
+The \code{arx_classifier()} is an autoregressive classification model for \code{epi_df}
+data that is used to predict a discrete class for each case under consideration.
+It is a direct forecaster in that it estimates the classes at a specific horizon
+or ahead value.
+
+To get a sense of how the \code{arx_classifier()} works, let's consider a simple
+example with minimal inputs. For this, we will use the built-in
+\code{covid_case_death_rates} that contains confirmed COVID-19 cases and deaths from
+JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this, we'll take
+a subset of data for five states over June 4, 2021 to December 31, 2021. Our
+objective is to predict whether the case rates are increasing when considering
+the 0, 7 and 14 day case rates:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{jhu <- covid_case_death_rates \%>\%
+  filter(
+    time_value >= "2021-06-04",
+    time_value <= "2021-12-31",
+    geo_value \%in\% c("ca", "fl", "tx", "ny", "nj")
+  )
+
+out <- arx_classifier(jhu, outcome = "case_rate", predictors = "case_rate")
+
+out$predictions
+#> # A tibble: 5 x 4
+#>   geo_value .pred_class forecast_date target_date
+#>   <chr>     <fct>       <date>        <date>     
+#> 1 ca        (-Inf,0.25] 2021-12-31    2022-01-07 
+#> 2 fl        (-Inf,0.25] 2021-12-31    2022-01-07 
+#> 3 nj        (-Inf,0.25] 2021-12-31    2022-01-07 
+#> 4 ny        (-Inf,0.25] 2021-12-31    2022-01-07 
+#> 5 tx        (-Inf,0.25] 2021-12-31    2022-01-07
+}\if{html}{\out{</div>}}
+
+The key takeaway from the predictions is that there are two prediction classes:
+(-Inf, 0.25] and (0.25, Inf). This is because for our goal of classification
+the classes must be discrete. The discretization of the real-valued outcome is
+controlled by the \code{breaks} argument, which defaults to 0.25. Such breaks will be
+automatically extended to cover the entire real line. For example, the default
+break of 0.25 is silently extended to breaks = c(-Inf, .25, Inf) and, therefore,
+results in two classes: \link{-Inf, 0.25} and (0.25, Inf). These two classes are
+used to discretize the outcome. The conversion of the outcome to such classes is
+handled internally. So if discrete classes already exist for the outcome in the
+\code{epi_df}, then we recommend to code a classifier from scratch using the
+\code{epi_workflow} framework for more control.
+
+The \code{trainer} is a \code{parsnip} model describing the type of estimation such that
+\code{mode = "classification"} is enforced. The two typical trainers that are used
+are \code{parsnip::logistic_reg()} for two classes or \code{parsnip::multinom_reg()} for
+more than two classes.
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{workflows::extract_spec_parsnip(out$epi_workflow)
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+}\if{html}{\out{</div>}}
+
+From the parsnip model specification, we can see that the trainer used is
+logistic regression, which is expected for our binary outcome. More complicated
+trainers like \code{parsnip::naive_Bayes()} or \code{parsnip::rand_forest()} may also be
+used (however, we will stick to the basics in this gentle introduction to the
+classifier).
+
+If you use the default trainer of logistic regression for binary classification
+and you decide against using the default break of 0.25, then you should only
+input one break so that there are two classification bins to properly
+dichotomize the outcome. For example, let's set a break of 0.5 instead of
+relying on the default of 0.25. We can do this by passing 0.5 to the \code{breaks}
+argument in \code{arx_class_args_list()} as follows:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{out_break_0.5 <- arx_classifier(
+  jhu,
+  outcome = "case_rate",
+  predictors = "case_rate",
+  args_list = arx_class_args_list(
+    breaks = 0.5
+  )
+)
+#> Warning: glm.fit: algorithm did not converge
+#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
+
+out_break_0.5$predictions
+#> # A tibble: 5 x 4
+#>   geo_value .pred_class forecast_date target_date
+#>   <chr>     <fct>       <date>        <date>     
+#> 1 ca        (-Inf,0.5]  2021-12-31    2022-01-07 
+#> 2 fl        (-Inf,0.5]  2021-12-31    2022-01-07 
+#> 3 nj        (-Inf,0.5]  2021-12-31    2022-01-07 
+#> 4 ny        (-Inf,0.5]  2021-12-31    2022-01-07 
+#> 5 tx        (-Inf,0.5]  2021-12-31    2022-01-07
+}\if{html}{\out{</div>}}
+
+Indeed, we can observe that the two \code{.pred_class} are now (-Inf, 0.5] and (0.5,
+Inf). See \code{help(arx_class_args_list)} for other available modifications.
+
+Additional arguments that may be supplied to \code{arx_class_args_list()} include the
+expected \code{lags} and \code{ahead} arguments for an autoregressive-type model. These
+have default values of 0, 7, and 14 days for the lags of the predictors and 7
+days ahead of the forecast date for predicting the outcome. There is also
+\code{n_training} to indicate the upper bound for the number of training rows per
+key. If you would like some practice with using this, then remove the filtering
+command to obtain data within "2021-06-04" and "2021-12-31" and instead set
+\code{n_training} to be the number of days between these two dates, inclusive of the
+end points. The end results should be the same. In addition to \code{n_training},
+there are \code{forecast_date} and \code{target_date} to specify the date that the
+forecast is created and intended, respectively. We will not dwell on such
+arguments here as they are not unique to this classifier or absolutely essential
+to understanding how it operates. The remaining arguments will be discussed
+organically, as they are needed to serve our purposes. For information on any
+remaining arguments that are not discussed here, please see the function
+documentation for a complete list and their definitions.
 }
 \examples{
 tiny_geos <- c("as", "mp", "vi", "gu", "pr")
diff --git a/vignettes/articles/smooth-qr.Rmd b/vignettes/articles/smooth-qr.Rmd
deleted file mode 100644
index ec07272aa..000000000
--- a/vignettes/articles/smooth-qr.Rmd
+++ /dev/null
@@ -1,544 +0,0 @@
----
-title: "Smooth quantile regression"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Smooth quantile regression}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = FALSE,
-  comment = "#>",
-  warning = FALSE,
-  message = FALSE,
-  out.width = "100%"
-)
-```
-
-# Introducing smooth quantile regression
-
-Whereas other time-series forecasting examples in this package have used
-(direct) models for single horizons, in multi-period forecasting, the goal is to
-(directly) forecast several horizons simultaneously. This is useful in
-epidemiological applications where decisions are based on the trend of a signal.
-
-The idea underlying smooth quantile regression is that set forecast targets can
-be approximated by a smooth curve. This novel approach from
-[Tuzhilina et al., 2022](https://arxiv.org/abs/2202.09723)
-enforces smoothness across the
-horizons and can be applied to point estimation by regression or interval
-prediction by quantile regression. Our focus in this vignette is the latter.
-
-# Built-in function for smooth quantile regression and its parameters
-
-The built-in smooth quantile regression function, `smooth_quantile_reg()`
-provides a model specification for smooth quantile regression that works under
-the tidymodels framework. It has the following parameters and default values:
-
-```{r, eval = FALSE}
-smooth_quantile_reg(
-  mode = "regression",
-  engine = "smoothqr",
-  outcome_locations = NULL,
-  quantile_levels = 0.5,
-  degree = 3L
-)
-```
-
-For smooth quantile regression, the type of model or `mode` is regression.
-
-The only `engine` that is currently supported is `smooth_qr()` from the
-[`smoothqr` package](https://dajmcdon.github.io/smoothqr/).
-
-The `outcome_locations` indicate the multiple horizon (ie. ahead) values. These
-should be specified by the user.
-
-The `quantile_levels` parameter is a vector of values that indicates the
-quantiles to be estimated. The default is the median (0.5 quantile).
-
-The `degree` parameter indicates the degree of the polynomials used for
-smoothing of the response. It should be no more than the number of aheads. If
-the degree is precisely equal to the number of aheads, then there is no
-smoothing. To better understand this parameter and how it works, we should look
-to its origins and how it is used in the model.
-
-# Model form
-
-Smooth quantile regression is linear auto-regressive, with the key feature being
-a transformation that forces the coefficients to satisfy a smoothing constraint.
-The purpose of this is for each model coefficient to be a smooth function of
-ahead values, and so each such coefficient is set to be a linear combination of
-smooth basis functions (such as a spline or a polynomial).
-
-The `degree` parameter controls the number of these polynomials used. It should
-be no greater than the number of responses. This is a tuning parameter, and so
-it can be chosen by performing a grid search with cross-validation. Intuitively,
-$d = 1$ corresponds to the constant model, $d = 2$ gives straight line
-forecasts, while $d = 3$ gives quadratic forecasts. Since a degree of 3 was
-found to work well in the tested applications (see Section 9 of
-[Tuzhilina et al., 2022](https://arxiv.org/abs/2202.09723)),
-it is the default value.
-
-# Demonstration of smooth quantile regression
-
-```{r, message = FALSE}
-library(epipredict)
-library(dplyr)
-library(purrr)
-library(ggplot2)
-theme_set(theme_bw())
-```
-
-We will now apply smooth quantile regression on the real data used for COVID-19
-forecasting. The built-in dataset we will use is a subset of JHU daily data on
-state cases and deaths. This sample data ranges from Dec. 31, 2020 to
-Dec. 31, 2021.
-
-```{r}
-edf <- epidatasets::covid_case_death_rates
-```
-
-We will set the forecast date to be November 30, 2021 so that we can produce
-forecasts for target dates of 1 to 28 days ahead. We construct our test data,
-`tedf` from the days beyond this.
-
-```{r}
-fd <- as.Date("2021-11-30")
-
-tedf <- edf %>% filter(time_value >= fd)
-```
-
-We will use the most recent 3 months worth of data up to the forecast date for
-training.
-
-```{r}
-edf <- edf %>% filter(time_value < fd, time_value >= fd - 90L)
-```
-
-And for plotting our focus will be on a subset of two states - California and
-Utah.
-
-```{r}
-geos <- c("ut", "ca")
-```
-
-Suppose that our goal with this data is to predict COVID-19 death rates at
-several horizons for each state. On day $t$, we want to predict new deaths $y$
-that are $a = 1,\dots, 28$ days ahead at locations $j$ using the death rates
-from today, 1 week ago, and 2 weeks ago. So for each location, we'll predict the
-median (0.5 quantile) for each of the target dates by using
-$$
-\hat{y}_{j}(t+a) = \alpha(a) + \sum_{l = 0}^2 \beta_{l}(a)  y_{j}(t - 7l)
-$$
-where $\beta_{l}(a) = \sum_{i=1}^d \theta_{il} h_i(a)$ is the smoothing
-constraint where ${h_1(a), \dots, h_d(a)}$ are the set of smooth basis functions
-and $d$ is a hyperparameter that manages the flexibility of $\beta_{l}(a)$.
-Remember that the goal is to have each $\beta_{l}(a)$ to be a smooth function of
-the aheads and that is achieved through imposing the smoothing constraint.
-
-Note that this model is intended to be simple and straightforward. Our only
-modification to this model is to add case rates as another predictive feature
-(we will leave it to the reader to incorporate additional features beyond this
-and the historical response values). We can update the basic model incorporate
-the $k = 2$ predictive features of case and death rates for each location j,
-$x_j(t) = (x_{j1}(t), x_{j2}(t))$ as follows:
-
-$$
-\hat{y}_{j}(t+a) = \alpha(a) + \sum_{k = 1}^2 \sum_{l = 0}^2 \beta_{kl}(a)  x_{jk}(t - 7l)
-$$
-where $\beta_{kl}(a) = \sum_{i=1}^d \theta_{ikl} h_i(a)$.
-
-Now, we will create our own forecaster from scratch by building up an
-`epi_workflow` (there is no canned forecaster that is currently available).
-Building our own forecaster allows for customization and control over the
-pre-processing and post-processing actions we wish to take.
-
-The pre-processing steps we take in our `epi_recipe` are simply to lag the
-predictor (by 0, 7, and 14 days) and lead the response by the multiple aheads
-specified by the function user.
-
-The post-processing layers we add to our `frosting` are nearly as simple. We
-first predict, unnest the prediction list-cols, omit NAs from them, and enforce
-that they are greater than 0.
-
-The third component of an to an `epi_workflow`, the model, is smooth quantile
-regression, which has three main arguments - the quantiles, aheads, and degree.
-
-After creating our `epi_workflow` with these components, we get our test data
-based on longest lag period and make the predictions.
-
-We input our forecaster into a function for ease of use.
-
-```{r}
-smooth_fc <- function(x, aheads = 1:28, degree = 3L, quantiles = 0.5, fd) {
-  rec <- epi_recipe(x) %>%
-    step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-    step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-    step_epi_ahead(death_rate, ahead = aheads)
-
-  f <- frosting() %>%
-    layer_predict() %>%
-    layer_unnest(.pred) %>%
-    layer_naomit(distn) %>%
-    layer_add_forecast_date() %>%
-    layer_threshold(distn)
-
-  ee <- smooth_quantile_reg(
-    quantile_levels = quantiles,
-    outcome_locations = aheads,
-    degree = degree
-  )
-
-  ewf <- epi_workflow(rec, ee, f)
-
-  the_fit <- ewf %>% fit(x)
-
-  latest <- get_test_data(rec, x)
-
-  preds <- predict(the_fit, new_data = latest) %>%
-    mutate(forecast_date = fd, target_date = fd + ahead) %>%
-    select(geo_value, target_date, distn, ahead) %>%
-    pivot_quantiles_wider(distn)
-
-  preds
-}
-```
-
-```{r load-stored-preds, echo=FALSE}
-smooth_preds_list <- readRDS("smooth-qr_smooth_preds_list.rds")
-baseline_preds <- readRDS("smooth-qr_baseline_preds.rds")
-smooth_preds <- smooth_preds_list %>%
-  filter(degree == 3L) %>%
-  select(geo_value:ahead, `0.5`)
-```
-
-Notice that we allow the function user to specify the aheads, degree, and
-quantile as they may want to change these parameter values. We also allow for
-input of the forecast date as we fixed that at the onset of this demonstration.
-
-We now can produce smooth quantile regression predictions for our problem:
-
-```{r, eval = FALSE}
-smooth_preds <- smooth_fc(edf, fd = fd)
-smooth_preds
-```
-
-```{r, echo=FALSE}
-smooth_preds
-smooth_preds <- smooth_preds_list %>%
-  filter(degree == 3L) %>%
-  select(-degree)
-```
-
-
-Most often, we're not going to want to limit ourselves to just predicting the
-median value as there is uncertainty about the predictions, so let's try to
-predict several different quantiles in addition to the median:
-
-```{r, eval = FALSE}
-several_quantiles <- c(.1, .25, .5, .75, .9)
-smooth_preds <- smooth_fc(edf, quantiles = several_quantiles, fd = fd)
-smooth_preds
-```
-
-```{r, echo = FALSE}
-several_quantiles <- c(.1, .25, .5, .75, .9)
-smooth_preds
-```
-
-We can see that we have different columns for the different quantile
-predictions.
-
-Let's visualize these results for the sample of two states. We will create a
-simple plotting function, under which the median predictions are an orange line
-and the surrounding quantiles are blue bands around this. For comparison, we
-will include the actual values over time as a black line.
-
-```{r}
-plot_preds <- function(preds, geos_to_plot = NULL, train_test_dat, fd) {
-  if (!is.null(geos_to_plot)) {
-    preds <- preds %>% filter(geo_value %in% geos_to_plot)
-    train_test_dat <- train_test_dat %>% filter(geo_value %in% geos_to_plot)
-  }
-
-  ggplot(preds) +
-    geom_ribbon(aes(target_date, ymin = `0.1`, ymax = `0.9`),
-      fill = "cornflowerblue", alpha = .8
-    ) +
-    geom_ribbon(aes(target_date, ymin = `0.25`, ymax = `0.75`),
-      fill = "#00488E", alpha = .8
-    ) +
-    geom_line(data = train_test_dat, aes(time_value, death_rate)) +
-    geom_line(aes(target_date, `0.5`), color = "orange") +
-    geom_vline(xintercept = fd) +
-    facet_wrap(~geo_value) +
-    scale_x_date(name = "", date_labels = "%b %Y", date_breaks = "2 months") +
-    ylab("Deaths per 100K inhabitants")
-}
-```
-
-Since we would like to plot the actual death rates for these states over time,
-we bind the training and testing data together and input this into our plotting
-function as follows:
-
-```{r, warning = FALSE}
-plot_preds(smooth_preds, geos, bind_rows(tedf, edf), fd)
-```
-
-We can see that the predictions are smooth curves for each state, as expected
-when using smooth quantile regression. In addition while the curvature of the
-forecasts matches that of the truth, the forecasts do not look remarkably
-accurate.
-
-## Varying the degrees parameter
-
-We can test the impact of different degrees by using the `map()` function.
-Noting that this may take some time to run, let's try out all degrees from 1
-to 7:
-
-```{r, eval = FALSE}
-smooth_preds_list <- map(1:7, function(x) {
-  smooth_fc(
-    edf,
-    degree = x,
-    quantiles = c(.1, .25, .5, .75, .9),
-    fd = fd
-  ) %>%
-    mutate(degree = x)
-}) %>% list_rbind()
-```
-
-One way to quantify the impact of these on the forecasting is to look at the
-mean absolute error (MAE) or mean squared error (MSE) over the degrees. We can
-select the degree that results in the lowest MAE.
-
-Since the MAE compares the predicted values to the actual values, we will first
-join the test data to the predicted data for our comparisons:
-```{r, message = FALSE}
-tedf_sub <- tedf %>%
-  rename(target_date = time_value, actual = death_rate) %>%
-  select(geo_value, target_date, actual)
-```
-
-And then compute the MAE for each of the degrees:
-```{r, message = FALSE}
-smooth_preds_df_deg <- smooth_preds_list %>%
-  left_join(tedf_sub, by = c("geo_value", "target_date")) %>%
-  group_by(degree) %>%
-  mutate(error = abs(`0.5` - actual)) %>%
-  summarise(mean = mean(error))
-
-# Arrange the MAE from smallest to largest
-smooth_preds_df_deg %>% arrange(mean)
-```
-
-Instead of just looking at the raw numbers, let's create a simple line plot to
-visualize how the MAE changes over degrees for this data:
-
-```{r}
-ggplot(smooth_preds_df_deg, aes(degree, mean)) +
-  geom_line() +
-  xlab("Degrees of freedom") +
-  ylab("Mean MAE")
-```
-
-We can see that the degree that results in the lowest MAE is 3. Hence, we could
-pick this degree for future forecasting work on this data.
-
-## A brief comparison between smoothing and no smoothing
-
-Now, we will briefly compare the results from using smooth quantile regression
-to those obtained without smoothing. The latter approach amounts to ordinary
-quantile regression to get predictions for the intended target date. The main
-drawback is that it ignores the fact that the responses all represent the same
-signal, just for different ahead values. In contrast, the smooth quantile
-regression approach utilizes this information about the data structure - the
-fact that the aheads in are not be independent of each other, but that they are
-naturally related over time by a smooth curve.
-
-To get the basic quantile regression results we can utilize the forecaster that
-we've already built. We can simply set the degree to be the number of ahead
-values to re-run the code without smoothing.
-
-```{r, eval = FALSE}
-baseline_preds <- smooth_fc(
-  edf,
-  degree = 28L, quantiles = several_quantiles, fd = fd
-)
-```
-
-And we can produce the corresponding plot to inspect the predictions obtained
-under the baseline model:
-
-```{r, warning = FALSE}
-plot_preds(baseline_preds, geos, bind_rows(tedf, edf), fd)
-```
-
-Unlike for smooth quantile regression, the resulting forecasts are not smooth
-curves, but rather jagged and irregular in shape.
-
-For a more formal comparison between the two approaches, we could compare the
-test performance in terms of accuracy through calculating either the, MAE or
-MSE, where the performance measure of choice can be calculated over over all
-times and locations for each ahead value
-
-
-```{r, message = FALSE}
-baseline_preds_mae_df <- baseline_preds %>%
-  left_join(tedf_sub, by = c("geo_value", "target_date")) %>%
-  group_by(ahead) %>%
-  mutate(error = abs(`0.5` - actual)) %>%
-  summarise(mean = mean(error)) %>%
-  mutate(type = "baseline")
-
-smooth_preds_mae_df <- smooth_preds %>%
-  left_join(tedf_sub, by = c("geo_value", "target_date")) %>%
-  group_by(ahead) %>%
-  mutate(error = abs(`0.5` - actual)) %>%
-  summarise(mean = mean(error)) %>%
-  mutate(type = "smooth")
-
-preds_mae_df <- bind_rows(baseline_preds_mae_df, smooth_preds_mae_df)
-
-ggplot(preds_mae_df, aes(ahead, mean, color = type)) +
-  geom_line() +
-  xlab("Ahead") +
-  ylab("Mean MAE") +
-  scale_color_manual(values = c("darkred", "#063970"), name = "")
-```
-
-or over all aheads, times, and locations for a single numerical summary.
-
-```{r}
-mean(baseline_preds_mae_df$mean)
-mean(smooth_preds_mae_df$mean)
-```
-
-The former shows that forecasts for the immediate future and for the distant
-future are more inaccurate for both models under consideration. The latter shows
-that the smooth quantile regression model and baseline models perform very
-similarly overall, with the smooth quantile regression model only slightly
-beating the baseline model in terms of overall average MAE.
-
-One other commonly used metric is the Weighted Interval Score
-(WIS, [Bracher et al., 2021](https://arxiv.org/pdf/2005.12881.pdf)),
-which a scoring rule that is based on the population quantiles. The point is to
-score the interval, whereas MAE only evaluates the accuracy of the point
-forecast.
-
-Let $F$ be a forecast composed of predicted quantiles $q_{\tau}$ for the set of
-quantile levels $\tau$. Then, in terms of the predicted quantiles, the WIS for
-target variable $Y$ is represented as follows
-([McDonald etal., 2021](https://www.pnas.org/doi/full/10.1073/pnas.2111453118)):
-
-$$
-WIS(F, Y) = 2 \sum_{\tau} \phi_{\tau} (Y - q_{\tau})
-$$
-where $\phi_{\tau}(x) = \tau |x|$ for $x \geq 0$
-and$\phi_{\tau}(x) = (1 - \tau) |x|$ for $x < 0$.
-
-This form is general as it can accommodate both symmetric and asymmetric
-quantile levels. If the quantile levels are symmetric, then we can alternatively
-express the WIS as a collection of central prediction intervals
-($\ell_{\alpha}, u_{\alpha}$) parametrized by the exclusion probability
-$\alpha$:
-
-$$
-WIS(F, Y) =  \sum_{\alpha} \{ (u_{\alpha} - \ell_{\alpha}) + 2 \cdot \text{dist}(Y, [\ell_{\alpha}, u_{\alpha}]) \}
-$$
-where $\text{dist}(a,S)$ is the smallest distance between point $a$ and an
-element of set $S$.
-
-While we implement the former representation, we mention this form because it
-shows the that the score can be decomposed into the addition of a sharpness
-component (first term in the summand) and an under/overprediction component
-(second term in the summand). This alternative representation is useful because
-from it, we more easily see the major limitation to the WIS, which is that the
-score tends to prioritize sharpness (how wide the interval is) relative to
-coverage (if the interval contains the truth).
-
-Now, we write a simple function for the first representation of the score that
-is compatible with the latest version of `epipredict` (adapted from the
-corresponding function in
-[smoothmpf-epipredict](https://github.com/dajmcdon/smoothmpf-epipredict)). The
-inputs for it are the actual and predicted values and the quantile levels.
-
-```{r}
-wis_dist_quantile <- function(actual, values, quantile_levels) {
-  2 * mean(pmax(
-    quantile_levels * (actual - values),
-    (1 - quantile_levels) * (values - actual),
-    na.rm = TRUE
-  ))
-}
-```
-
-Next, we apply the `wis_dist_quantile` function to get a WIS score for each
-state on each target date. We then compute the mean WIS for each ahead value
-over all of the states. The results for each of the smooth and baseline
-forecasters are shown in a similar style line plot as we chose for MAE:
-
-```{r}
-smooth_preds_wis_df <- smooth_preds %>%
-  left_join(tedf_sub, by = c("geo_value", "target_date")) %>%
-  rowwise() %>%
-  mutate(wis = wis_dist_quantile(
-    actual, c(`0.1`, `0.25`, `0.5`, `0.75`, `0.9`),
-    several_quantiles
-  )) %>%
-  group_by(ahead) %>%
-  summarise(mean = mean(wis)) %>%
-  mutate(type = "smooth")
-
-baseline_preds_wis_df <- baseline_preds %>%
-  left_join(tedf_sub, by = c("geo_value", "target_date")) %>%
-  rowwise() %>%
-  mutate(wis = wis_dist_quantile(
-    actual, c(`0.1`, `0.25`, `0.5`, `0.75`, `0.9`),
-    several_quantiles
-  )) %>%
-  group_by(ahead) %>%
-  summarise(mean = mean(wis)) %>%
-  mutate(type = "baseline")
-
-preds_wis_df <- bind_rows(smooth_preds_wis_df, baseline_preds_wis_df)
-
-ggplot(preds_wis_df, aes(ahead, mean, color = type)) +
-  geom_line() +
-  xlab("Ahead") +
-  ylab("Mean WIS") +
-  scale_color_manual(values = c("darkred", "#063970"), name = "")
-```
-
-The results are consistent with what we saw for MAE: The forecasts for the near
-and distant future tend to be inaccurate for both models. The smooth quantile
-regression model only slightly outperforms the baseline model.
-
-Though averaging the WIS score over location and time tends to be the primary
-aggregation scheme used in evaluation and model comparisons (see, for example,
-[McDonald et al., 2021](https://www.pnas.org/doi/full/10.1073/pnas.2111453118)),
-we can also obtain a single numerical summary by averaging over the aheads,
-times, and locations:
-
-```{r}
-mean(baseline_preds_wis_df$mean)
-mean(smooth_preds_wis_df$mean)
-```
-
-Overall, both perspectives agree that the smooth quantile regression model tends
-to perform only slightly better than the baseline model in terms of average WIS,
-illustrating the difficulty of this forecasting problem.
-
-# What we've learned in a nutshell
-
-Smooth quantile regression is used in multi-period forecasting for predicting
-several horizons simultaneously with a single smooth curve. It operates under
-the key assumption that the future of the response can be approximated well by a
-smooth curve.
-
-# Attribution
-
-The information presented on smooth quantile regression is from
-[Tuzhilina et al., 2022](https://arxiv.org/abs/2202.09723).
diff --git a/vignettes/arx-classifier.Rmd b/vignettes/arx-classifier.Rmd
deleted file mode 100644
index efc82ebaf..000000000
--- a/vignettes/arx-classifier.Rmd
+++ /dev/null
@@ -1,274 +0,0 @@
----
-title: "Auto-regressive classifier"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Auto-regressive classifier}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-source(here::here("vignettes/_common.R"))
-```
-
-## Load required packages
-
-```{r, message = FALSE, warning = FALSE}
-library(dplyr)
-library(purrr)
-library(ggplot2)
-library(epipredict)
-```
-
-## Introducing the ARX classifier
-
-The `arx_classifier()` is an autoregressive classification model for `epi_df`
-data that is used to predict a discrete class for each case under consideration.
-It is a direct forecaster in that it estimates the classes at a specific horizon
-or ahead value.
-
-To get a sense of how the `arx_classifier()` works, let's consider a simple
-example with minimal inputs. For this, we will use the built-in
-`covid_case_death_rates` that contains confirmed COVID-19 cases and deaths from
-JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this, we'll take
-a subset of data for five states over June 4, 2021 to December 31, 2021. Our
-objective is to predict whether the case rates are increasing when considering
-the 0, 7 and 14 day case rates:
-
-```{r}
-jhu <- covid_case_death_rates %>%
-  filter(
-    time_value >= "2021-06-04",
-    time_value <= "2021-12-31",
-    geo_value %in% c("ca", "fl", "tx", "ny", "nj")
-  )
-
-out <- arx_classifier(jhu, outcome = "case_rate", predictors = "case_rate")
-
-out$predictions
-```
-
-The key takeaway from the predictions is that there are two prediction classes:
-(-Inf, 0.25] and (0.25, Inf). This is because for our goal of classification
-the classes must be discrete. The discretization of the real-valued outcome is
-controlled by the `breaks` argument, which defaults to 0.25. Such breaks will be
-automatically extended to cover the entire real line. For example, the default
-break of 0.25 is silently extended to breaks = c(-Inf, .25, Inf) and, therefore,
-results in two classes: [-Inf, 0.25] and (0.25, Inf). These two classes are
-used to discretize the outcome. The conversion of the outcome to such classes is
-handled internally. So if discrete classes already exist for the outcome in the
-`epi_df`, then we recommend to code a classifier from scratch using the
-`epi_workflow` framework for more control.
-
-The `trainer` is a `parsnip` model describing the type of estimation such that
-`mode = "classification"` is enforced. The two typical trainers that are used
-are `parsnip::logistic_reg()` for two classes or `parsnip::multinom_reg()` for
-more than two classes.
-
-```{r}
-workflows::extract_spec_parsnip(out$epi_workflow)
-```
-
-From the parsnip model specification, we can see that the trainer used is
-logistic regression, which is expected for our binary outcome. More complicated
-trainers like `parsnip::naive_Bayes()` or `parsnip::rand_forest()` may also be
-used (however, we will stick to the basics in this gentle introduction to the
-classifier).
-
-If you use the default trainer of logistic regression for binary classification
-and you decide against using the default break of 0.25, then you should only
-input one break so that there are two classification bins to properly
-dichotomize the outcome. For example, let's set a break of 0.5 instead of
-relying on the default of 0.25. We can do this by passing 0.5 to the `breaks`
-argument in `arx_class_args_list()` as follows:
-
-```{r}
-out_break_0.5 <- arx_classifier(
-  jhu,
-  outcome = "case_rate",
-  predictors = "case_rate",
-  args_list = arx_class_args_list(
-    breaks = 0.5
-  )
-)
-
-out_break_0.5$predictions
-```
-Indeed, we can observe that the two `.pred_class` are now (-Inf, 0.5] and (0.5,
-Inf). See `help(arx_class_args_list)` for other available modifications.
-
-Additional arguments that may be supplied to `arx_class_args_list()` include the
-expected `lags` and `ahead` arguments for an autoregressive-type model. These
-have default values of 0, 7, and 14 days for the lags of the predictors and 7
-days ahead of the forecast date for predicting the outcome. There is also
-`n_training` to indicate the upper bound for the number of training rows per
-key. If you would like some practice with using this, then remove the filtering
-command to obtain data within "2021-06-04" and "2021-12-31" and instead set
-`n_training` to be the number of days between these two dates, inclusive of the
-end points. The end results should be the same. In addition to `n_training`,
-there are `forecast_date` and `target_date` to specify the date that the
-forecast is created and intended, respectively. We will not dwell on such
-arguments here as they are not unique to this classifier or absolutely essential
-to understanding how it operates. The remaining arguments will be discussed
-organically, as they are needed to serve our purposes. For information on any
-remaining arguments that are not discussed here, please see the function
-documentation for a complete list and their definitions.
-
-## Example of using the ARX classifier
-
-Now, to demonstrate the power and utility of this built-in arx classifier, we
-will loosely adapt the classification example that was written from scratch in
-`vignette("preprocessing-and-models")`. However, to keep things simple and not
-merely a direct translation, we will only consider two prediction categories and
-leave the extension to three as an exercise for the reader.
-
-To motivate this example, a major use of autoregressive classification models is
-to predict upswings or downswings like in hotspot prediction models to
-anticipate the direction of the outcome (see [McDonald, Bien, Green, Hu, et al.
-(2021)](https://www.pnas.org/doi/full/10.1073/pnas.2111453118) for more on
-these). In our case, one simple question that such models can help answer is...
-Do we expect that the future will have increased case rates or not relative to
-the present?
-
-To answer this question, we can create a predictive model for upswings and
-downswings of case rates rather than the raw case rates themselves. For this
-situation, our target is to predict whether there is an increase in case rates
-or not. Following
-[McDonald, Bien, Green, Hu, et al.(2021)](https://www.pnas.org/doi/full/10.1073/pnas.2111453118),
-we look at the
-relative change between $Y_{l,t}$ and $Y_{l, t+a}$, where the former is the case
-rate at location $l$ at time $t$ and the latter is the rate for that location at
-time $t+a$. Using these variables, we define a categorical response variable
-with two classes
-
-$$\begin{align}
-Z_{l,t} = \left\{\begin{matrix}
-\text{up,} & \text{if } Y_{l,t}^\Delta > 0.25\\
-\text{not up,} & \text{otherwise}
-\end{matrix}\right.
-\end{align}$$
-where $Y_{l,t}^\Delta = (Y_{l, t} - Y_{l, t-7} / Y_{l, t-7}$. If $Y_{l,t}^\Delta$ > 0.25, meaning that the number of new cases over the week has increased by over 25\%, then $Z_{l,t}$ is up. This is the criteria for location $l$ to be a hotspot at time $t$. On the other hand, if $Y_{l,t}^\Delta$ \leq 0.25$, then then $Z_{l,t}$ is categorized as not up, meaning that there has not been a >25\% increase in the new cases over the past week.
-
-The logistic regression model we use to predict this binary response can be
-considered to be a simplification of the multinomial regression model presented
-in `vignette("preprocessing-and-models")`:
-
-$$\begin{align}
-\pi_{\text{up}}(x) &= Pr(Z_{l, t} = \text{up}|x) = \frac{e^{g_{\text{up}}(x)}}{1 + e^{g_{\text{up}}(x)}}, \\
-\pi_{\text{not up}}(x)&= Pr(Z_{l, t} = \text{not up}|x) = 1 - Pr(Z_{l, t} = \text{up}|x)  = \frac{1}{1 + e^{g_{\text{up}}(x)}}
-\end{align}$$
-where
-
-$$
-g_{\text{up}}(x) = \log\left ( \frac{\Pr(Z_{l, t} = \text{up} \vert x)}{\Pr(Z_{l, t} = \text{not up} \vert x)} \right ) = \beta_{10} + \beta_{11}Y_{l,t}^\Delta + \beta_{12}Y_{l,t-7}^\Delta + \beta_{13}Y_{l,t-14}^\Delta.
-$$
-
-Now then, we will operate on the same subset of the `covid_case_death_rates`
-that we used in our above example. This time, we will use it to investigate
-whether the number of newly reported cases over the past 7 days has increased by
-at least 25% compared to the preceding week for our sample of states.
-
-Notice that by using the `arx_classifier()` function we've completely eliminated
-the need to manually categorize the response variable and implement
-pre-processing steps, which was necessary in
-`vignette("preprocessing-and-models")`.
-
-```{r}
-log_res <- arx_classifier(
-  jhu,
-  outcome = "case_rate",
-  predictors = "case_rate",
-  args_list = arx_class_args_list(
-    breaks = 0.25 / 7 # division by 7 gives weekly not daily
-  )
-)
-
-log_res$epi_workflow
-```
-
-Comparing the pre-processing steps for this to those in the other vignette, we
-can see that they are not precisely the same, but they cover the same essentials
-of transforming `case_rate` to the growth rate scale (`step_growth_rate()`),
-lagging the predictors (`step_epi_lag()`), leading the response
-(`step_epi_ahead()`), which are both constructed from the growth rates, and
-constructing the binary classification response variable (`step_mutate()`).
-
-On this topic, it is important to understand that we are not actually concerned
-about the case values themselves. Rather we are concerned whether the quantity
-of cases in the future is a lot larger than that in the present. For this
-reason, the outcome does not remain as cases, but rather it is transformed by
-using either growth rates (as the predictors and outcome in our example are) or
-lagged differences. While the latter is closer to the requirements for the
-[2022-23 CDC Flusight Hospitalization Experimental Target](https://github.com/cdcepi/Flusight-forecast-data/blob/745511c436923e1dc201dea0f4181f21a8217b52/data-experimental/README.md),
-and it is conceptually easy to understand because it is simply the change of the
-value for the horizon, it is not the default. The default is `growth_rate`. One
-reason for this choice is because the growth rate is on a rate scale, not on the
-absolute scale, so it fosters comparability across locations without any
-conscious effort (on the other hand, when using the `lag_difference` one would
-need to take care to operate on rates per 100k and not raw counts). We utilize
-`epiprocess::growth_rate()` to create the outcome using some of the additional
-arguments. One important argument for the growth rate calculation is the
-`method`. Only `rel_change` for relative change should be used as the method
-because the test data is the only data that is accessible and the other methods
-require access to the training data.
-
-The other optional arguments for controlling the growth rate calculation (that
-can be inputted as `additional_gr_args`) can be found in the documentation for
-`epiprocess::growth_rate()` and the related
-`vignette("growth_rate", package = "epiprocess")`.
-
-### Visualizing the results
-
-To visualize the prediction classes across the states for the target date, we
-can plot our results as a heatmap. However, if we were to plot the results for
-only one target date, like our 7-day ahead predictions, then that would be a
-pretty sad heatmap (which would look more like a bar chart than a heatmap)... So
-instead of doing that, let's get predictions for several aheads and plot a
-heatmap across the target dates. To get the predictions across several ahead
-values, we will use the map function in the same way that we did in other
-vignettes:
-
-```{r}
-multi_log_res <- map(1:40, ~ arx_classifier(
-  jhu,
-  outcome = "case_rate",
-  predictors = "case_rate",
-  args_list = arx_class_args_list(
-    breaks = 0.25 / 7, # division by 7 gives weekly not daily
-    ahead = .x
-  )
-)$predictions) %>% list_rbind()
-```
-
-We can plot a the heatmap of the results over the aheads to see if there's
-anything novel or interesting to take away:
-
-```{r}
-ggplot(multi_log_res, aes(target_date, geo_value, fill = .pred_class)) +
-  geom_tile() +
-  ylab("State") +
-  xlab("Target date") +
-  scale_fill_brewer(palette = "Set1")
-```
-
-While there is a bit of variability near to the end, we can clearly see that
-there are upswings for all states starting from the beginning of January 2022,
-which we can recall was when there was a massive spike in cases for many states.
-So our results seem to align well with what actually happened at the beginning
-of January 2022.
-
-## A brief reflection
-
-The most noticeable benefit of using the `arx_classifier()` function is the
-simplification and reduction of the manual implementation of the classifier from
-about 30 down to 3 lines. However, as we noted before, the trade-off for
-simplicity is control over the precise pre-processing, post-processing, and
-additional features embedded in the coding of a classifier. So the good thing is
-that `epipredict` provides both - a built-in `arx_classifer()` or the means to
-implement your own classifier from scratch by using the `epi_workflow`
-framework. And which you choose will depend on the circumstances. Our advice is
-to start with using the built-in classifier for ostensibly simple projects and
-begin to implement your own when the modelling project takes a complicated turn.
-To get some practice on coding up a classifier by hand, consider translating
-this binary classification model example to an `epi_workflow`, akin to that in
-`vignette("preprocessing-and-models")`.

From 2f4fa89d3959dfd4b1572f29ec7e51d2e662e1a9 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 4 Mar 2025 18:07:52 -0600
Subject: [PATCH 355/387] follow up on first half of epipredict.Rmd

---
 vignettes/epipredict.Rmd | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 338187c16..fd85912ff 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -7,6 +7,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+# Introduction
+
 ```{r, include = FALSE}
 source(here::here("vignettes/_common.R"))
 ```
@@ -61,7 +63,7 @@ components:
     `step`](https://recipes.tidymodels.org/reference/index.html)
   * _Trainer_: train a model on data, resulting in a fitted model object.
     Examples include linear regression, quantile regression, or [any `{parsnip}`
-    engine](https://parsnip.tidymodels.org/reference/index.html).
+    engine](https://www.tidymodels.org/find/parsnip/).
   * _Postprocessor_: unique to `{epipredict}`; used to transform the
     predictions after the model has been fit, such as
     - generating quantiles from purely point-prediction models,
@@ -69,8 +71,8 @@ components:
     rates back to counts
     - generally adapting the format of the prediction to its eventual use.
 
-The rest of the "getting started" vignette will focus on using and modifying the canned forecasters.
-Check out the ["guts" vignette](preprocessing-and-models) for examples of using the forecaster
+The rest of this "Get Started" vignette will focus on using and modifying the canned forecasters.
+Check out the [Custom Epiworkflows vignette](preprocessing-and-models) for examples of using the forecaster
 framework to make more complex, custom forecasters.
 
 If you are interested in time series in a non-panel data context, you may also
@@ -188,7 +190,7 @@ hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 
 See the function documentation for `arx_args_list()` for more examples of the modifications available.
 If you want to make further modifications, you will need a custom
-workflow; see the ["guts" vignette](custom_epiworkflows) for details.
+workflow; see the [Custom Epiworkflows vignette](custom_epiworkflows) for details.
 
 ## Generating multiple aheads
 Frequently, one doesn't want just a forecast for a single day, but a trajectory
@@ -436,7 +438,7 @@ An `epi_workflow()` consists of 3 parts:
   5 of as these well. You can inspect the layers more closely by running
   `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
 
-See the ["guts" vignette](custom_epiworkflows) for recreating and then
+See the [Custom Epiworkflows vignette](custom_epiworkflows) for recreating and then
 extending `four_week_ahead` using the custom forecaster framework.
 
 ## Mathematical description

From 500373625d95f6fa218dcde2944c1c37e82790c6 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 5 Mar 2025 11:24:25 -0600
Subject: [PATCH 356/387] avoid [link] parsing

---
 R/arx_classifier.R    |  12 ++---
 man/arx_classifier.Rd | 103 ++++++++++++++++++++++--------------------
 2 files changed, 59 insertions(+), 56 deletions(-)

diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 456796790..b53d7dd15 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -34,14 +34,14 @@
 #' ```
 #'
 #' The key takeaway from the predictions is that there are two prediction
-#'   classes: (-Inf, 0.25] and (0.25, Inf). This is because for our goal of
+#'   classes: `(-Inf, 0.25]` and `(0.25, Inf)`. This is because for our goal of
 #'   classification the classes must be discrete. The discretization of the
 #'   real-valued outcome is controlled by the `breaks` argument, which defaults
-#'   to 0.25. Such breaks will be automatically extended to cover the entire
-#'   real line. For example, the default break of 0.25 is silently extended to
-#'   breaks = c(-Inf, .25, Inf) and, therefore, results in two classes: [-Inf,
-#'   0.25] and (0.25, Inf). These two classes are used to discretize the
-#'   outcome. The conversion of the outcome to such classes is handled
+#'   to `0.25`. Such breaks will be automatically extended to cover the entire
+#'   real line. For example, the default break of `0.25` is silently extended to
+#'   `breaks = c(-Inf, .25, Inf)` and, therefore, results in two classes:
+#'   `[-Inf, 0.25]` and `(0.25, Inf)`. These two classes are used to discretize
+#'   the outcome. The conversion of the outcome to such classes is handled
 #'   internally. So if discrete classes already exist for the outcome in the
 #'   `epi_df`, then we recommend to code a classifier from scratch using the
 #'   `epi_workflow` framework for more control.
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index d9d632d90..293e05af0 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -49,17 +49,17 @@ target horizon.
 }
 \details{
 The \code{arx_classifier()} is an autoregressive classification model for \code{epi_df}
-data that is used to predict a discrete class for each case under consideration.
-It is a direct forecaster in that it estimates the classes at a specific horizon
-or ahead value.
+data that is used to predict a discrete class for each case under
+consideration.  It is a direct forecaster in that it estimates the classes
+at a specific horizon or ahead value.
 
 To get a sense of how the \code{arx_classifier()} works, let's consider a simple
 example with minimal inputs. For this, we will use the built-in
-\code{covid_case_death_rates} that contains confirmed COVID-19 cases and deaths from
-JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this, we'll take
-a subset of data for five states over June 4, 2021 to December 31, 2021. Our
-objective is to predict whether the case rates are increasing when considering
-the 0, 7 and 14 day case rates:
+\code{covid_case_death_rates} that contains confirmed COVID-19 cases and deaths
+from JHU CSSE for all states over Dec 31, 2020 to Dec 31, 2021. From this,
+we'll take a subset of data for five states over June 4, 2021 to December
+31, 2021. Our objective is to predict whether the case rates are increasing
+when considering the 0, 7 and 14 day case rates:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{jhu <- covid_case_death_rates \%>\%
   filter(
@@ -81,22 +81,23 @@ out$predictions
 #> 5 tx        (-Inf,0.25] 2021-12-31    2022-01-07
 }\if{html}{\out{</div>}}
 
-The key takeaway from the predictions is that there are two prediction classes:
-(-Inf, 0.25] and (0.25, Inf). This is because for our goal of classification
-the classes must be discrete. The discretization of the real-valued outcome is
-controlled by the \code{breaks} argument, which defaults to 0.25. Such breaks will be
-automatically extended to cover the entire real line. For example, the default
-break of 0.25 is silently extended to breaks = c(-Inf, .25, Inf) and, therefore,
-results in two classes: \link{-Inf, 0.25} and (0.25, Inf). These two classes are
-used to discretize the outcome. The conversion of the outcome to such classes is
-handled internally. So if discrete classes already exist for the outcome in the
+The key takeaway from the predictions is that there are two prediction
+classes: \verb{(-Inf, 0.25]} and \verb{(0.25, Inf)}. This is because for our goal of
+classification the classes must be discrete. The discretization of the
+real-valued outcome is controlled by the \code{breaks} argument, which defaults
+to \code{0.25}. Such breaks will be automatically extended to cover the entire
+real line. For example, the default break of \code{0.25} is silently extended to
+\code{breaks = c(-Inf, .25, Inf)} and, therefore, results in two classes:
+\verb{[-Inf, 0.25]} and \verb{(0.25, Inf)}. These two classes are used to discretize
+the outcome. The conversion of the outcome to such classes is handled
+internally. So if discrete classes already exist for the outcome in the
 \code{epi_df}, then we recommend to code a classifier from scratch using the
 \code{epi_workflow} framework for more control.
 
-The \code{trainer} is a \code{parsnip} model describing the type of estimation such that
-\code{mode = "classification"} is enforced. The two typical trainers that are used
-are \code{parsnip::logistic_reg()} for two classes or \code{parsnip::multinom_reg()} for
-more than two classes.
+The \code{trainer} is a \code{parsnip} model describing the type of estimation such
+that \code{mode = "classification"} is enforced. The two typical trainers that
+are used are \code{parsnip::logistic_reg()} for two classes or
+\code{parsnip::multinom_reg()} for more than two classes.
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{workflows::extract_spec_parsnip(out$epi_workflow)
 #> Logistic Regression Model Specification (classification)
@@ -105,17 +106,17 @@ more than two classes.
 }\if{html}{\out{</div>}}
 
 From the parsnip model specification, we can see that the trainer used is
-logistic regression, which is expected for our binary outcome. More complicated
-trainers like \code{parsnip::naive_Bayes()} or \code{parsnip::rand_forest()} may also be
-used (however, we will stick to the basics in this gentle introduction to the
-classifier).
-
-If you use the default trainer of logistic regression for binary classification
-and you decide against using the default break of 0.25, then you should only
-input one break so that there are two classification bins to properly
-dichotomize the outcome. For example, let's set a break of 0.5 instead of
-relying on the default of 0.25. We can do this by passing 0.5 to the \code{breaks}
-argument in \code{arx_class_args_list()} as follows:
+logistic regression, which is expected for our binary outcome. More
+complicated trainers like \code{parsnip::naive_Bayes()} or
+\code{parsnip::rand_forest()} may also be used (however, we will stick to the
+basics in this gentle introduction to the classifier).
+
+If you use the default trainer of logistic regression for binary
+classification and you decide against using the default break of 0.25, then
+you should only input one break so that there are two classification bins
+to properly dichotomize the outcome. For example, let's set a break of 0.5
+instead of relying on the default of 0.25. We can do this by passing 0.5 to
+the \code{breaks} argument in \code{arx_class_args_list()} as follows:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{out_break_0.5 <- arx_classifier(
   jhu,
@@ -139,24 +140,26 @@ out_break_0.5$predictions
 #> 5 tx        (-Inf,0.5]  2021-12-31    2022-01-07
 }\if{html}{\out{</div>}}
 
-Indeed, we can observe that the two \code{.pred_class} are now (-Inf, 0.5] and (0.5,
-Inf). See \code{help(arx_class_args_list)} for other available modifications.
-
-Additional arguments that may be supplied to \code{arx_class_args_list()} include the
-expected \code{lags} and \code{ahead} arguments for an autoregressive-type model. These
-have default values of 0, 7, and 14 days for the lags of the predictors and 7
-days ahead of the forecast date for predicting the outcome. There is also
-\code{n_training} to indicate the upper bound for the number of training rows per
-key. If you would like some practice with using this, then remove the filtering
-command to obtain data within "2021-06-04" and "2021-12-31" and instead set
-\code{n_training} to be the number of days between these two dates, inclusive of the
-end points. The end results should be the same. In addition to \code{n_training},
-there are \code{forecast_date} and \code{target_date} to specify the date that the
-forecast is created and intended, respectively. We will not dwell on such
-arguments here as they are not unique to this classifier or absolutely essential
-to understanding how it operates. The remaining arguments will be discussed
-organically, as they are needed to serve our purposes. For information on any
-remaining arguments that are not discussed here, please see the function
+Indeed, we can observe that the two \code{.pred_class} are now (-Inf, 0.5] and
+(0.5, Inf). See \code{help(arx_class_args_list)} for other available
+modifications.
+
+Additional arguments that may be supplied to \code{arx_class_args_list()} include
+the expected \code{lags} and \code{ahead} arguments for an autoregressive-type model.
+These have default values of 0, 7, and 14 days for the lags of the
+predictors and 7 days ahead of the forecast date for predicting the
+outcome. There is also \code{n_training} to indicate the upper bound for the
+number of training rows per key. If you would like some practice with using
+this, then remove the filtering command to obtain data within "2021-06-04"
+and "2021-12-31" and instead set \code{n_training} to be the number of days
+between these two dates, inclusive of the end points. The end results
+should be the same. In addition to \code{n_training}, there are \code{forecast_date}
+and \code{target_date} to specify the date that the forecast is created and
+intended, respectively. We will not dwell on such arguments here as they
+are not unique to this classifier or absolutely essential to understanding
+how it operates. The remaining arguments will be discussed organically, as
+they are needed to serve our purposes. For information on any remaining
+arguments that are not discussed here, please see the function
 documentation for a complete list and their definitions.
 }
 \examples{

From 28063be435f5fd75a3751ef63296b2be3e5d515f Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 5 Mar 2025 16:43:23 -0600
Subject: [PATCH 357/387] reorganize reference page

---
 _pkgdown.yml | 84 ++++++++++++++++++++++++++++++----------------------
 1 file changed, 49 insertions(+), 35 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 7bcaa007b..ce1facdfa 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -9,7 +9,7 @@ template:
 
 navbar:
   structure:
-    left:  [intro, workflows, backtesting, reference, articles, news]
+    left: [intro, workflows, backtesting, reference, articles, news]
     right: [search, github, lightswitch]
   components:
     workflows:
@@ -48,63 +48,81 @@ reference:
       - contains("forecaster")
       - contains("classifier")
 
-  - title: Forecaster modifications
+  - subtitle: Forecaster modifications
     desc: Constructors to modify forecaster arguments and utilities to produce `epi_workflow` objects
     contents:
       - contains("args_list")
       - contains("_epi_workflow")
+  ##########################
+  - title: Steps and Layers
 
-  - title: Helper functions for Hub submission
+  - subtitle: Epi recipe preprocessing steps
+    desc: >
+      Note that any `{recipes}`
+      [`step`](https://recipes.tidymodels.org/reference/index.html) is also valid
     contents:
-      - flusight_hub_formatter
+      - starts_with("step_")
 
-  - title: Parsnip engines
-    desc: Prediction methods not available elsewhere
+  - subtitle: Frosting post-processing layers
     contents:
-      - quantile_reg
-      - smooth_quantile_reg
-      - grf_quantiles
+      - starts_with("layer_")
 
-  - title: Custom panel data forecasting workflows
+  ##########################
+  - title: Epiworkflows
+  - subtitle: Basic forecasting workflow functions
     contents:
       - epi_recipe
       - epi_workflow
       - add_epi_recipe
-      - adjust_epi_recipe
-      - Add_model
-      - predict.epi_workflow
       - fit.epi_workflow
-      - augment.epi_workflow
-      - forecast.epi_workflow
-
-  - title: Epi recipe preprocessing steps
-    contents:
-      - starts_with("step_")
-      - contains("bake")
 
-  - title: Epi recipe verification checks
-    contents:
-      - check_enough_data
-
-  - title: Forecast postprocessing
-    desc: Create a series of postprocessing operations
+  - subtitle: Forecast post-processing workflow functions
+    desc: Create and apply series of post-processing operations
     contents:
       - frosting
       - ends_with("_frosting")
-      - get_test_data
       - tidy.frosting
+      - contains("slather")
 
-  - title: Frosting layers
+  - subtitle: Prediction
+    desc: Methods for prediction and modifying predictions
     contents:
-      - contains("layer")
-      - contains("slather")
+      - predict.epi_workflow
+      - augment.epi_workflow
+      - get_test_data
+      - forecast.epi_workflow
 
+  - subtitle: Modifying forecasting epiworkflows
+    desc: modify or inspect an existing recipe, workflow, or frosting
+    contents:
+      - adjust_epi_recipe
+      - Add_model
+      - add_layer
+      - layer-processors
+      - update.layer
+
+  ##########################
   - title: Automatic forecast visualization
     contents:
       - autoplot.epi_workflow
       - autoplot.canned_epipred
 
-  - title: Utilities for quantile distribution processing
+  ##########################
+  - title: Parsnip engines
+    desc: Prediction methods not available elsewhere
+    contents:
+      - quantile_reg
+      - smooth_quantile_reg
+      - grf_quantiles
+
+  ##########################
+  - title: Utilities
+    contents:
+      - flusight_hub_formatter
+      - clean_f_name
+      - check_enough_data
+
+  - subtitle: Utilities for quantile distribution processing
     contents:
       - dist_quantiles
       - contains("quantile_pred")
@@ -112,7 +130,3 @@ reference:
       - nested_quantiles
       - weighted_interval_score
       - starts_with("pivot_quantiles")
-
-  - title: Other utilities
-    contents:
-      - clean_f_name

From c2bc4bab2676c499ee1dc10f96cd568bda71683c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 5 Mar 2025 16:44:58 -0600
Subject: [PATCH 358/387] postprocessing -> post-processing

---
 NEWS.md                                  |  2 +-
 R/epi_workflow.R                         |  2 +-
 R/frosting.R                             | 14 +++++++-------
 R/layer_add_forecast_date.R              |  8 ++++----
 R/layer_add_target_date.R                |  4 ++--
 R/layer_point_from_distn.R               |  2 +-
 R/layer_predict.R                        |  4 ++--
 R/layer_threshold_preds.R                |  2 +-
 R/layers.R                               |  6 +++---
 man/apply_frosting.Rd                    |  4 ++--
 man/epi_workflow.Rd                      |  2 +-
 man/frosting.Rd                          |  8 ++++----
 man/layer_add_forecast_date.Rd           | 10 +++++-----
 man/layer_add_target_date.Rd             |  6 +++---
 man/layer_point_from_distn.Rd            |  2 +-
 man/layer_predict.Rd                     |  4 ++--
 man/layer_threshold.Rd                   |  2 +-
 man/slather.Rd                           |  6 +++---
 tests/testthat/test-population_scaling.R |  6 +++---
 19 files changed, 47 insertions(+), 47 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index bbcdb2eb4..512de8d76 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -70,7 +70,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - training window step debugged
 - `min_train_window` argument removed from canned forecasters
 - add forecasters
-- implement postprocessing
+- implement post-processing
 - vignettes avaliable
 - arx_forecaster
 - pkgdown
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 81b443e7b..6fdb7a846 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -1,7 +1,7 @@
 #' Create an epi_workflow
 #'
 #' This is a container object that unifies preprocessing, fitting, prediction,
-#' and postprocessing for predictive modeling on epidemiological data. It extends
+#' and post-processing for predictive modeling on epidemiological data. It extends
 #' the functionality of a [workflows::workflow()] to handle the typical panel
 #' data structures found in this field. This extension is handled completely
 #' internally, and should be invisible to the user. For all intents and purposes,
diff --git a/R/frosting.R b/R/frosting.R
index cb0fa916e..84331c9ac 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -246,10 +246,10 @@ new_frosting <- function() {
 }
 
 
-#' Create frosting for postprocessing predictions
+#' Create frosting for post-processing predictions
 #'
-#' This generates a postprocessing container (much like `recipes::recipe()`)
-#' to hold steps for postprocessing predictions.
+#' This generates a post-processing container (much like `recipes::recipe()`)
+#' to hold steps for post-processing predictions.
 #'
 #' The arguments are currently placeholders and must be NULL
 #'
@@ -260,7 +260,7 @@ new_frosting <- function() {
 #' @export
 #'
 #' @examples
-#' # Toy example to show that frosting can be created and added for postprocessing
+#' # Toy example to show that frosting can be created and added for post-processing
 #' f <- frosting()
 #' wf <- epi_workflow() %>% add_frosting(f)
 #'
@@ -322,9 +322,9 @@ extract_frosting.epi_workflow <- function(x, ...) {
   }
 }
 
-#' Apply postprocessing to a fitted workflow
+#' Apply post-processing to a fitted workflow
 #'
-#' This function is intended for internal use. It implements postprocessing
+#' This function is intended for internal use. It implements post-processing
 #' inside of the `predict()` method for a fitted workflow.
 #'
 #' @param workflow An object of class workflow
@@ -342,7 +342,7 @@ apply_frosting <- function(workflow, ...) {
 apply_frosting.default <- function(workflow, components, ...) {
   if (has_postprocessor(workflow)) {
     cli_abort(c(
-      "Postprocessing is only available for epi_workflows currently.",
+      "Post-processing is only available for epi_workflows currently.",
       i = "Can you use `epi_workflow()` instead of `workflow()`?"
     ))
   }
diff --git a/R/layer_add_forecast_date.R b/R/layer_add_forecast_date.R
index 3e62bafb0..72bc33703 100644
--- a/R/layer_add_forecast_date.R
+++ b/R/layer_add_forecast_date.R
@@ -1,4 +1,4 @@
-#' Postprocessing step to add the forecast date
+#' Post-processing step to add the forecast date
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param forecast_date The forecast date to add as a column to the `epi_df`.
@@ -7,7 +7,7 @@
 #'   values.  If there is a `step_adjust_latency` step present, it uses the
 #'   `forecast_date` as set in that function. Otherwise, it uses the maximum
 #'   `time_value` across the data used for pre-processing, fitting the model,
-#'   and postprocessing.
+#'   and post-processing.
 #' @param id a random id string
 #'
 #' @return an updated `frosting` postprocessor
@@ -15,9 +15,9 @@
 #' @details To use this function, either specify a forecast date or leave the
 #'  forecast date unspecifed here. In the latter case, the forecast date will
 #'  be set as the maximum time value from the data used in pre-processing,
-#'  fitting the model, and postprocessing. In any case, when the forecast date is
+#'  fitting the model, and post-processing. In any case, when the forecast date is
 #'  less than the maximum `as_of` value (from the data used pre-processing,
-#'  model fitting, and postprocessing), an appropriate warning will be thrown.
+#'  model fitting, and post-processing), an appropriate warning will be thrown.
 #'
 #' @export
 #' @examples
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index bd97862ca..46f413bd2 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -1,4 +1,4 @@
-#' Postprocessing step to add the target date
+#' Post-processing step to add the target date
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param target_date The target date to add as a column to the `epi_df`. If
@@ -6,7 +6,7 @@
 #'   `step_adjust_latency` or in a `layer_forecast_date`), then it is the
 #'   forecast date plus `ahead` (from `step_epi_ahead` in the `epi_recipe`).
 #'   Otherwise, it is the maximum `time_value` (from the data used in
-#'   pre-processing, fitting the model, and postprocessing) plus `ahead`, where
+#'   pre-processing, fitting the model, and post-processing) plus `ahead`, where
 #'   `ahead` has been specified in preprocessing. The user may override these by
 #'   specifying a target date of their own (of the form "yyyy-mm-dd").
 #' @param id a random id string
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index a67e3e079..07f524470 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -1,6 +1,6 @@
 #' Converts distributional forecasts to point forecasts
 #'
-#' This function adds a postprocessing layer to extract a point forecast from
+#' This function adds a post-processing layer to extract a point forecast from
 #' a distributional forecast. NOTE: With default arguments, this will remove
 #' information, so one should usually call this AFTER `layer_quantile_distn()`
 #' or set the `name` argument to something specific.
diff --git a/R/layer_predict.R b/R/layer_predict.R
index 0f4c33e11..76868bd44 100644
--- a/R/layer_predict.R
+++ b/R/layer_predict.R
@@ -1,8 +1,8 @@
-#' Prediction layer for postprocessing
+#' Prediction layer for post-processing
 #'
 #' Implements prediction on a fitted `epi_workflow`. One may want different
 #' types of prediction, and to potentially apply this after some amount of
-#' postprocessing. This would typically be the first layer in a `frosting`
+#' post-processing. This would typically be the first layer in a `frosting`
 #' postprocessor.
 #'
 #' @seealso `parsnip::predict.model_fit()`
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 2869fff07..0e28bfa10 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -1,6 +1,6 @@
 #' Lower and upper thresholds for predicted values
 #'
-#' This postprocessing step is used to set prediction values that are
+#' This post-processing step is used to set prediction values that are
 #' smaller than the lower threshold or higher than the upper threshold equal
 #' to the threshold values.
 #'
diff --git a/R/layers.R b/R/layers.R
index 01cb19dba..c098172a9 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -178,11 +178,11 @@ detect_layer.workflow <- function(x, name, ...) {
 
 #' Spread a layer of frosting on a fitted workflow
 #'
-#' Slathering frosting means to implement a postprocessing layer. When
-#' creating a new postprocessing layer, you must implement an S3 method
+#' Slathering frosting means to implement a post-processing layer. When
+#' creating a new post-processing layer, you must implement an S3 method
 #' for this function
 #'
-#' @param object a workflow with `frosting` postprocessing steps
+#' @param object a workflow with `frosting` post-processing steps
 #' @param components a list of components containing model information. These
 #'   will be updated and returned by the layer. These should be
 #'   * `mold` - the output of calling `hardhat::mold()` on the workflow. This
diff --git a/man/apply_frosting.Rd b/man/apply_frosting.Rd
index ef18796cc..ece3261e8 100644
--- a/man/apply_frosting.Rd
+++ b/man/apply_frosting.Rd
@@ -5,7 +5,7 @@
 \alias{apply_frosting.default}
 \alias{apply_frosting.epi_recipe}
 \alias{apply_frosting.epi_workflow}
-\title{Apply postprocessing to a fitted workflow}
+\title{Apply post-processing to a fitted workflow}
 \usage{
 apply_frosting(workflow, ...)
 
@@ -39,6 +39,6 @@ and predict on}
 \code{\link[=slather]{slather()}} for supported layers}
 }
 \description{
-This function is intended for internal use. It implements postprocessing
+This function is intended for internal use. It implements post-processing
 inside of the \code{predict()} method for a fitted workflow.
 }
diff --git a/man/epi_workflow.Rd b/man/epi_workflow.Rd
index 59e3d5c8f..2e5aea938 100644
--- a/man/epi_workflow.Rd
+++ b/man/epi_workflow.Rd
@@ -25,7 +25,7 @@ A new \code{epi_workflow} object.
 }
 \description{
 This is a container object that unifies preprocessing, fitting, prediction,
-and postprocessing for predictive modeling on epidemiological data. It extends
+and post-processing for predictive modeling on epidemiological data. It extends
 the functionality of a \code{\link[workflows:workflow]{workflows::workflow()}} to handle the typical panel
 data structures found in this field. This extension is handled completely
 internally, and should be invisible to the user. For all intents and purposes,
diff --git a/man/frosting.Rd b/man/frosting.Rd
index e36100bae..c00875125 100644
--- a/man/frosting.Rd
+++ b/man/frosting.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/frosting.R
 \name{frosting}
 \alias{frosting}
-\title{Create frosting for postprocessing predictions}
+\title{Create frosting for post-processing predictions}
 \usage{
 frosting(layers = NULL, requirements = NULL)
 }
@@ -15,14 +15,14 @@ frosting(layers = NULL, requirements = NULL)
 A frosting object.
 }
 \description{
-This generates a postprocessing container (much like \code{recipes::recipe()})
-to hold steps for postprocessing predictions.
+This generates a post-processing container (much like \code{recipes::recipe()})
+to hold steps for post-processing predictions.
 }
 \details{
 The arguments are currently placeholders and must be NULL
 }
 \examples{
-# Toy example to show that frosting can be created and added for postprocessing
+# Toy example to show that frosting can be created and added for post-processing
 f <- frosting()
 wf <- epi_workflow() \%>\% add_frosting(f)
 
diff --git a/man/layer_add_forecast_date.Rd b/man/layer_add_forecast_date.Rd
index 14c7864bc..6370962cc 100644
--- a/man/layer_add_forecast_date.Rd
+++ b/man/layer_add_forecast_date.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/layer_add_forecast_date.R
 \name{layer_add_forecast_date}
 \alias{layer_add_forecast_date}
-\title{Postprocessing step to add the forecast date}
+\title{Post-processing step to add the forecast date}
 \usage{
 layer_add_forecast_date(
   frosting,
@@ -19,7 +19,7 @@ that when the forecast date is left unspecified, it is set to one of two
 values.  If there is a \code{step_adjust_latency} step present, it uses the
 \code{forecast_date} as set in that function. Otherwise, it uses the maximum
 \code{time_value} across the data used for pre-processing, fitting the model,
-and postprocessing.}
+and post-processing.}
 
 \item{id}{a random id string}
 }
@@ -27,15 +27,15 @@ and postprocessing.}
 an updated \code{frosting} postprocessor
 }
 \description{
-Postprocessing step to add the forecast date
+Post-processing step to add the forecast date
 }
 \details{
 To use this function, either specify a forecast date or leave the
 forecast date unspecifed here. In the latter case, the forecast date will
 be set as the maximum time value from the data used in pre-processing,
-fitting the model, and postprocessing. In any case, when the forecast date is
+fitting the model, and post-processing. In any case, when the forecast date is
 less than the maximum \code{as_of} value (from the data used pre-processing,
-model fitting, and postprocessing), an appropriate warning will be thrown.
+model fitting, and post-processing), an appropriate warning will be thrown.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index c9f43c5f1..034462be7 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/layer_add_target_date.R
 \name{layer_add_target_date}
 \alias{layer_add_target_date}
-\title{Postprocessing step to add the target date}
+\title{Post-processing step to add the target date}
 \usage{
 layer_add_target_date(
   frosting,
@@ -18,7 +18,7 @@ there's a forecast date specified upstream (either in a
 \code{step_adjust_latency} or in a \code{layer_forecast_date}), then it is the
 forecast date plus \code{ahead} (from \code{step_epi_ahead} in the \code{epi_recipe}).
 Otherwise, it is the maximum \code{time_value} (from the data used in
-pre-processing, fitting the model, and postprocessing) plus \code{ahead}, where
+pre-processing, fitting the model, and post-processing) plus \code{ahead}, where
 \code{ahead} has been specified in preprocessing. The user may override these by
 specifying a target date of their own (of the form "yyyy-mm-dd").}
 
@@ -28,7 +28,7 @@ specifying a target date of their own (of the form "yyyy-mm-dd").}
 an updated \code{frosting} postprocessor
 }
 \description{
-Postprocessing step to add the target date
+Post-processing step to add the target date
 }
 \details{
 By default, this function assumes that a value for \code{ahead}
diff --git a/man/layer_point_from_distn.Rd b/man/layer_point_from_distn.Rd
index 329f62d5b..3e770e912 100644
--- a/man/layer_point_from_distn.Rd
+++ b/man/layer_point_from_distn.Rd
@@ -28,7 +28,7 @@ will overwrite the \code{.pred} column, removing the distribution information.}
 an updated \code{frosting} postprocessor.
 }
 \description{
-This function adds a postprocessing layer to extract a point forecast from
+This function adds a post-processing layer to extract a point forecast from
 a distributional forecast. NOTE: With default arguments, this will remove
 information, so one should usually call this AFTER \code{layer_quantile_distn()}
 or set the \code{name} argument to something specific.
diff --git a/man/layer_predict.Rd b/man/layer_predict.Rd
index 9a7f12a46..7955114bf 100644
--- a/man/layer_predict.Rd
+++ b/man/layer_predict.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/layer_predict.R
 \name{layer_predict}
 \alias{layer_predict}
-\title{Prediction layer for postprocessing}
+\title{Prediction layer for post-processing}
 \usage{
 layer_predict(
   frosting,
@@ -54,7 +54,7 @@ An updated \code{frosting} object
 \description{
 Implements prediction on a fitted \code{epi_workflow}. One may want different
 types of prediction, and to potentially apply this after some amount of
-postprocessing. This would typically be the first layer in a \code{frosting}
+post-processing. This would typically be the first layer in a \code{frosting}
 postprocessor.
 }
 \examples{
diff --git a/man/layer_threshold.Rd b/man/layer_threshold.Rd
index 875390ced..0ecca7e65 100644
--- a/man/layer_threshold.Rd
+++ b/man/layer_threshold.Rd
@@ -35,7 +35,7 @@ Default value is \code{Inf}.}
 an updated \code{frosting} postprocessor
 }
 \description{
-This postprocessing step is used to set prediction values that are
+This post-processing step is used to set prediction values that are
 smaller than the lower threshold or higher than the upper threshold equal
 to the threshold values.
 }
diff --git a/man/slather.Rd b/man/slather.Rd
index dd556b629..6f6450ba4 100644
--- a/man/slather.Rd
+++ b/man/slather.Rd
@@ -7,7 +7,7 @@
 slather(object, components, workflow, new_data, ...)
 }
 \arguments{
-\item{object}{a workflow with \code{frosting} postprocessing steps}
+\item{object}{a workflow with \code{frosting} post-processing steps}
 
 \item{components}{a list of components containing model information. These
 will be updated and returned by the layer. These should be
@@ -34,7 +34,7 @@ and predict on}
 The \code{components} list. In the same format after applying any updates.
 }
 \description{
-Slathering frosting means to implement a postprocessing layer. When
-creating a new postprocessing layer, you must implement an S3 method
+Slathering frosting means to implement a post-processing layer. When
+creating a new post-processing layer, you must implement an S3 method
 for this function
 }
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index f2efde3c0..f31473f96 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -88,8 +88,8 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
   expect_equal(ncol(b), 5L)
 })
 
-## Postprocessing
-test_that("Postprocessing workflow works and values correct", {
+## Post-processing
+test_that("Post-processing workflow works and values correct", {
   jhu <- epidatasets::cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
@@ -149,7 +149,7 @@ test_that("Postprocessing workflow works and values correct", {
   expect_equal(p$.pred_scaled, p$.pred * c(2, 3))
 })
 
-test_that("Postprocessing to get cases from case rate", {
+test_that("Post-processing to get cases from case rate", {
   jhu <- covid_case_death_rates %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, case_rate)

From 34d7a05835a536fe49e63cc493c297324970ca7e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 6 Mar 2025 17:53:48 -0600
Subject: [PATCH 359/387] lots of reference updates

---
 DEVELOPMENT.md                  |  6 ++-
 R/arx_classifier.R              |  6 +--
 R/arx_forecaster.R              | 42 +++++++++--------
 R/epi_recipe.R                  | 12 ++---
 R/epi_workflow.R                | 80 ++++++++++++++++++---------------
 R/extrapolate_quantiles.R       | 18 +++++++-
 R/flatline_forecaster.R         | 41 +++++++++++++----
 R/frosting.R                    |  3 +-
 R/get_test_data.R               |  9 ++--
 R/layer_add_target_date.R       | 17 +++----
 R/layer_population_scaling.R    | 23 +++++-----
 R/layer_predict.R               |  2 +-
 R/layer_predictive_distn.R      |  8 ++--
 R/layer_quantile_distn.R        |  2 +
 R/layer_residual_quantiles.R    |  5 +++
 R/layer_threshold_preds.R       | 14 ++++--
 R/layer_unnest.R                | 36 +++++++++++++++
 R/layers.R                      | 12 +++--
 R/pivot_quantiles.R             | 14 +++---
 R/step_epi_naomit.R             | 13 ++++--
 R/step_epi_shift.R              | 25 +++++------
 R/step_epi_slide.R              |  7 +--
 R/step_growth_rate.R            |  5 ++-
 R/step_lag_difference.R         | 11 ++++-
 R/step_population_scaling.R     | 30 +++++++------
 R/step_training_window.R        |  8 +++-
 _pkgdown.yml                    |  8 +++-
 inst/pkgdown-watch.R            |  3 +-
 man/add_epi_recipe.Rd           | 24 +++++-----
 man/add_frosting.Rd             |  6 ++-
 man/arx_class_args_list.Rd      |  2 +-
 man/arx_class_epi_workflow.Rd   |  8 ++--
 man/arx_classifier.Rd           |  8 ++--
 man/arx_fcast_epi_workflow.Rd   | 15 +++----
 man/arx_forecaster.Rd           | 39 +++++++++-------
 man/augment.epi_workflow.Rd     |  5 ++-
 man/epi_workflow.Rd             | 14 +++---
 man/extrapolate_quantiles.Rd    | 19 +++++++-
 man/fit-epi_workflow.Rd         |  6 +--
 man/flatline_forecaster.Rd      | 33 +++++++++++---
 man/forecast.epi_workflow.Rd    | 34 +++++++-------
 man/get_test_data.Rd            |  9 ++--
 man/layer_add_target_date.Rd    | 17 +++----
 man/layer_population_scaling.Rd | 23 +++++-----
 man/layer_predict.Rd            |  2 +-
 man/layer_predictive_distn.Rd   |  8 ++--
 man/layer_quantile_distn.Rd     |  2 +
 man/layer_residual_quantiles.Rd |  5 ++-
 man/layer_threshold.Rd          | 14 ++++--
 man/layer_unnest.Rd             | 37 ++++++++++++++-
 man/pivot_quantiles_longer.Rd   |  6 +--
 man/pivot_quantiles_wider.Rd    |  8 ++--
 man/predict-epi_workflow.Rd     | 22 ++++-----
 man/slather.Rd                  | 12 +++--
 man/step_epi_naomit.Rd          | 13 ++++--
 man/step_epi_shift.Rd           | 25 +++++------
 man/step_epi_slide.Rd           |  7 +--
 man/step_growth_rate.Rd         |  5 ++-
 man/step_lag_difference.Rd      | 12 ++++-
 man/step_population_scaling.Rd  | 30 ++++++-------
 man/step_training_window.Rd     |  8 +++-
 61 files changed, 598 insertions(+), 340 deletions(-)

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 27ed2bf9f..c52032cda 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -35,12 +35,14 @@ R -e 'devtools::document()'
 R -e 'pkgdown::build_site()'
 ```
 
-Note that sometimes the caches from either `pkgdown` or `knitr` can cause difficulties. To clear those, run `make`, with either `clean_knitr`, `clean_site`, or `clean` (which does both).
+Note that sometimes the caches from either `pkgdown` or `knitr` can cause
+difficulties. To clear those, run `make`, with either `clean_knitr`,
+`clean_site`, or `clean` (which does both).
 
 If you work without R Studio and want to iterate on documentation, you might
 find [this
 script](https://gist.github.com/gadenbuie/d22e149e65591b91419e41ea5b2e0621)
-helpful.
+helpful. For updating references, you will need to manually call `pkgdown::build_reference()`.
 
 ## Versioning
 
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index b53d7dd15..9122435b1 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -166,9 +166,7 @@ arx_classifier <- function(
   }
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
-  preds <- forecast(
-    wf,
-  ) %>%
+  preds <- forecast(wf) %>%
     as_tibble() %>%
     select(-time_value)
 
@@ -347,7 +345,7 @@ arx_class_epi_workflow <- function(
 #'   be created using growth rates (as the predictors are) or lagged
 #'   differences. The second case is closer to the requirements for the
 #'   [2022-23 CDC Flusight Hospitalization Experimental Target](https://github.com/cdcepi/Flusight-forecast-data/blob/745511c436923e1dc201dea0f4181f21a8217b52/data-experimental/README.md).
-#'   See the Classification Vignette for details of how to create a reasonable
+#'   See the [Classification chapter from the forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/arx-classifier.html) Vignette for details of how to create a reasonable
 #'   baseline for this case. Selecting `"growth_rate"` (the default) uses
 #'   [epiprocess::growth_rate()] to create the outcome using some of the
 #'   additional arguments below. Choosing `"lag_difference"` instead simply
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index f988490fd..d063f8134 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -1,26 +1,29 @@
 #' Direct autoregressive forecaster with covariates
 #'
 #' This is an autoregressive forecasting model for
-#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
-#' that it estimates a model for a particular target horizon.
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct"
+#' forecasting, meaning that it estimates a model for a particular target
+#' horizon of `outcome` based on the lags of the `predictors`. See the [Get
+#' started vignette](../articles/epipredict.html) for some worked examples and
+#' [Custom epi_workflows vignette](../articles/custom_epiworkflows.html) for a
+#' recreation using a custom `epi_workflow()`.
 #'
 #'
 #' @param epi_data An `epi_df` object
-#' @param outcome A character (scalar) specifying the outcome (in the
-#'   `epi_df`).
+#' @param outcome A character (scalar) specifying the outcome (in the `epi_df`).
 #' @param predictors A character vector giving column(s) of predictor variables.
-#'   This defaults to the `outcome`. However, if manually specified, only those variables
-#'   specifically mentioned will be used. (The `outcome` will not be added.)
-#'   By default, equals the outcome. If manually specified, does not add the
-#'   outcome variable, so make sure to specify it.
-#' @param trainer A `{parsnip}` model describing the type of estimation.
-#'   For now, we enforce `mode = "regression"`.
-#' @param args_list A list of customization arguments to determine
-#'   the type of forecasting model. See [arx_args_list()].
+#'   This defaults to the `outcome`. However, if manually specified, only those
+#'   variables specifically mentioned will be used. (The `outcome` will not be
+#'   added.)  By default, equals the outcome. If manually specified, does not
+#'   add the outcome variable, so make sure to specify it.
+#' @param trainer A `{parsnip}` model describing the type of estimation.  For
+#'   now, we enforce `mode = "regression"`.
+#' @param args_list A list of customization arguments to determine the type of
+#'   forecasting model. See [arx_args_list()].
 #'
-#' @return A list with (1) `predictions` an `epi_df` of predicted values
-#'   and (2) `epi_workflow`, a list that encapsulates the entire estimation
-#'   workflow
+#' @return An `arx_fcast`, with the fields `predictions` and `epi_workflow`.
+#'   `predictions` is an `epi_df` of predicted values while `epi_workflow()` is
+#'   the fit workflow used to make those predictions
 #' @export
 #' @seealso [arx_fcast_epi_workflow()], [arx_args_list()]
 #'
@@ -29,15 +32,18 @@
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
 #' out <- arx_forecaster(
-#'   jhu, "death_rate",
+#'   jhu,
+#'   "death_rate",
 #'   c("case_rate", "death_rate")
 #' )
 #'
-#' out <- arx_forecaster(jhu, "death_rate",
+#' out <- arx_forecaster(jhu,
+#'   "death_rate",
 #'   c("case_rate", "death_rate"),
 #'   trainer = quantile_reg(),
 #'   args_list = arx_args_list(quantile_levels = 1:9 / 10)
 #' )
+#' out
 arx_forecaster <- function(
     epi_data,
     outcome,
@@ -60,7 +66,7 @@ arx_forecaster <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
 
 
-  preds <- forecast(wf, forecast_date = forecast_date) %>%
+  preds <- forecast(wf) %>%
     as_tibble() %>%
     select(-time_value)
 
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index dae445f53..3f90e40b1 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -232,9 +232,10 @@ is_epi_recipe <- function(x) {
 
 
 
-#' Add an `epi_recipe` to a workflow
+#' Given an `epi_recipe`, add it to, remove it from, or update it in an
+#' `epi_workflow`
 #'
-#' @seealso [workflows::add_recipe()]
+#' @description
 #' - `add_recipe()` specifies the terms of the model and any preprocessing that
 #'   is required through the usage of a recipe.
 #'
@@ -244,9 +245,9 @@ is_epi_recipe <- function(x) {
 #' recipe with the new one.
 #'
 #' @details
-#' `add_epi_recipe` has the same behaviour as
-#' [workflows::add_recipe()] but sets a different
-#' default blueprint to automatically handle [epiprocess::epi_df][epiprocess::as_epi_df] data.
+#' `add_epi_recipe()` has the same behaviour as [workflows::add_recipe()] but
+#'   sets a different default blueprint to automatically handle
+#'   `epiprocess::epi_df()` data.
 #'
 #' @param x A `workflow` or `epi_workflow`
 #'
@@ -265,6 +266,7 @@ is_epi_recipe <- function(x) {
 #' `x`, updated with a new recipe preprocessor.
 #'
 #' @export
+#' @seealso [workflows::add_recipe()]
 #' @examples
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-08-01")
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 6fdb7a846..c3b1c4fbc 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -1,19 +1,20 @@
 #' Create an epi_workflow
 #'
 #' This is a container object that unifies preprocessing, fitting, prediction,
-#' and post-processing for predictive modeling on epidemiological data. It extends
-#' the functionality of a [workflows::workflow()] to handle the typical panel
-#' data structures found in this field. This extension is handled completely
-#' internally, and should be invisible to the user. For all intents and purposes,
-#' this operates exactly like a [workflows::workflow()]. For more details
-#' and numerous examples, see there.
+#' and post-processing for predictive modeling on epidemiological data. It
+#' extends the functionality of a [workflows::workflow()] to handle the typical
+#' panel data structures found in this field. This extension is handled
+#' completely internally, and should be invisible to the user. For all intents
+#' and purposes, this operates exactly like a [workflows::workflow()]. For some
+#' `{epipredict}` specific examples, see the [custom epiworkflows
+#' vignette](../articles/custom_epiworkflows.html).
 #'
 #' @inheritParams workflows::workflow
 #' @param postprocessor An optional postprocessor to add to the workflow.
 #'   Currently only `frosting` is allowed using, `add_frosting()`.
 #'
 #' @return A new `epi_workflow` object.
-#' @seealso workflows::workflow
+#' @seealso [workflows::workflow()]
 #' @importFrom rlang is_null
 #' @importFrom stats predict
 #' @importFrom generics fit
@@ -62,9 +63,9 @@ is_epi_workflow <- function(x) {
 #' Fit an `epi_workflow` object
 #'
 #' @description
-#' This is the `fit()` method for an `epi_workflow` object that
+#' This is the `fit()` method for an `epi_workflow()` object that
 #' estimates parameters for a given model from a set of data.
-#' Fitting an `epi_workflow` involves two main steps, which are
+#' Fitting an `epi_workflow()` involves two main steps, which are
 #' preprocessing the data and fitting the underlying parsnip model.
 #'
 #' @inheritParams workflows::fit.workflow
@@ -79,7 +80,7 @@ is_epi_workflow <- function(x) {
 #' @return The `epi_workflow` object, updated with a fit parsnip
 #' model in the `object$fit$fit` slot.
 #'
-#' @seealso workflows::fit-workflow
+#' @seealso [workflows::fit-workflow()]
 #'
 #' @name fit-epi_workflow
 #' @export
@@ -111,20 +112,20 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #' Predict from an epi_workflow
 #'
 #' @description
-#' This is the `predict()` method for a fit epi_workflow object. The nice thing
-#' about predicting from an epi_workflow is that it will:
+#' This is the `predict()` method for a fit epi_workflow object. The 3 steps that this implements are:
 #'
-#' - Preprocess `new_data` using the preprocessing method specified when the
-#'   workflow was created and fit. This is accomplished using
-#'   [hardhat::forge()], which will apply any formula preprocessing or call
-#'   [recipes::bake()] if a recipe was supplied.
+#' - Preprocessing `new_data` using the preprocessing method specified when the
+#'   epi_workflow was created and fit. This is accomplished using
+#'   `recipes::bake()` if a recipe was supplied. Note that this is a slightly
+#'   different `bake` operation than the one occuring during the fit. Any `step`
+#'   that has `skip = TRUE` isn't applied during prediction; for example in
+#'   `step_epi_naomit()`, `all_outcomes()` isn't `NA` omitted, since doing so
+#'   would drop the exact `time_values` we are trying to predict.
 #'
-#' - Call [parsnip::predict.model_fit()] for you using the underlying fit
+#' - Calling `parsnip::predict.model_fit()` for you using the underlying fit
 #'   parsnip model.
 #'
-#' - Ensure that the returned object is an [epiprocess::epi_df][epiprocess::as_epi_df] where
-#'   possible. Specifically, the output will have `time_value` and
-#'   `geo_value` columns as well as the prediction.
+#' - `slather()` any frosting that has been included in the `epi_workflow`.
 #'
 #' @param object An epi_workflow that has been fit by
 #'   [workflows::fit.workflow()]
@@ -136,7 +137,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' @return
 #' A data frame of model predictions, with as many rows as `new_data` has.
-#' If `new_data` is an `epi_df` or a data frame with `time_value` or
+#' If `new_data` is an `epi_df()` or a data frame with `time_value` or
 #' `geo_value` columns, then the result will have those as well.
 #'
 #' @name predict-epi_workflow
@@ -177,6 +178,11 @@ predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), .
 
 #' Augment data with predictions
 #'
+#' `augment()`, unlike `forecast()`, has the goal of modifying the training
+#' data, rather than just producing new forecasts. It does a prediction on
+#' `new_data`, which will produce a prediction for most `time_values`, and then
+#' adds `.pred` as a column to `new_data` and returns the resulting join.
+#'
 #' @param x A trained epi_workflow
 #' @param new_data A epi_df of predictors
 #' @param ... Arguments passed on to the predict method.
@@ -228,26 +234,30 @@ print.epi_workflow <- function(x, ...) {
 }
 
 
-#' Produce a forecast from an epi workflow
+#' Produce a forecast from just an epi workflow
+#'
+#' `forecast.epi_workflow` predicts by restricting the training data to the
+#' latest available data, and predicting on that. It binds together
+#' `get_test_data()` and `predict()`.
 #'
 #' @param object An epi workflow.
-#' @param ... Not used.
-#' @param n_recent Integer or NULL. If filling missing data with locf = TRUE,
-#' how far back are we willing to tolerate missing data? Larger values allow
-#' more filling. The default NULL will determine this from the the recipe. For
-#' example, suppose n_recent = 3, then if the 3 most recent observations in any
-#' geo_value are all NA’s, we won’t be able to fill anything, and an error
-#' message will be thrown. (See details.)
-#' @param forecast_date By default, this is set to the maximum time_value in x.
-#' But if there is data latency such that recent NA's should be filled, this may
-#' be after the last available time_value.
 #'
 #' @return A forecast tibble.
 #'
 #' @export
-forecast.epi_workflow <- function(object, ..., n_recent = NULL, forecast_date = NULL) {
-  rlang::check_dots_empty()
-
+#' @examples
+#' jhu <- covid_case_death_rates %>%
+#'   filter(time_value > "2021-08-01")
+#'
+#' r <- epi_recipe(jhu) %>%
+#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+#'   step_epi_ahead(death_rate, ahead = 7) %>%
+#'   step_epi_naomit()
+#'
+#' epi_workflow(r, parsnip::linear_reg()) %>%
+#'   fit(jhu) %>%
+#'   forecast()
+forecast.epi_workflow <- function(object) {
   if (!object$trained) {
     cli_abort(c(
       "You cannot `forecast()` a {.cls workflow} that has not been trained.",
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index c7a9a3b6b..ec68d8256 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -1,4 +1,16 @@
-#' Summarize a distribution with a set of quantiles
+#' Extrapolate the quantiles to new quantile levels
+#'
+#' This both interpolates between quantile levels already defined in `x` and
+#' extrapolates quantiles outside their bounds. The interpolation method is
+#' determined by the `quantile` argument `middle`, which can be either `"cubic"`
+#' for a (hyman) cubic spline interpolation, or `"linear"` for simple linear
+#' interpolation.
+#'
+#' There is only one extrapolation method for values greater than the largest
+#' known quantile level or smaller than the smallest known quantile level. It
+#' assumes a roughly exponential tail, whose decay rate and offset is derived
+#' from the slope of the two most extreme quantile levels on a logistic scale.
+#' See the internal function `tail_extrapolate()` for the exact implementation.
 #'
 #' This function takes a `quantile_pred` vector and returns the same
 #' type of object, expanded to include
@@ -20,7 +32,9 @@
 #' @examples
 #' dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 #' # extra quantiles are appended
-#' as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+#' as_tibble(extrapolate_quantiles(dstn, probs = c(0.25, 0.5, 0.75)))
+#'
+#' extrapolate_quantiles(dstn, probs = c(0.0001, 0.25, 0.5, 0.75, 0.99999))
 extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
   UseMethod("extrapolate_quantiles")
 }
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index bf7ecb5b0..617d703e7 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -1,18 +1,41 @@
 #' Predict the future with today's value
 #'
-#' This is a simple forecasting model for
-#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent
-#' observation as the
-#' forecast for any future date, and produces intervals based on the quantiles
-#' of the residuals of such a "flatline" forecast over all available training
-#' data.
+#' @description This is a simple forecasting model for
+#'   [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent
+#'   observation as the forecast for any future date, and produces intervals
+#'   based on the quantiles of the residuals of such a "flatline" forecast over
+#'   all available training data.
 #'
 #' By default, the predictive intervals are computed separately for each
-#' combination of key values (`geo_value` + any additional keys) in the
-#' `epi_data` argument.
+#'   combination of key values (`geo_value` + any additional keys) in the
+#'   `epi_data` argument.
 #'
 #' This forecaster is very similar to that used by the
-#' [COVID19ForecastHub](https://covid19forecasthub.org)
+#'   [COVID19ForecastHub](https://covid19forecasthub.org)
+#'
+#' @details
+#'  Here is (roughly) the code for the `flatline_forecaster()` applied to the
+#'   `case_rate` for `epidatasets::covid_case_death_rates`.
+#'
+#' ```{r}
+#' jhu <- covid_case_death_rates %>%
+#'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
+#' r <- epi_recipe(covid_case_death_rates) %>%
+#'   step_epi_ahead(case_rate, ahead = 7, skip = TRUE) %>%
+#'   recipes::update_role(case_rate, new_role = "predictor") %>%
+#'   recipes::add_role(all_of(key_colnames(jhu)), new_role = "predictor")
+#'
+#' f <- frosting() %>%
+#'   layer_predict() %>%
+#'   layer_residual_quantiles() %>%
+#'   layer_add_forecast_date() %>%
+#'   layer_add_target_date() %>%
+#'   layer_threshold(starts_with(".pred"))
+#'
+#' eng <- linear_reg() %>% set_engine("flatline")
+#' wf <- epi_workflow(r, eng, f) %>% fit(jhu)
+#' preds <- forecast(wf)
+#' ```
 #'
 #' @param epi_data An [epiprocess::epi_df][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
diff --git a/R/frosting.R b/R/frosting.R
index 84331c9ac..65115cbcf 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -1,4 +1,5 @@
-#' Add frosting to a workflow
+#' Given a `frosting()`, add it to, remove it from, or update it in an
+#' `epi_workflow`
 #'
 #' @param x A workflow
 #' @param frosting A frosting object created using `frosting()`.
diff --git a/R/get_test_data.R b/R/get_test_data.R
index 442272a2f..fd01f10e2 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -1,10 +1,9 @@
 #' Get test data for prediction based on longest lag period
 #'
-#' Based on the longest lag period in the recipe,
-#' `get_test_data()` creates an [epi_df][epiprocess::as_epi_df]
-#' with columns `geo_value`, `time_value`
-#' and other variables in the original dataset,
-#' which will be used to create features necessary to produce forecasts.
+#' If `predict()` is given the full training dataset, it will produce a forecast
+#' for every day which has enough data. For most cases, this is far more
+#' forecasts than is necessary. `get_test_data()` is designed to restrict the given dataset to the minimum amount needed to produce a forecast on the `forecast_date`.
+#' Primarily this is based on the longest lag period in the recipe.
 #'
 #' The minimum required (recent) data to produce a forecast is equal to
 #' the maximum lag requested (on any predictor) plus the longest horizon
diff --git a/R/layer_add_target_date.R b/R/layer_add_target_date.R
index 46f413bd2..8c60dfbfc 100644
--- a/R/layer_add_target_date.R
+++ b/R/layer_add_target_date.R
@@ -13,14 +13,15 @@
 #'
 #' @return an updated `frosting` postprocessor
 #'
-#' @details By default, this function assumes that a value for `ahead`
-#' has been specified in a preprocessing step (most likely in
-#' `step_epi_ahead`). Then, `ahead` is added to the `forecast_date`
-#' in the test data to get the target date. `forecast_date` can be set in 3 ways:
-#' 1. `step_adjust_latency`, which typically uses the training `epi_df`'s `as_of`
-#' 2. `layer_add_forecast_date`, which inherits from 1 if not manually specifed
-#' 3. if none of those are the case, it is simply the maximum `time_value` over
-#'   every dataset used (prep, training, and prediction).
+#' @details By default, this function assumes that a value for `ahead` has been
+#'   specified in a preprocessing step (most likely in `step_epi_ahead`). Then,
+#'   `ahead` is added to the `forecast_date` in the test data to get the target
+#'   date. `forecast_date` itself can be set in 3 ways:
+#' 1. The default `forecast_date` is simply the maximum `time_value` over every
+#'   dataset used (prep, training, and prediction).
+#' 2. if `step_adjust_latency` is present, it will typically use the training
+#'   `epi_df`'s `as_of`
+#' 3. `layer_add_forecast_date`, which inherits from 2 if not manually specifed
 #'
 #' @export
 #' @examples
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index a3183a0ae..91aae77c6 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -1,15 +1,15 @@
 #' Convert per-capita predictions to raw scale
 #'
-#' `layer_population_scaling` creates a specification of a frosting layer
-#' that will "undo" per-capita scaling. Typical usage would
-#' load a dataset that contains state-level population, and use it to convert
-#' predictions made from a rate-scale model to raw scale by multiplying by
-#' the population.
-#' Although, it is worth noting that there is nothing special about "population".
-#' The function can be used to scale by any variable. Population is the
-#' standard use case in the epidemiology forecasting scenario. Any value
-#' passed will *multiply* the selected variables while the `rate_rescaling`
-#' argument is a common *divisor* of the selected variables.
+#' `layer_population_scaling` creates a specification of a frosting layer that
+#' will "undo" per-capita scaling done in `step_population_scaling()`. Typical
+#' usage would set `df` to be a dataset that contains state-level population,
+#' and use it to convert predictions made from a raw scale model to rate-scale
+#' by dividing by the population.
+#' Although, it is worth noting that there is nothing special about
+#' "population", and  the function can be used to scale by any variable.
+#' Population is the standard use case in the epidemiology forecasting scenario.
+#' Any value passed will *multiply* the selected variables while the
+#' `rate_rescaling` argument is a common *divisor* of the selected variables.
 #'
 #' @param frosting a `frosting` postprocessor. The layer will be added to the
 #'   sequence of operations for this frosting.
@@ -17,7 +17,8 @@
 #'   for this step. See [recipes::selections()] for more details.
 #' @param df a data frame that contains the population data to be used for
 #'   inverting the existing scaling.
-#' @param by A (possibly named) character vector of variables to join by.
+#' @param by A (possibly named) character vector of variables to join `df` onto
+#'   the `epi_df` by.
 #'
 #' If `NULL`, the default, the function will try to infer a reasonable set of
 #' columns. First, it will try to join by all variables in the test data with
diff --git a/R/layer_predict.R b/R/layer_predict.R
index 76868bd44..623ab3391 100644
--- a/R/layer_predict.R
+++ b/R/layer_predict.R
@@ -5,7 +5,7 @@
 #' post-processing. This would typically be the first layer in a `frosting`
 #' postprocessor.
 #'
-#' @seealso `parsnip::predict.model_fit()`
+#' @seealso [parsnip::predict.model_fit()]
 #'
 #' @inheritParams parsnip::predict.model_fit
 #' @param frosting a frosting object
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 824593f8d..8b6e170ab 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -5,9 +5,11 @@
 #' This function calculates an _approximation_ to a parametric predictive
 #' distribution. Predictive distributions from linear models require
 #' `x* (X'X)^{-1} x*`
-#' along with the degrees of freedom. This function approximates both. It
-#' should be reasonably accurate for models fit using `lm` when the new point
-#' `x*` isn't too far from the bulk of the data.
+#' along with the degrees of freedom. This function approximates both. It should
+#' be reasonably accurate for models fit using `lm` when the new point `x*`
+#' isn't too far from the bulk of the data. Outside of that specific case, it is
+#' recommended to use `layer_residual_quantiles()`, or if you are working with a
+#' model that produces distributional predictions, use `layer_quantile_distn()`.
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index e07713e00..a0af380a2 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -1,6 +1,8 @@
 #' Returns predictive quantiles
 #'
 #' This function calculates quantiles when the prediction was _distributional_.
+#' If the model producing the forecast is not distributional, it is recommended
+#' to use `layer_residual_quantiles()` instead.
 #'
 #' Currently, the only distributional modes/engines are
 #' * `quantile_reg()`
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 6b32d2921..37087f9d5 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -1,5 +1,10 @@
 #' Creates predictions based on residual quantiles
 #'
+#' This function calculates quantiles based on the empirical quantiles of the
+#' model's residuals. If the model producing the forecast is distributional, it
+#' is recommended to use `layer_residual_quantiles()` instead, as those will be
+#' significantly more accurate.
+#'
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
 #' @param quantile_levels numeric vector of probabilities with values in (0,1)
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 0e28bfa10..9e8d3bbae 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -1,8 +1,16 @@
 #' Lower and upper thresholds for predicted values
 #'
-#' This post-processing step is used to set prediction values that are
-#' smaller than the lower threshold or higher than the upper threshold equal
-#' to the threshold values.
+#' This post-processing step is used to set prediction values that are smaller
+#' than the lower threshold or higher than the upper threshold equal to the
+#' threshold values.
+
+#' @details
+#' Making case count predictions strictly positive is a typical example usage.
+#'   It can be called before or after the quantiles are created using
+#'   `layer_quantile_distn()` since the quantiles are an inherent part of the
+#'   result from `layer_predict()` for distributional models, but must be called
+#'   after `layer_residual_quantiles()`, since the quantiles for that case don't
+#'   exist until after that layer.
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
diff --git a/R/layer_unnest.R b/R/layer_unnest.R
index a6fc9f0af..ffac480e9 100644
--- a/R/layer_unnest.R
+++ b/R/layer_unnest.R
@@ -1,5 +1,12 @@
 #' Unnest prediction list-cols
 #'
+#' For any model that produces forecasts for multiple outcomes, such as multiple
+#' aheads, the resulting prediction is a list of forecasts inside a column of
+#' the prediction tibble, which is not an ideal format. This layer "lengthens"
+#' the result, moving each outcome to a separate row, in the same manner as
+#' `tidyr::unnest()` would. At the moment, the only such engine is
+#' `smooth_quantile_reg()`.
+#'
 #' @param frosting a `frosting` postprocessor
 #' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
 #'   expressions separated by commas. Variable names can be used as if they
@@ -9,6 +16,35 @@
 #'
 #' @return an updated `frosting` postprocessor
 #' @export
+#' @examples
+#' jhu <- covid_case_death_rates %>%
+#'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
+#'
+#' aheads <- 1:7
+#'
+#' r <- epi_recipe(jhu) %>%
+#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
+#'   step_epi_ahead(death_rate, ahead = aheads) %>%
+#'   step_epi_naomit()
+#'
+#' wf <- epi_workflow(
+#'   r,
+#'   smooth_quantile_reg(
+#'     quantile_levels = c(.05, .1, .25, .5, .75, .9, .95),
+#'     outcome_locations = aheads
+#'   )
+#' ) %>%
+#'   fit(jhu)
+#'
+#' f <- frosting() %>%
+#'   layer_predict() %>%
+#'   layer_naomit() %>%
+#'   layer_unnest(.pred)
+#'
+#' wf1 <- wf %>% add_frosting(f)
+#'
+#' p <- forecast(wf1)
+#' p
 layer_unnest <- function(frosting, ..., id = rand_id("unnest")) {
   arg_is_chr_scalar(id)
 
diff --git a/R/layers.R b/R/layers.R
index c098172a9..b35dceaf2 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -178,9 +178,12 @@ detect_layer.workflow <- function(x, name, ...) {
 
 #' Spread a layer of frosting on a fitted workflow
 #'
-#' Slathering frosting means to implement a post-processing layer. When
-#' creating a new post-processing layer, you must implement an S3 method
-#' for this function
+#' Slathering frosting means to implement a post-processing layer. It is the
+#' post-processing equivalent of `bake` for a recipe. Given a layer, it applies
+#' the actual transformation of that layer. When creating a new post-processing
+#' layer, you must implement an S3 method for this function. Generally, you will
+#' not need to call this function directly, as it will be used indirectly during
+#' `predict`.
 #'
 #' @param object a workflow with `frosting` post-processing steps
 #' @param components a list of components containing model information. These
@@ -200,7 +203,8 @@ detect_layer.workflow <- function(x, name, ...) {
 #'
 #' @param ... additional arguments used by methods. Currently unused.
 #'
-#' @return The `components` list. In the same format after applying any updates.
+#' @return The `components` list, in the same format as before, after applying
+#'   any updates.
 #' @export
 slather <- function(object, components, workflow, new_data, ...) {
   UseMethod("slather")
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index e4b5b3320..75fff6e3e 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -34,9 +34,9 @@ nested_quantiles <- function(x) {
 
 #' Pivot a column containing `quantile_pred` longer
 #'
-#' A column that contains `quantile_pred` will be "lengthened" with
-#' the quantile levels serving as 1 column and the values as another. If
-#' multiple columns are selected, these will be prefixed with the column name.
+#' Selected columns that contain `quantile_pred` will be "lengthened" with the
+#' `quantile_level`s in one column and the `value`s in another. If multiple
+#' columns are selected, these will be prefixed with the column name.
 #'
 #' @param .data A data frame, or a data frame extension such as a tibble or
 #'   epi_df.
@@ -68,10 +68,10 @@ pivot_quantiles_longer <- function(.data, ...) {
 
 #' Pivot a column containing `quantile_pred` wider
 #'
-#' Any selected columns that contain `quantile_pred` will be "widened" with
-#' the "taus" (quantile) serving as names and the values in the data frame.
-#' When pivoting multiple columns, the original column name will be used as
-#' a prefix.
+#' Any selected columns that contain `quantile_pred` will be "widened" with the
+#' "taus" (quantile) serving as column names and the values in the corresponding
+#' column.  When pivoting multiple columns, the original column name will be
+#' used as a prefix.
 #'
 #' @inheritParams pivot_quantiles_longer
 #'
diff --git a/R/step_epi_naomit.R b/R/step_epi_naomit.R
index bfe8a4faa..0544bc5f9 100644
--- a/R/step_epi_naomit.R
+++ b/R/step_epi_naomit.R
@@ -2,10 +2,15 @@
 #'
 #' @param recipe Recipe to be used for omission steps
 #'
-#' @return Omits NA's from both predictors and outcomes at training time
-#'   to fit the model. Also only omits associated predictors and not
-#'   outcomes at prediction time due to lack of response and avoidance
-#'   of data loss.
+#' @return Omits NA's from both predictors and outcomes at training time to fit
+#'   the model. Also only omits associated predictors and not outcomes at
+#'   prediction time due to lack of response and avoidance of data loss. Given a
+#'   `recipe`, this step is literally equivalent to
+#'   ```{r, eval=FALSE}
+#'    recipe %>%
+#'      recipes::step_naomit(all_predictors(), skip = FALSE) %>%
+#'      recipes::step_naomit(all_outcomes(), skip = TRUE)
+#'   ```
 #' @export
 #' @examples
 #' covid_case_death_rates %>%
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 8d68a2ef9..862c224e5 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -1,12 +1,11 @@
 #' Create a shifted predictor
 #'
 #' `step_epi_lag` and `step_epi_ahead` create a *specification* of a recipe step
-#'   that will add new columns of shifted data. The former will created a lag
-#'   column, while the latter will create a lead column. Shifted data will
-#'   by default include NA values where the shift was induced.
-#'   These can be properly removed with [step_epi_naomit()], or you may
-#'   specify an alternative filler value with the `default`
-#'   argument.
+#'   that will add new columns of shifted data. The `step_epi_lag` will created
+#'   a lagged `predictor` column, while `step_epi_ahead` will create a leading
+#'   `outcome` column. Shifted data will by default include NA values where the
+#'   shift was induced.  These can be properly removed with [step_epi_naomit()],
+#'   or you may specify an alternative filler value with the `default` argument.
 #'
 #'
 #' @param recipe A recipe object. The step will be added to the
@@ -30,14 +29,14 @@
 #' @param id A unique identifier for the step
 #' @template step-return
 #'
-#' @details The step assumes that the data are already _in the proper sequential
-#'  order_ for shifting.
+#' @details The step assumes that the data's `time_value` column is already _in
+#'   the proper sequential order_ for shifting.
 #'
-#'  While `{recipes}` provides a function `step_lag()`, it assumes that the data
-#'  have no breaks in the sequence of `time_values`, and has no ability to group by
-#'  `geo_value` or other keys. Our `lag/ahead` functions also appropriately adjust
-#'  the amount of data to avoid accidentally dropping recent predictors from the
-#'  test data.
+#'  Our `lag/ahead` functions respect the `geo_value` and `other_keys` of the
+#'   `epi_df`, and allow for discontiguous `time_value`s. Both of these features
+#'   are noticably lacking from `recipe::step_lag()`.
+#'  Our `lag/ahead` functions also appropriately adjust the amount of data to
+#'   avoid accidentally dropping recent predictors from the test data.
 #'
 #' The `prefix` and `id` arguments are unchangeable to ensure that the code runs
 #' properly and to avoid inconsistency with naming. For `step_epi_ahead`, they
diff --git a/R/step_epi_slide.R b/R/step_epi_slide.R
index 4cf0e7acf..564d525d9 100644
--- a/R/step_epi_slide.R
+++ b/R/step_epi_slide.R
@@ -1,8 +1,9 @@
 #' Calculate a rolling window transformation
 #'
-#' `step_epi_slide()` creates a *specification* of a recipe step
-#'   that will generate one or more new columns of derived data by "sliding"
-#'   a computation along existing data.
+#' `step_epi_slide()` creates a *specification* of a recipe step that will
+#'   generate one or more new columns of derived data by "sliding" a computation
+#'   along existing data. This is a wrapper around `epiprocess::epi_slide()`
+#'   to allow its use within an `epi_recipe()`.
 #'
 #' @inheritParams step_epi_lag
 #' @param .f A function in one of the following formats:
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index 5497c2957..80b5bf682 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -1,7 +1,8 @@
 #' Calculate a growth rate
 #'
-#' `step_growth_rate()` creates a *specification* of a recipe step
-#'   that will generate one or more new columns of derived data.
+#' `step_growth_rate()` creates a *specification* of a recipe step that will
+#'   generate one or more new columns of derived data. This is a wrapper around
+#'   `epiprocess::growth_rate()` to allow its use within an `epi_recipe()`.
 #'
 #'
 #' @inheritParams step_epi_lag
diff --git a/R/step_lag_difference.R b/R/step_lag_difference.R
index 2b0af00f2..1c38b0659 100644
--- a/R/step_lag_difference.R
+++ b/R/step_lag_difference.R
@@ -1,7 +1,14 @@
 #' Calculate a lagged difference
 #'
-#' `step_lag_difference()` creates a *specification* of a recipe step
-#'   that will generate one or more new columns of derived data.
+#' `step_lag_difference()` creates a *specification* of a recipe step that will
+#'   generate one or more new columns of derived data. For each column in the
+#'   specification, `step_lag_difference()` will calculate the difference
+#'   between the values at a distance of `horizon`. For example, with
+#'   `horizon=1`, this would simply be the difference between adjacent days.
+#'
+#' Much like `step_epi_lag()` this step works with the actual time values (so if
+#' there are gaps it will fill with `NA` values), and respects the grouping
+#' inherent in the `epi_df()` as specified by `geo_value` and `other_keys`.
 #'
 #'
 #' @inheritParams step_epi_lag
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index bb60d039a..cd3a54f83 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -1,20 +1,22 @@
 #' Convert raw scale predictions to per-capita
 #'
-#' `step_population_scaling` creates a specification of a recipe step
-#' that will perform per-capita scaling. Typical usage would
-#' load a dataset that contains state-level population, and use it to convert
-#' predictions made from a raw scale model to rate-scale by dividing by
-#' the population.
-#' Although, it is worth noting that there is nothing special about "population".
-#' The function can be used to scale by any variable. Population is the
-#' standard use case in the epidemiology forecasting scenario. Any value
-#' passed will *divide* the selected variables while the `rate_rescaling`
-#' argument is a common *multiplier* of the selected variables.
+#' `step_population_scaling()` creates a specification of a recipe step that
+#' will perform per-capita scaling. Typical usage would set `df` to be a dataset
+#' that contains state-level population, and use it to convert predictions made
+#' from a raw scale model to rate-scale by dividing by the population.
+#' Although, it is worth noting that there is nothing special about
+#' "population", and the function can be used to scale by any variable.
+#' Population is the standard use case in the epidemiology forecasting scenario.
+#' Any value passed will *divide* the selected variables while the
+#' `rate_rescaling` argument is a common *multiplier* of the selected variables.
 #'
 #' @inheritParams step_epi_lag
-#' @param df a data frame that contains the population data to be used for
-#'   inverting the existing scaling.
-#' @param by A (possibly named) character vector of variables to join by.
+#' @param role For model terms created by this step, what analysis role should
+#'   they be assigned?
+#' @param df a data frame containing the scaling data (such as population). The
+#'   target column is divided by the value in `df_pop_col`.
+#' @param by A (possibly named) character vector of variables to join `df` onto
+#'   the `epi_df` by.
 #'
 #' If `NULL`, the default, the function will try to infer a reasonable set of
 #' columns. First, it will try to join by all variables in the training/test
@@ -41,7 +43,7 @@
 #' @param rate_rescaling Sometimes raw scales are "per 100K" or "per 1M".
 #' Adjustments can be made here. For example, if the original
 #' scale is "per 100K", then set `rate_rescaling = 1e5` to get rates.
-#' @param create_new TRUE to create a new column and keep the original column
+#' @param create_new `TRUE` to create a new column and keep the original column
 #' in the `epi_df`
 #' @param suffix a character. The suffix added to the column name if
 #' `create_new = TRUE`. Default to "_scaled".
diff --git a/R/step_training_window.R b/R/step_training_window.R
index eafc076c7..51361b4f9 100644
--- a/R/step_training_window.R
+++ b/R/step_training_window.R
@@ -14,8 +14,12 @@
 #' @inheritParams step_epi_lag
 #' @template step-return
 #'
-#' @details Note that `step_epi_lead()` and `step_epi_lag()` should come
-#' after any filtering step.
+#' @details It is recommended to do this after any `step_epi_ahead()`,
+#'   `step_epi_lag()`, or `step_epi_naomit()` steps. If `step_training_window()`
+#'   happens first, there will be less than `n_training` remaining examples,
+#'   since either leading or lagging will introduce `NA`'s later removed by
+#'   `step_epi_naomit()`. Typical usage will have this function applied after
+#'   every other step.
 #'
 #' @export
 #'
diff --git a/_pkgdown.yml b/_pkgdown.yml
index ce1facdfa..32da5cae7 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -93,7 +93,9 @@ reference:
       - forecast.epi_workflow
 
   - subtitle: Modifying forecasting epiworkflows
-    desc: modify or inspect an existing recipe, workflow, or frosting
+    desc: >
+      Modify or inspect an existing recipe, workflow, or frosting. See also [the
+      article on the topic](../articles/update.html)
     contents:
       - adjust_epi_recipe
       - Add_model
@@ -109,7 +111,9 @@ reference:
 
   ##########################
   - title: Parsnip engines
-    desc: Prediction methods not available elsewhere
+    desc: >
+      Prediction methods not available in the [general parsnip
+      repository](https://www.tidymodels.org/find/parsnip/)
     contents:
       - quantile_reg
       - smooth_quantile_reg
diff --git a/inst/pkgdown-watch.R b/inst/pkgdown-watch.R
index 6541a1365..bd23406a3 100644
--- a/inst/pkgdown-watch.R
+++ b/inst/pkgdown-watch.R
@@ -13,6 +13,7 @@
 rlang::check_installed(c("pkgdown", "servr", "devtools", "here", "cli", "fs"))
 library(pkgdown)
 pkg <- pkgdown::as_pkgdown(here::here())
+devtools::document(here::here())
 devtools::build_readme()
 pkgdown::build_articles(pkg)
 pkgdown::build_site(pkg, lazy = FALSE, examples = FALSE, devel = TRUE, preview = FALSE)
@@ -39,7 +40,7 @@ servr::httw(
     refs <- grep("man.+R(m?d)?$", files, value = TRUE)
     if (length(refs)) {
       # Doesn't work for me, so I run it manually.
-      # pkgdown::build_reference(pkg, preview = FALSE, examples = FALSE, lazy = FALSE) # nolint: commented_code_linter
+      # pkgdown::build_reference(pkg) # nolint: commented_code_linter
     }
 
     pkgdown <- grep("pkgdown", files, value = TRUE)
diff --git a/man/add_epi_recipe.Rd b/man/add_epi_recipe.Rd
index 0135cfd6f..f5cb32a73 100644
--- a/man/add_epi_recipe.Rd
+++ b/man/add_epi_recipe.Rd
@@ -4,7 +4,8 @@
 \alias{add_epi_recipe}
 \alias{remove_epi_recipe}
 \alias{update_epi_recipe}
-\title{Add an \code{epi_recipe} to a workflow}
+\title{Given an \code{epi_recipe}, add it to, remove it from, or update it in an
+\code{epi_workflow}}
 \usage{
 add_epi_recipe(x, recipe, ..., blueprint = default_epi_recipe_blueprint())
 
@@ -30,12 +31,18 @@ might be done automatically by the underlying model.}
 \code{x}, updated with a new recipe preprocessor.
 }
 \description{
-Add an \code{epi_recipe} to a workflow
+\itemize{
+\item \code{add_recipe()} specifies the terms of the model and any preprocessing that
+is required through the usage of a recipe.
+\item \code{remove_recipe()} removes the recipe as well as any downstream objects
+\item \code{update_recipe()} first removes the recipe, then replaces the previous
+recipe with the new one.
+}
 }
 \details{
-\code{add_epi_recipe} has the same behaviour as
-\code{\link[workflows:add_recipe]{workflows::add_recipe()}} but sets a different
-default blueprint to automatically handle \link[epiprocess:epi_df]{epiprocess::epi_df} data.
+\code{add_epi_recipe()} has the same behaviour as \code{\link[workflows:add_recipe]{workflows::add_recipe()}} but
+sets a different default blueprint to automatically handle
+\code{epiprocess::epi_df()} data.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
@@ -65,11 +72,4 @@ workflow
 }
 \seealso{
 \code{\link[workflows:add_recipe]{workflows::add_recipe()}}
-\itemize{
-\item \code{add_recipe()} specifies the terms of the model and any preprocessing that
-is required through the usage of a recipe.
-\item \code{remove_recipe()} removes the recipe as well as any downstream objects
-\item \code{update_recipe()} first removes the recipe, then replaces the previous
-recipe with the new one.
-}
 }
diff --git a/man/add_frosting.Rd b/man/add_frosting.Rd
index 825747487..6a38b359e 100644
--- a/man/add_frosting.Rd
+++ b/man/add_frosting.Rd
@@ -4,7 +4,8 @@
 \alias{add_frosting}
 \alias{remove_frosting}
 \alias{update_frosting}
-\title{Add frosting to a workflow}
+\title{Given a \code{frosting()}, add it to, remove it from, or update it in an
+\code{epi_workflow}}
 \usage{
 add_frosting(x, frosting, ...)
 
@@ -23,7 +24,8 @@ update_frosting(x, frosting, ...)
 \code{x}, updated with a new frosting postprocessor
 }
 \description{
-Add frosting to a workflow
+Given a \code{frosting()}, add it to, remove it from, or update it in an
+\code{epi_workflow}
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 40bb48ca9..7359c8764 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -67,7 +67,7 @@ latency is large. If this is \code{FALSE}, that warning is turned off.}
 be created using growth rates (as the predictors are) or lagged
 differences. The second case is closer to the requirements for the
 \href{https://github.com/cdcepi/Flusight-forecast-data/blob/745511c436923e1dc201dea0f4181f21a8217b52/data-experimental/README.md}{2022-23 CDC Flusight Hospitalization Experimental Target}.
-See the Classification Vignette for details of how to create a reasonable
+See the \href{https://cmu-delphi.github.io/delphi-tooling-book/arx-classifier.html}{Classification chapter from the forecasting book} Vignette for details of how to create a reasonable
 baseline for this case. Selecting \code{"growth_rate"} (the default) uses
 \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}} to create the outcome using some of the
 additional arguments below. Choosing \code{"lag_difference"} instead simply
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index 9b048463c..be4ecedef 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -23,10 +23,10 @@ If discrete classes are already in the \code{epi_df}, it is recommended to
 code up a classifier from scratch using \code{\link[=epi_recipe]{epi_recipe()}}.}
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
-This defaults to the \code{outcome}. However, if manually specified, only those variables
-specifically mentioned will be used. (The \code{outcome} will not be added.)
-By default, equals the outcome. If manually specified, does not add the
-outcome variable, so make sure to specify it.}
+This defaults to the \code{outcome}. However, if manually specified, only those
+variables specifically mentioned will be used. (The \code{outcome} will not be
+added.)  By default, equals the outcome. If manually specified, does not
+add the outcome variable, so make sure to specify it.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation. For
 now, we enforce \code{mode = "classification"}. Typical values are
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index 293e05af0..61cb8db8c 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -23,10 +23,10 @@ If discrete classes are already in the \code{epi_df}, it is recommended to
 code up a classifier from scratch using \code{\link[=epi_recipe]{epi_recipe()}}.}
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
-This defaults to the \code{outcome}. However, if manually specified, only those variables
-specifically mentioned will be used. (The \code{outcome} will not be added.)
-By default, equals the outcome. If manually specified, does not add the
-outcome variable, so make sure to specify it.}
+This defaults to the \code{outcome}. However, if manually specified, only those
+variables specifically mentioned will be used. (The \code{outcome} will not be
+added.)  By default, equals the outcome. If manually specified, does not
+add the outcome variable, so make sure to specify it.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation.
 For now, we enforce \code{mode = "classification"}. Typical values are
diff --git a/man/arx_fcast_epi_workflow.Rd b/man/arx_fcast_epi_workflow.Rd
index 87d503bc6..4aa97ff45 100644
--- a/man/arx_fcast_epi_workflow.Rd
+++ b/man/arx_fcast_epi_workflow.Rd
@@ -15,21 +15,20 @@ arx_fcast_epi_workflow(
 \arguments{
 \item{epi_data}{An \code{epi_df} object}
 
-\item{outcome}{A character (scalar) specifying the outcome (in the
-\code{epi_df}).}
+\item{outcome}{A character (scalar) specifying the outcome (in the \code{epi_df}).}
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
-This defaults to the \code{outcome}. However, if manually specified, only those variables
-specifically mentioned will be used. (The \code{outcome} will not be added.)
-By default, equals the outcome. If manually specified, does not add the
-outcome variable, so make sure to specify it.}
+This defaults to the \code{outcome}. However, if manually specified, only those
+variables specifically mentioned will be used. (The \code{outcome} will not be
+added.)  By default, equals the outcome. If manually specified, does not
+add the outcome variable, so make sure to specify it.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation. For
 now, we enforce \code{mode = "regression"}. May be \code{NULL} if you'd like to
 decide later.}
 
-\item{args_list}{A list of customization arguments to determine
-the type of forecasting model. See \code{\link[=arx_args_list]{arx_args_list()}}.}
+\item{args_list}{A list of customization arguments to determine the type of
+forecasting model. See \code{\link[=arx_args_list]{arx_args_list()}}.}
 }
 \value{
 An unfitted \code{epi_workflow}.
diff --git a/man/arx_forecaster.Rd b/man/arx_forecaster.Rd
index ff820b8c8..85c25c115 100644
--- a/man/arx_forecaster.Rd
+++ b/man/arx_forecaster.Rd
@@ -15,45 +15,50 @@ arx_forecaster(
 \arguments{
 \item{epi_data}{An \code{epi_df} object}
 
-\item{outcome}{A character (scalar) specifying the outcome (in the
-\code{epi_df}).}
+\item{outcome}{A character (scalar) specifying the outcome (in the \code{epi_df}).}
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
-This defaults to the \code{outcome}. However, if manually specified, only those variables
-specifically mentioned will be used. (The \code{outcome} will not be added.)
-By default, equals the outcome. If manually specified, does not add the
-outcome variable, so make sure to specify it.}
+This defaults to the \code{outcome}. However, if manually specified, only those
+variables specifically mentioned will be used. (The \code{outcome} will not be
+added.)  By default, equals the outcome. If manually specified, does not
+add the outcome variable, so make sure to specify it.}
 
-\item{trainer}{A \code{{parsnip}} model describing the type of estimation.
-For now, we enforce \code{mode = "regression"}.}
+\item{trainer}{A \code{{parsnip}} model describing the type of estimation.  For
+now, we enforce \code{mode = "regression"}.}
 
-\item{args_list}{A list of customization arguments to determine
-the type of forecasting model. See \code{\link[=arx_args_list]{arx_args_list()}}.}
+\item{args_list}{A list of customization arguments to determine the type of
+forecasting model. See \code{\link[=arx_args_list]{arx_args_list()}}.}
 }
 \value{
-A list with (1) \code{predictions} an \code{epi_df} of predicted values
-and (2) \code{epi_workflow}, a list that encapsulates the entire estimation
-workflow
+An \code{arx_fcast}, with the fields \code{predictions} and \code{epi_workflow}.
+\code{predictions} is an \code{epi_df} of predicted values while \code{epi_workflow()} is
+the fit workflow used to make those predictions
 }
 \description{
 This is an autoregressive forecasting model for
-\link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
-that it estimates a model for a particular target horizon.
+\link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct"
+forecasting, meaning that it estimates a model for a particular target
+horizon of \code{outcome} based on the lags of the \code{predictors}. See the \href{../articles/epipredict.html}{Get started vignette} for some worked examples and
+\href{../articles/custom_epiworkflows.html}{Custom epi_workflows vignette} for a
+recreation using a custom \code{epi_workflow()}.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
   dplyr::filter(time_value >= as.Date("2021-12-01"))
 
 out <- arx_forecaster(
-  jhu, "death_rate",
+  jhu,
+  "death_rate",
   c("case_rate", "death_rate")
 )
 
-out <- arx_forecaster(jhu, "death_rate",
+out <- arx_forecaster(jhu,
+  "death_rate",
   c("case_rate", "death_rate"),
   trainer = quantile_reg(),
   args_list = arx_args_list(quantile_levels = 1:9 / 10)
 )
+out
 }
 \seealso{
 \code{\link[=arx_fcast_epi_workflow]{arx_fcast_epi_workflow()}}, \code{\link[=arx_args_list]{arx_args_list()}}
diff --git a/man/augment.epi_workflow.Rd b/man/augment.epi_workflow.Rd
index 8007a4d30..cbc807ebb 100644
--- a/man/augment.epi_workflow.Rd
+++ b/man/augment.epi_workflow.Rd
@@ -17,5 +17,8 @@
 new_data with additional columns containing the predicted values
 }
 \description{
-Augment data with predictions
+\code{augment()}, unlike \code{forecast()}, has the goal of modifying the training
+data, rather than just producing new forecasts. It does a prediction on
+\code{new_data}, which will produce a prediction for most \code{time_values}, and then
+adds \code{.pred} as a column to \code{new_data} and returns the resulting join.
 }
diff --git a/man/epi_workflow.Rd b/man/epi_workflow.Rd
index 2e5aea938..c84626363 100644
--- a/man/epi_workflow.Rd
+++ b/man/epi_workflow.Rd
@@ -25,12 +25,12 @@ A new \code{epi_workflow} object.
 }
 \description{
 This is a container object that unifies preprocessing, fitting, prediction,
-and post-processing for predictive modeling on epidemiological data. It extends
-the functionality of a \code{\link[workflows:workflow]{workflows::workflow()}} to handle the typical panel
-data structures found in this field. This extension is handled completely
-internally, and should be invisible to the user. For all intents and purposes,
-this operates exactly like a \code{\link[workflows:workflow]{workflows::workflow()}}. For more details
-and numerous examples, see there.
+and post-processing for predictive modeling on epidemiological data. It
+extends the functionality of a \code{\link[workflows:workflow]{workflows::workflow()}} to handle the typical
+panel data structures found in this field. This extension is handled
+completely internally, and should be invisible to the user. For all intents
+and purposes, this operates exactly like a \code{\link[workflows:workflow]{workflows::workflow()}}. For some
+\code{{epipredict}} specific examples, see the \href{../articles/custom_epiworkflows.html}{custom epiworkflows vignette}.
 }
 \examples{
 jhu <- covid_case_death_rates
@@ -46,5 +46,5 @@ wf <- epi_workflow(r, parsnip::linear_reg())
 wf
 }
 \seealso{
-workflows::workflow
+\code{\link[workflows:workflow]{workflows::workflow()}}
 }
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index bd460dbe9..5b6b97ff0 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/extrapolate_quantiles.R
 \name{extrapolate_quantiles}
 \alias{extrapolate_quantiles}
-\title{Summarize a distribution with a set of quantiles}
+\title{Extrapolate the quantiles to new quantile levels}
 \usage{
 extrapolate_quantiles(x, probs, replace_na = TRUE, ...)
 }
@@ -22,6 +22,19 @@ of \code{x} will now have a superset
 of the original \code{quantile_values} (the union of those and \code{probs}).
 }
 \description{
+This both interpolates between quantile levels already defined in \code{x} and
+extrapolates quantiles outside their bounds. The interpolation method is
+determined by the \code{quantile} argument \code{middle}, which can be either \code{"cubic"}
+for a (hyman) cubic spline interpolation, or \code{"linear"} for simple linear
+interpolation.
+}
+\details{
+There is only one extrapolation method for values greater than the largest
+known quantile level or smaller than the smallest known quantile level. It
+assumes a roughly exponential tail, whose decay rate and offset is derived
+from the slope of the two most extreme quantile levels on a logistic scale.
+See the internal function \code{tail_extrapolate()} for the exact implementation.
+
 This function takes a \code{quantile_pred} vector and returns the same
 type of object, expanded to include
 \emph{additional} quantiles computed at \code{probs}. If you want behaviour more
@@ -31,5 +44,7 @@ appropriate.
 \examples{
 dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 # extra quantiles are appended
-as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+as_tibble(extrapolate_quantiles(dstn, probs = c(0.25, 0.5, 0.75)))
+
+extrapolate_quantiles(dstn, probs = c(0.0001, 0.25, 0.5, 0.75, 0.99999))
 }
diff --git a/man/fit-epi_workflow.Rd b/man/fit-epi_workflow.Rd
index 83b3b9f51..ef0f4ff40 100644
--- a/man/fit-epi_workflow.Rd
+++ b/man/fit-epi_workflow.Rd
@@ -22,9 +22,9 @@ The \code{epi_workflow} object, updated with a fit parsnip
 model in the \code{object$fit$fit} slot.
 }
 \description{
-This is the \code{fit()} method for an \code{epi_workflow} object that
+This is the \code{fit()} method for an \code{epi_workflow()} object that
 estimates parameters for a given model from a set of data.
-Fitting an \code{epi_workflow} involves two main steps, which are
+Fitting an \code{epi_workflow()} involves two main steps, which are
 preprocessing the data and fitting the underlying parsnip model.
 }
 \examples{
@@ -40,5 +40,5 @@ wf
 
 }
 \seealso{
-workflows::fit-workflow
+\code{\link[workflows:fit-workflow]{workflows::fit-workflow()}}
 }
diff --git a/man/flatline_forecaster.Rd b/man/flatline_forecaster.Rd
index f78e2f931..cc789bac2 100644
--- a/man/flatline_forecaster.Rd
+++ b/man/flatline_forecaster.Rd
@@ -21,12 +21,10 @@ ahead (unique horizon) for each unique combination of \code{key_vars}.
 \description{
 This is a simple forecasting model for
 \link[epiprocess:epi_df]{epiprocess::epi_df} data. It uses the most recent
-observation as the
-forecast for any future date, and produces intervals based on the quantiles
-of the residuals of such a "flatline" forecast over all available training
-data.
-}
-\details{
+observation as the forecast for any future date, and produces intervals
+based on the quantiles of the residuals of such a "flatline" forecast over
+all available training data.
+
 By default, the predictive intervals are computed separately for each
 combination of key values (\code{geo_value} + any additional keys) in the
 \code{epi_data} argument.
@@ -34,6 +32,29 @@ combination of key values (\code{geo_value} + any additional keys) in the
 This forecaster is very similar to that used by the
 \href{https://covid19forecasthub.org}{COVID19ForecastHub}
 }
+\details{
+Here is (roughly) the code for the \code{flatline_forecaster()} applied to the
+\code{case_rate} for \code{epidatasets::covid_case_death_rates}.
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{jhu <- covid_case_death_rates \%>\%
+  filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
+r <- epi_recipe(covid_case_death_rates) \%>\%
+  step_epi_ahead(case_rate, ahead = 7, skip = TRUE) \%>\%
+  recipes::update_role(case_rate, new_role = "predictor") \%>\%
+  recipes::add_role(all_of(key_colnames(jhu)), new_role = "predictor")
+
+f <- frosting() \%>\%
+  layer_predict() \%>\%
+  layer_residual_quantiles() \%>\%
+  layer_add_forecast_date() \%>\%
+  layer_add_target_date() \%>\%
+  layer_threshold(starts_with(".pred"))
+
+eng <- linear_reg() \%>\% set_engine("flatline")
+wf <- epi_workflow(r, eng, f) \%>\% fit(jhu)
+preds <- forecast(wf)
+}\if{html}{\out{</div>}}
+}
 \examples{
 jhu <- covid_case_death_rates \%>\%
   dplyr::filter(time_value >= as.Date("2021-12-01"))
diff --git a/man/forecast.epi_workflow.Rd b/man/forecast.epi_workflow.Rd
index 22f8cf4bb..74944e61d 100644
--- a/man/forecast.epi_workflow.Rd
+++ b/man/forecast.epi_workflow.Rd
@@ -2,29 +2,31 @@
 % Please edit documentation in R/epi_workflow.R
 \name{forecast.epi_workflow}
 \alias{forecast.epi_workflow}
-\title{Produce a forecast from an epi workflow}
+\title{Produce a forecast from just an epi workflow}
 \usage{
-\method{forecast}{epi_workflow}(object, ..., n_recent = NULL, forecast_date = NULL)
+\method{forecast}{epi_workflow}(object)
 }
 \arguments{
 \item{object}{An epi workflow.}
-
-\item{...}{Not used.}
-
-\item{n_recent}{Integer or NULL. If filling missing data with locf = TRUE,
-how far back are we willing to tolerate missing data? Larger values allow
-more filling. The default NULL will determine this from the the recipe. For
-example, suppose n_recent = 3, then if the 3 most recent observations in any
-geo_value are all NA’s, we won’t be able to fill anything, and an error
-message will be thrown. (See details.)}
-
-\item{forecast_date}{By default, this is set to the maximum time_value in x.
-But if there is data latency such that recent NA's should be filled, this may
-be after the last available time_value.}
 }
 \value{
 A forecast tibble.
 }
 \description{
-Produce a forecast from an epi workflow
+\code{forecast.epi_workflow} predicts by restricting the training data to the
+latest available data, and predicting on that. It binds together
+\code{get_test_data()} and \code{predict()}.
+}
+\examples{
+jhu <- covid_case_death_rates \%>\%
+  filter(time_value > "2021-08-01")
+
+r <- epi_recipe(jhu) \%>\%
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
+  step_epi_ahead(death_rate, ahead = 7) \%>\%
+  step_epi_naomit()
+
+epi_workflow(r, parsnip::linear_reg()) \%>\%
+  fit(jhu) \%>\%
+  forecast()
 }
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index 16359b9c3..3c3317067 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -17,11 +17,10 @@ An object of the same type as \code{x} with columns \code{geo_value}, \code{time
 keys, as well other variables in the original dataset.
 }
 \description{
-Based on the longest lag period in the recipe,
-\code{get_test_data()} creates an \link[epiprocess:epi_df]{epi_df}
-with columns \code{geo_value}, \code{time_value}
-and other variables in the original dataset,
-which will be used to create features necessary to produce forecasts.
+If \code{predict()} is given the full training dataset, it will produce a forecast
+for every day which has enough data. For most cases, this is far more
+forecasts than is necessary. \code{get_test_data()} is designed to restrict the given dataset to the minimum amount needed to produce a forecast on the \code{forecast_date}.
+Primarily this is based on the longest lag period in the recipe.
 }
 \details{
 The minimum required (recent) data to produce a forecast is equal to
diff --git a/man/layer_add_target_date.Rd b/man/layer_add_target_date.Rd
index 034462be7..f10178898 100644
--- a/man/layer_add_target_date.Rd
+++ b/man/layer_add_target_date.Rd
@@ -31,15 +31,16 @@ an updated \code{frosting} postprocessor
 Post-processing step to add the target date
 }
 \details{
-By default, this function assumes that a value for \code{ahead}
-has been specified in a preprocessing step (most likely in
-\code{step_epi_ahead}). Then, \code{ahead} is added to the \code{forecast_date}
-in the test data to get the target date. \code{forecast_date} can be set in 3 ways:
+By default, this function assumes that a value for \code{ahead} has been
+specified in a preprocessing step (most likely in \code{step_epi_ahead}). Then,
+\code{ahead} is added to the \code{forecast_date} in the test data to get the target
+date. \code{forecast_date} itself can be set in 3 ways:
 \enumerate{
-\item \code{step_adjust_latency}, which typically uses the training \code{epi_df}'s \code{as_of}
-\item \code{layer_add_forecast_date}, which inherits from 1 if not manually specifed
-\item if none of those are the case, it is simply the maximum \code{time_value} over
-every dataset used (prep, training, and prediction).
+\item The default \code{forecast_date} is simply the maximum \code{time_value} over every
+dataset used (prep, training, and prediction).
+\item if \code{step_adjust_latency} is present, it will typically use the training
+\code{epi_df}'s \code{as_of}
+\item \code{layer_add_forecast_date}, which inherits from 2 if not manually specifed
 }
 }
 \examples{
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 1b4c3d7d9..25164bbd9 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -26,7 +26,8 @@ for this step. See \code{\link[recipes:selections]{recipes::selections()}} for m
 \item{df}{a data frame that contains the population data to be used for
 inverting the existing scaling.}
 
-\item{by}{A (possibly named) character vector of variables to join by.
+\item{by}{A (possibly named) character vector of variables to join \code{df} onto
+the \code{epi_df} by.
 
 If \code{NULL}, the default, the function will try to infer a reasonable set of
 columns. First, it will try to join by all variables in the test data with
@@ -67,16 +68,16 @@ in the \code{epi_df}.}
 an updated \code{frosting} postprocessor
 }
 \description{
-\code{layer_population_scaling} creates a specification of a frosting layer
-that will "undo" per-capita scaling. Typical usage would
-load a dataset that contains state-level population, and use it to convert
-predictions made from a rate-scale model to raw scale by multiplying by
-the population.
-Although, it is worth noting that there is nothing special about "population".
-The function can be used to scale by any variable. Population is the
-standard use case in the epidemiology forecasting scenario. Any value
-passed will \emph{multiply} the selected variables while the \code{rate_rescaling}
-argument is a common \emph{divisor} of the selected variables.
+\code{layer_population_scaling} creates a specification of a frosting layer that
+will "undo" per-capita scaling done in \code{step_population_scaling()}. Typical
+usage would set \code{df} to be a dataset that contains state-level population,
+and use it to convert predictions made from a raw scale model to rate-scale
+by dividing by the population.
+Although, it is worth noting that there is nothing special about
+"population", and  the function can be used to scale by any variable.
+Population is the standard use case in the epidemiology forecasting scenario.
+Any value passed will \emph{multiply} the selected variables while the
+\code{rate_rescaling} argument is a common \emph{divisor} of the selected variables.
 }
 \examples{
 jhu <- cases_deaths_subset \%>\%
diff --git a/man/layer_predict.Rd b/man/layer_predict.Rd
index 7955114bf..d678c9fae 100644
--- a/man/layer_predict.Rd
+++ b/man/layer_predict.Rd
@@ -84,5 +84,5 @@ p2 <- predict(wf2, latest)
 p2
 }
 \seealso{
-\code{parsnip::predict.model_fit()}
+\code{\link[parsnip:predict.model_fit]{parsnip::predict.model_fit()}}
 }
diff --git a/man/layer_predictive_distn.Rd b/man/layer_predictive_distn.Rd
index 0e4b17cdb..7f5464513 100644
--- a/man/layer_predictive_distn.Rd
+++ b/man/layer_predictive_distn.Rd
@@ -37,7 +37,9 @@ residual quantiles added to the prediction
 This function calculates an \emph{approximation} to a parametric predictive
 distribution. Predictive distributions from linear models require
 \verb{x* (X'X)^\{-1\} x*}
-along with the degrees of freedom. This function approximates both. It
-should be reasonably accurate for models fit using \code{lm} when the new point
-\verb{x*} isn't too far from the bulk of the data.
+along with the degrees of freedom. This function approximates both. It should
+be reasonably accurate for models fit using \code{lm} when the new point \verb{x*}
+isn't too far from the bulk of the data. Outside of that specific case, it is
+recommended to use \code{layer_residual_quantiles()}, or if you are working with a
+model that produces distributional predictions, use \code{layer_quantile_distn()}.
 }
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index 21c38b812..9ce3cd57d 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -32,6 +32,8 @@ quantiles will be added to the predictions.
 }
 \description{
 This function calculates quantiles when the prediction was \emph{distributional}.
+If the model producing the forecast is not distributional, it is recommended
+to use \code{layer_residual_quantiles()} instead.
 }
 \details{
 Currently, the only distributional modes/engines are
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index 16e16cea8..df1320a88 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -37,7 +37,10 @@ an updated \code{frosting} postprocessor with additional columns of the
 residual quantiles added to the prediction
 }
 \description{
-Creates predictions based on residual quantiles
+This function calculates quantiles based on the empirical quantiles of the
+model's residuals. If the model producing the forecast is distributional, it
+is recommended to use \code{layer_residual_quantiles()} instead, as those will be
+significantly more accurate.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/layer_threshold.Rd b/man/layer_threshold.Rd
index 0ecca7e65..77f0a9aa2 100644
--- a/man/layer_threshold.Rd
+++ b/man/layer_threshold.Rd
@@ -35,9 +35,17 @@ Default value is \code{Inf}.}
 an updated \code{frosting} postprocessor
 }
 \description{
-This post-processing step is used to set prediction values that are
-smaller than the lower threshold or higher than the upper threshold equal
-to the threshold values.
+This post-processing step is used to set prediction values that are smaller
+than the lower threshold or higher than the upper threshold equal to the
+threshold values.
+}
+\details{
+Making case count predictions strictly positive is a typical example usage.
+It can be called before or after the quantiles are created using
+\code{layer_quantile_distn()} since the quantiles are an inherent part of the
+result from \code{layer_predict()} for distributional models, but must be called
+after \code{layer_residual_quantiles()}, since the quantiles for that case don't
+exist until after that layer.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/layer_unnest.Rd b/man/layer_unnest.Rd
index 3c25608a6..dfa1f4dc1 100644
--- a/man/layer_unnest.Rd
+++ b/man/layer_unnest.Rd
@@ -20,5 +20,40 @@ be used to select a range of variables.}
 an updated \code{frosting} postprocessor
 }
 \description{
-Unnest prediction list-cols
+For any model that produces forecasts for multiple outcomes, such as multiple
+aheads, the resulting prediction is a list of forecasts inside a column of
+the prediction tibble, which is not an ideal format. This layer "lengthens"
+the result, moving each outcome to a separate row, in the same manner as
+\code{tidyr::unnest()} would. At the moment, the only such engine is
+\code{smooth_quantile_reg()}.
+}
+\examples{
+jhu <- covid_case_death_rates \%>\%
+  filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
+
+aheads <- 1:7
+
+r <- epi_recipe(jhu) \%>\%
+  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
+  step_epi_ahead(death_rate, ahead = aheads) \%>\%
+  step_epi_naomit()
+
+wf <- epi_workflow(
+  r,
+  smooth_quantile_reg(
+    quantile_levels = c(.05, .1, .25, .5, .75, .9, .95),
+    outcome_locations = aheads
+  )
+) \%>\%
+  fit(jhu)
+
+f <- frosting() \%>\%
+  layer_predict() \%>\%
+  layer_naomit() \%>\%
+  layer_unnest(.pred)
+
+wf1 <- wf \%>\% add_frosting(f)
+
+p <- forecast(wf1)
+p
 }
diff --git a/man/pivot_quantiles_longer.Rd b/man/pivot_quantiles_longer.Rd
index b872d0c97..3c112b12f 100644
--- a/man/pivot_quantiles_longer.Rd
+++ b/man/pivot_quantiles_longer.Rd
@@ -19,9 +19,9 @@ can be selected for this operation.}
 An object of the same class as \code{.data}.
 }
 \description{
-A column that contains \code{quantile_pred} will be "lengthened" with
-the quantile levels serving as 1 column and the values as another. If
-multiple columns are selected, these will be prefixed with the column name.
+Selected columns that contain \code{quantile_pred} will be "lengthened" with the
+\code{quantile_level}s in one column and the \code{value}s in another. If multiple
+columns are selected, these will be prefixed with the column name.
 }
 \examples{
 d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
diff --git a/man/pivot_quantiles_wider.Rd b/man/pivot_quantiles_wider.Rd
index 01f85a70a..bba08e292 100644
--- a/man/pivot_quantiles_wider.Rd
+++ b/man/pivot_quantiles_wider.Rd
@@ -19,10 +19,10 @@ can be selected for this operation.}
 An object of the same class as \code{.data}
 }
 \description{
-Any selected columns that contain \code{quantile_pred} will be "widened" with
-the "taus" (quantile) serving as names and the values in the data frame.
-When pivoting multiple columns, the original column name will be used as
-a prefix.
+Any selected columns that contain \code{quantile_pred} will be "widened" with the
+"taus" (quantile) serving as column names and the values in the corresponding
+column.  When pivoting multiple columns, the original column name will be
+used as a prefix.
 }
 \examples{
 d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
diff --git a/man/predict-epi_workflow.Rd b/man/predict-epi_workflow.Rd
index 0b605d556..c04097c47 100644
--- a/man/predict-epi_workflow.Rd
+++ b/man/predict-epi_workflow.Rd
@@ -47,22 +47,22 @@ time points at which the survival probability or hazard is estimated.
 }
 \value{
 A data frame of model predictions, with as many rows as \code{new_data} has.
-If \code{new_data} is an \code{epi_df} or a data frame with \code{time_value} or
+If \code{new_data} is an \code{epi_df()} or a data frame with \code{time_value} or
 \code{geo_value} columns, then the result will have those as well.
 }
 \description{
-This is the \code{predict()} method for a fit epi_workflow object. The nice thing
-about predicting from an epi_workflow is that it will:
+This is the \code{predict()} method for a fit epi_workflow object. The 3 steps that this implements are:
 \itemize{
-\item Preprocess \code{new_data} using the preprocessing method specified when the
-workflow was created and fit. This is accomplished using
-\code{\link[hardhat:forge]{hardhat::forge()}}, which will apply any formula preprocessing or call
-\code{\link[recipes:bake]{recipes::bake()}} if a recipe was supplied.
-\item Call \code{\link[parsnip:predict.model_fit]{parsnip::predict.model_fit()}} for you using the underlying fit
+\item Preprocessing \code{new_data} using the preprocessing method specified when the
+epi_workflow was created and fit. This is accomplished using
+\code{recipes::bake()} if a recipe was supplied. Note that this is a slightly
+different \code{bake} operation than the one occuring during the fit. Any \code{step}
+that has \code{skip = TRUE} isn't applied during prediction; for example in
+\code{step_epi_naomit()}, \code{all_outcomes()} isn't \code{NA} omitted, since doing so
+would drop the exact \code{time_values} we are trying to predict.
+\item Calling \code{parsnip::predict.model_fit()} for you using the underlying fit
 parsnip model.
-\item Ensure that the returned object is an \link[epiprocess:epi_df]{epiprocess::epi_df} where
-possible. Specifically, the output will have \code{time_value} and
-\code{geo_value} columns as well as the prediction.
+\item \code{slather()} any frosting that has been included in the \code{epi_workflow}.
 }
 }
 \examples{
diff --git a/man/slather.Rd b/man/slather.Rd
index 6f6450ba4..3219d0e32 100644
--- a/man/slather.Rd
+++ b/man/slather.Rd
@@ -31,10 +31,14 @@ and predict on}
 \item{...}{additional arguments used by methods. Currently unused.}
 }
 \value{
-The \code{components} list. In the same format after applying any updates.
+The \code{components} list, in the same format as before, after applying
+any updates.
 }
 \description{
-Slathering frosting means to implement a post-processing layer. When
-creating a new post-processing layer, you must implement an S3 method
-for this function
+Slathering frosting means to implement a post-processing layer. It is the
+post-processing equivalent of \code{bake} for a recipe. Given a layer, it applies
+the actual transformation of that layer. When creating a new post-processing
+layer, you must implement an S3 method for this function. Generally, you will
+not need to call this function directly, as it will be used indirectly during
+\code{predict}.
 }
diff --git a/man/step_epi_naomit.Rd b/man/step_epi_naomit.Rd
index faf7484da..aa9208d89 100644
--- a/man/step_epi_naomit.Rd
+++ b/man/step_epi_naomit.Rd
@@ -10,10 +10,15 @@ step_epi_naomit(recipe)
 \item{recipe}{Recipe to be used for omission steps}
 }
 \value{
-Omits NA's from both predictors and outcomes at training time
-to fit the model. Also only omits associated predictors and not
-outcomes at prediction time due to lack of response and avoidance
-of data loss.
+Omits NA's from both predictors and outcomes at training time to fit
+the model. Also only omits associated predictors and not outcomes at
+prediction time due to lack of response and avoidance of data loss. Given a
+\code{recipe}, this step is literally equivalent to
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{ recipe \%>\%
+   recipes::step_naomit(all_predictors(), skip = FALSE) \%>\%
+   recipes::step_naomit(all_outcomes(), skip = TRUE)
+}\if{html}{\out{</div>}}
 }
 \description{
 Unified NA omission wrapper function for recipes
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index e2140248d..3291d95dd 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -61,22 +61,21 @@ sequence of any existing operations.
 }
 \description{
 \code{step_epi_lag} and \code{step_epi_ahead} create a \emph{specification} of a recipe step
-that will add new columns of shifted data. The former will created a lag
-column, while the latter will create a lead column. Shifted data will
-by default include NA values where the shift was induced.
-These can be properly removed with \code{\link[=step_epi_naomit]{step_epi_naomit()}}, or you may
-specify an alternative filler value with the \code{default}
-argument.
+that will add new columns of shifted data. The \code{step_epi_lag} will created
+a lagged \code{predictor} column, while \code{step_epi_ahead} will create a leading
+\code{outcome} column. Shifted data will by default include NA values where the
+shift was induced.  These can be properly removed with \code{\link[=step_epi_naomit]{step_epi_naomit()}},
+or you may specify an alternative filler value with the \code{default} argument.
 }
 \details{
-The step assumes that the data are already \emph{in the proper sequential
-order} for shifting.
+The step assumes that the data's \code{time_value} column is already \emph{in
+the proper sequential order} for shifting.
 
-While \code{{recipes}} provides a function \code{step_lag()}, it assumes that the data
-have no breaks in the sequence of \code{time_values}, and has no ability to group by
-\code{geo_value} or other keys. Our \code{lag/ahead} functions also appropriately adjust
-the amount of data to avoid accidentally dropping recent predictors from the
-test data.
+Our \code{lag/ahead} functions respect the \code{geo_value} and \code{other_keys} of the
+\code{epi_df}, and allow for discontiguous \code{time_value}s. Both of these features
+are noticably lacking from \code{recipe::step_lag()}.
+Our \code{lag/ahead} functions also appropriately adjust the amount of data to
+avoid accidentally dropping recent predictors from the test data.
 
 The \code{prefix} and \code{id} arguments are unchangeable to ensure that the code runs
 properly and to avoid inconsistency with naming. For \code{step_epi_ahead}, they
diff --git a/man/step_epi_slide.Rd b/man/step_epi_slide.Rd
index 4e7ba8bed..1a104c1bc 100644
--- a/man/step_epi_slide.Rd
+++ b/man/step_epi_slide.Rd
@@ -75,9 +75,10 @@ An updated version of \code{recipe} with the new step added to the
 sequence of any existing operations.
 }
 \description{
-\code{step_epi_slide()} creates a \emph{specification} of a recipe step
-that will generate one or more new columns of derived data by "sliding"
-a computation along existing data.
+\code{step_epi_slide()} creates a \emph{specification} of a recipe step that will
+generate one or more new columns of derived data by "sliding" a computation
+along existing data. This is a wrapper around \code{epiprocess::epi_slide()}
+to allow its use within an \code{epi_recipe()}.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index e76da1100..999d818ed 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -68,8 +68,9 @@ An updated version of \code{recipe} with the new step added to the
 sequence of any existing operations.
 }
 \description{
-\code{step_growth_rate()} creates a \emph{specification} of a recipe step
-that will generate one or more new columns of derived data.
+\code{step_growth_rate()} creates a \emph{specification} of a recipe step that will
+generate one or more new columns of derived data. This is a wrapper around
+\code{epiprocess::growth_rate()} to allow its use within an \code{epi_recipe()}.
 }
 \examples{
 tiny_geos <- c("as", "mp", "vi", "gu", "pr")
diff --git a/man/step_lag_difference.Rd b/man/step_lag_difference.Rd
index 6151bee84..325bcf05c 100644
--- a/man/step_lag_difference.Rd
+++ b/man/step_lag_difference.Rd
@@ -43,8 +43,16 @@ An updated version of \code{recipe} with the new step added to the
 sequence of any existing operations.
 }
 \description{
-\code{step_lag_difference()} creates a \emph{specification} of a recipe step
-that will generate one or more new columns of derived data.
+\code{step_lag_difference()} creates a \emph{specification} of a recipe step that will
+generate one or more new columns of derived data. For each column in the
+specification, \code{step_lag_difference()} will calculate the difference
+between the values at a distance of \code{horizon}. For example, with
+\code{horizon=1}, this would simply be the difference between adjacent days.
+}
+\details{
+Much like \code{step_epi_lag()} this step works with the actual time values (so if
+there are gaps it will fill with \code{NA} values), and respects the grouping
+inherent in the \code{epi_df()} as specified by \code{geo_value} and \code{other_keys}.
 }
 \examples{
 r <- epi_recipe(covid_case_death_rates) \%>\%
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 427e896ed..7340762ac 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -26,12 +26,13 @@ sequence of operations for this recipe.}
 for this step. See \code{\link[recipes:selections]{recipes::selections()}} for more details.}
 
 \item{role}{For model terms created by this step, what analysis role should
-they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.}
+they be assigned?}
 
-\item{df}{a data frame that contains the population data to be used for
-inverting the existing scaling.}
+\item{df}{a data frame containing the scaling data (such as population). The
+target column is divided by the value in \code{df_pop_col}.}
 
-\item{by}{A (possibly named) character vector of variables to join by.
+\item{by}{A (possibly named) character vector of variables to join \code{df} onto
+the \code{epi_df} by.
 
 If \code{NULL}, the default, the function will try to infer a reasonable set of
 columns. First, it will try to join by all variables in the training/test
@@ -61,7 +62,7 @@ This should be one column.}
 Adjustments can be made here. For example, if the original
 scale is "per 100K", then set \code{rate_rescaling = 1e5} to get rates.}
 
-\item{create_new}{TRUE to create a new column and keep the original column
+\item{create_new}{\code{TRUE} to create a new column and keep the original column
 in the \code{epi_df}}
 
 \item{suffix}{a character. The suffix added to the column name if
@@ -80,16 +81,15 @@ the computations for subsequent operations.}
 Scales raw data by the population
 }
 \description{
-\code{step_population_scaling} creates a specification of a recipe step
-that will perform per-capita scaling. Typical usage would
-load a dataset that contains state-level population, and use it to convert
-predictions made from a raw scale model to rate-scale by dividing by
-the population.
-Although, it is worth noting that there is nothing special about "population".
-The function can be used to scale by any variable. Population is the
-standard use case in the epidemiology forecasting scenario. Any value
-passed will \emph{divide} the selected variables while the \code{rate_rescaling}
-argument is a common \emph{multiplier} of the selected variables.
+\code{step_population_scaling()} creates a specification of a recipe step that
+will perform per-capita scaling. Typical usage would set \code{df} to be a dataset
+that contains state-level population, and use it to convert predictions made
+from a raw scale model to rate-scale by dividing by the population.
+Although, it is worth noting that there is nothing special about
+"population", and the function can be used to scale by any variable.
+Population is the standard use case in the epidemiology forecasting scenario.
+Any value passed will \emph{divide} the selected variables while the
+\code{rate_rescaling} argument is a common \emph{multiplier} of the selected variables.
 }
 \examples{
 jhu <- cases_deaths_subset \%>\%
diff --git a/man/step_training_window.Rd b/man/step_training_window.Rd
index 42f6b9a95..86c9a2952 100644
--- a/man/step_training_window.Rd
+++ b/man/step_training_window.Rd
@@ -40,8 +40,12 @@ observations in \code{time_value} per group, where the groups are formed
 based on the remaining \code{epi_keys}.
 }
 \details{
-Note that \code{step_epi_lead()} and \code{step_epi_lag()} should come
-after any filtering step.
+It is recommended to do this after any \code{step_epi_ahead()},
+\code{step_epi_lag()}, or \code{step_epi_naomit()} steps. If \code{step_training_window()}
+happens first, there will be less than \code{n_training} remaining examples,
+since either leading or lagging will introduce \code{NA}'s later removed by
+\code{step_epi_naomit()}. Typical usage will have this function applied after
+every other step.
 }
 \examples{
 tib <- tibble(

From b61a30bb2d20909ad93efe3a0c6bb23460c79cb8 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 6 Mar 2025 18:04:08 -0600
Subject: [PATCH 360/387] forecast needs `...` as a generic

---
 R/epi_workflow.R             | 3 ++-
 man/forecast.epi_workflow.Rd | 4 +++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index c3b1c4fbc..6aac401b2 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -241,6 +241,7 @@ print.epi_workflow <- function(x, ...) {
 #' `get_test_data()` and `predict()`.
 #'
 #' @param object An epi workflow.
+#' @param ... Not used.
 #'
 #' @return A forecast tibble.
 #'
@@ -257,7 +258,7 @@ print.epi_workflow <- function(x, ...) {
 #' epi_workflow(r, parsnip::linear_reg()) %>%
 #'   fit(jhu) %>%
 #'   forecast()
-forecast.epi_workflow <- function(object) {
+forecast.epi_workflow <- function(object, ...) {
   if (!object$trained) {
     cli_abort(c(
       "You cannot `forecast()` a {.cls workflow} that has not been trained.",
diff --git a/man/forecast.epi_workflow.Rd b/man/forecast.epi_workflow.Rd
index 74944e61d..feae32f68 100644
--- a/man/forecast.epi_workflow.Rd
+++ b/man/forecast.epi_workflow.Rd
@@ -4,10 +4,12 @@
 \alias{forecast.epi_workflow}
 \title{Produce a forecast from just an epi workflow}
 \usage{
-\method{forecast}{epi_workflow}(object)
+\method{forecast}{epi_workflow}(object, ...)
 }
 \arguments{
 \item{object}{An epi workflow.}
+
+\item{...}{Not used.}
 }
 \value{
 A forecast tibble.

From ea276f352e520b41b747c1ef55a64e0bec7e4215 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 14 Mar 2025 15:28:56 -0500
Subject: [PATCH 361/387] include climate, only calculate necessary days

---
 R/autoplot.R                       |  8 ++-
 R/climatological_forecaster.R      | 67 +++++++++++++-------
 R/step_climate.R                   | 99 +++++++++++++++++-------------
 man/roll_modular_multivec.Rd       | 15 +++--
 tests/testthat/test-step_climate.R |  2 +-
 vignettes/epipredict.Rmd           | 39 +++++++++++-
 6 files changed, 156 insertions(+), 74 deletions(-)

diff --git a/R/autoplot.R b/R/autoplot.R
index 07fe56466..236419c4e 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -114,8 +114,12 @@ autoplot.epi_workflow <- function(
   keys <- c("geo_value", "time_value", "key")
   mold_roles <- names(mold$extras$roles)
   # extract the relevant column names for plotting
-  old_name_y <- unlist(strsplit(names(y), "_"))
-  new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
+  if (starts_with_impl("ahead_", names(y)) || starts_with_impl("lag_", names(y))) {
+    old_name_y <- unlist(strsplit(names(y), "_"))
+    new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
+  } else {
+    new_name_y <- names(y)
+  }
   if (is.null(plot_data)) {
     # the outcome has shifted, so we need to shift it forward (or back)
     # by the corresponding amount
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index e29d7d4a3..592fcccc0 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -115,10 +115,42 @@ climatological_forecaster <- function(epi_data,
     mean = function(x, w) mean(x, na.rm = TRUE),
     median = function(x, w) stats::median(x, na.rm = TRUE)
   )
-  # get the point predictions
   keys <- key_colnames(epi_data, exclude = "time_value")
-  epi_data <- epi_data %>% mutate(.idx = time_aggr(time_value), .weights = 1)
-  climate_center <- epi_data %>%
+  # Get the prediction geo and .idx for the target date(s)
+  predictions <- epi_data %>%
+    select(all_of(keys)) %>%
+    dplyr::distinct() %>%
+    mutate(forecast_date = forecast_date, .idx = time_aggr(forecast_date))
+  predictions <-
+    map(horizon, ~ {
+      predictions %>%
+        mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
+    }) %>%
+    purrr::list_rbind() %>%
+    mutate(
+      .idx = .idx %% modulus,
+      .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
+    )
+  # get the distinct .idx for the target date(s)
+  distinct_target_idx <- predictions$.idx %>% unique()
+  # get all of the idx's within the window of the target .idxs
+  entries <- map(distinct_target_idx, \(idx) within_window(idx, window_size, modulus)) %>%
+    do.call(c, .) %>%
+    unique()
+  # for the center, we need those within twice the window, since for each point
+  # we're subtracting out the center to generate the quantiles
+  entries_double_window <- map(entries, \(idx) within_window(idx, window_size, modulus)) %>%
+    do.call(c, .) %>%
+    unique()
+
+  epi_data_target <-
+    epi_data %>%
+    mutate(.idx = time_aggr(time_value), .weights = 1)
+  # get the point predictions
+  climate_center <-
+    epi_data_target %>%
+    filter(.idx %in% entries_double_window) %>%
+    mutate(.idx = time_aggr(time_value), .weights = 1) %>%
     select(.idx, .weights, all_of(c(outcome, keys))) %>%
     dplyr::reframe(
       roll_modular_multivec(
@@ -136,7 +168,10 @@ climatological_forecaster <- function(epi_data,
       probs = args_list$quantile_levels, na.rm = TRUE, type = 8
     )))
   }
-  climate_quantiles <- epi_data %>%
+  # add on the centers and subtract them out before computing the quantiles
+  climate_quantiles <-
+    epi_data_target %>%
+    filter(.idx %in% entries) %>%
     left_join(climate_center, by = c(".idx", keys)) %>%
     mutate({{ outcome }} := !!sym_outcome - .pred) %>%
     select(.idx, .weights, all_of(c(outcome, args_list$quantile_by_key))) %>%
@@ -147,31 +182,17 @@ climatological_forecaster <- function(epi_data,
       ),
       .by = all_of(args_list$quantile_by_key)
     ) %>%
-    rename(.pred_distn = climate_pred) %>%
-    mutate(.pred_distn = hardhat::quantile_pred(do.call(rbind, .pred_distn), args_list$quantile_levels))
+    mutate(.pred_distn = hardhat::quantile_pred(do.call(rbind, climate_pred), args_list$quantile_levels)) %>%
+    select(-climate_pred)
   # combine them together
   climate_table <- climate_center %>%
-    left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
+    inner_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
     mutate(.pred_distn = .pred_distn + .pred)
-  # create the predictions
-  predictions <- epi_data %>%
-    select(all_of(keys)) %>%
-    dplyr::distinct() %>%
-    mutate(forecast_date = forecast_date, .idx = time_aggr(forecast_date))
-  predictions <- map(horizon, ~ {
-    predictions %>%
-      mutate(.idx = .idx + .x, target_date = forecast_date + ttype_dur(.x))
-  }) %>%
-    purrr::list_rbind() %>%
-    mutate(
-      .idx = .idx %% modulus,
-      .idx = dplyr::case_when(.idx == 0 ~ modulus, TRUE ~ .idx)
-    ) %>%
+  predictions <- predictions %>%
     left_join(climate_table, by = c(".idx", keys)) %>%
     select(-.idx)
   if (args_list$nonneg) {
-    predictions <- mutate(
-      predictions,
+    predictions <- predictions %>% mutate(
       .pred = snap(.pred, 0, Inf),
       .pred_distn = snap(.pred_distn, 0, Inf)
     )
diff --git a/R/step_climate.R b/R/step_climate.R
index 6e2817faf..fa505298d 100644
--- a/R/step_climate.R
+++ b/R/step_climate.R
@@ -338,57 +338,74 @@ print.step_climate <- function(x, width = max(20, options()$width - 30), ...) {
 }
 
 #' group col by .idx values and sum windows around each .idx value
-#' @param .idx the relevant periodic part of time value, e.g. the week number
-#' @param col the list of values indexed by `.idx`
-#' @param weights how much to weigh each particular datapoint
-#' @param aggr the aggregation function, probably Quantile, mean or median
+#' @param idx_in the relevant periodic part of time value, e.g. the week number,
+#'   limited to the relevant range
+#' @param col the list of values indexed by `idx_in`
+#' @param weights how much to weigh each particular datapoint (also indexed by
+#'   `idx_in`)
+#' @param aggr the aggregation function, probably Quantile, mean, or median
 #' @param window_size the number of .idx entries before and after to include in
 #'   the aggregation
-#' @param modulus the maximum value of `.idx`
+#' @param modulus the number of days/weeks/months in the year, not including any
+#'   leap days/weeks
 #' @importFrom lubridate %m-%
 #' @keywords internal
-roll_modular_multivec <- function(col, .idx, weights, aggr, window_size, modulus) {
-  tib <- tibble(col = col, weights = weights, .idx = .idx) |>
+roll_modular_multivec <- function(col, idx_in, weights, aggr, window_size, modulus) {
+  # make a tibble where data gives the list of all datapoints with the
+  # corresponding .idx
+  tib <- tibble(col = col, weights = weights, .idx = idx_in) |>
     arrange(.idx) |>
     tidyr::nest(data = c(col, weights), .by = .idx)
-  out <- double(modulus + 1)
-  for (iter in seq_along(out)) {
-    # +1 from 1-indexing
-    entries <- (iter - window_size):(iter + window_size) %% modulus
-    entries[entries == 0] <- modulus
-    # note that because we are 1-indexing, we're looking for indices that are 1
-    # larger than the actual day/week in the year
-    if (modulus == 365) {
-      # we need to grab just the window around the leap day on the leap day
-      if (iter == 366) {
-        # there's an extra data point in front of the leap day
-        entries <- (59 - window_size):(59 + window_size - 1) %% modulus
-        entries[entries == 0] <- modulus
-        # adding in the leap day itself
-        entries <- c(entries, 999)
-      } else if ((59 %in% entries) || (60 %in% entries)) {
-        # if we're on the Feb/March boundary for daily data, we need to add in the
-        # leap day data
-        entries <- c(entries, 999)
-      }
-    } else if (modulus == 52) {
-      # we need to grab just the window around the leap week on the leap week
-      if (iter == 53) {
-        entries <- (53 - window_size):(53 + window_size - 1) %% 52
-        entries[entries == 0] <- 52
-        entries <- c(entries, 999)
-      } else if ((52 %in% entries) || (1 %in% entries)) {
-        # if we're on the year boundary for weekly data, we need to add in the
-        # leap week data (which is the extra week at the end)
-        entries <- c(entries, 999)
-      }
-    }
-    out[iter] <- with(
+  # storage for the results, includes all possible time indexes
+  out <- tibble(.idx = c(1:modulus, 999), climate_pred = double(modulus + 1))
+  for (tib_idx in tib$.idx) {
+    entries <- within_window(tib_idx, window_size, modulus)
+    out$climate_pred[out$.idx == tib_idx] <- with(
       purrr::list_rbind(tib %>% filter(.idx %in% entries) %>% pull(data)),
       aggr(col, weights)
     )
   }
-  tibble(.idx = unique(tib$.idx), climate_pred = out[seq_len(nrow(tib))])
+  # filter to only the ones we actually computed
+  out %>% filter(.idx %in% idx_in)
+}
+
+#' generate the idx values within `window_size` of `target_idx` given that our
+#' time value is of the type matching modulus
+#' @param target_idx the time index which we're drawing the window around
+#' @param window_size the size of the window on one side of `target_idx`
+#' @param modulus the number of days/weeks/months in the year, not including any leap days/weeks
+#' @keywords internal
+within_window <- function(target_idx, window_size, modulus) {
+  entries <- (target_idx - window_size):(target_idx + window_size) %% modulus
+  entries[entries == 0] <- modulus
+  # note that because we are 1-indexing, we're looking for indices that are 1
+  # larger than the actual day/week in the year
+  if (modulus == 365) {
+    # we need to grab just the window around the leap day on the leap day
+    if (target_idx == 999) {
+      # there's an extra data point in front of the leap day
+      entries <- (59 - window_size):(59 + window_size - 1) %% modulus
+      entries[entries == 0] <- modulus
+      # adding in the leap day itself
+      entries <- c(entries, 999)
+    } else if ((59 %in% entries) || (60 %in% entries)) {
+      # if we're on the Feb/March boundary for daily data, we need to add in the
+      # leap day data
+      entries <- c(entries, 999)
+    }
+  } else if (modulus == 52) {
+    # we need to grab just the window around the leap week on the leap week
+    if (target_idx == 999) {
+      entries <- (53 - window_size):(53 + window_size - 1) %% 52
+      entries[entries == 0] <- 52
+      entries <- c(entries, 999)
+    } else if ((52 %in% entries) || (1 %in% entries)) {
+      # if we're on the year boundary for weekly data, we need to add in the
+      # leap week data (which is the extra week at the end)
+      entries <- c(entries, 999)
+    }
+  }
+  entries
 }
 
 
diff --git a/man/roll_modular_multivec.Rd b/man/roll_modular_multivec.Rd
index b01304cc6..83c32aa41 100644
--- a/man/roll_modular_multivec.Rd
+++ b/man/roll_modular_multivec.Rd
@@ -4,21 +4,24 @@
 \alias{roll_modular_multivec}
 \title{group col by .idx values and sum windows around each .idx value}
 \usage{
-roll_modular_multivec(col, .idx, weights, aggr, window_size, modulus)
+roll_modular_multivec(col, idx_in, weights, aggr, window_size, modulus)
 }
 \arguments{
-\item{col}{the list of values indexed by \code{.idx}}
+\item{col}{the list of values indexed by \code{idx_in}}
 
-\item{.idx}{the relevant periodic part of time value, e.g. the week number}
+\item{idx_in}{the relevant periodic part of time value, e.g. the week number,
+limited to the relevant range}
 
-\item{weights}{how much to weigh each particular datapoint}
+\item{weights}{how much to weigh each particular datapoint (also indexed by
+\code{idx_in})}
 
-\item{aggr}{the aggregation function, probably Quantile, mean or median}
+\item{aggr}{the aggregation function, probably Quantile, mean, or median}
 
 \item{window_size}{the number of .idx entries before and after to include in
 the aggregation}
 
-\item{modulus}{the maximum value of \code{.idx}}
+\item{modulus}{the number of days/weeks/months in the year, not including any
+leap days/weeks}
 }
 \description{
 group col by .idx values and sum windows around each .idx value
diff --git a/tests/testthat/test-step_climate.R b/tests/testthat/test-step_climate.R
index 3dca9ec67..231908b98 100644
--- a/tests/testthat/test-step_climate.R
+++ b/tests/testthat/test-step_climate.R
@@ -110,7 +110,7 @@ test_that("prep/bake steps create the correct training data with an incomplete y
   r <- epi_recipe(x) %>% step_climate(y, time_type = "epiweek")
   p <- prep(r, x)
 
-  expected_res <- tibble(.idx = c(1:44, 999), climate_y = c(2, 3, 3, 4:25, 25, 25, 25:12, 12, 11, 11, 10))
+  expected_res <- tibble(.idx = c(1:44, 999), climate_y = c(2, 3, 3, 4:25, 25, 25, 25:12, 12, 11, 11, 2))
   expect_equal(p$steps[[1]]$climate_table, expected_res)
 
   b <- bake(p, new_data = NULL)
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index fd85912ff..70e153c63 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -45,6 +45,7 @@ We currently provide the following basic forecasters:
   
   * _Flatline forecaster_: predicts as the median the most recently seen value 
     with increasingly wide quantiles.
+  * _Climatological forecaster_: predicts the median and quantiles based on the historical values around the same date in previous years.
   * _Autoregressive forecaster_: fits a model (e.g. linear regression) on
     lagged data to predict quantiles for continuous values.
   * _Autoregressive classifier_: fits a model (e.g. logistic regression) on
@@ -243,7 +244,6 @@ all_flatlines <- lapply(
       outcome = "death_rate",
       args_list = flatline_args_list(
         ahead = days_ahead,
-        quantile_levels = c(0.05, 0.5, 0.95)
       )
     )
   }
@@ -262,6 +262,43 @@ autoplot(
 Note that the `cdc_baseline_forecaster` is a slight modification of this method
 for use in [the CDC COVID19 Forecasting Hub](https://covid19forecasthub.org/).
 
+### `climatological_forecaster()`
+A different kind of baseline, the `climatological_forecaster()` forecasts the
+point forecast and quantiles based on the historical values for this time of
+year, rather than extrapolating from recent values.
+For example, on the same dataset as above:
+```{r make-climatological-forecast, warning=FALSE}
+all_climate <- climatological_forecaster(
+  covid_case_death_rates_extended |>
+    filter(time_value <= forecast_date, geo_value %in% used_locations),
+  outcome = "death_rate",
+  args_list = climate_args_list(
+    forecast_horizon = seq(0, 28),
+    window_size = 14,
+    time_type = "day",
+    forecast_date = forecast_date
+  )
+)
+workflow <- all_climate$epi_workflow
+results <- all_climate$predictions
+autoplot(
+  object = workflow,
+  predictions = results,
+  plot_data = covid_case_death_rates_extended |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+)
+```
+
+Note that we're using `covid_case_death_rates_extended` rather than
+`covid_case_death_rates`, since it starts in March of 2020 rather than December.
+Without at least a year's worth of historical data, it is impossible to do a
+climatological model.
+Even with only one year as we have here the resulting forecasts are unreliable.
+
+One feature of the climatological baseline is that it forecasts multiple aheads
+simultaneously.
+This is possible for `arx_forecaster()`, but only using `trainer =
+smooth_quantile_reg()`, which is built to handle multiple aheads simultaneously.
+
 ### `arx_classifier()`
 
 The most complicated of the canned forecasters, `arx_classifier` first

From c3287d21f681c8f587382d0b813a8e42a963c6e0 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 18 Mar 2025 16:42:57 -0500
Subject: [PATCH 362/387] Adding short blurb on cdc_flatline

---
 man/step_adjust_latency.Rd |  4 ++--
 vignettes/epipredict.Rmd   | 36 +++++++++++++++++++++++++++++++++---
 2 files changed, 35 insertions(+), 5 deletions(-)

diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 437811b39..019434409 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -268,8 +268,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 70e153c63..cfd6d24f0 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -251,7 +251,6 @@ all_flatlines <- lapply(
 # same plotting code as in the arx multi-ahead case
 workflow <- all_flatlines[[1]]$epi_workflow
 results <- purrr::map_df(all_flatlines, ~ `$`(., "predictions"))
-results %>% filter(target_date == max(target_date))
 autoplot(
   object = workflow,
   predictions = results,
@@ -259,8 +258,39 @@ autoplot(
 )
 ```
 
-Note that the `cdc_baseline_forecaster` is a slight modification of this method
-for use in [the CDC COVID19 Forecasting Hub](https://covid19forecasthub.org/).
+### `cdc_baseline_forecaster()`
+
+This is a different method of generating a flatline forecast, used as a baseline
+for [COVID19ForecastHub](https://covid19forecasthub.org).
+
+```{r make-cdc-forecast, warning=FALSE}
+all_cdc_flatline <- 
+cdc_baseline_forecaster(
+  covid_case_death_rates |>
+    filter(time_value <= forecast_date, geo_value %in% used_locations),
+  outcome = "death_rate",
+  args_list = cdc_baseline_args_list(
+    aheads = 1:28,
+    data_frequency = "1 day"
+  )
+)
+# same plotting code as in the arx multi-ahead case
+workflow <- all_cdc_flatline$epi_workflow
+results <- all_cdc_flatline$predictions
+autoplot(
+  object = workflow,
+  predictions = results,
+  plot_data = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+)
+```
+
+The median is the same, but the quantiles are generated using
+`layer_cdc_flatline_quantiles()` instead of `layer_residual_quantiles()`.
+Both rely on the computing the quantiles of the residuals, but this model
+extrapolates the quantiles by repeatedly sampling the initial quantiles to
+generate the next quantiles.
+This results in much smoother quantiles, but ones that only capture the
+one-ahead uncertainty.
 
 ### `climatological_forecaster()`
 A different kind of baseline, the `climatological_forecaster()` forecasts the

From 01f1d22b84f6a299a45114fbc872c3fa8dff9824 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 18 Mar 2025 17:36:09 -0500
Subject: [PATCH 363/387] extra details for symmetrize

---
 R/arx_forecaster.R              | 11 +++++++----
 R/layer_residual_quantiles.R    | 10 +++++++---
 man/arx_args_list.Rd            | 11 +++++++----
 man/cdc_baseline_args_list.Rd   | 11 +++++++----
 man/climate_args_list.Rd        | 11 +++++++----
 man/flatline_args_list.Rd       | 11 +++++++----
 man/layer_residual_quantiles.Rd |  6 +++++-
 man/step_adjust_latency.Rd      |  4 ++--
 vignettes/epipredict.Rmd        | 18 +++++++++---------
 9 files changed, 58 insertions(+), 35 deletions(-)

diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index d063f8134..56034bffa 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -268,10 +268,13 @@ arx_fcast_epi_workflow <- function(
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
 #'   prediction intervals. These are created by computing the quantiles of
 #'   training residuals. A `NULL` value will result in point forecasts only.
-#' @param symmetrize Logical. The default `TRUE` calculates
-#'   symmetric prediction intervals. This argument only applies when
-#'   residual quantiles are used. It is not applicable with
-#'   `trainer = quantile_reg()`, for example.
+#' @param symmetrize Logical. The default `TRUE` calculates symmetric prediction
+#'   intervals. This argument only applies when residual quantiles are used. It
+#'   is not applicable with `trainer = quantile_reg()`, for example. This is
+#'   achieved by including both the residuals and their negation. Typically, one
+#'   would only want non-symmetric quantiles when increasing trajectories are
+#'   quite different from decreasing ones, such as a strictly postive variable
+#'   near zero.
 #' @param nonneg Logical. The default `TRUE` enforces nonnegative predictions
 #'   by hard-thresholding at 0.
 #' @param quantile_by_key Character vector. Groups residuals by listed keys
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 37087f9d5..779bf36b6 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -10,7 +10,11 @@
 #' @param quantile_levels numeric vector of probabilities with values in (0,1)
 #'   referring to the desired quantile. Note that 0.5 will always be included
 #'   even if left out by the user.
-#' @param symmetrize logical. If `TRUE` then interval will be symmetric.
+#' @param symmetrize logical. If `TRUE` then the interval will be symmetric.
+#'   This is achieved by including both the residuals and their negations.
+#'   Typically, one would only want non-symmetric quantiles when increasing
+#'   trajectories are quite different from decreasing ones, such as a strictly
+#'   postive variable near zero.
 #' @param by_key A character vector of keys to group the residuals by before
 #'   calculating quantiles. The default, `c()` performs no grouping.
 #' @param name character. The name for the output column.
@@ -96,7 +100,7 @@ slather.layer_residual_quantiles <-
       return(components)
     }
 
-    s <- ifelse(object$symmetrize, -1, NA)
+    symmetric <- ifelse(object$symmetrize, -1, NA)
     r <- grab_residuals(the_fit, components)
 
     ## Handle any grouping requests
@@ -131,7 +135,7 @@ slather.layer_residual_quantiles <-
 
     r <- r %>%
       summarize(dstn = quantile_pred(matrix(quantile(
-        c(.resid, s * .resid),
+        c(.resid, symmetric * .resid),
         probs = object$quantile_levels, na.rm = TRUE
       ), nrow = 1), quantile_levels = object$quantile_levels))
     # Check for NA
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index 650c4a614..ca00bcc5c 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -66,10 +66,13 @@ latency is large. If this is \code{FALSE}, that warning is turned off.}
 prediction intervals. These are created by computing the quantiles of
 training residuals. A \code{NULL} value will result in point forecasts only.}
 
-\item{symmetrize}{Logical. The default \code{TRUE} calculates
-symmetric prediction intervals. This argument only applies when
-residual quantiles are used. It is not applicable with
-\code{trainer = quantile_reg()}, for example.}
+\item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
+intervals. This argument only applies when residual quantiles are used. It
+is not applicable with \code{trainer = quantile_reg()}, for example. This is
+achieved by including both the residuals and their negation. Typically, one
+would only want non-symmetric quantiles when increasing trajectories are
+quite different from decreasing ones, such as a strictly postive variable
+near zero.}
 
 \item{nonneg}{Logical. The default \code{TRUE} enforces nonnegative predictions
 by hard-thresholding at 0.}
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 4a8c13113..2c557e5dc 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -51,10 +51,13 @@ These samples are spaced evenly on the (0, 1) scale, F_X(x) resulting
 in linear interpolation on the X scale. This is achieved with
 \code{\link[stats:quantile]{stats::quantile()}} Type 7 (the default for that function).}
 
-\item{symmetrize}{Logical. The default \code{TRUE} calculates
-symmetric prediction intervals. This argument only applies when
-residual quantiles are used. It is not applicable with
-\code{trainer = quantile_reg()}, for example.}
+\item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
+intervals. This argument only applies when residual quantiles are used. It
+is not applicable with \code{trainer = quantile_reg()}, for example. This is
+achieved by including both the residuals and their negation. Typically, one
+would only want non-symmetric quantiles when increasing trajectories are
+quite different from decreasing ones, such as a strictly postive variable
+near zero.}
 
 \item{nonneg}{Logical. Force all predictive intervals be non-negative.
 Because non-negativity is forced \emph{before} propagating forward, this
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
index 3a889e5c7..bb1ef7eab 100644
--- a/man/climate_args_list.Rd
+++ b/man/climate_args_list.Rd
@@ -46,10 +46,13 @@ rolling average, centered at each day.}
 prediction intervals. These are created by computing the quantiles of
 training residuals. A \code{NULL} value will result in point forecasts only.}
 
-\item{symmetrize}{Logical. The default \code{TRUE} calculates
-symmetric prediction intervals. This argument only applies when
-residual quantiles are used. It is not applicable with
-\code{trainer = quantile_reg()}, for example.}
+\item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
+intervals. This argument only applies when residual quantiles are used. It
+is not applicable with \code{trainer = quantile_reg()}, for example. This is
+achieved by including both the residuals and their negation. Typically, one
+would only want non-symmetric quantiles when increasing trajectories are
+quite different from decreasing ones, such as a strictly postive variable
+near zero.}
 
 \item{nonneg}{Logical. The default \code{TRUE} enforces nonnegative predictions
 by hard-thresholding at 0.}
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 626bcb6f1..7c9bb0399 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -44,10 +44,13 @@ will determine this automatically as \code{forecast_date + ahead}.}
 prediction intervals. These are created by computing the quantiles of
 training residuals. A \code{NULL} value will result in point forecasts only.}
 
-\item{symmetrize}{Logical. The default \code{TRUE} calculates
-symmetric prediction intervals. This argument only applies when
-residual quantiles are used. It is not applicable with
-\code{trainer = quantile_reg()}, for example.}
+\item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
+intervals. This argument only applies when residual quantiles are used. It
+is not applicable with \code{trainer = quantile_reg()}, for example. This is
+achieved by including both the residuals and their negation. Typically, one
+would only want non-symmetric quantiles when increasing trajectories are
+quite different from decreasing ones, such as a strictly postive variable
+near zero.}
 
 \item{nonneg}{Logical. The default \code{TRUE} enforces nonnegative predictions
 by hard-thresholding at 0.}
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index df1320a88..e879677f3 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -23,7 +23,11 @@ layer_residual_quantiles(
 referring to the desired quantile. Note that 0.5 will always be included
 even if left out by the user.}
 
-\item{symmetrize}{logical. If \code{TRUE} then interval will be symmetric.}
+\item{symmetrize}{logical. If \code{TRUE} then the interval will be symmetric.
+This is achieved by including both the residuals and their negations.
+Typically, one would only want non-symmetric quantiles when increasing
+trajectories are quite different from decreasing ones, such as a strictly
+postive variable near zero.}
 
 \item{by_key}{A character vector of keys to group the residuals by before
 calculating quantiles. The default, \code{c()} performs no grouping.}
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 019434409..437811b39 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -268,8 +268,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
-#> work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index cfd6d24f0..6b7fb790c 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -264,16 +264,16 @@ This is a different method of generating a flatline forecast, used as a baseline
 for [COVID19ForecastHub](https://covid19forecasthub.org).
 
 ```{r make-cdc-forecast, warning=FALSE}
-all_cdc_flatline <- 
-cdc_baseline_forecaster(
-  covid_case_death_rates |>
-    filter(time_value <= forecast_date, geo_value %in% used_locations),
-  outcome = "death_rate",
-  args_list = cdc_baseline_args_list(
-    aheads = 1:28,
-    data_frequency = "1 day"
+all_cdc_flatline <-
+  cdc_baseline_forecaster(
+    covid_case_death_rates |>
+      filter(time_value <= forecast_date, geo_value %in% used_locations),
+    outcome = "death_rate",
+    args_list = cdc_baseline_args_list(
+      aheads = 1:28,
+      data_frequency = "1 day"
+    )
   )
-)
 # same plotting code as in the arx multi-ahead case
 workflow <- all_cdc_flatline$epi_workflow
 results <- all_cdc_flatline$predictions

From 84ed412ae3519a1550ef64841f276e5d6cfe6fe7 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 28 Mar 2025 13:21:58 -0400
Subject: [PATCH 364/387] epipredict.Rmd

---
 vignettes/epipredict.Rmd | 138 +++++++++++++++++++++------------------
 1 file changed, 74 insertions(+), 64 deletions(-)

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 6b7fb790c..88a047828 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Get started with `{epipredict}`"
+title: "Get started with epipredict"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Get started with `{epipredict}`}
@@ -42,8 +42,8 @@ Towards that end, `{epipredict}` provides two main classes of tools:
 
 A set of basic, easy-to-use "canned" forecasters that work out of the box.
 We currently provide the following basic forecasters:
-  
-  * _Flatline forecaster_: predicts as the median the most recently seen value 
+
+  * _Flatline forecaster_: predicts as the median the most recently seen value
     with increasingly wide quantiles.
   * _Climatological forecaster_: predicts the median and quantiles based on the historical values around the same date in previous years.
   * _Autoregressive forecaster_: fits a model (e.g. linear regression) on
@@ -58,7 +58,7 @@ We currently provide the following basic forecasters:
 A framework for creating custom forecasters out of modular components, from
 which the canned forecasters were created.  There are three types of
 components:
- 
+
   * _Preprocessor_: transform the data before model training, such as converting
     counts to rates, creating smoothed columns, or [any `{recipes}`
     `step`](https://recipes.tidymodels.org/reference/index.html)
@@ -194,12 +194,12 @@ If you want to make further modifications, you will need a custom
 workflow; see the [Custom Epiworkflows vignette](custom_epiworkflows) for details.
 
 ## Generating multiple aheads
-Frequently, one doesn't want just a forecast for a single day, but a trajectory
-of forecasts for several weeks.
-We can do this with `arx_forecaster()` by looping over aheads; for
-example, to predict every day over a 4-week time period:
+We often want to generate a a trajectory
+of forecasts over a range of dates, rather than for a single day.
+We can do this with `arx_forecaster()` by looping over aheads.
+For example, to predict every day over a 4-week time period:
 
-```{r temp-thing}
+```{r aheads-loop}
 all_canned_results <- lapply(
   seq(0, 28),
   \(days_ahead) {
@@ -261,7 +261,7 @@ autoplot(
 ### `cdc_baseline_forecaster()`
 
 This is a different method of generating a flatline forecast, used as a baseline
-for [COVID19ForecastHub](https://covid19forecasthub.org).
+for [the CDC COVID-19 Forecasting Hub](https://covid19forecasthub.org).
 
 ```{r make-cdc-forecast, warning=FALSE}
 all_cdc_flatline <-
@@ -284,17 +284,18 @@ autoplot(
 )
 ```
 
-The median is the same, but the quantiles are generated using
+`cdc_baseline_forecaster()` and `flatline_forecaster()` generate medians in the same way, 
+but `cdc_baseline_forecaster()`'s quantiles are generated using
 `layer_cdc_flatline_quantiles()` instead of `layer_residual_quantiles()`.
-Both rely on the computing the quantiles of the residuals, but this model
-extrapolates the quantiles by repeatedly sampling the initial quantiles to
-generate the next quantiles.
+Both quantile-generating methods use the residuals to compute quantiles, but 
+`layer_cdc_flatline_quantiles()` extrapolates the quantiles by repeatedly 
+sampling the initial quantiles to generate the next set.
 This results in much smoother quantiles, but ones that only capture the
 one-ahead uncertainty.
 
 ### `climatological_forecaster()`
-A different kind of baseline, the `climatological_forecaster()` forecasts the
-point forecast and quantiles based on the historical values for this time of
+The `climatological_forecaster()` is a different kind of baseline. It produces a
+point forecast and quantiles based on the historical values for a given time of
 year, rather than extrapolating from recent values.
 For example, on the same dataset as above:
 ```{r make-climatological-forecast, warning=FALSE}
@@ -318,11 +319,12 @@ autoplot(
 )
 ```
 
-Note that we're using `covid_case_death_rates_extended` rather than
-`covid_case_death_rates`, since it starts in March of 2020 rather than December.
+Note that to have enough training data for this method, we're using 
+`covid_case_death_rates_extended`, which starts in March 2020, rather than
+`covid_case_death_rates`, which starts in December.
 Without at least a year's worth of historical data, it is impossible to do a
 climatological model.
-Even with only one year as we have here the resulting forecasts are unreliable.
+Even with one year of data, as we have here, the resulting forecasts are unreliable.
 
 One feature of the climatological baseline is that it forecasts multiple aheads
 simultaneously.
@@ -331,10 +333,9 @@ smooth_quantile_reg()`, which is built to handle multiple aheads simultaneously.
 
 ### `arx_classifier()`
 
-The most complicated of the canned forecasters, `arx_classifier` first
-translates the outcome into a growth rate, and then classifies that growth rate
-into bins.
-For example, on the same dataset and `forecast_date` as above, we get:
+Unlike the other canned forecasters, `arx_classifier` predicts binned growth rate.
+The forecaster converts the raw outcome variable into a growth rate, which it then bins and predicts, using bin thresholds provided by the user.
+For example, on the same dataset and `forecast_date` as above, this model outputs:
 
 ```{r discrete-rt}
 classifier <- arx_classifier(
@@ -352,14 +353,18 @@ classifier <- arx_classifier(
 classifier$predictions
 ```
 
-The prediction splits into 4 cases: `(-∞, -0.01)`, `(-0.01, 0.01)`,  `(0.01,
-0.1)`, and `(0.1, ∞)`.
-In this case, the classifier put all 4 of the states in the same category,
-`(0.01, 0.1)`. **TODO** _effected by the old data._
-The number and size of the categories is controlled by `breaks`, which gives the
-boundary values.
+The number and size of the growth rate categories is controlled by `breaks`, which define the
+bin boundaries.
+
+In this example, the custom `breaks` passed to `arx_class_args_list()` correspond to 4 bins:
+`(-∞, -0.01)`, `(-0.01, 0.01)`,  `(0.01, 0.1)`, and `(0.1, ∞)`.
+The bins can be interpreted as: the outcome variable is decreasing, approximately stable, slightly increasing, or increasing quickly.
 
-For comparison, the growth rates for the `target_date`, as computed using
+The returned `predictions` assigns each state to one of the growth rate bins.
+In this case, the classifier expects the growth rate for all 4 of the states to fall into the same category,
+`(-0.01, 0.01]`.
+
+To see how this model performed, let's compare to the actual growth rates for the `target_date`, as computed using
 `{epiprocess}`:
 
 ```{r growth_rate_results}
@@ -373,24 +378,29 @@ growth_rates <- covid_case_death_rates |>
 growth_rates |> filter(time_value == "2021-08-14")
 ```
 
-Unfortunately, this forecast was not particularly accurate, since for example
-`-1.39` is not remotely in the interval `(-0.01, 0.01]`.
+Unfortunately, this forecast was not particularly accurate. All real growth rates were larger than the predicted growth rates, with California (real growth rate `-1.39`) not remotely in the interval (`(-0.01, 0.01]`).
 
 
 ## Fitting multi-key panel data
 
-If you have multiple keys that are set in the `epi_df` as `other_keys`,
-`arx_forecaster` will automatically group by those as well.
-For example, predicting the number of graduates in each of the categories in `grad_employ` from above:
+If multiple keys are set in the `epi_df` as `other_keys`,
+`arx_forecaster` will automatically group by those in addition to the required geographic key.
+For example, predicting the number of graduates in each of the categories in `grad_employ_subset` from above:
 
 ```{r multi_key_forecast, warning=FALSE}
 # only fitting a subset, otherwise there are ~550 distinct pairs, which is bad for plotting
 edu_quals <- c("Undergraduate degree", "Professional degree")
 geo_values <- c("Quebec", "British Columbia")
-grad_forecast <- arx_forecaster(
-  grad_employ_subset |>
+
+grad_employ <- grad_employ_subset |>
     filter(time_value < 2017) |>
-    filter(edu_qual %in% edu_quals, geo_value %in% geo_values),
+    filter(edu_qual %in% edu_quals, geo_value %in% geo_values)
+
+grad_employ
+
+grad_forecast <- arx_forecaster(
+  grad_employ |>
+    filter(time_value < 2017),
   outcome = "num_graduates",
   predictors = c("num_graduates"),
   args_list = arx_args_list(
@@ -402,19 +412,20 @@ grad_forecast <- arx_forecaster(
 autoplot(
   grad_forecast$epi_workflow,
   grad_forecast$predictions,
-  grad_employ_subset |>
-    filter(edu_qual %in% edu_quals, geo_value %in% geo_values),
+  grad_employ,
 )
 ```
 
-The 8 graphs are all pairs of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
+The 8 graphs represent all combinations of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
 ## Fitting a non-geo-pooled model
 
-Because our internal methods fit a single model, to fit a non-geo-pooled model
-that has a different fit for each geography, one either needs a multi-level
-engine (which at the moment parsnip doesn't support), or one needs to map over
+The methods shown so far fit a single model across all geographic regions.
+This is called "geo-pooling". 
+To fit a non-geo-pooled model that fits each geography separately, one either needs a multi-level
+engine (which at the moment `{parsnip}` doesn't support), or one needs to loop over
 geographies.
+Here, we're using `purrr::map` to perform the loop.
 
 ```{r fit_non_geo_pooled, warning=FALSE}
 geo_values <- covid_case_death_rates |>
@@ -441,12 +452,11 @@ all_fits <-
 map_df(all_fits, ~ pluck(., "predictions"))
 ```
 
-This is both 56 times slower[^7], and uses far less data to fit each model.
-If the geographies are at all comparable, for example by normalization, we would
- get much better results by pooling.
+Fitting separate models for each geography is both 56 times slower[^7] than geo-pooling, and fits each model on far less data.
+If a dataset contains relatively few observations for each geography, fitting a geo-pooled model is likely to produce better, more stable results.
+However, geo-pooling can only be used if values are comparable in meaning and scale across geographies or can be made comparable, for example by normalization.
 
-If we wanted to build a geo-aware model, such as one that sets the constant in a
- linear regression fit to be different for each geography, we would need to build a [Custom workflow](custom_epiworkflows) with geography as a factor.
+If we wanted to build a geo-aware model, such as a linear regression with a different intercept for each geography, we would need to build a [custom workflow](custom_epiworkflows) with geography as a factor.
 
 # Anatomy of a canned forecaster
 ## Code object
@@ -468,21 +478,21 @@ four_week_ahead <- arx_forecaster(
 
 `four_week_ahead` has two components: an `epi_workflow`, and a table of
 `predictions`.
-The table of predictions is simply a tibble of the predictions,
+The table of predictions is a simple tibble,
 
 ```{r show_predictions}
 four_week_ahead$predictions
 ```
 
-`.pred` gives the point/median prediction, while `.pred_distn` is a
+where `.pred` gives the point/median prediction, and `.pred_distn` is a
 `dist_quantiles()` object representing a distribution through various quantile
 levels.
 The `[6]` in the name refers to the number of quantiles that have been
-explicitly created[^4]; by default, this covers a 90% prediction interval, or 5%
-and 95%.
+explicitly created[^4].
+By default, `.pred_distn` covers a 90% prediction interval, reporting the 5% and 95% quantiles.
 
 The `epi_workflow` is a significantly more complicated object, extending a
-`workflows::workflow()`  to include post-processing:
+`workflows::workflow()`  to include post-processing steps:
 
 ```{r show_workflow}
 four_week_ahead$epi_workflow
@@ -491,17 +501,17 @@ four_week_ahead$epi_workflow
 An `epi_workflow()` consists of 3 parts:
 
 - `Preprocessor`: a collection of steps that transform the data to be ready for
-      modelling. They come from this package or [any of the recipes
-      steps](https://recipes.tidymodels.org/reference/index.html);
-      `four_week_ahead` has 5 of these, and you can inspect them more closely by
+      modelling. Steps can be custom, as are those included in this package, 
+      or [be defined in `{recipes}`](https://recipes.tidymodels.org/reference/index.html).
+      `four_week_ahead` has 5 steps; you can inspect them more closely by
       running `hardhat::extract_recipe(four_week_ahead$epi_workflow)`.[^6]
 - `Model`: the actual model that does the fitting, given by a
-  `parsnip::model_spec`; `four_week_ahead` has the default of
-  `parsnip::linear_reg()`, which is a wrapper from `{parsnip}` for
+  `parsnip::model_spec`. `four_week_ahead` uses the default of
+  `parsnip::linear_reg()`, which is a `{parsnip}` wrapper for
   `stats::lm()`. You can inspect the model more closely by running
   `hardhat::extract_fit_recipe(four_week_ahead$epi_workflow)`.
 - `Postprocessor`: a collection of layers to be applied to the resulting
-  forecast, internal to this package. `four_week_ahead` just so happens to have
+  forecast. Layers are internal to this package. `four_week_ahead` just so happens to have
   5 of as these well. You can inspect the layers more closely by running
   `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
 
@@ -510,7 +520,7 @@ extending `four_week_ahead` using the custom forecaster framework.
 
 ## Mathematical description
 
-Let's describe in more detail the actual fit model for a more minimal version of
+Let's look at the mathematical details of the model in more detail, using a minimal version of
 `four_week_ahead`:
 
 ```{r, four_week_again}
@@ -537,9 +547,9 @@ $$
 For example, $a_1$ is `lag_0_death_rate` above, with a value of `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"] `,
 while $a_5$ is `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"] `.
 
-The training data for fitting this linear model is created by creating a series
-of columns shifted by the appropriate amount; this makes it so that each row
-without `NA` values is a training point to fit the coefficients $a_0,\ldots, a_6$.
+The training data for fitting this linear model is constructed within the `arx_forecaster()` function by shifting a series
+of columns the appropriate amount -- based on the requested `lags`.
+Each row containing no `NA` values is used as a training observation to fit the coefficients $a_0,\ldots, a_6$.
 
 [^4]: in the case of a `{parsnip}` engine which doesn't explicitly predict
     quantiles, these quantiles are created using `layer_residual_quantiles()`,

From 9bbf7bf7ce3dfa296d0f3536f0e077c445f6725d Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 3 Apr 2025 18:28:52 -0400
Subject: [PATCH 365/387] backtesting.rmd

---
 vignettes/backtesting.Rmd | 189 +++++++++++++++++++-------------------
 1 file changed, 96 insertions(+), 93 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 581bb31ae..cc0e4617e 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -11,39 +11,23 @@ vignette: >
 source(here::here("vignettes/_common.R"))
 ```
 
-```{r pkgs, message=FALSE}
-library(epipredict)
-library(epiprocess)
-library(epidatr)
-library(data.table)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-library(magrittr)
-library(purrr)
-```
-
 Backtesting is a crucial step in the development of forecasting models. It
-involves testing the model on historical data to see how well it performs. This
-is important because it allows us to see how well the model generalizes to new
-data and to identify any potential issues with the model. In the context of
+involves testing the model on historical time periods to see how well it generalizes to new
+data.
+
+In the context of
 epidemiological forecasting, to do backtesting accurately, we need to account
-for the fact that the data available at the time of the forecast would have been
-different from the data available at the time of the backtest. This is because
-new data is constantly being collected and added to the dataset, which can
-affect the accuracy of the forecast.
-
-For this reason, it is important to use version-faithful forecasting, where the
-model is trained on data that would have been available at the time of the
-forecast. This ensures that the model is tested on data that is as close as
-possible to what would have been available in real-time; training and making
-predictions on finalized data can lead to an overly optimistic sense of accuracy
+for the fact that the data available at _the time of the forecast_ would have been
+different from the data available at the time of the _backtest_.
+This is because
+new data is constantly being collected and added to the dataset, and old data potentially revised.
+Training and making
+predictions only on finalized data can lead to overly optimistic estimates of accuracy
 (see, for example, [McDonald et al.
 (2021)](https://www.pnas.org/content/118/51/e2111453118/) and the references
 therein).
 
-In the `{epiprocess}` package, we provide `epix_slide()`, a function that allows
-a convenient way to perform version-faithful forecasting by only using the data as
+In the `{epiprocess}` package, we provide the function `epix_slide()` to help conviently perform version-faithful forecasting by only using the data as
 it would have been available at forecast reference time.
 In this vignette, we will demonstrate how to use `epix_slide()` to backtest an
 auto-regressive forecaster constructed using `arx_forecaster()` on historical
@@ -51,6 +35,19 @@ COVID-19 case data from the US and Canada.
 
 # Getting case data from US states into an `epi_archive`
 
+```{r pkgs, message=FALSE}
+# Setup
+library(epipredict)
+library(epiprocess)
+library(epidatr)
+library(data.table)
+library(dplyr)
+library(tidyr)
+library(ggplot2)
+library(magrittr)
+library(purrr)
+```
+
 First, we create an `epi_archive()` to store the version history of the
 percentage of doctor's visits with CLI (COVID-like illness) computed from
 medical insurance claims and the number of new confirmed COVID-19 cases per
@@ -78,13 +75,13 @@ doctor_visits <- pub_covidcast(
   time_values = epirange(20200601, 20211201),
   issues = epirange(20200601, 20211201)
 ) |>
+  # The version date column is called `issue` in the Epidata API. Rename it.
   rename(version = issue, percent_cli = value) |>
   as_epi_archive(compactify = TRUE)
 ```
 
-`issues` is the name for `version` in the Epidata API.
-In the interest of computational speed, we only use the 4 state dataset limited
-to 2020-2021, but the full archive can be used in the same way and has performed
+In the interest of computational speed, we limit the dataset to 4 states and 
+2020-2021, but the full archive can be used in the same way and has performed
 well in the past.
 
 We choose this dataset in particular partly because it is revision heavy; for
@@ -104,11 +101,11 @@ percent_cli_data <- bind_rows(
       mutate(version = .x)
   ) |>
     bind_rows() |>
-    mutate(version_faithful = TRUE),
-  # Latest data for the version-faithless forecasts
+    mutate(version_faithful = "Version faithful"),
+  # Latest data for the version-un-faithful forecasts
   doctor_visits |>
     epix_as_of(doctor_visits$versions_end) |>
-    mutate(version_faithful = FALSE)
+    mutate(version_faithful = "Version un-faithful")
 )
 
 p0 <-
@@ -125,18 +122,18 @@ p0 <-
 ```
 </details>
 
-```{r plot_just_revisioning, warn = FALSE}
+```{r plot_just_revisioning, warn = FALSE, message = FALSE}
 p0
 ```
 
 The snapshots are taken on the first of each month, with the vertical dashed
-line representing the sample date for the time series of the corresponding
+line representing the issue date for the time series of the corresponding
 color.
 For example, the snapshot on March 1st, 2021 is aquamarine, and increases to
 slightly over 10.
 Every series is necessarily to the left of the snapshot date (since all known
 values must happen before the snapshot is taken[^4]).
-The grey line at the center of all the various snapshots represents the "final
+The grey line overlaying the various snapshots represents the "final
 value", which is just the snapshot at the last version in the archive (the
 `versions_end`).
 
@@ -146,12 +143,12 @@ The drop in January 2021 in the snapshot on `2021-02-01` was initially reported
 as much steeper than it eventually turned out to be, while in the period after
 that the values were initially reported as higher than they actually were.
 
-A feature that is common in both real-time forecasting and retrospective
-forecasting is data latency.
+Handling data latency is important in both real-time forecasting and retrospective
+forecasting.
 Looking at the very first snapshot, `2020-08-01` (the red dotted
 vertical line), there is a noticeable gap between the forecast date and the end
 of the red time-series to its left.
-In fact, if we take a snapshot and get the last `time_value`:
+In fact, if we take a snapshot and get the last `time_value`,
 
 ```{r}
 doctor_visits |>
@@ -160,7 +157,7 @@ doctor_visits |>
   max()
 ```
 
-The last day of data is the 25th, a entire week before `2020-08-01`.
+the last day of data is the 25th, a entire week before `2020-08-01`.
 This can require some effort to work around, especially if the latency is
 variable; see `step_adjust_latency()` for some methods included in this package.
 Much of that functionality is built into `arx_forecaster()` using the parameter
@@ -172,16 +169,16 @@ Much of that functionality is built into `arx_forecaster()` using the parameter
 One of the most common use cases of `epiprocess::epi_archive()` object is for
 accurate model back-testing.
 
-To start, let's use a simple autoregressive forecaster to predict the percentage
-of doctor's hospital visits with CLI (COVID-like illness) (`percent_cli`) 14
+To start, let's use a simple autoregressive forecaster to predict `percent_cli`, the percentage
+of doctor's hospital visits associated with COVID-like illness, 14
 days in the future. 
 For increased accuracy we will use quantile regression.
 
 ## Comparing a single day and ahead
 
-As a sanity check before we backtest the entire dataset, let's look at
-forecasting a single day in the middle of the dataset. 
-One way to do this is by setting the `.version` argument for `epix_slide()`:
+As a sanity check before we backtest the _entire_ dataset, let's 
+forecast a single day in the middle of the dataset. 
+We can do this by setting the `.version` argument in `epix_slide()`:
 
 ```{r single_version, warn = FALSE}
 forecast_date <- as.Date("2021-04-06")
@@ -198,8 +195,8 @@ forecasts <- doctor_visits |>
   )
 ```
 
-As truth data, we'll compare with the `epix_as_of()` to generate a snapshot of
-the archive at the last date[^1].
+We need truth data to compare our forecast against. We can construct it by using `epix_as_of()` to snapshot
+the archive at the last available date[^1].
 
 ```{r compare_single_with_result}
 forecasts |>
@@ -211,20 +208,20 @@ forecasts |>
   select(geo_value, forecast_date, .pred, `0.05`, `0.95`, percent_cli)
 ```
 
-`.pred` corresponds to the point forecast (median) and the `0.05`, `0.95`
-correspond to those respective quantiles.
-The forecasts fall within the prediction interval, so our
+`.pred` corresponds to the point forecast (median), and `0.05` and `0.95`
+correspond to the 5th and 95th quantiles.
+The `percent_cli` truth data falls within the prediction intervals, so our
 implementation passes a simple validation.
 
-## Comparing version faithful and version faithless forecasts
+## Comparing version faithful and version un-faithful forecasts
+
+Now let's compare the behavior of this forecaster, both properly considering data versioning 
+("version faithful") and ignoring data versions ("version un-faithful").
 
-Now let's go ahead and slide this forecaster in a version faithless way and a
-version faithful way.
-For the version faithless way, to still use `epix_slide` to do the backtesting
-we need to snapshot the latest version of the data, and then make a faux archive
-by setting `version = time_value`[^2].
-This has the effect of simulating a data set that receives the final version
-updates every day.
+For the version un-faithful approach, we need to do some setup if we want to use `epix_slide` for backtesting.
+We want to simulate a data set that receives finalized updates every day, that is, a data set with no revisions.
+To do this, we will snapshot the latest version of the data to create a synthetic data set, and convert it into an archive
+where `version = time_value`[^2].
 
 ```{r}
 archive_cases_dv_subset_faux <- doctor_visits |>
@@ -233,9 +230,9 @@ archive_cases_dv_subset_faux <- doctor_visits |>
   as_epi_archive()
 ```
 
-For the version faithful way, we will simply use the true `epi_archive` object.
-To reduce typing, we'll create `forecast_wrapper()` to cover mapping across
-aheads and some variations we will use later.
+For the version faithful approach, we will continue using the original `epi_archive` object containing all version updates.
+
+We will also create the helper function `forecast_wrapper()` to let us easily map across aheads.
 
 ```{r arx-kweek-preliminaries, warning = FALSE}
 forecast_wrapper <- function(
@@ -259,13 +256,12 @@ forecast_wrapper <- function(
 }
 ```
 
-Note that we have used the parameter`adjust_latency` mentioned above, which
-comes up because on any given forecast date, such as `2020-08-01` the latest
-data to be released may be several days old.
+Note that in the helper function, we're using the parameter `adjust_latency`.
+We need to use it because the most recently released data may still be several days old on any given forecast date; 
 `adjust_latency` will modify the forecaster to compensate[^5].
 See the function `step_adjust_latency()` for more details and examples.
 
-And now we generate the forecasts for both the version faithful and faithless
+Now that we're set up, we can generate forecasts for both the version faithful and un-faithful
 archives, and bind the results together.
 
 ```{r generate_forecasts, warning = FALSE}
@@ -276,13 +272,13 @@ forecast_dates <- seq(
 )
 aheads <- c(1, 7, 14, 21, 28)
 
-version_faithless <- archive_cases_dv_subset_faux |>
+version_unfaithful <- archive_cases_dv_subset_faux |>
   epix_slide(
     ~ forecast_wrapper(.x, aheads, "percent_cli", "percent_cli"),
     .before = 120,
     .versions = forecast_dates
   ) |>
-  mutate(version_faithful = FALSE)
+  mutate(version_faithful = "Version un-faithful")
 
 version_faithful <- doctor_visits |>
   epix_slide(
@@ -290,25 +286,25 @@ version_faithful <- doctor_visits |>
     .before = 120,
     .versions = forecast_dates
   ) |>
-  mutate(version_faithful = TRUE)
+  mutate(version_faithful = "Version faithful")
 
 forecasts <-
   bind_rows(
-    version_faithless,
+    version_unfaithful,
     version_faithful
   )
 ```
 
-Here, `arx_forecaster()` does all the heavy lifting.
-It creates leads of the target (respecting time stamps and locations) along with
-lags of the features (here, the response and doctors visits), estimates a
+`arx_forecaster()` does all the heavy lifting.
+It creates and lags copies of the features (here, the response and doctors visits), 
+creates and leads copies of the target (respecting time stamps and locations), fits a
 forecasting model using the specified engine, creates predictions, and
-non-parametric confidence bands.
+creates non-parametric confidence bands.
 
-To see how the predictions compare, we plot them on top of the latest case
-rates, using the same versioning plotting method as above.
-Note that even though we've fitted the model on four states (ca, fl, tx, and
-ny), we'll just display the results for two states, California (CA) and Florida
+To see how the version faithful and un-faithful predictions compare, let's plot them on top of the latest case
+rates, using the same versioned plotting method as above.
+Note that even though we fit the model on four states (California, Texas, Florida, and
+New York), we'll just display the results for two states, California (CA) and Florida
 (FL), to get a sense of the model performance while keeping the graphic simple.
 
 <details>
@@ -372,30 +368,37 @@ p2 <-
 p1
 ```
 
-For California above and Florida below, neither approach produces amazingly
-accurate forecasts. 
-The difference between the forecasts produced is quite striking however,
-especially in the single day ahead forecasts.
+The version faithful and un-faithful forecasts look moderately similar except for the 1 day horizons  
+(although neither approach produces amazingly accurate forecasts).
 
-In the version faithful case for California above, the December 2020 forecast
-starts at nearly 30, whereas the equivalent version faithless forecast at least
-starts at the correct value, even if it is overly pessimistic about the eventual
-peak that will occur.
+In the version faithful case for California, the March 2021 forecast (turquoise)
+starts at a value just above 10, which is very well lined up with reported values leading up to that forecast.
+The measured and forecasted trends are also concordant (both increasingly moderately fast).
 
-In the version faithful case for Florida below, the late 2021 forecasts, such as
-September are wildly off base, routinely estimating zero cases, thanks to the
-versioned data systematically under-reporting.
-There is a similar if somewhat less extreme effect on December 2020 as in
-California.
+Because the data for this time period was later adjusted down with a decreasing trend, the March 2021 forecast looks quite bad compared to finalized data.
 
-Without using the data as you would have seen it at the time as in the version
-faithful case, you potentially have no insight into what kind of performance you
-can expect in practice.
+The equivalent version un-faithful forecast starts at a value of 5, which is in line with the finalized data but would have been out of place compared to the version data.
 
 ```{r show-plot2, warning = FALSE, echo=FALSE}
 p2
 ```
 
+Now let's look at Florida.
+In the version faithful case, the three late-2021 forecasts (purples and pinks) starting in September predict very low values, near 0.
+The trend leading up to each forecast shows a substantial decrease, so these forecasts seem appropriate and we would expect them to score fairly well on various performance metrics when compared to the versioned data.
+
+In hindsight, we know that early versions of the data systematically under-reported COVID-related doctor visits such that these forecasts don't actually perform well compared to _finalized_ data.
+In this example, version faithful forecasts predicted values at or near 0 while finalized data shows values in the 5-10 range.
+As a result, the version un-faithful forecasts for these same dates are quite a bit higher, and would perform well when scored using the finalized data and poorly with versioned data.
+
+In general, the longer ago a forecast was made, the worse its performance is compared to finalized data. Finalized data accumulates revisions over time that make it deviate more and more from the non-finalized data a model was trained on.
+Forecasts trained solely on finalized data will of course appear to perform better when scored on finalized data, but will have unknown performance on the non-finalized data we need to use if we want timely predictions.
+
+Without using data that would have been available on the actual forecast date, 
+you have little insight into what level of performance you
+can expect in practice.
+
+
 
 [^1]: For forecasting a single day like this, we could have actually just used
     `doctor_visits |> epix_as_of(forecast_date)` to get the relevant snapshot, and then fed that into `arx_forecaster()` as we did in the [landing

From bb4025c4fa6f3191724d7e71fce76453bf5387ca Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Fri, 4 Apr 2025 19:12:41 -0400
Subject: [PATCH 366/387] first half custom_epiworkflows.Rmd

---
 vignettes/custom_epiworkflows.Rmd | 262 +++++++++++++++++-------------
 1 file changed, 150 insertions(+), 112 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 400fce26c..6ff6ee6e0 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -24,9 +24,12 @@ used_locations <- c("ca", "ma", "ny", "tx")
 library(epidatr)
 ```
 
-To get a better handle on custom `epi_workflow()`s, lets recreate and then
-modify the example of `four_week_ahead` from the [landing
-page](../index.html#motivating-example)
+If you want to do custom data preprocessing or fit a model that isn't included in the canned workflows, you'll need to write a custom `epi_workflow()`.
+
+To get understand how to work with custom `epi_workflow()`s, let's recreate and then
+modify the `four_week_ahead` example from the [landing
+page](../index.html#motivating-example).
+Let's first remind ourselves how to use a simple canned workflow:
 
 ```{r make-four-forecasts, warning=FALSE}
 training_data <- covid_case_death_rates |>
@@ -43,77 +46,92 @@ four_week_ahead <- arx_forecaster(
 )
 four_week_ahead$epi_workflow
 ```
+
 # Anatomy of an `epi_workflow`
-An `epi_workflow()` is an extension of a `workflows::workflow()` to handle panel
+
+An `epi_workflow()` is an extension of a `workflows::workflow()` that handles panel
 data and post-processing.
-It consists of 3 components, shown above:
-
-1. Preprocessor: transform the data before model training and prediction, such
-  as convert counts to rates, create smoothed columns, or [any of the recipes
-  steps](https://recipes.tidymodels.org/reference/index.html).
-  Think of it as a more flexible `formula` that you would pass to `lm()`: `y ~
-  x1 + log(x2) + lag(x1, 5)`.
-  The above model has 6 of these steps.
-  In general, there are 2 broad classes of transformation that `{recipes}`
-  handles:
-    - Transforms of both training and test data that are always applied.
-      Examples include taking the log of a variable, leading or lagging,
-      filtering out rows, handling dummy variables, calculating growth rates,
-      etc.
-    - Operations that fit parameters during training to apply during prediction,
-      such as centering by the mean.
-      This is a major benefit of `{recipes}`, since it prevents data leakage,
-      where information about the test/predict time data "leaks" into the
-      parameters. <!-- TODO unsure if worth even keeping, as we effectively
-      can't have data leakage. -->
-      However, the main mechanism we rely on to prevent data leakage is proper
-      [backtesting](backtesting.html).
-      For the case of centering, we need to store the mean of the predictor from
-      the training data and use that value on the prediction data rather than
-      accidentally calculating the mean of the test predictor for centering.
-2. Trainer: use a `{parsnip}` model to train a model on data, resulting in a
-   fitted model object.
-  Examples include linear regression, quantile regression, or [any parsnip
-  engine](https://www.tidymodels.org/find/parsnip/). 
-  Parsnip serves as a front-end that abstracts away the differences in interface
-  between a wide collection of statistical models.
-3. Postprocessor: unique to this package, and used to format and modify the
-   prediction after the model has been fit.
-   Each operation is a `layer_`, and the stack of layers is known as `frosting()`,
-   continuing the metaphor of baking a cake established in the recipe.
-   Some example operations include:
-    - generate quantiles from purely point-prediction models,
-    - undo operations done in the steps, such as convert back to counts from rates
-    - threshold so the forecast doesn't include negative values
-    - generally adapt the format of the prediction to it's eventual use.
+All `epi_workflows`, including simple and canned workflows, consist of 3 components.
+
+### Preprocessor
+
+A preprocessor transforms the data before model training and prediction.
+Transformations can include converting counts to rates, applying a running average 
+to columns, or [any of the `step`s found in `{recipes}`](https://recipes.tidymodels.org/reference/index.html).
+
+You can think of a preprocessor as a more flexible `formula` that you would pass to `lm()`: `y ~ x1 + log(x2) + lag(x1, 5)`.
+The simple model above internally runs 6 of these steps, such as creating lagged predictor columns.
+
+In general, there are 2 broad classes of transformation that `{recipes}` `step`s handle:
+
+- Operations that are applied to both training and test data without using stored information.
+  Examples include taking the log of a variable, leading or lagging columns,
+  filtering out rows, handling dummy variables, calculating growth rates,
+  etc.
+- Operations that rely on stored information (parameters fit during training) to modify train and test data.
+  Examples include centering by the mean, and normalizing the variance (whitening).
+
+We differentiate between these types of transformations because the second type can result in information leakage if not done properly.
+Information leakage or data leakage happens when a system has access to information that would not have been available at prediction time and could change our evaluation of the model's real-world performance.
+
+In the case of centering, we need to store the mean of the predictor from
+the training data and use that value on the prediction data, rather than
+using the mean of the test predictor for centering or including test data in the mean calculation.
+
+A major benefit of `{recipes}` is that it prevents information leakage. 
+<!-- TODO unsure if worth even keeping, as we effectively can't have data leakage. -->
+However, the _main_ mechanism we rely on to prevent data leakage is proper
+[backtesting](backtesting.html).
+
+### Trainer
+
+A trainer fits a `{parsnip}` model on data, and outputs a fitted model object.
+Examples include linear regression, quantile regression, or [any `{parsnip}` engine](https://www.tidymodels.org/find/parsnip/).
+The `{parsnip}` front-end abstracts away the differences in interface between a wide collection of statistical models.
+
+### Postprocessor
+
+Postprocessors are unique to this `{epipredict}`.
+A postprocessor modifies and formats the prediction after a model has been fit.
+
+Each operation within a postprocessor is called a `layer_`, and the stack of layers is known as `frosting()`,
+continuing the metaphor of baking a cake established in `{recipes}`.
+Some example operations include:
+
+- generating quantiles from purely point-prediction models
+- reverting transformations done in prior steps, such as converting from rates back to counts
+- thresholding forecasts to remove negative values
+- generally adapting the format of the prediction to a downstream use.
 
 # Recreating `four_week_ahead` in an `epi_workflow()`
-To be able to extend this beyond what `arx_forecaster()` itself will let us do,
-we first need to understand how to recreate it using a custom `epi_workflow()`.
 
-To use a custom workflow, there are a couple of steps:
+To understand how to create custom workflows, let's first recreate the simple canned `arx_forecaster()` from scratch.
 
-1. define the `epi_recipe()`, which contains the preprocessing steps
-2. define the `frosting()` which contains the post-processing layers
+We'll think through the following sub-steps:
+
+1. Define the `epi_recipe()`, which contains the preprocessing steps
+2. Define the `frosting()` which contains the post-processing layers
 3. Combine these with a trainer such as `quantile_reg()` into an
    `epi_workflow()`, which we can then fit on the training data
 4. `fit()` the workflow on some data
-5. grab the right prediction data using `get_test_data()` and apply the fit data
+5. Grab the right prediction data using `get_test_data()` and apply the fit data
    to generate a prediction
 
 ## Define the `epi_recipe()`
-To do this, we'll first take a look at the steps as they're found in
-`four_week_ahead`:
+
+The steps found in `four_week_ahead` look like:
 
 ```{r inspect_fwa_steps, warning=FALSE}
 hardhat::extract_recipe(four_week_ahead$epi_workflow)
 ```
 
-So there are 6 steps we will need to recreate.
-One thing to note about the extracted recipe is that it has already been
-trained; for steps such as `recipes::step_BoxCox()` which have parameters, this means that their
-parameters have been calculated.
-Before defining steps, we need to create an `epi_recipe()` to hold them
+There are 6 steps we will need to recreate.
+Note that all steps in the extracted recipe are marked as already been
+`Trained`. For steps such as `recipes::step_BoxCox()` that have parameters that change their behavior, this means that their
+parameters have already been calculated based on the training data set.
+
+Let's create an `epi_recipe()` to hold the 6 steps:
+
 ```{r make_recipe}
 four_week_recipe <- epi_recipe(
   covid_case_death_rates |>
@@ -121,15 +139,18 @@ four_week_recipe <- epi_recipe(
 )
 ```
 
-The data here, `covid_case_death_rates` doesn't strictly need to be the actual
-dataset on which you are going to train. 
-However, it should have the same columns and the same metadata, such as `as_of`
-or `other_keys`; it is typically easiest to just use the training data itself.
+The data set passed to `epi_recipe` isn't required to be the actual
+data set on which you are going to train the model. 
+However, it should have the same columns and the same metadata (such as `as_of`
+and `other_keys`); it is typically easiest just to use the training data itself.
 
+This means that you can use the same workflow for multiple data sets as long as the format remains the same.
+This might be useful if you continue to get updates to a data set over time and you want to train a new instance of the same model.
 
-Then we add each step via pipes; in principle the order matters, though for this
+Then we can append each `step` using pipes. In principle, the order matters, though for this
 recipe only `step_epi_naomit()` and `step_training_window()` depend on the steps
-before them:
+before them.
+The other steps can be thought of as setting parameters that help specify later processing and computation.
 
 ```{r make_steps}
 four_week_recipe <- four_week_recipe |>
@@ -140,31 +161,31 @@ four_week_recipe <- four_week_recipe |>
   step_training_window()
 ```
 
-One thing to note: we only added 5 steps here because `step_epi_naomit()` is
-actually a wrapper around adding 2 base `step_naomit()`s, one for
-`all_predictors()` and one for `all_outcomes()`, differing in their treatment at
-predict time.
+Note we said before that `four_week_ahead` contained 6 steps.
+We've only added _5_ top-level steps here because `step_epi_naomit()` is
+actually a wrapper around adding two `step_naomit()`s, one for
+`all_predictors()` and one for `all_outcomes()`.
+The `step_naomit()`s differ in their treatment of the data at predict time.
 
-`step_epi_lag()` and `step_epi_ahead()` both accept tidy syntax, so if for
-example we wanted the same lags for both `case_rate` and `death_rate`, we could
-have done `step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
+`step_epi_lag()` and `step_epi_ahead()` both accept ["tidy" syntax](https://dplyr.tidyverse.org/reference/select.html) so processing can be applied to multiple columns at once.
+For example, if we wanted to use the same lags for both `case_rate` and `death_rate`, we could
+specify them in a single step, like `step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
 
-In general for the `{recipes}` package, steps assign roles, such as `predictor`
-and `outcome` to columns, either by adding new columns or adjusting existing
-ones. 
-`step_epi_lag()` for example, creates a new column for each lag with the name
-`lag_x_column_name` and assigns them as predictors, while `step_epi_ahead()`
-creates `ahead_x_column_name` columns and assigns them as outcomes.
+In general, `{recipes}` `step`s assign roles (`predictor`, `outcome`) to columns either by adding new columns or adjusting existing
+ones.
+`step_epi_lag()`, for example, creates a new column for each lag with the name
+`lag_x_column_name` and labels them each with the `predictor` role.
+`step_epi_ahead()` creates `ahead_x_column_name` columns and labels each with the `outcome` role.
 
-One way to inspect the roles assigned is to use `prep()`:
+We can inspect assigned roles with `prep()` to make sure that we are training on the correct columns:
 
 ```{r prep_recipe}
 prepped <- four_week_recipe |> prep(training_data)
 prepped$term_info |> print(n = 14)
 ```
 
-The way to inspect the columns created is by using `bake()` on the resulting
-recipe:
+We can inspect newly-created columns by running `bake()` on the
+recipe so far:
 
 ```{r bake_recipe}
 four_week_recipe |>
@@ -172,23 +193,24 @@ four_week_recipe |>
   bake(training_data)
 ```
 
-This is also useful for debugging malfunctioning pipelines; if you define
-`four_week_recipe` only up to the step that is misbehaving, you can get a
-partial evaluation to see the exact data the step is being applied to.
-It also allows you to see the exact data that the parsnip model is fitting on.
+This is also useful for debugging malfunctioning pipelines.
+You can run `prep()` and `bake()` on a new recipe containing a subset of `step`s -- all `step`s from the beginning up to the one that is misbehaving -- from the full, original recipe.
+This will return an evaluation of the `recipe` up to that point so that you can see the data that the misbehaving `step` is being applied to.
+It also allows you to see the exact data that a later `{parsnip}` model is trained on.
 
 ## Define the `frosting()`
-Since the post-processing frosting layers[^1] are unique to this package, to
-inspect them we use `extract_frosting()` from `{epipredict}`:
+
+The post-processing `frosting` layers[^1] found in `four_week_ahead` look like:
 
 ```{r inspect_fwa_layers, warning=FALSE}
 epipredict::extract_frosting(four_week_ahead$epi_workflow)
 ```
 
-The above gives us detailed descriptions of the arguments to the functions named
-above in the postprocessor of `four_week_ahead$epiworkflow`.
-Creating the layers is a similar process, except with frosting instead of a
-`recipe`[^2]:
+Note: since `frosting` is unique to this package, we've defined a custom function `extract_frosting()` to inspect these steps.
+
+Using the detailed information in the output above,
+we can recreate the layers similar to how we defined the
+`recipe` `step`s[^2]:
 
 ```{r make_frosting}
 four_week_layers <- frosting() |>
@@ -199,22 +221,23 @@ four_week_layers <- frosting() |>
   layer_threshold()
 ```
 
+`layer_predict()` needs to be included in every postprocessor to actually train the model.
 
-Most layers will work for any engine or steps; `layer_predict()` you will want
-to call in every case.
-There are a couple of layers, however, which depend on whether the engine used predicts quantiles or point estimates.
+Most layers work with any engine or `step`s.
+There are a couple of layers, however, which depend on whether the engine predicts quantiles or point estimates.
 
 The layers that are only supported by point estimate engines (such as
-`linear_reg()`) are
+`linear_reg()`) are:
 
 - `layer_residual_quantiles()`: the preferred method of generating quantiles for
-  non-quantile models, it uses the error residuals of the engine. 
-  This will work for most parsnip engines.
-- `layer_predictive_distn()`: alternate method of generating quantiles, it uses
+  models that don't generate quantiles themselves.
+  This function uses the error residuals of the engine to calculate quantiles. 
+  This will work for most `{parsnip}` engines.
+- `layer_predictive_distn()`: alternate method of generating quantiles using
   an approximate parametric distribution. This will work for linear regression
   specifically.
   
-TODO check this
+<!-- TODO check this -->
   
 On the other hand, the layers that are only supported by quantile estimating
 engines (such as `quantile_reg()`) are
@@ -223,11 +246,13 @@ engines (such as `quantile_reg()`) are
   If they differ from the ones actually fit, they will be interpolated and/or
   extrapolated.
 - `layer_point_from_distn()`: this adds the median quantile as a point estimate,
-  and should be called after `layer_quantile_distn()` if called at all. 
+  and, if called, should be included after `layer_quantile_distn()`. 
 
 ## Fitting an `epi_workflow()`
 
-Given that we now have a recipe and some layers, we need to assemble the workflow:
+Now that we have a recipe and some layers, we can assemble the workflow.
+This is as simple as passing the component preprocessor, model, and postprocessor into `epi_workflow()`.
+
 ```{r workflow_building}
 four_week_workflow <- epi_workflow(
   four_week_recipe,
@@ -236,15 +261,19 @@ four_week_workflow <- epi_workflow(
 )
 ```
 
-And fit it to recreate `four_week_ahead$epi_workflow`
+After fitting it, we will have recreated `four_week_ahead$epi_workflow`.
+
 ```{r workflow_fitting}
 fit_workflow <- four_week_workflow |> fit(training_data)
 ```
 
+Running `fit()` calculates all preprocessor-required parameters, and trains the model on the data passed in `fit()`.
+However, it does not generate any predictions; predictions need to be created in a separate step.
+
 ## Predicting
 
-To do a prediction, we need to first narrow the dataset down to the relevant
-datapoints:
+To make a prediction, we need to first narrow a data set down to the relevant observations.
+This process removes observations that will not be used for training because, for example, they contain missing values or <!-- TODO other reasons?-->.
 
 ```{r grab_data}
 relevant_data <- get_test_data(
@@ -253,13 +282,15 @@ relevant_data <- get_test_data(
 )
 ```
 
-With a fit workflow and test data in hand, we can actually make our predictions:
+In this example, we're creating `relevant_data` from `training_data`, but the data set we want predictions for could be an entirely new data set.
+
+With a trained workflow and data in hand, we can actually make our predictions:
 
 ```{r workflow_pred}
 fit_workflow |> predict(relevant_data)
 ```
 
-Note that if we had simply plugged `training_data` into `predict()` we still get
+Note that if we simply plug `training_data` into `predict()` we will still get
 predictions:
 
 ```{r workflow_pred_training}
@@ -267,8 +298,9 @@ fit_workflow |> predict(training_data)
 ```
 
 The resulting tibble is 800 rows long, however.
-This produces forecasts for not just the actual `forecast_date`, but for every
-day in the dataset it has enough data to actually make a prediction.
+Not running `get_test_data()` means that we're providing irrelevant data along with relevant, valid data.
+Passing the non-subsetted data set produces forecasts for not just the requested `forecast_date`, but for every
+day in the data set that has sufficient data to produce a prediction.
 To narrow this down, we could filter to rows where the `time_value` matches the `forecast_date`:
 
 ```{r workflow_pred_training_filter}
@@ -282,12 +314,18 @@ data.
 
 # Extending `four_week_ahead`
 
-There are many ways we could modify `four_week_ahead`; one simple modification
-would be to include a growth rate estimate as part of the model.
-Another would be to convert from rates to counts, for example if that were the
-preferred prediction format.
-Another would be to include a time component to the prediction (useful if we
-expect there to be a strong seasonal component).
+Now that we've recreated `four_week_ahead`, we can start modifying parts of it to get custom behavior.
+
+There are many ways we could modify `four_week_ahead`. We might consider:
+
+- Including a growth rate estimate as part of the model
+- Converting from rates to counts, for example if that were the
+preferred prediction format
+- Including a time component to the prediction, useful if we
+expect there to be a strong seasonal component
+- Scaling by some factor
+
+We will demo a couple of these modifications below.
 
 ## Growth rate
 

From 05f1507008fd2b6981004f8a744e8949781693df Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Mon, 7 Apr 2025 16:57:48 -0400
Subject: [PATCH 367/387] second half custom_epiworkflows.Rmd

---
 vignettes/custom_epiworkflows.Rmd | 140 ++++++++++++++++--------------
 1 file changed, 73 insertions(+), 67 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 6ff6ee6e0..915376a29 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -91,10 +91,10 @@ The `{parsnip}` front-end abstracts away the differences in interface between a
 
 ### Postprocessor
 
-Postprocessors are unique to this `{epipredict}`.
+Postprocessors are unique to `{epipredict}`.
 A postprocessor modifies and formats the prediction after a model has been fit.
 
-Each operation within a postprocessor is called a `layer_`, and the stack of layers is known as `frosting()`,
+Each operation within a postprocessor is called a "layer" (functions are named `layer_*`), and the stack of layers is known as `frosting()`,
 continuing the metaphor of baking a cake established in `{recipes}`.
 Some example operations include:
 
@@ -314,23 +314,23 @@ data.
 
 # Extending `four_week_ahead`
 
-Now that we've recreated `four_week_ahead`, we can start modifying parts of it to get custom behavior.
+Now that we know how to create `four_week_ahead` from scratch, we can start modifying the workflow to get custom behavior.
 
 There are many ways we could modify `four_week_ahead`. We might consider:
 
-- Including a growth rate estimate as part of the model
-- Converting from rates to counts, for example if that were the
-preferred prediction format
-- Including a time component to the prediction, useful if we
-expect there to be a strong seasonal component
-- Scaling by some factor
+- Converting from rates to counts
+- Including a growth rate estimate as a predictor
+- Including a time component as a predictor -- useful if we
+expect there to be a strong seasonal component to the outcome
+- Scaling by a factor
 
 We will demo a couple of these modifications below.
 
 ## Growth rate
 
-One feature that may potentially improve our forecast is looking at the growth
-rate
+Let's say we're interested in including growth rate as a predictor in our model because
+we think it may potentially improve our forecast.
+We can easily create a new growth rate column as a step in the `epi_recipe`.
 
 ```{r growth_rate_recipe}
 growth_rate_recipe <- epi_recipe(
@@ -341,6 +341,7 @@ growth_rate_recipe <- epi_recipe(
   step_epi_lag(death_rate, lag = c(0, 7, 14)) |>
   step_epi_ahead(death_rate, ahead = 4 * 7) |>
   step_epi_naomit() |>
+  # Calculate growth rate from death rate column.
   step_growth_rate(death_rate) |>
   step_training_window()
 ```
@@ -354,7 +355,8 @@ growth_rate_recipe |>
   select(
     geo_value, time_value, case_rate,
     death_rate, gr_7_rel_change_death_rate
-  )
+  ) |>
+  tail()
 ```
 
 And the role:
@@ -396,8 +398,7 @@ gr_predictions <- gr_fit_workflow |>
 <details>
 <summary> Plot </summary>
 
-Plotting the result; this is reusing some code from the landing page to print
-the forecast date.
+We'll reuse some code from the landing page to plot the result.
 
 ```{r plotting}
 forecast_date_label <-
@@ -416,7 +417,7 @@ result_plot <- autoplot(
 ) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label %>% filter(.response_name == "death_rate"),
+    data = forecast_date_label |> filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
     size = 3, hjust = "right"
   ) +
@@ -428,14 +429,13 @@ result_plot <- autoplot(
 result_plot
 ```
 
-TODO `get_test_data` isn't actually working here...
+<!-- TODO `get_test_data` isn't actually working here... -->
 
 ## Population scaling
 
-Suppose we're sending our predictions to someone who is looking to understand
-counts, rather than rates.
-Then we can adjust just the frosting to get a forecast for the counts from our
-rates forecaster:
+Suppose we want to modify our predictions to apply to counts, rather than rates.
+To do that, we can adjust _just_ the `frosting` to perform post-processing on our existing rates forecaster.
+Since rates are calculated as counts per 100 000 people, we will convert back to counts by multiplying rates by the factor $\frac{regional \text{ } population}{100000}$.
 
 ```{r rate_scale}
 count_layers <-
@@ -445,15 +445,18 @@ count_layers <-
   layer_population_scaling(
     .pred,
     .pred_distn,
+    # `df` contains scaling values for all regions; in this case it is the state populations
     df = epidatasets::state_census,
     df_pop_col = "pop",
     create_new = FALSE,
+    # `rate_rescaling` gives the denominator of the existing rate predictions
     rate_rescaling = 1e5,
     by = c("geo_value" = "abbr")
   ) |>
   layer_add_forecast_date() |>
   layer_add_target_date() |>
   layer_threshold()
+
 # building the new workflow
 count_workflow <- epi_workflow(
   four_week_recipe,
@@ -464,66 +467,67 @@ count_pred_data <- get_test_data(four_week_recipe, training_data)
 count_predictions <- count_workflow |>
   fit(training_data) |>
   predict(count_pred_data)
+
 count_predictions
 ```
 
-which are 2-3 orders of magnitude larger than the corresponding rates above.
-`df` represents the scaling value; in this case it is the state populations,
-while `rate_rescaling` gives the denominator of the rate (our fit values were
-per 100,000).
-
 # Custom classifier workflow
 
-As a more complicated example of the kind of pipeline that you can build using
-this framework, here is an example of a hotspot prediction model, which predicts
-whether the case rates are increasing (`up`), decreasing (`down`) or flat
+Let's work through an example of a more complicated kind of pipeline you can build using
+the `epipredict` framework.
+This is a hotspot prediction model, which predicts whether case rates are increasing (`up`), decreasing (`down`) or flat
 (`flat`).
-This comes from a paper by McDonald, Bien, Green, Hu et al[^3], and roughly
+The model comes from a paper by McDonald, Bien, Green, Hu et al[^3], and roughly
 serves as an extension of `arx_classifier()`.
 
-First, we need to add a factor version of the `geo_value`, so that it can
-actually be used as a feature.
+First, we need to add a factor version of `geo_value`, so that it can be used as a feature.
 
 ```{r training_factor}
 training_data <-
-  training_data %>%
+  training_data |>
   mutate(geo_value_factor = as.factor(geo_value))
 ```
 
 Then we put together the recipe, using a combination of base `{recipe}`
-functions such as `add_role()` and `step_dummy()`, and `{epipreict}` functions
+functions such as `add_role()` and `step_dummy()`, and `{epipredict}` functions
 such as `step_growth_rate()`.
 
 ```{r class_recipe}
-classifier_recipe <- epi_recipe(training_data) %>%
-  add_role(time_value, new_role = "predictor") %>%
-  step_dummy(geo_value_factor) %>%
-  step_growth_rate(case_rate, role = "none", prefix = "gr_") %>%
-  step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) %>%
-  step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") %>%
-  # note recipes::step_cut() has a bug in it, or we could use that here
+classifier_recipe <- epi_recipe(training_data) |>
+  # Turn `time_value` into predictor
+  add_role(time_value, new_role = "predictor") |>
+  # Turn `geo_value_factor` into predictor
+  step_dummy(geo_value_factor) |>
+  # Create and lag growth rate
+  step_growth_rate(case_rate, role = "none", prefix = "gr_") |>
+  step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) |>
+  step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") |>
+  # Divide growth rate into 3 bins.
+  # Note `recipes::step_cut()` has a bug, or we could use that here.
   step_mutate(
     response = cut(
       ahead_7_gr_7_rel_change_case_rate,
-      breaks = c(-Inf, -0.2, 0.25, Inf) / 7, # division gives weekly not daily
+      # Define bin thresholds.
+      # Divide by 7 to create weekly values.
+      breaks = c(-Inf, -0.2, 0.25, Inf) / 7,
       labels = c("down", "flat", "up")
     ),
     role = "outcome"
-  ) %>%
-  step_rm(has_role("none"), has_role("raw")) %>%
+  ) |>
+  # Drop unused columns.
+  step_rm(has_role("none"), has_role("raw")) |>
   step_epi_naomit()
 ```
 
+This adds as predictors:
 
-Roughly, this adds as predictors:
+- time value (via `add_role()`)
+- `geo_value` (via `step_dummy()` and the previous `as.factor()`)
+- growth rate of case rate, both at prediction time (no lag), and lagged by one and two weeks
 
-1. the time value (via `add_role()`)
-2. the `geo_value` (via `step_dummy()` and the `as.factor()` above)
-3. the growth rate, both at prediction time and lagged by one and two weeks
-
-The outcome is created by composing several steps together: `step_epi_ahead()`
-creates a column with the growth rate one week into the future, while
-`step_mutate()` creates a factor with the 3 values:
+The outcome variable is created by composing several steps together. `step_epi_ahead()`
+creates a column with the growth rate one week into the future, and
+`step_mutate()` turns that column into a factor with 3 possible values,
 
 $$
  Z_{\ell, t}=
@@ -538,35 +542,37 @@ where $Y^{\Delta}_{\ell, t}$ is the growth rate at location $\ell$ and time $t$.
 `up` means that the `case_rate` is has increased by at least 25%, while `down`
 means it has decreased by at least 20%.
 
-Note that both `step_growth_rate()` and `step_epi_ahead()` assign the role
-`none` explicitly; this is because they're used as intermediate steps to create
-both predictors and the outcome.
-`step_rm()` drops them after they're done, along with the original `raw` columns
-`death_rate` and `case_rate` (both `geo_value` and `time_value` are retained
-because their roles have been reassigned).
+Note that in both `step_growth_rate()` and `step_epi_ahead()` we explicitly assign the role
+`none`. This is because those columns are used as intermediaries to create
+predictor and outcome columns.
+Afterwards, `step_rm()` drops the temporary columns, along with the original `role = "raw"` columns
+`death_rate` and `case_rate`. Both `geo_value_factor` and `time_value` are retained
+because their roles have been reassigned.
 
 
-To fit a classification model like this, we will need to use a parsnip model
-with mode classification; the simplest example is `multinomial_reg()`.
-We don't actually need to apply any layers, so we can skip adding one to the `epiworkflow()`:
+To fit a classification model like this, we will need to use a `{parsnip}` model
+that has `mode = "classification"`.
+The simplest example of a `{parsnip}` `classification`-`mode` model is `multinomial_reg()`.
+We don't need to do any post-processing, so we can skip adding `layer`s to the `epiworkflow()`.
+So our workflow looks like:
 
 ```{r, warning=FALSE}
 wf <- epi_workflow(
   classifier_recipe,
   multinom_reg()
-) %>%
+) |>
   fit(training_data)
 
-forecast(wf) %>% filter(!is.na(.pred_class))
+forecast(wf) |> filter(!is.na(.pred_class))
 ```
 
-And comparing the result with the actual growth rates at that point:
+And comparing the result with the actual growth rates at that point in time,
 ```{r growth_rate_results}
 growth_rates <- covid_case_death_rates |>
   filter(geo_value %in% used_locations) |>
   group_by(geo_value) |>
   mutate(
-    # multiply by 7 to get to weekly equivalents
+    # Multiply by 7 to estimate weekly equivalents
     case_gr = growth_rate(x = time_value, y = case_rate) * 7
   ) |>
   ungroup()
@@ -574,11 +580,11 @@ growth_rates <- covid_case_death_rates |>
 growth_rates |> filter(time_value == "2021-08-01")
 ```
 
-So they're all increasing at significantly higher than 25% per week (36%-62%),
-which matches the classification.
+we see that they're all significantly higher than 25% per week (36%-62%),
+which matches the classification model's predictions.
 
 
-See the [tooling book](https://cmu-delphi.github.io/delphi-tooling-book/preprocessing-and-models.html) for a more in depth discussion of this example.
+See the [tooling book](https://cmu-delphi.github.io/delphi-tooling-book/preprocessing-and-models.html) for a more in-depth discussion of this example.
 
 
 [^1]: Think of baking a cake, where adding the frosting is the last step in the

From c9361ae6a51b4f0172efd7c134f38758f50d3dd2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 9 Apr 2025 14:27:57 -0500
Subject: [PATCH 368/387] various requested changes

---
 man/step_adjust_latency.Rd        |  4 +-
 vignettes/backtesting.Rmd         |  2 +-
 vignettes/custom_epiworkflows.Rmd | 65 +++++++++++++++++++++----------
 3 files changed, 47 insertions(+), 24 deletions(-)

diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 437811b39..c909452eb 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -268,8 +268,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous
+#> `step_epi_lag`s won't work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index cc0e4617e..ec7e991a5 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -122,7 +122,7 @@ p0 <-
 ```
 </details>
 
-```{r plot_just_revisioning, warn = FALSE, message = FALSE}
+```{r plot_just_revisioning, echo = FALSE, warn = FALSE, message = FALSE}
 p0
 ```
 
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 915376a29..763bdb998 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -19,14 +19,17 @@ library(recipes)
 library(epipredict)
 library(epiprocess)
 library(ggplot2)
+library(rlang) # for %@%
 forecast_date <- as.Date("2021-08-01")
 used_locations <- c("ca", "ma", "ny", "tx")
 library(epidatr)
 ```
 
 If you want to do custom data preprocessing or fit a model that isn't included in the canned workflows, you'll need to write a custom `epi_workflow()`.
+An `epi_workflow()` is a sub-class of a `workflows::workflow()` from the
+`{workflows}` package designed to handle panel data specifically.
 
-To get understand how to work with custom `epi_workflow()`s, let's recreate and then
+To understand how to work with custom `epi_workflow()`s, let's recreate and then
 modify the `four_week_ahead` example from the [landing
 page](../index.html#motivating-example).
 Let's first remind ourselves how to use a simple canned workflow:
@@ -133,9 +136,11 @@ parameters have already been calculated based on the training data set.
 Let's create an `epi_recipe()` to hold the 6 steps:
 
 ```{r make_recipe}
-four_week_recipe <- epi_recipe(
-  covid_case_death_rates |>
+filtered_data <- covid_case_death_rates |>
     filter(time_value <= forecast_date, geo_value %in% used_locations)
+four_week_recipe <- epi_recipe(
+  filtered_data,
+  reference_date = (filtered_data %@% metadata)$as_of
 )
 ```
 
@@ -171,21 +176,28 @@ The `step_naomit()`s differ in their treatment of the data at predict time.
 For example, if we wanted to use the same lags for both `case_rate` and `death_rate`, we could
 specify them in a single step, like `step_epi_lag(ends_with("rate"), lag = c(0, 7, 14))`.
 
-In general, `{recipes}` `step`s assign roles (`predictor`, `outcome`) to columns either by adding new columns or adjusting existing
+In general, `{recipes}` `step`s assign roles (such as `predictor`, or `outcome`,
+see the [Roles vignette for
+details](https://recipes.tidymodels.org/articles/Roles.html)) to columns either
+by adding new columns or adjusting existing
 ones.
 `step_epi_lag()`, for example, creates a new column for each lag with the name
 `lag_x_column_name` and labels them each with the `predictor` role.
-`step_epi_ahead()` creates `ahead_x_column_name` columns and labels each with the `outcome` role.
+`step_epi_ahead()` creates `ahead_x_column_name` columns and labels each with
+the `outcome` role.
 
-We can inspect assigned roles with `prep()` to make sure that we are training on the correct columns:
+In general, to inspect the 'prepared' steps, we can run `prep()`, which fits any
+parameters used in the recipe, calculates new columns, and assigns roles[^4].
+For example, we can use `prep()` to make sure that we are training on the
+correct columns:
 
 ```{r prep_recipe}
 prepped <- four_week_recipe |> prep(training_data)
 prepped$term_info |> print(n = 14)
 ```
 
-We can inspect newly-created columns by running `bake()` on the
-recipe so far:
+`bake()` applies a prepared recipe to a (potentially new) dataset to create the dataset as handed to the `epi_workflow()`.
+We can inspect newly-created columns by running `bake()` on the recipe so far:
 
 ```{r bake_recipe}
 four_week_recipe |>
@@ -243,7 +255,7 @@ On the other hand, the layers that are only supported by quantile estimating
 engines (such as `quantile_reg()`) are
 
 - `layer_quantile_distn()`: adds the specified quantiles.
-  If they differ from the ones actually fit, they will be interpolated and/or
+  If the quantile levels specified differ from the ones actually fit, they will be interpolated and/or
   extrapolated.
 - `layer_point_from_distn()`: this adds the median quantile as a point estimate,
   and, if called, should be included after `layer_quantile_distn()`. 
@@ -272,8 +284,7 @@ However, it does not generate any predictions; predictions need to be created in
 
 ## Predicting
 
-To make a prediction, we need to first narrow a data set down to the relevant observations.
-This process removes observations that will not be used for training because, for example, they contain missing values or <!-- TODO other reasons?-->.
+To make a prediction, it helps to narrow the data set down to the relevant observations using `get_test_data()`. Not doing this will still fit, but it will predict on every day in the data-set, and not just on the `reference_date`.
 
 ```{r grab_data}
 relevant_data <- get_test_data(
@@ -299,7 +310,7 @@ fit_workflow |> predict(training_data)
 
 The resulting tibble is 800 rows long, however.
 Not running `get_test_data()` means that we're providing irrelevant data along with relevant, valid data.
-Passing the non-subsetted data set produces forecasts for not just the requested `forecast_date`, but for every
+Passing the non-subsetted data set produces forecasts for not just the requested `reference_date`, but for every
 day in the data set that has sufficient data to produce a prediction.
 To narrow this down, we could filter to rows where the `time_value` matches the `forecast_date`:
 
@@ -356,6 +367,7 @@ growth_rate_recipe |>
     geo_value, time_value, case_rate,
     death_rate, gr_7_rel_change_death_rate
   ) |>
+  arrange(geo_value, time_value) |>
   tail()
 ```
 
@@ -484,7 +496,8 @@ First, we need to add a factor version of `geo_value`, so that it can be used as
 
 ```{r training_factor}
 training_data <-
-  training_data |>
+  covid_case_death_rates |>
+  filter(time_value <= forecast_date, geo_value %in% used_locations) |>
   mutate(geo_value_factor = as.factor(geo_value))
 ```
 
@@ -496,7 +509,7 @@ such as `step_growth_rate()`.
 classifier_recipe <- epi_recipe(training_data) |>
   # Turn `time_value` into predictor
   add_role(time_value, new_role = "predictor") |>
-  # Turn `geo_value_factor` into predictor
+  # Turn `geo_value_factor` into predictor by adding indicators for each value
   step_dummy(geo_value_factor) |>
   # Create and lag growth rate
   step_growth_rate(case_rate, role = "none", prefix = "gr_") |>
@@ -514,15 +527,15 @@ classifier_recipe <- epi_recipe(training_data) |>
     ),
     role = "outcome"
   ) |>
-  # Drop unused columns.
+  # Drop unused columns, not strictly necessary
   step_rm(has_role("none"), has_role("raw")) |>
   step_epi_naomit()
 ```
 
 This adds as predictors:
 
-- time value (via `add_role()`)
-- `geo_value` (via `step_dummy()` and the previous `as.factor()`)
+- time value as a continuous variable (via `add_role()`)
+- `geo_value` as a set of indicator variables (via `step_dummy()` and the previous `as.factor()`)
 - growth rate of case rate, both at prediction time (no lag), and lagged by one and two weeks
 
 The outcome variable is created by composing several steps together. `step_epi_ahead()`
@@ -553,17 +566,25 @@ because their roles have been reassigned.
 To fit a classification model like this, we will need to use a `{parsnip}` model
 that has `mode = "classification"`.
 The simplest example of a `{parsnip}` `classification`-`mode` model is `multinomial_reg()`.
-We don't need to do any post-processing, so we can skip adding `layer`s to the `epiworkflow()`.
-So our workflow looks like:
+The needed layers are more or less the same as the `linear_reg()` regression layers, with the addition that we need to remove some `NA` values:
+
+```{r, warning=FALSE}
+frost <- frosting() |>
+  layer_naomit(starts_with(".pred")) |>
+  layer_add_forecast_date() |>
+  layer_add_target_date() |>
+  layer_threshold()
+```
 
 ```{r, warning=FALSE}
 wf <- epi_workflow(
   classifier_recipe,
-  multinom_reg()
+  multinom_reg(),
+  frost
 ) |>
   fit(training_data)
 
-forecast(wf) |> filter(!is.na(.pred_class))
+forecast(wf)
 ```
 
 And comparing the result with the actual growth rates at that point in time,
@@ -596,3 +617,5 @@ See the [tooling book](https://cmu-delphi.github.io/delphi-tooling-book/preproce
 [^3]: McDonald, Bien, Green, Hu, et al. “Can auxiliary indicators improve
     COVID-19 forecasting and hotspot prediction?.” Proceedings of the National
     Academy of Sciences 118.51 (2021): e2111453118. doi:10.1073/pnas.2111453118
+
+[^4]: Note that `prep()` and `bake()` are standard `{recipes}` functions, so any discussion of them there applies just as well here. For example in the [guide to creating a new step](https://www.tidymodels.org/learn/develop/recipes/#create-the-prep-method).

From cdf37304a79304bcdd148491ed1fc42aec606fd7 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 9 Apr 2025 16:17:21 -0400
Subject: [PATCH 369/387] backtesting version un/faithful clarification

---
 vignettes/backtesting.Rmd | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index ec7e991a5..d0113e5ac 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -387,17 +387,19 @@ Now let's look at Florida.
 In the version faithful case, the three late-2021 forecasts (purples and pinks) starting in September predict very low values, near 0.
 The trend leading up to each forecast shows a substantial decrease, so these forecasts seem appropriate and we would expect them to score fairly well on various performance metrics when compared to the versioned data.
 
-In hindsight, we know that early versions of the data systematically under-reported COVID-related doctor visits such that these forecasts don't actually perform well compared to _finalized_ data.
+However in hindsight, we know that early versions of the data systematically under-reported COVID-related doctor visits such that these forecasts don't actually perform well compared to _finalized_ data.
 In this example, version faithful forecasts predicted values at or near 0 while finalized data shows values in the 5-10 range.
 As a result, the version un-faithful forecasts for these same dates are quite a bit higher, and would perform well when scored using the finalized data and poorly with versioned data.
 
 In general, the longer ago a forecast was made, the worse its performance is compared to finalized data. Finalized data accumulates revisions over time that make it deviate more and more from the non-finalized data a model was trained on.
-Forecasts trained solely on finalized data will of course appear to perform better when scored on finalized data, but will have unknown performance on the non-finalized data we need to use if we want timely predictions.
+Forecasts _trained_ on finalized data will of course appear to perform better when _scored_ on finalized data, but will have unknown performance on the non-finalized data we need to use if we want timely predictions.
 
 Without using data that would have been available on the actual forecast date, 
 you have little insight into what level of performance you
 can expect in practice.
 
+Good performance of a version un-faithful model is a mirage; it is only achievable if the training data has no revisions.
+If a data source has any revisions, version un-faithful-level performance is unachievable when making forecasts in real time.
 
 
 [^1]: For forecasting a single day like this, we could have actually just used

From 730411053715939bc06c2ffb65f04d87006b2981 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 9 Apr 2025 17:00:50 -0400
Subject: [PATCH 370/387] why comparing to final data

---
 vignettes/backtesting.Rmd | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index d0113e5ac..0e8b35871 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -198,6 +198,11 @@ forecasts <- doctor_visits |>
 We need truth data to compare our forecast against. We can construct it by using `epix_as_of()` to snapshot
 the archive at the last available date[^1].
 
+_Note:_ We always want to compare our forecasts to actual (most recently reported) values because that is the outcome we care about.
+`as_of` data is useful for understanding why we're getting the forecasts we're getting, but `as_of` values are not the real outcome
+Therefore, it's not meaningful to use them for evaluating the performance of a forecast.
+Unfortunately, it's not uncommon for revisions to cause poor (final) performance of a forecaster that was decent at the time of the forecast.
+
 ```{r compare_single_with_result}
 forecasts |>
   inner_join(

From 4df2535daf49e98d958f55b193a8734d7b7e73da Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 9 Apr 2025 17:33:47 -0500
Subject: [PATCH 371/387] fixing backtest truth data plot

---
 README.Rmd                                    |   3 ++-
 README.md                                     |  19 +++++++++---------
 man/figures/README-date-1.png                 | Bin 239191 -> 277865 bytes
 man/figures/README-show-processed-data-1.png  | Bin 299152 -> 326552 bytes
 man/figures/README-show-single-forecast-1.png | Bin 261019 -> 292042 bytes
 vignettes/backtesting.Rmd                     |  16 ++++++++++++---
 vignettes/epipredict.Rmd                      |   4 ++--
 7 files changed, 27 insertions(+), 15 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index e52112ba2..1adc09bc6 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -330,7 +330,8 @@ And as a tibble of quantile level-value pairs:
 ```{r pivot_wider}
 four_week_ahead$predictions |>
   select(-.pred) |>
-  pivot_quantiles_longer(.pred_distn)
+  pivot_quantiles_longer(.pred_distn) |>
+  select(geo_value, forecast_date, target_date, quantile = .pred_distn_quantile_level, value = .pred_distn_value)
 ```
 
 The yellow dot gives the median prediction, while the blue intervals give the
diff --git a/README.md b/README.md
index 25024c4d1..24093478a 100644
--- a/README.md
+++ b/README.md
@@ -294,16 +294,17 @@ And as a tibble of quantile level-value pairs:
 ``` r
 four_week_ahead$predictions |>
   select(-.pred) |>
-  pivot_quantiles_longer(.pred_distn)
+  pivot_quantiles_longer(.pred_distn) |>
+  select(geo_value, forecast_date, target_date, quantile = .pred_distn_quantile_level, value = .pred_distn_value)
 #> # A tibble: 20 × 5
-#>   geo_value values quantile_levels forecast_date target_date
-#>   <chr>      <dbl>           <dbl> <date>        <date>     
-#> 1 ca        0.0425            0.1  2021-08-01    2021-08-29 
-#> 2 ca        0.0803            0.25 2021-08-01    2021-08-29 
-#> 3 ca        0.115             0.5  2021-08-01    2021-08-29 
-#> 4 ca        0.150             0.75 2021-08-01    2021-08-29 
-#> 5 ca        0.187             0.9  2021-08-01    2021-08-29 
-#> 6 ma        0                 0.1  2021-08-01    2021-08-29 
+#>   geo_value forecast_date target_date quantile  value
+#>   <chr>     <date>        <date>         <dbl>  <dbl>
+#> 1 ca        2021-08-01    2021-08-29      0.1  0.198 
+#> 2 ca        2021-08-01    2021-08-29      0.25 0.285 
+#> 3 ca        2021-08-01    2021-08-29      0.5  0.345 
+#> 4 ca        2021-08-01    2021-08-29      0.75 0.405 
+#> 5 ca        2021-08-01    2021-08-29      0.9  0.491 
+#> 6 ma        2021-08-01    2021-08-29      0.1  0.0277
 #> # ℹ 14 more rows
 ```
 
diff --git a/man/figures/README-date-1.png b/man/figures/README-date-1.png
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zlW#D8eN(n|@7a@dUkuAUGjweRt1#Q!|Hrnjp_JN`=MB3^YaQ)SPVH;)l0AC%G+tg{
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zS1eS+(Fd_Uaw0=aX<fLm2qzG^F#1m$ktDSd?|M=Bor6r7=-I?tf*Ix_Gs5QaDvN#l
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z;ntDAvA5nm?<*=Tuv<bqh|Y#@Gp6abdS5a~{Ep#uWZIrQ(aKZ00Hama*8P~n9a49n
zgra|Z%8*};&DxWHdOiN3kCAjYiT5x)B#kY^m)ev4eyKF8bT#{>(EMLq=g7bB>9=1{
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diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 0e8b35871..3fef41f28 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -320,6 +320,13 @@ geo_choose <- "ca"
 forecasts_filtered <- forecasts |>
   filter(geo_value == geo_choose) |>
   mutate(time_value = version)
+# we need to add the ground truth data to the version faithful plot as well
+plotting_data <- bind_rows(
+  percent_cli_data,
+  percent_cli_data %>%
+    filter(version_faithful == "Version un-faithful") %>%
+    mutate(version_faithful = "Version faithful")
+)
 
 p1 <- # first plotting the forecasts as bands, lines and points
   ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
@@ -327,10 +334,13 @@ p1 <- # first plotting the forecasts as bands, lines and points
   geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
   # the forecast date
-  geom_vline(data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful), aes(color = factor(version), xintercept = version), lty = 2) +
+  geom_vline(
+    data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
+    aes(color = factor(version), xintercept = version),
+    lty = 2) +
   # the underlying data
   geom_line(
-    data = percent_cli_data |> filter(geo_value == geo_choose),
+    data = plotting_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
@@ -357,7 +367,7 @@ p2 <-
     aes(color = factor(version), xintercept = version), lty = 2
   ) +
   geom_line(
-    data = percent_cli_data |> filter(geo_value == geo_choose),
+    data = plotting_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = factor(version)),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 88a047828..d1600663d 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "Get started with epipredict"
+title: "Get started with `epipredict`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Get started with `{epipredict}`}
+  %\VignetteIndexEntry{Get started with `epipredict`}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---

From 3aad6b89f019b8f62a5edb3b6a88706bd2e5a427 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 9 Apr 2025 18:57:48 -0400
Subject: [PATCH 372/387] backtesting.rmd comment fixes

---
 vignettes/backtesting.Rmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 3fef41f28..6121a0499 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -261,8 +261,8 @@ forecast_wrapper <- function(
 }
 ```
 
-Note that in the helper function, we're using the parameter `adjust_latency`.
-We need to use it because the most recently released data may still be several days old on any given forecast date; 
+_Note:_ In the helper function, we're using the parameter `adjust_latency`.
+We need to use it because the most recently released data may still be several days old on any given forecast date (lag > 0); 
 `adjust_latency` will modify the forecaster to compensate[^5].
 See the function `step_adjust_latency()` for more details and examples.
 
@@ -302,7 +302,7 @@ forecasts <-
 
 `arx_forecaster()` does all the heavy lifting.
 It creates and lags copies of the features (here, the response and doctors visits), 
-creates and leads copies of the target (respecting time stamps and locations), fits a
+creates and leads copies of the target while respecting timestamps and locations, fits a
 forecasting model using the specified engine, creates predictions, and
 creates non-parametric confidence bands.
 

From 5469dda3d4e57c20d7b1ac1badb7e2024e340ba6 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Wed, 9 Apr 2025 19:16:34 -0400
Subject: [PATCH 373/387] add alternate step names and say if optional/not

---
 vignettes/custom_epiworkflows.Rmd | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 763bdb998..5440c2a93 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -52,16 +52,20 @@ four_week_ahead$epi_workflow
 
 # Anatomy of an `epi_workflow`
 
-An `epi_workflow()` is an extension of a `workflows::workflow()` that handles panel
-data and post-processing.
-All `epi_workflows`, including simple and canned workflows, consist of 3 components.
+An `epi_workflow()` is an extension of a `workflows::workflow()` that is specially designed to handle panel
+data, and to apply custom post-processing steps to the output of a model.
+All `epi_workflows`, including simple and canned workflows, consist of 3 components, a preprocessor, trainer, and postprocessor.
 
 ### Preprocessor
 
-A preprocessor transforms the data before model training and prediction.
+A preprocessor (also called a recipe) transforms the data before model training and prediction.
 Transformations can include converting counts to rates, applying a running average 
 to columns, or [any of the `step`s found in `{recipes}`](https://recipes.tidymodels.org/reference/index.html).
 
+All workflows must include a preprocessor.
+The most basic preprocessor just assigns roles to columns, telling the model in the next step which to use as predictors or the outcome.
+
+However, preprocessors can do much more.
 You can think of a preprocessor as a more flexible `formula` that you would pass to `lm()`: `y ~ x1 + log(x2) + lag(x1, 5)`.
 The simple model above internally runs 6 of these steps, such as creating lagged predictor columns.
 
@@ -88,14 +92,19 @@ However, the _main_ mechanism we rely on to prevent data leakage is proper
 
 ### Trainer
 
-A trainer fits a `{parsnip}` model on data, and outputs a fitted model object.
+A trainer (aso called a model or engine) fits a `{parsnip}` model on data, and outputs a fitted model object.
 Examples include linear regression, quantile regression, or [any `{parsnip}` engine](https://www.tidymodels.org/find/parsnip/).
 The `{parsnip}` front-end abstracts away the differences in interface between a wide collection of statistical models.
 
+All workflows must include a model.
+
 ### Postprocessor
 
 Postprocessors are unique to `{epipredict}`.
-A postprocessor modifies and formats the prediction after a model has been fit.
+A postprocessor (also known as frosting) modifies and formats the prediction after a model has been fit.
+
+The postprocessor is _optional_.
+It only needs to be included in a workflow if you need to process the model output.
 
 Each operation within a postprocessor is called a "layer" (functions are named `layer_*`), and the stack of layers is known as `frosting()`,
 continuing the metaphor of baking a cake established in `{recipes}`.

From b481a2839cee34b19cfa85cda24d0d995c0f08f9 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 10 Apr 2025 09:49:16 -0400
Subject: [PATCH 374/387] get_test_data help

---
 vignettes/custom_epiworkflows.Rmd | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 5440c2a93..dbe844479 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -293,7 +293,8 @@ However, it does not generate any predictions; predictions need to be created in
 
 ## Predicting
 
-To make a prediction, it helps to narrow the data set down to the relevant observations using `get_test_data()`. Not doing this will still fit, but it will predict on every day in the data-set, and not just on the `reference_date`.
+To make a prediction, it helps to narrow the data set down to the relevant observations using `get_test_data()`.
+We can still generate predictions without doing this first, but it will predict on _every_ day in the data-set, and not just on the `reference_date`.
 
 ```{r grab_data}
 relevant_data <- get_test_data(
@@ -302,7 +303,7 @@ relevant_data <- get_test_data(
 )
 ```
 
-In this example, we're creating `relevant_data` from `training_data`, but the data set we want predictions for could be an entirely new data set.
+In this example, we're creating `relevant_data` from `training_data`, but the data set we want predictions for could be entirely new data, unrelated to the one we used when building the workflow.
 
 With a trained workflow and data in hand, we can actually make our predictions:
 
@@ -310,7 +311,7 @@ With a trained workflow and data in hand, we can actually make our predictions:
 fit_workflow |> predict(relevant_data)
 ```
 
-Note that if we simply plug `training_data` into `predict()` we will still get
+Note that if we simply plug the full `training_data` into `predict()` we will still get
 predictions:
 
 ```{r workflow_pred_training}
@@ -318,7 +319,6 @@ fit_workflow |> predict(training_data)
 ```
 
 The resulting tibble is 800 rows long, however.
-Not running `get_test_data()` means that we're providing irrelevant data along with relevant, valid data.
 Passing the non-subsetted data set produces forecasts for not just the requested `reference_date`, but for every
 day in the data set that has sufficient data to produce a prediction.
 To narrow this down, we could filter to rows where the `time_value` matches the `forecast_date`:
@@ -329,8 +329,9 @@ fit_workflow |>
   filter(time_value == forecast_date)
 ```
 
-This can be useful for cases where `get_test_data()` doesn't pull sufficient
-data.
+This can be useful for as a workaround when `get_test_data()` fails to pull enough
+data to produce a forecast.
+This is generally a problem when the recipe (preprocessor) is sufficiently complicated, and `get_test_data()` can't determine precisely what data is required.
 
 # Extending `four_week_ahead`
 

From 764b7f951ea0754b9fbb215232e1d5a55b4851c4 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 10 Apr 2025 09:51:12 -0400
Subject: [PATCH 375/387] get_test_data forecasts identical

---
 vignettes/custom_epiworkflows.Rmd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index dbe844479..e25af3454 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -329,9 +329,10 @@ fit_workflow |>
   filter(time_value == forecast_date)
 ```
 
-This can be useful for as a workaround when `get_test_data()` fails to pull enough
+This can be useful as a workaround when `get_test_data()` fails to pull enough
 data to produce a forecast.
 This is generally a problem when the recipe (preprocessor) is sufficiently complicated, and `get_test_data()` can't determine precisely what data is required.
+The forecasts generated with `filter` and `get_test_data` are identical.
 
 # Extending `four_week_ahead`
 

From c716235b07dc337f913c26d3c75192232622922d Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 10 Apr 2025 10:03:09 -0400
Subject: [PATCH 376/387] clarify changing frosting with model

---
 vignettes/custom_epiworkflows.Rmd | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index e25af3454..5667d0961 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -390,8 +390,9 @@ prepped <- growth_rate_recipe |>
 prepped$term_info |> filter(grepl("gr", variable))
 ```
 
-To demonstrate the changes in the layers that come along with it, we will use
-`quantile_reg()` as the model, which requires changing from `layer_residual_quantiles()` to `layer_quantile_distn()` and `layer_point_from_distn()`:
+Let's say we want to use `quantile_reg()` as the model.
+Because `quantile_reg()` outputs quantiles only, we need to change our `frosting` to convert a quantile distribution into quantiles and point predictions.
+To do that, we need to switch out `layer_residual_quantiles()` (used for converting point + residuals output, e.g. from `linear_reg()` into quantiles) for `layer_quantile_distn()` and `layer_point_from_distn()`:
 ```{r layer_and_fit}
 growth_rate_layers <- frosting() |>
   layer_predict() |>

From 42b146dda953ff4394f054ac7fde7f57eda2e141 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 10 Apr 2025 10:09:11 -0400
Subject: [PATCH 377/387] classifier chunk comments

---
 vignettes/custom_epiworkflows.Rmd | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 5667d0961..af0968af0 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -519,16 +519,16 @@ such as `step_growth_rate()`.
 
 ```{r class_recipe}
 classifier_recipe <- epi_recipe(training_data) |>
-  # Turn `time_value` into predictor
+  # Label `time_value` as predictor and do no other processing
   add_role(time_value, new_role = "predictor") |>
-  # Turn `geo_value_factor` into predictor by adding indicators for each value
+  # Use one-hot encoding on `geo_value_factor` and label each resulting column as a predictor
   step_dummy(geo_value_factor) |>
-  # Create and lag growth rate
+  # Create and lag `case_rate` growth rate
   step_growth_rate(case_rate, role = "none", prefix = "gr_") |>
   step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) |>
   step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") |>
-  # Divide growth rate into 3 bins.
-  # Note `recipes::step_cut()` has a bug, or we could use that here.
+  # Divide growth rate into 3 bins, and label as outcome variable
+  # Note `recipes::step_cut()` has a bug that prevents us from using it here
   step_mutate(
     response = cut(
       ahead_7_gr_7_rel_change_case_rate,
@@ -539,7 +539,8 @@ classifier_recipe <- epi_recipe(training_data) |>
     ),
     role = "outcome"
   ) |>
-  # Drop unused columns, not strictly necessary
+  # Drop unused columns based on role assignments. This is not strictly 
+  # necessary, as columns with roles unused in the model will be ignored anyway.
   step_rm(has_role("none"), has_role("raw")) |>
   step_epi_naomit()
 ```

From ef99e42a47a3af72b673ee7220c4ee9c2e9d5b88 Mon Sep 17 00:00:00 2001
From: nmdefries <42820733+nmdefries@users.noreply.github.com>
Date: Thu, 10 Apr 2025 10:17:11 -0400
Subject: [PATCH 378/387] model-specific layers

---
 vignettes/custom_epiworkflows.Rmd | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index af0968af0..ef69e510c 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -227,7 +227,7 @@ The post-processing `frosting` layers[^1] found in `four_week_ahead` look like:
 epipredict::extract_frosting(four_week_ahead$epi_workflow)
 ```
 
-Note: since `frosting` is unique to this package, we've defined a custom function `extract_frosting()` to inspect these steps.
+_Note_: since `frosting` is unique to this package, we've defined a custom function `extract_frosting()` to inspect these steps.
 
 Using the detailed information in the output above,
 we can recreate the layers similar to how we defined the
@@ -245,10 +245,10 @@ four_week_layers <- frosting() |>
 `layer_predict()` needs to be included in every postprocessor to actually train the model.
 
 Most layers work with any engine or `step`s.
-There are a couple of layers, however, which depend on whether the engine predicts quantiles or point estimates.
+There are a couple of layers, however, that depend on whether the engine predicts quantiles or point estimates.
 
-The layers that are only supported by point estimate engines (such as
-`linear_reg()`) are:
+The following layers are only supported by point estimate engines, such as
+`linear_reg()`:
 
 - `layer_residual_quantiles()`: the preferred method of generating quantiles for
   models that don't generate quantiles themselves.
@@ -260,14 +260,14 @@ The layers that are only supported by point estimate engines (such as
   
 <!-- TODO check this -->
   
-On the other hand, the layers that are only supported by quantile estimating
-engines (such as `quantile_reg()`) are
+On the other hand, the following layers are only supported by engines that 
+output quantiles, such as `quantile_reg()`:
 
 - `layer_quantile_distn()`: adds the specified quantiles.
-  If the quantile levels specified differ from the ones actually fit, they will be interpolated and/or
+  If the user-requested quantile levels differ from the ones actually fit, they will be interpolated and/or
   extrapolated.
-- `layer_point_from_distn()`: this adds the median quantile as a point estimate,
-  and, if called, should be included after `layer_quantile_distn()`. 
+- `layer_point_from_distn()`: this adds the middle quantile (median) as a point estimate,
+  and, if used, should be included after `layer_quantile_distn()`. 
 
 ## Fitting an `epi_workflow()`
 

From a958343c6c64cbd37f0d07dd29d46e6ff71e6949 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 15 Apr 2025 12:27:02 -0500
Subject: [PATCH 379/387] removing resolved todos

---
 vignettes/custom_epiworkflows.Rmd | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index ef69e510c..8e23981a7 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -86,7 +86,6 @@ the training data and use that value on the prediction data, rather than
 using the mean of the test predictor for centering or including test data in the mean calculation.
 
 A major benefit of `{recipes}` is that it prevents information leakage. 
-<!-- TODO unsure if worth even keeping, as we effectively can't have data leakage. -->
 However, the _main_ mechanism we rely on to prevent data leakage is proper
 [backtesting](backtesting.html).
 
@@ -258,8 +257,6 @@ The following layers are only supported by point estimate engines, such as
   an approximate parametric distribution. This will work for linear regression
   specifically.
   
-<!-- TODO check this -->
-  
 On the other hand, the following layers are only supported by engines that 
 output quantiles, such as `quantile_reg()`:
 
@@ -453,8 +450,6 @@ result_plot <- autoplot(
 result_plot
 ```
 
-<!-- TODO `get_test_data` isn't actually working here... -->
-
 ## Population scaling
 
 Suppose we want to modify our predictions to apply to counts, rather than rates.

From 2056e0aee2a880c5e9a7af98d9625fe6b64316d2 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Thu, 1 May 2025 19:51:20 -0500
Subject: [PATCH 380/387] dan's simple suggestions

---
 NEWS.md                           |   2 +-
 R/autoplot.R                      |  40 +++++-----
 README.Rmd                        |  16 ++--
 README.md                         |  25 +++---
 man/autoplot-epipred.Rd           |   6 +-
 man/grf_quantiles.Rd              |   4 +-
 man/step_adjust_latency.Rd        |  50 ++++++------
 vignettes/backtesting.Rmd         |   3 +-
 vignettes/custom_epiworkflows.Rmd |   6 +-
 vignettes/epipredict.Rmd          | 121 ++++++++++++++++++------------
 10 files changed, 149 insertions(+), 124 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 512de8d76..814bfb67c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -38,7 +38,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Replace `dist_quantiles()` with `hardhat::quantile_pred()`
 - Allow `quantile()` to threshold to an interval if desired (#434)
 - `arx_forecaster()` detects if there's enough data to predict
-- Add `plot_data` to `autoplot` so that forecasts can be plotted against the values they're predicting
+- Add `observed_response` to `autoplot` so that forecasts can be plotted against the values they're predicting
 
 ## Bug fixes
 
diff --git a/R/autoplot.R b/R/autoplot.R
index 236419c4e..3295992c7 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -16,7 +16,7 @@ ggplot2::autoplot
 #' @param object,x An `epi_workflow`
 #' @param predictions A data frame with predictions. If `NULL`, only the
 #'   original data is shown.
-#' @param plot_data An epi_df of the data to plot against. This is for the case
+#' @param observed_response An epi_df of the data to plot against. This is for the case
 #'   where you have the actual results to compare the forecast against.
 #' @param .levels A numeric vector of levels to plot for any prediction bands.
 #'   More than 3 levels begins to be difficult to see.
@@ -85,7 +85,7 @@ NULL
 autoplot.epi_workflow <- function(
     object,
     predictions = NULL,
-    plot_data = NULL,
+    observed_response = NULL,
     .levels = c(.5, .8, .9), ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
@@ -120,10 +120,10 @@ autoplot.epi_workflow <- function(
   } else {
     new_name_y <- names(y)
   }
-  if (is.null(plot_data)) {
+  if (is.null(observed_response)) {
     # the outcome has shifted, so we need to shift it forward (or back)
     # by the corresponding amount
-    plot_data <- bind_cols(mold$extras$roles[mold_roles %in% keys], y)
+    observed_response <- bind_cols(mold$extras$roles[mold_roles %in% keys], y)
     if (starts_with_impl("ahead_", names(y))) {
       shift <- as.numeric(old_name_y[2])
     } else if (starts_with_impl("lag_", names(y))) {
@@ -133,21 +133,22 @@ autoplot.epi_workflow <- function(
       new_name_y <- names(y)
       shift <- 0
     }
-    plot_data <- rename(plot_data, !!new_name_y := !!names(y))
+    observed_response <- rename(observed_response, !!new_name_y := !!names(y))
     if (!is.null(shift)) {
-      plot_data <- mutate(plot_data, time_value = time_value + shift)
+      observed_response <- mutate(observed_response, time_value = time_value + shift)
     }
     other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
-    plot_data <- as_epi_df(plot_data,
+    observed_response <- as_epi_df(observed_response,
       as_of = object$fit$meta$as_of,
       other_keys = other_keys
     )
   }
   if (is.null(predictions)) {
     return(autoplot(
-      plot_data, new_name_y,
+      observed_response, new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
-      .facet_filter = {{ .facet_filter }}
+      .facet_filter = {{ .facet_filter }},
+      .max_facets = .max_facets
     ))
   }
 
@@ -157,27 +158,29 @@ autoplot.epi_workflow <- function(
     }
     predictions <- rename(predictions, time_value = target_date)
   }
-  pred_cols_ok <- hardhat::check_column_names(predictions, key_colnames(plot_data))
+  pred_cols_ok <- hardhat::check_column_names(predictions, key_colnames(observed_response))
   if (!pred_cols_ok$ok) {
     cli_warn(c(
       "`predictions` is missing required variables: {.var {pred_cols_ok$missing_names}}.",
       i = "Plotting the original data."
     ))
     return(autoplot(
-      plot_data, !!new_name_y,
+      observed_response, !!new_name_y,
       .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
-      .facet_filter = {{ .facet_filter }}
+      .facet_filter = {{ .facet_filter }},
+      .max_facets = .max_facets
     ))
   }
 
   # First we plot the history, always faceted by everything
-  bp <- autoplot(plot_data, !!new_name_y,
+  bp <- autoplot(observed_response, !!new_name_y,
     .color_by = "none", .facet_by = "all_keys",
-    .base_color = "black", .facet_filter =  {{ .facet_filter }}
+    .base_color = "black", .facet_filter = {{ .facet_filter }},
+    .max_facets = .max_facets
   )
 
   # Now, prepare matching facets in the predictions
-  ek <- epi_keys_only(plot_data)
+  ek <- epi_keys_only(observed_response)
   predictions <- predictions %>%
     mutate(
       .facets = interaction(!!!rlang::syms(as.list(ek)), sep = " / "),
@@ -215,7 +218,7 @@ autoplot.epi_workflow <- function(
 #' @export
 #' @rdname autoplot-epipred
 autoplot.canned_epipred <- function(
-    object, plot_data = NULL, ...,
+    object, observed_response = NULL, ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
@@ -230,9 +233,10 @@ autoplot.canned_epipred <- function(
   predictions <- object$predictions %>%
     rename(time_value = target_date)
 
-  autoplot(ewf, predictions, plot_data, ...,
+  autoplot(ewf, predictions, observed_response, ...,
     .color_by = .color_by, .facet_by = .facet_by,
-    .base_color = .base_color, .facet_filter = {{ .facet_filter }}
+    .base_color = .base_color, .facet_filter = {{ .facet_filter }},
+    .max_facets = .max_facets
   )
 }
 
diff --git a/README.Rmd b/README.Rmd
index 1adc09bc6..2fc0b2970 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -124,7 +124,7 @@ library(ggplot2)
 ```
 </details>
 
-To demonstrate using [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for forecasting, say we want to
+To demonstrate using [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for forecasting, suppose we want to
 predict COVID-19 deaths per 100k people for each of a subset of states
 
 ```{r subset_geos}
@@ -226,8 +226,8 @@ cases_deaths <-
 ```
 </details>
 
-After downloading and cleaning the cases and deaths data, we can plot
-a subset of the states, marking the desired forecast date:
+After downloading and cleaning deaths per capita, as well as cases per 100k people, we can plot
+a subset of the states, marking the desired forecast date with a vertical line:
 
 <details>
 <summary> Plot </summary>
@@ -287,8 +287,8 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 ```
 
-In our model setup, we are defining as predictors case rate lagged 0-3
-days, one week, and two weeks, and death rate lagged 0-2 weeks.
+In our model setup, we are using as predictors the case rate lagged 0-3
+days, one week, and two weeks, and the death rate lagged 0-2 weeks.
 The result `four_week_ahead` is both a fitted model object which could be used
 any time in the future to create different forecasts, and a set of predicted
 values (and prediction intervals) for each location 28 days after the forecast
@@ -296,7 +296,7 @@ date.
 
 Plotting the prediction intervals on the true values for our location subset[^2]:
 
-[^2]: Alternatively, you could call `autoplot(four_week_ahead, plot_data =
+[^2]: Alternatively, you could call `autoplot(four_week_ahead, observed_response =
     cases_deaths)` to get the full collection of forecasts. This is too busy for
     the space we have for plotting here.
 
@@ -310,14 +310,14 @@ restricted_predictions <-
   mutate(.response_name = "death_rate")
 forecast_plot <-
   four_week_ahead |>
-  autoplot(plot_data = cases_deaths) +
+  autoplot(observed_response = cases_deaths) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label %>% filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
     size = 3, hjust = "right"
   ) +
-  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 </details>
diff --git a/README.md b/README.md
index 24093478a..8303f5b3e 100644
--- a/README.md
+++ b/README.md
@@ -62,8 +62,8 @@ library(ggplot2)
 
 To demonstrate using
 [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) for
-forecasting, say we want to predict COVID-19 deaths per 100k people for
-each of a subset of states
+forecasting, suppose we want to predict COVID-19 deaths per 100k people
+for each of a subset of states
 
 ``` r
 used_locations <- c("ca", "ma", "ny", "tx")
@@ -173,8 +173,9 @@ cases_deaths <-
 
 </details>
 
-After downloading and cleaning the cases and deaths data, we can plot a
-subset of the states, marking the desired forecast date:
+After downloading and cleaning deaths per capita, as well as cases per
+100k people, we can plot a subset of the states, marking the desired
+forecast date with a vertical line:
 
 <details>
 <summary>
@@ -234,7 +235,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-03-03 14:43:07.
+#> This forecaster was fit on 2025-04-09 17:23:00.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -251,8 +252,8 @@ four_week_ahead
 #> 
 ```
 
-In our model setup, we are defining as predictors case rate lagged 0-3
-days, one week, and two weeks, and death rate lagged 0-2 weeks. The
+In our model setup, we are using as predictors the case rate lagged 0-3
+days, one week, and two weeks, and the death rate lagged 0-2 weeks. The
 result `four_week_ahead` is both a fitted model object which could be
 used any time in the future to create different forecasts, and a set of
 predicted values (and prediction intervals) for each location 28 days
@@ -274,14 +275,14 @@ restricted_predictions <-
   mutate(.response_name = "death_rate")
 forecast_plot <-
   four_week_ahead |>
-  autoplot(plot_data = cases_deaths) +
+  autoplot(observed_response = cases_deaths) +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
     data = forecast_date_label %>% filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
     size = 3, hjust = "right"
   ) +
-  scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
+  scale_x_date(date_breaks = "3 months", date_labels = "%y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
@@ -337,9 +338,9 @@ email, or the InsightNet Slack.
     ago.
 
 [^2]: Alternatively, you could call
-    `autoplot(four_week_ahead, plot_data = cases_deaths)` to get the
-    full collection of forecasts. This is too busy for the space we have
-    for plotting here.
+    `autoplot(four_week_ahead, observed_response = cases_deaths)` to get
+    the full collection of forecasts. This is too busy for the space we
+    have for plotting here.
 
 [^3]: Note that these are not the same quantiles that we fit when
     creating `four_week_ahead`. They are extrapolated from those
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index e92864664..03cba3e2d 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -11,7 +11,7 @@
 \method{autoplot}{epi_workflow}(
   object,
   predictions = NULL,
-  plot_data = NULL,
+  observed_response = NULL,
   .levels = c(0.5, 0.8, 0.9),
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
@@ -24,7 +24,7 @@
 
 \method{autoplot}{canned_epipred}(
   object,
-  plot_data = NULL,
+  observed_response = NULL,
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
@@ -44,7 +44,7 @@
 \item{predictions}{A data frame with predictions. If \code{NULL}, only the
 original data is shown.}
 
-\item{plot_data}{An epi_df of the data to plot against. This is for the case
+\item{observed_response}{An epi_df of the data to plot against. This is for the case
 where you have the actual results to compare the forecast against.}
 
 \item{.levels}{A numeric vector of levels to plot for any prediction bands.
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index 2e4b8bcbb..ce40b6845 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -52,8 +52,8 @@ details, see \href{https://grf-labs.github.io/grf/articles/categorical_inputs.ht
 #> Model fit template:
 #> grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~integer(1), 
 #>     x), num.trees = integer(1), min.node.size = min_rows(~integer(1), 
-#>     x), quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95), 
-#>     num.threads = 1L, seed = stats::runif(1, 0, .Machine$integer.max))
+#>     x), quantiles = c(0.1, 0.5, 0.9), num.threads = 1L, seed = stats::runif(1, 
+#>     0, .Machine$integer.max))
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index c909452eb..93cc72502 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -151,8 +151,7 @@ toy_recipe \%>\%
 #> 4 ca        2015-01-14   103    10
 #> 5 ma        2015-01-11    20     6
 #> 6 ma        2015-01-12    23     6
-#> 7 ma        2015-01-13    25     6
-#> 8 ma        2015-01-14    25     6
+#> # i 2 more rows
 }\if{html}{\out{</div>}}
 }
 
@@ -179,19 +178,15 @@ toy_recipe \%>\%
 #> * as_of     = 2015-01-14
 #> 
 #> # A tibble: 21 x 7
-#>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
-#>  1 ca        2015-01-10    NA    NA      NA      NA       100
-#>  2 ca        2015-01-11   100     5      NA      NA       103
-#>  3 ca        2015-01-12   103    10      NA      NA        NA
-#>  4 ca        2015-01-13    NA    NA      NA      NA        NA
-#>  5 ca        2015-01-14    NA    NA     100      NA        NA
-#>  6 ca        2015-01-15    NA    NA     103       5        NA
-#>  7 ca        2015-01-16    NA    NA      NA      10        NA
-#>  8 ca        2015-01-17    NA    NA      NA      NA        NA
-#>  9 ca        2015-01-18    NA    NA      NA      NA        NA
-#> 10 ca        2015-01-19    NA    NA      NA      NA        NA
-#> # i 11 more rows
+#>   geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
+#>   <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#> 1 ca        2015-01-10    NA    NA      NA      NA       100
+#> 2 ca        2015-01-11   100     5      NA      NA       103
+#> 3 ca        2015-01-12   103    10      NA      NA        NA
+#> 4 ca        2015-01-13    NA    NA      NA      NA        NA
+#> 5 ca        2015-01-14    NA    NA     100      NA        NA
+#> 6 ca        2015-01-15    NA    NA     103       5        NA
+#> # i 15 more rows
 }\if{html}{\out{</div>}}
 
 The maximum latency in column \code{a} is 2 days, so the lag is increased to 3,
@@ -227,18 +222,15 @@ toy_recipe \%>\%
 #> * as_of     = 2015-01-14
 #> 
 #> # A tibble: 10 x 6
-#>    geo_value time_value     a     b lag_0_a ahead_3_a
-#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
-#>  1 ca        2015-01-08    NA    NA      NA       100
-#>  2 ca        2015-01-09    NA    NA      NA       103
-#>  3 ca        2015-01-11   100     5     100        NA
-#>  4 ca        2015-01-12   103    10     103        NA
-#>  5 ma        2015-01-08    NA    NA      NA        20
-#>  6 ma        2015-01-09    NA    NA      NA        23
-#>  7 ma        2015-01-10    NA    NA      NA        25
-#>  8 ma        2015-01-11    20     6      20        NA
-#>  9 ma        2015-01-12    23    NA      23        NA
-#> 10 ma        2015-01-13    25    NA      25        NA
+#>   geo_value time_value     a     b lag_0_a ahead_3_a
+#>   <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#> 1 ca        2015-01-08    NA    NA      NA       100
+#> 2 ca        2015-01-09    NA    NA      NA       103
+#> 3 ca        2015-01-11   100     5     100        NA
+#> 4 ca        2015-01-12   103    10     103        NA
+#> 5 ma        2015-01-08    NA    NA      NA        20
+#> 6 ma        2015-01-09    NA    NA      NA        23
+#> # i 4 more rows
 }\if{html}{\out{</div>}}
 
 Even though we're doing a 1 day ahead forecast, because our worst latency
@@ -268,8 +260,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous
-#> `step_epi_lag`s won't work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
+#> modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 6121a0499..058d6a5b6 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -337,7 +337,8 @@ p1 <- # first plotting the forecasts as bands, lines and points
   geom_vline(
     data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
     aes(color = factor(version), xintercept = version),
-    lty = 2) +
+    lty = 2
+  ) +
   # the underlying data
   geom_line(
     data = plotting_data |> filter(geo_value == geo_choose),
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 8e23981a7..6419504bd 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -145,7 +145,7 @@ Let's create an `epi_recipe()` to hold the 6 steps:
 
 ```{r make_recipe}
 filtered_data <- covid_case_death_rates |>
-    filter(time_value <= forecast_date, geo_value %in% used_locations)
+  filter(time_value <= forecast_date, geo_value %in% used_locations)
 four_week_recipe <- epi_recipe(
   filtered_data,
   reference_date = (filtered_data %@% metadata)$as_of
@@ -433,7 +433,7 @@ forecast_date_label <-
 result_plot <- autoplot(
   object = gr_fit_workflow,
   predictions = gr_predictions,
-  plot_data = covid_case_death_rates |>
+  observed_response = covid_case_death_rates |>
     filter(geo_value %in% used_locations, time_value > "2021-07-01")
 ) +
   geom_vline(aes(xintercept = forecast_date)) +
@@ -534,7 +534,7 @@ classifier_recipe <- epi_recipe(training_data) |>
     ),
     role = "outcome"
   ) |>
-  # Drop unused columns based on role assignments. This is not strictly 
+  # Drop unused columns based on role assignments. This is not strictly
   # necessary, as columns with roles unused in the model will be ignored anyway.
   step_rm(has_role("none"), has_role("raw")) |>
   step_epi_naomit()
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index d1600663d..2753e3772 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -43,14 +43,15 @@ Towards that end, `{epipredict}` provides two main classes of tools:
 A set of basic, easy-to-use "canned" forecasters that work out of the box.
 We currently provide the following basic forecasters:
 
-  * _Flatline forecaster_: predicts as the median the most recently seen value
+  * `flatline_forecaster()`: predicts as the median the most recently seen value
     with increasingly wide quantiles.
-  * _Climatological forecaster_: predicts the median and quantiles based on the historical values around the same date in previous years.
-  * _Autoregressive forecaster_: fits a model (e.g. linear regression) on
-    lagged data to predict quantiles for continuous values.
-  * _Autoregressive classifier_: fits a model (e.g. logistic regression) on
-    lagged data to predict a binned version of the growth rate.
-  * _CDC FluSight flatline forecaster_: a variant of the flatline forecaster that is
+  * `climatological_forecaster()`: predicts the median and quantiles based on the historical values around the same date in previous years.
+  * `arx_forecaster()`: an AutoRegressive eXogenous feature forecaster, which
+    fits a model (e.g. linear regression) on lagged data to predict quantiles
+    for continuous values.
+  * `arx_classifier()`: fits a model (e.g. logistic regression) on lagged data
+    to predict a binned version of the growth rate.
+  * `cdc_baseline_forecaster()`: a variant of the flatline forecaster that is
     used as a baseline in the CDC's [FluSight forecasting competition](https://www.cdc.gov/flu-forecasting/about/index.html).
 
 ## Forecasting framework
@@ -83,6 +84,9 @@ For a more in-depth treatment with some practical applications, see also the
 [Forecasting Book](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 # Panel forecasting basics
+
+This section gives basic usage examples for the package beyond the most basic usage of `arx_forecaster()` for forecasting a single ahead using the default engine.
+
 ## Example data
 
 The forecasting methods in this package are designed to work with panel time
@@ -122,13 +126,14 @@ While this package was designed with epidemiology in mind, many of the
 techniques are more broadly applicable.
 
 ## Customizing `arx_forecaster()`
+
 Let's expand on the basic example presented on the [landing
 page](../index.html#motivating-example), starting with adjusting some parameters in
 `arx_forecaster()`.
 
-The `trainer` argument allows us to set the fitting engine. We can use either one of the
-included engines, such as `quantile_reg()`, or one of the relevant [parsnip
-models](https://www.tidymodels.org/find/parsnip/):
+The `trainer` argument allows us to set the fitting engine. We can use either
+one of the relevant [parsnip models](https://www.tidymodels.org/find/parsnip/),
+or one of the included engines, such as `smooth_quantile_reg()`:
 
 ```{r make-forecasts, warning=FALSE}
 two_week_ahead <- arx_forecaster(
@@ -146,8 +151,10 @@ hardhat::extract_fit_engine(two_week_ahead$epi_workflow)
 
 The default trainer is `parsnip::linear_reg()`, which generates quantiles after
 the fact in the post-processing layers, rather than as part of the model.
-While this does work, it is generally preferable to use `quantile_reg()`, as the
-quantiles generated in post-processing can be poorly behaved.
+While these post-processing layers will produce prediction intervals for an
+arbitrary trainer, it is generally preferable to use `quantile_reg()` (or an
+alternative that produces statistically justifiable prediction intervals), as
+the quantiles generated in post-processing can be poorly behaved.
 `quantile_reg()` on the other hand directly estimates a different linear model
 for each quantile, reflected in the several different columns for `tau` above.
 
@@ -168,8 +175,8 @@ two_week_ahead <- arx_forecaster(
 )
 ```
 
-Any other customization is routed through `arx_args_list()`; for example, if we
-wanted to increase the number of quantiles fit:
+Other customization is possible via `args_list = arx_args_list()`; for
+example, if we wanted to increase the number of quantiles fit:
 
 ```{r make-quantile-levels-forecasts, warning=FALSE}
 two_week_ahead <- arx_forecaster(
@@ -194,6 +201,7 @@ If you want to make further modifications, you will need a custom
 workflow; see the [Custom Epiworkflows vignette](custom_epiworkflows) for details.
 
 ## Generating multiple aheads
+
 We often want to generate a a trajectory
 of forecasts over a range of dates, rather than for a single day.
 We can do this with `arx_forecaster()` by looping over aheads.
@@ -219,17 +227,23 @@ all_canned_results <- lapply(
 # pull out the workflow and the predictions to be able to
 #  effectively use autoplot
 workflow <- all_canned_results[[1]]$epi_workflow
-results <- purrr::map_df(all_canned_results, ~ `$`(., "predictions"))
+results <- all_canned_results |>
+  purrr::map(~ `$`(., "predictions")) |>
+  list_rbind()
 autoplot(
   object = workflow,
   predictions = results,
-  plot_data = covid_case_death_rates |>
+  observed_response = covid_case_death_rates |>
     filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
 ## Other canned forecasters
+
+This section gives a brief example of each of the canned forecasters.
+
 ### `flatline_forecaster()`
+
 The simplest model we provide is the `flatline_forecaster()`, which predicts a
 flat line (with quantiles generated from the residuals using
 `layer_residual_quantiles()`).
@@ -250,11 +264,13 @@ all_flatlines <- lapply(
 )
 # same plotting code as in the arx multi-ahead case
 workflow <- all_flatlines[[1]]$epi_workflow
-results <- purrr::map_df(all_flatlines, ~ `$`(., "predictions"))
+results <- all_flatlines |>
+  purrr::map(~ `$`(., "predictions")) |>
+  list_rbind()
 autoplot(
   object = workflow,
   predictions = results,
-  plot_data = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
@@ -280,7 +296,7 @@ results <- all_cdc_flatline$predictions
 autoplot(
   object = workflow,
   predictions = results,
-  plot_data = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
@@ -294,6 +310,7 @@ This results in much smoother quantiles, but ones that only capture the
 one-ahead uncertainty.
 
 ### `climatological_forecaster()`
+
 The `climatological_forecaster()` is a different kind of baseline. It produces a
 point forecast and quantiles based on the historical values for a given time of
 year, rather than extrapolating from recent values.
@@ -315,7 +332,7 @@ results <- all_climate$predictions
 autoplot(
   object = workflow,
   predictions = results,
-  plot_data = covid_case_death_rates_extended |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = covid_case_death_rates_extended |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
@@ -347,22 +364,22 @@ classifier <- arx_classifier(
   args_list = arx_class_args_list(
     lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
     ahead = 2 * 7,
-    breaks = c(-0.01, 0.01, 0.1)
+    breaks = 0.25 / 7
   )
 )
 classifier$predictions
 ```
 
-The number and size of the growth rate categories is controlled by `breaks`, which define the
-bin boundaries.
+The number and size of the growth rate categories is controlled by `breaks`,
+which define the bin boundaries.
 
-In this example, the custom `breaks` passed to `arx_class_args_list()` correspond to 4 bins:
-`(-∞, -0.01)`, `(-0.01, 0.01)`,  `(0.01, 0.1)`, and `(0.1, ∞)`.
+In this example, the custom `breaks` passed to `arx_class_args_list()` correspond to 2 bins:
+`(-∞, 0.0357]` and `(0.0357, ∞)`.
 The bins can be interpreted as: the outcome variable is decreasing, approximately stable, slightly increasing, or increasing quickly.
 
 The returned `predictions` assigns each state to one of the growth rate bins.
 In this case, the classifier expects the growth rate for all 4 of the states to fall into the same category,
-`(-0.01, 0.01]`.
+`(-∞, 0.0357]`.
 
 To see how this model performed, let's compare to the actual growth rates for the `target_date`, as computed using
 `{epiprocess}`:
@@ -378,7 +395,7 @@ growth_rates <- covid_case_death_rates |>
 growth_rates |> filter(time_value == "2021-08-14")
 ```
 
-Unfortunately, this forecast was not particularly accurate. All real growth rates were larger than the predicted growth rates, with California (real growth rate `-1.39`) not remotely in the interval (`(-0.01, 0.01]`).
+The accuracy is 50%, since all 4 states were predicted to be in the interval `(-Inf, 0.0357]`, while two,  `ca` and `ny` actually were.
 
 
 ## Fitting multi-key panel data
@@ -393,8 +410,8 @@ edu_quals <- c("Undergraduate degree", "Professional degree")
 geo_values <- c("Quebec", "British Columbia")
 
 grad_employ <- grad_employ_subset |>
-    filter(time_value < 2017) |>
-    filter(edu_qual %in% edu_quals, geo_value %in% geo_values)
+  filter(time_value < 2017) |>
+  filter(edu_qual %in% edu_quals, geo_value %in% geo_values)
 
 grad_employ
 
@@ -449,7 +466,9 @@ all_fits <-
         )
       )
   })
-map_df(all_fits, ~ pluck(., "predictions"))
+all_fits |>
+  map(~ pluck(., "predictions")) |>
+  list_rbind()
 ```
 
 Fitting separate models for each geography is both 56 times slower[^7] than geo-pooling, and fits each model on far less data.
@@ -459,6 +478,9 @@ However, geo-pooling can only be used if values are comparable in meaning and sc
 If we wanted to build a geo-aware model, such as a linear regression with a different intercept for each geography, we would need to build a [custom workflow](custom_epiworkflows) with geography as a factor.
 
 # Anatomy of a canned forecaster
+
+This section describes the resulting object from `arx_forecaster()`, an `arx_fcast` object, along with a fairly minimal description of the actual mathematical model used for `arx_forecaster()`.
+
 ## Code object
 Let's dissect the forecaster we trained back on the [landing
 page](../index.html#motivating-example):
@@ -476,8 +498,8 @@ four_week_ahead <- arx_forecaster(
 )
 ```
 
-`four_week_ahead` has two components: an `epi_workflow`, and a table of
-`predictions`.
+`four_week_ahead` has three components: an `epi_workflow`, a table of
+`predictions`, and a list of `metadata`.
 The table of predictions is a simple tibble,
 
 ```{r show_predictions}
@@ -485,11 +507,11 @@ four_week_ahead$predictions
 ```
 
 where `.pred` gives the point/median prediction, and `.pred_distn` is a
-`dist_quantiles()` object representing a distribution through various quantile
+`hardhat::quantile_pred()` object representing a distribution through various quantile
 levels.
-The `[6]` in the name refers to the number of quantiles that have been
-explicitly created[^4].
-By default, `.pred_distn` covers a 90% prediction interval, reporting the 5% and 95% quantiles.
+The `5` in `<qtls(5)>` refers to the number of quantiles that have been
+explicitly created, while the [0.234] is the median value[^4].
+By default, `.pred_distn` covers the quantiles `c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)`.
 
 The `epi_workflow` is a significantly more complicated object, extending a
 `workflows::workflow()`  to include post-processing steps:
@@ -500,17 +522,19 @@ four_week_ahead$epi_workflow
 
 An `epi_workflow()` consists of 3 parts:
 
-- `Preprocessor`: a collection of steps that transform the data to be ready for
+- `preprocessor`: a collection of steps that transform the data to be ready for
       modelling. Steps can be custom, as are those included in this package, 
       or [be defined in `{recipes}`](https://recipes.tidymodels.org/reference/index.html).
       `four_week_ahead` has 5 steps; you can inspect them more closely by
       running `hardhat::extract_recipe(four_week_ahead$epi_workflow)`.[^6]
-- `Model`: the actual model that does the fitting, given by a
-  `parsnip::model_spec`. `four_week_ahead` uses the default of
-  `parsnip::linear_reg()`, which is a `{parsnip}` wrapper for
-  `stats::lm()`. You can inspect the model more closely by running
+- `spec`: a `parsnip::model_spec` which includes both the model parameters and
+  an engine to fit those parameters to the training data as prepared by
+  `preprocessor`.  `four_week_ahead` uses the default of
+  `parsnip::linear_reg()`, which is a `{parsnip}` wrapper for several linear
+  regression engines, by default `stats::lm()`. You can inspect the model more
+  closely by running
   `hardhat::extract_fit_recipe(four_week_ahead$epi_workflow)`.
-- `Postprocessor`: a collection of layers to be applied to the resulting
+- `postprocessor`: a collection of layers to be applied to the resulting
   forecast. Layers are internal to this package. `four_week_ahead` just so happens to have
   5 of as these well. You can inspect the layers more closely by running
   `epipredict::extract_layers(four_week_ahead$epi_workflow)`.
@@ -537,15 +561,18 @@ four_week_small <- arx_forecaster(
 hardhat::extract_fit_engine(four_week_small$epi_workflow)
 ```
 
-If $d_t$ is the death rate on day $t$ and $c_t$ is the case rate, then the model
-we're fitting is:
+If $d_{t,j}$ is the death rate on day $t$ at location $j$ and $c_{t,j}$ is the
+associated case rate, then the model we're fitting is:
 
 $$
-d_{t+28} = a_0 + a_1 d_t + a_2 d_{t-7} + a_3 d_{t-14} + a_4 c_t + a_5 c_{t-7} + a_6 c_{t-14}.
+\begin{aligned}
+d_{t+28, j} = & a_0 + a_1 d_{t,j} + a_2 d_{t-7,j} + a_3 d_{t-14, j} +\\
+     & a_4 c_{t, j} + a_5 c_{t-7, j} + a_6 c_{t-14, j} + \varepsilon_{t,j}.
+\end{aligned}
 $$
 
-For example, $a_1$ is `lag_0_death_rate` above, with a value of `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"] `,
-while $a_5$ is `r hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"] `.
+For example, $a_1$ is `lag_0_death_rate` above, with a value of `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"], 3)`,
+while $a_5$ is `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"], 4) `.
 
 The training data for fitting this linear model is constructed within the `arx_forecaster()` function by shifting a series
 of columns the appropriate amount -- based on the requested `lags`.

From ef1fd582ecf2b0e053d5dc15b37064ca584a15c7 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 2 May 2025 15:34:09 -0500
Subject: [PATCH 381/387] move pkgdown-watch, better climate ex, some wording

---
 DEVELOPMENT.md                          |  3 +-
 README.Rmd                              |  2 +-
 README.md                               | 10 ++---
 inst/pkgdown-watch.R => pkgdown-watch.R |  0
 vignettes/epipredict.Rmd                | 58 +++++++++++++------------
 5 files changed, 38 insertions(+), 35 deletions(-)
 rename inst/pkgdown-watch.R => pkgdown-watch.R (100%)

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index c52032cda..8922a7e69 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -40,8 +40,7 @@ difficulties. To clear those, run `make`, with either `clean_knitr`,
 `clean_site`, or `clean` (which does both).
 
 If you work without R Studio and want to iterate on documentation, you might
-find [this
-script](https://gist.github.com/gadenbuie/d22e149e65591b91419e41ea5b2e0621)
+find `Rscript pkgdown-watch.R` useful.
 helpful. For updating references, you will need to manually call `pkgdown::build_reference()`.
 
 ## Versioning
diff --git a/README.Rmd b/README.Rmd
index 2fc0b2970..28ae1409c 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -334,7 +334,7 @@ four_week_ahead$predictions |>
   select(geo_value, forecast_date, target_date, quantile = .pred_distn_quantile_level, value = .pred_distn_value)
 ```
 
-The yellow dot gives the median prediction, while the blue intervals give the
+The orange dot gives the point prediction, while the blue intervals give the
 25-75%, the 10-90%, and 2.5-97.5%  inter-quantile ranges[^4].
 For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
diff --git a/README.md b/README.md
index 8303f5b3e..15e14c693 100644
--- a/README.md
+++ b/README.md
@@ -309,11 +309,11 @@ four_week_ahead$predictions |>
 #> # ℹ 14 more rows
 ```
 
-The yellow dot gives the median prediction, while the blue intervals
-give the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^3].
-For this particular day and these locations, the forecasts are
-relatively accurate, with the true data being at least within the 10-90%
-interval. A couple of things to note:
+The orange dot gives the point prediction, while the blue intervals give
+the 25-75%, the 10-90%, and 2.5-97.5% inter-quantile ranges[^3]. For
+this particular day and these locations, the forecasts are relatively
+accurate, with the true data being at least within the 10-90% interval.
+A couple of things to note:
 
 1.  `epipredict` methods are primarily direct forecasters; this means we
     don’t need to predict 1, 2,…, 27 days ahead to then predict 28 days
diff --git a/inst/pkgdown-watch.R b/pkgdown-watch.R
similarity index 100%
rename from inst/pkgdown-watch.R
rename to pkgdown-watch.R
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 2753e3772..60856aa0c 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -102,14 +102,9 @@ Let's look at an example `epi_df`:
 covid_case_death_rates
 ```
 
-This dataset uses a single key, `geo_value`, and two separate
-time series, `case_rate` and `death_rate`.
-The keys are represented in "long" format, with separate columns for the key and
-the value, while separate time series are represented in "wide" format with each
-time series stored in a separate column.
-
-`{epiprocess}` is designed to handle data that always has a geographic key, and
-potentially other key values, such as age, ethnicity, or other demographic
+An `epi_df` always has a `geo_value` and a `time_value` as keys, along with some number of value columns, in this case `case_rate` and `death_rate`.
+Each of these has an associated `geo_type` (state) and `time_type` (day), for which there are some utilities.
+While this `geo_value` and `time_value` are the minimal set of keys, the functions of `{epiprocess}` and `{epipredict}` are designed to accommodate other key values, such as age, ethnicity, or other demographic
 information.
 For example, `grad_employ_subset` from `{epidatasets}` also has both `age_group`
 and `edu_qual` as additional keys:
@@ -314,17 +309,26 @@ one-ahead uncertainty.
 The `climatological_forecaster()` is a different kind of baseline. It produces a
 point forecast and quantiles based on the historical values for a given time of
 year, rather than extrapolating from recent values.
-For example, on the same dataset as above:
+Among our forecasters, it is the only one well suited for forecasts at long time horizons.
+
+Since it requires multiple years of data and a roughly seasonal signal, the dataset we've been using for demonstrations so far is poor example for a climate forecast[^8].
+Instead, we'll use the fluview ILI dataset, which is weekly influenza like illness data for hhs regions, going back to 1997.
+
+
+We'll predict the 2023/24 season using all previous data, including 2020-2022, the two years where there was approximately no seasonal flu, forecasting from the start of the season, `2023-10-08`:
+
 ```{r make-climatological-forecast, warning=FALSE}
+fluview_hhs <- pub_fluview(regions = paste0("hhs", 1:10), epiweeks = epirange(100001,222201))
+fluview <- fluview_hhs %>% select(geo_value = region, time_value = epiweek, issue, ili) %>% as_epi_archive() %>% epix_as_of_current()
+
 all_climate <- climatological_forecaster(
-  covid_case_death_rates_extended |>
-    filter(time_value <= forecast_date, geo_value %in% used_locations),
-  outcome = "death_rate",
+  fluview %>% filter(time_value < "2023-10-08"),
+  outcome = "ili",
   args_list = climate_args_list(
     forecast_horizon = seq(0, 28),
-    window_size = 14,
-    time_type = "day",
-    forecast_date = forecast_date
+    time_type = "week",
+    quantile_by_key = "geo_value",
+    forecast_date = as.Date("2023-10-08")
   )
 )
 workflow <- all_climate$epi_workflow
@@ -332,21 +336,18 @@ results <- all_climate$predictions
 autoplot(
   object = workflow,
   predictions = results,
-  observed_response = covid_case_death_rates_extended |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = fluview %>% filter(time_value >= "2023-10-08", time_value < "2024-05-01") %>% mutate(geo_value = factor(geo_value, levels = paste0("hhs", 1:10)))
 )
 ```
 
-Note that to have enough training data for this method, we're using 
-`covid_case_death_rates_extended`, which starts in March 2020, rather than
-`covid_case_death_rates`, which starts in December.
-Without at least a year's worth of historical data, it is impossible to do a
-climatological model.
-Even with one year of data, as we have here, the resulting forecasts are unreliable.
 
 One feature of the climatological baseline is that it forecasts multiple aheads
-simultaneously.
+simultaneously; here we do so for the entire season of 28 weeks.
 This is possible for `arx_forecaster()`, but only using `trainer =
-smooth_quantile_reg()`, which is built to handle multiple aheads simultaneously.
+smooth_quantile_reg()`, which is built to handle multiple aheads simultaneously[^9].
+
+A pure climatological forecast can be thought of as forecasting a typical year so far.
+The 2023/24 had some regions, such as `hhs10` which were quite close to the typical year, and some, such as `hhs2` that were frequently outside even the 90% prediction band (the lightest shown above).
 
 ### `arx_classifier()`
 
@@ -410,7 +411,6 @@ edu_quals <- c("Undergraduate degree", "Professional degree")
 geo_values <- c("Quebec", "British Columbia")
 
 grad_employ <- grad_employ_subset |>
-  filter(time_value < 2017) |>
   filter(edu_qual %in% edu_quals, geo_value %in% geo_values)
 
 grad_employ
@@ -429,8 +429,8 @@ grad_forecast <- arx_forecaster(
 autoplot(
   grad_forecast$epi_workflow,
   grad_forecast$predictions,
-  grad_employ,
-)
+  observed_response = grad_employ,
+) + geom_vline(aes(xintercept = 2016))
 ```
 
 The 8 graphs represent all combinations of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
@@ -590,3 +590,7 @@ Each row containing no `NA` values is used as a training observation to fit the
     `hardhat::extract_preprocessor(four_week_ahead$epi_workflow)`
 
 [^7]: the number of geographies
+
+[^8]: It has only a year of data, which is barely enough to run the method without errors, let alone get a meaningful prediction.
+
+[^9]: Though not 28 weeks into the future! Such a forecast will be an absurd extrapolation.

From 4a9f43e55653e7134870b1ec34ae40aad30d4da9 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 2 May 2025 17:23:10 -0500
Subject: [PATCH 382/387] moving library, geo-pooling phrasing

---
 vignettes/epipredict.Rmd | 52 +++++++++++++++++++++++-----------------
 1 file changed, 30 insertions(+), 22 deletions(-)

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 60856aa0c..26e488dcc 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -13,20 +13,6 @@ vignette: >
 source(here::here("vignettes/_common.R"))
 ```
 
-```{r setup, message=FALSE, include = FALSE}
-library(dplyr)
-library(parsnip)
-library(workflows)
-library(recipes)
-library(epidatasets)
-library(epipredict)
-library(epiprocess)
-library(ggplot2)
-library(purrr)
-forecast_date <- as.Date("2021-08-01")
-used_locations <- c("ca", "ma", "ny", "tx")
-library(epidatr)
-```
 
 At a high level, the goal of `{epipredict}` is to make it easy to run simple machine
 learning and statistical forecasters for epidemiological data.
@@ -86,6 +72,27 @@ For a more in-depth treatment with some practical applications, see also the
 # Panel forecasting basics
 
 This section gives basic usage examples for the package beyond the most basic usage of `arx_forecaster()` for forecasting a single ahead using the default engine.
+Before we start actually building forecasters, lets import some relevant libraries
+
+```{r setup, message=FALSE}
+library(dplyr)
+library(parsnip)
+library(workflows)
+library(recipes)
+library(epidatasets)
+library(epipredict)
+library(epiprocess)
+library(ggplot2)
+library(purrr)
+library(epidatr)
+```
+
+And our default forecasting date and selected states (we will use these to limit the data to make discussion easier):
+
+```{r}
+forecast_date <- as.Date("2021-08-01")
+used_locations <- c("ca", "ma", "ny", "tx")
+```
 
 ## Example data
 
@@ -435,14 +442,11 @@ autoplot(
 
 The 8 graphs represent all combinations of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
-## Fitting a non-geo-pooled model
+## Fitting a forecaster without geo-pooling
 
-The methods shown so far fit a single model across all geographic regions.
-This is called "geo-pooling". 
-To fit a non-geo-pooled model that fits each geography separately, one either needs a multi-level
-engine (which at the moment `{parsnip}` doesn't support), or one needs to loop over
-geographies.
-Here, we're using `purrr::map` to perform the loop.
+The methods shown so far fit a single model across all geographic regions, treating them as if they are independently and identically distributed (see [Mathematical description] for an explicit model example).
+This is called "geo-pooling".
+In the context of `{epipredict}`, the simplest way to avoid geo-pooling and use different parameters for each geography is to loop over the `geo_value`s:
 
 ```{r fit_non_geo_pooled, warning=FALSE}
 geo_values <- covid_case_death_rates |>
@@ -475,7 +479,9 @@ Fitting separate models for each geography is both 56 times slower[^7] than geo-
 If a dataset contains relatively few observations for each geography, fitting a geo-pooled model is likely to produce better, more stable results.
 However, geo-pooling can only be used if values are comparable in meaning and scale across geographies or can be made comparable, for example by normalization.
 
-If we wanted to build a geo-aware model, such as a linear regression with a different intercept for each geography, we would need to build a [custom workflow](custom_epiworkflows) with geography as a factor.
+If we wanted to build a geo-aware model, such as a linear regression with a
+different intercept for each geography, we would need to build a [custom
+workflow](custom_epiworkflows) with geography as a factor.
 
 # Anatomy of a canned forecaster
 
@@ -573,6 +579,8 @@ $$
 
 For example, $a_1$ is `lag_0_death_rate` above, with a value of `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"], 3)`,
 while $a_5$ is `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"], 4) `.
+Note that unlike `d_{t,j}` or `c_{t,j}`, these *don't* depend on either the time $t$ or the location $j$.
+This is what make it a geo-pooled model.
 
 The training data for fitting this linear model is constructed within the `arx_forecaster()` function by shifting a series
 of columns the appropriate amount -- based on the requested `lags`.

From 9f0af0a535e76376e7e16c2f0f2a974836164761 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 13 May 2025 12:13:54 -0500
Subject: [PATCH 383/387] fit -> estimate

---
 vignettes/epipredict.Rmd | 48 ++++++++++++++++++++++++----------------
 vignettes/panel-data.Rmd | 10 ++++-----
 2 files changed, 34 insertions(+), 24 deletions(-)

diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 26e488dcc..6288594f8 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -20,7 +20,7 @@ To do this, we have extended the [tidymodels](https://www.tidymodels.org/)
 framework to handle the case of panel time-series data.
 
 Our hope is that it is easy for users with epidemiological training and some statistical knowledge to
-fit baseline models, while also allowing those with more nuanced statistical
+estimate baseline models, while also allowing those with more nuanced statistical
 understanding to create complex custom models using the same framework.
 Towards that end, `{epipredict}` provides two main classes of tools:
 
@@ -33,7 +33,7 @@ We currently provide the following basic forecasters:
     with increasingly wide quantiles.
   * `climatological_forecaster()`: predicts the median and quantiles based on the historical values around the same date in previous years.
   * `arx_forecaster()`: an AutoRegressive eXogenous feature forecaster, which
-    fits a model (e.g. linear regression) on lagged data to predict quantiles
+    estimates a model (e.g. linear regression) on lagged data to predict quantiles
     for continuous values.
   * `arx_classifier()`: fits a model (e.g. logistic regression) on lagged data
     to predict a binned version of the growth rate.
@@ -133,7 +133,7 @@ Let's expand on the basic example presented on the [landing
 page](../index.html#motivating-example), starting with adjusting some parameters in
 `arx_forecaster()`.
 
-The `trainer` argument allows us to set the fitting engine. We can use either
+The `trainer` argument allows us to set the computational engine. We can use either
 one of the relevant [parsnip models](https://www.tidymodels.org/find/parsnip/),
 or one of the included engines, such as `smooth_quantile_reg()`:
 
@@ -383,7 +383,8 @@ which define the bin boundaries.
 
 In this example, the custom `breaks` passed to `arx_class_args_list()` correspond to 2 bins:
 `(-∞, 0.0357]` and `(0.0357, ∞)`.
-The bins can be interpreted as: the outcome variable is decreasing, approximately stable, slightly increasing, or increasing quickly.
+The bins can be interpreted as: `death_rate` is decreasing/growing slowly,
+ or `death_rate` is growing quickly.
 
 The returned `predictions` assigns each state to one of the growth rate bins.
 In this case, the classifier expects the growth rate for all 4 of the states to fall into the same category,
@@ -403,14 +404,16 @@ growth_rates <- covid_case_death_rates |>
 growth_rates |> filter(time_value == "2021-08-14")
 ```
 
-The accuracy is 50%, since all 4 states were predicted to be in the interval `(-Inf, 0.0357]`, while two,  `ca` and `ny` actually were.
+The accuracy is 50%, since all 4 states were predicted to be in the interval
+`(-Inf, 0.0357]`, while two,  `ca` and `ny` actually were.
 
 
-## Fitting multi-key panel data
+## Handling multi-key panel data
 
-If multiple keys are set in the `epi_df` as `other_keys`,
-`arx_forecaster` will automatically group by those in addition to the required geographic key.
-For example, predicting the number of graduates in each of the categories in `grad_employ_subset` from above:
+If multiple keys are set in the `epi_df` as `other_keys`, `arx_forecaster` will
+automatically group by those in addition to the required geographic key.
+For example, predicting the number of graduates in each of the categories in
+`grad_employ_subset` from above:
 
 ```{r multi_key_forecast, warning=FALSE}
 # only fitting a subset, otherwise there are ~550 distinct pairs, which is bad for plotting
@@ -442,9 +445,9 @@ autoplot(
 
 The 8 graphs represent all combinations of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).
 
-## Fitting a forecaster without geo-pooling
+## Estimating models without geo-pooling
 
-The methods shown so far fit a single model across all geographic regions, treating them as if they are independently and identically distributed (see [Mathematical description] for an explicit model example).
+The methods shown so far estimate a single model across all geographic regions, treating them as if they are independently and identically distributed (see [Mathematical description] for an explicit model example).
 This is called "geo-pooling".
 In the context of `{epipredict}`, the simplest way to avoid geo-pooling and use different parameters for each geography is to loop over the `geo_value`s:
 
@@ -475,7 +478,7 @@ all_fits |>
   list_rbind()
 ```
 
-Fitting separate models for each geography is both 56 times slower[^7] than geo-pooling, and fits each model on far less data.
+Estimating separate models for each geography is both 56 times slower[^7] than geo-pooling, and uses far less data for each estimate.
 If a dataset contains relatively few observations for each geography, fitting a geo-pooled model is likely to produce better, more stable results.
 However, geo-pooling can only be used if values are comparable in meaning and scale across geographies or can be made comparable, for example by normalization.
 
@@ -568,7 +571,7 @@ hardhat::extract_fit_engine(four_week_small$epi_workflow)
 ```
 
 If $d_{t,j}$ is the death rate on day $t$ at location $j$ and $c_{t,j}$ is the
-associated case rate, then the model we're fitting is:
+associated case rate, then the corresponding model is:
 
 $$
 \begin{aligned}
@@ -577,14 +580,21 @@ d_{t+28, j} = & a_0 + a_1 d_{t,j} + a_2 d_{t-7,j} + a_3 d_{t-14, j} +\\
 \end{aligned}
 $$
 
-For example, $a_1$ is `lag_0_death_rate` above, with a value of `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"], 3)`,
-while $a_5$ is `r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"], 4) `.
-Note that unlike `d_{t,j}` or `c_{t,j}`, these *don't* depend on either the time $t$ or the location $j$.
+For example, $a_1$ is `lag_0_death_rate` above, with a value of `r
+round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"],
+3)`,
+while $a_5$ is `r
+round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"],
+4) `.
+Note that unlike `d_{t,j}` or `c_{t,j}`, these *don't* depend on either the time
+$t$ or the location $j$.
 This is what make it a geo-pooled model.
 
-The training data for fitting this linear model is constructed within the `arx_forecaster()` function by shifting a series
-of columns the appropriate amount -- based on the requested `lags`.
-Each row containing no `NA` values is used as a training observation to fit the coefficients $a_0,\ldots, a_6$.
+The training data for estimating the parameters of this linear model is
+constructed within the `arx_forecaster()` function by shifting a series of
+columns the appropriate amount -- based on the requested `lags`.
+Each row containing no `NA` values in the predictors is used as a training observation to fit the
+coefficients $a_0,\ldots, a_6$.
 
 [^4]: in the case of a `{parsnip}` engine which doesn't explicitly predict
     quantiles, these quantiles are created using `layer_residual_quantiles()`,
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index eeb0fb7ad..1396ebba7 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -109,7 +109,7 @@ sample_n(employ, 6)
 ```
 
 In the following sections, we will go over pre-processing the data in the
-`epi_recipe` framework, and fitting a model and making predictions within the
+`epi_recipe` framework, and estimating a model and making predictions within the
 `epipredict` framework and using the package's canned forecasters.
 
 # Autoregressive (AR) model to predict number of graduates in a year
@@ -213,9 +213,9 @@ our `epi_recipe`:
 `lag_2_num_graduates_prop` correspond to $y_{tijk}$, $y_{t-1,ijk}$, and $y_{t-2,ijk}$
 respectively.
 
-## Model fitting and prediction
+## Model estimation and prediction
 
-Since our goal for now is to fit a simple autoregressive model, we can use
+Since our goal for now is to estimate a simple autoregressive model, we can use
 [`parsnip::linear_reg()`](
   https://parsnip.tidymodels.org/reference/linear_reg.html) with the default
 engine `lm`, which fits a linear regression using ordinary least squares.
@@ -333,9 +333,9 @@ rx <- epi_recipe(employ_small) %>%
 bake_and_show_sample(rx, employ_small)
 ```
 
-## Model fitting & post-processing
+## Model estimation & post-processing
 
-Before fitting our model and making predictions, let's add some post-processing
+Before estimating our model and making predictions, let's add some post-processing
 steps using a few [`frosting`](
   https://cmu-delphi.github.io/epipredict/reference/frosting.html) layers to do
 a few things:

From c342680988af520ac228fa4e37a3a35f5d6e6233 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 16 Jun 2025 16:46:04 -0500
Subject: [PATCH 384/387] recommended edits

---
 NAMESPACE                                     |   1 +
 R/arx_classifier.R                            |  22 ++---
 R/arx_forecaster.R                            |  12 +--
 R/climatological_forecaster.R                 |   4 +-
 R/epi_recipe.R                                |   3 +-
 R/epi_workflow.R                              |  19 ++--
 R/extrapolate_quantiles.R                     |  11 ++-
 R/frosting.R                                  |   3 +-
 R/get_test_data.R                             |  11 ++-
 R/layer_population_scaling.R                  |   8 +-
 R/layer_residual_quantiles.R                  |   9 +-
 R/layer_threshold_preds.R                     |   8 +-
 R/layer_unnest.R                              |   2 +-
 R/pivot_quantiles.R                           |  42 ++++----
 R/step_epi_shift.R                            |   7 +-
 R/step_population_scaling.R                   |  12 +--
 R/step_training_window.R                      |   4 +-
 README.md                                     |   2 +-
 _pkgdown.yml                                  |   2 +-
 man/add_epi_recipe.Rd                         |   3 +-
 man/add_frosting.Rd                           |   6 +-
 man/arx_args_list.Rd                          |   3 +-
 man/arx_class_epi_workflow.Rd                 |   5 +-
 man/arx_classifier.Rd                         |  25 +++--
 man/arx_fcast_epi_workflow.Rd                 |   5 +-
 man/arx_forecaster.Rd                         |   9 +-
 man/cdc_baseline_args_list.Rd                 |   3 +-
 man/climate_args_list.Rd                      |   3 +-
 man/extrapolate_quantiles.Rd                  |  11 ++-
 man/figures/README-show-single-forecast-1.png | Bin 292042 -> 283978 bytes
 man/flatline_args_list.Rd                     |   3 +-
 man/forecast.epi_workflow.Rd                  |   2 +-
 man/get_test_data.Rd                          |  11 ++-
 man/grf_quantiles.Rd                          |   4 +-
 man/layer_population_scaling.Rd               |   8 +-
 man/layer_residual_quantiles.Rd               |   9 +-
 man/layer_threshold.Rd                        |   8 +-
 man/layer_unnest.Rd                           |   2 +-
 ...quantiles_longer.Rd => pivot_quantiles.Rd} |  23 ++++-
 man/pivot_quantiles_wider.Rd                  |  35 -------
 man/predict-epi_workflow.Rd                   |  17 ++--
 man/step_adjust_latency.Rd                    |  46 +++++----
 man/step_epi_shift.Rd                         |   7 +-
 man/step_population_scaling.Rd                |  12 +--
 man/step_training_window.Rd                   |   4 +-
 man/within_window.Rd                          |  21 ++++
 vignettes/backtesting.Rmd                     |  93 ++++++++++--------
 vignettes/custom_epiworkflows.Rmd             |  71 +++++++------
 48 files changed, 316 insertions(+), 315 deletions(-)
 rename man/{pivot_quantiles_longer.Rd => pivot_quantiles.Rd} (54%)
 delete mode 100644 man/pivot_quantiles_wider.Rd
 create mode 100644 man/within_window.Rd

diff --git a/NAMESPACE b/NAMESPACE
index c2fa94943..96cf927c5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -190,6 +190,7 @@ export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(pivot_longer)
+export(pivot_quantiles)
 export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
 export(pivot_wider)
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 9122435b1..57812233b 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -34,17 +34,17 @@
 #' ```
 #'
 #' The key takeaway from the predictions is that there are two prediction
-#'   classes: `(-Inf, 0.25]` and `(0.25, Inf)`. This is because for our goal of
-#'   classification the classes must be discrete. The discretization of the
-#'   real-valued outcome is controlled by the `breaks` argument, which defaults
-#'   to `0.25`. Such breaks will be automatically extended to cover the entire
-#'   real line. For example, the default break of `0.25` is silently extended to
-#'   `breaks = c(-Inf, .25, Inf)` and, therefore, results in two classes:
-#'   `[-Inf, 0.25]` and `(0.25, Inf)`. These two classes are used to discretize
-#'   the outcome. The conversion of the outcome to such classes is handled
-#'   internally. So if discrete classes already exist for the outcome in the
-#'   `epi_df`, then we recommend to code a classifier from scratch using the
-#'   `epi_workflow` framework for more control.
+#'   classes: `(-Inf, 0.25]` and `(0.25, Inf)`: the classes to predict must be
+#'   discrete. The discretization of the real-valued outcome is controlled by
+#'   the `breaks` argument, which defaults to `0.25`. Such breaks will be
+#'   automatically extended to cover the entire real line. For example, the
+#'   default break of `0.25` is silently extended to `breaks = c(-Inf, .25,
+#'   Inf)` and, therefore, results in two classes: `[-Inf, 0.25]` and `(0.25,
+#'   Inf)`. These two classes are used to discretize the outcome. The conversion
+#'   of the outcome to such classes is handled internally. So if discrete
+#'   classes already exist for the outcome in the `epi_df`, then we recommend to
+#'   code a classifier from scratch using the `epi_workflow` framework for more
+#'   control.
 #'
 #' The `trainer` is a `parsnip` model describing the type of estimation such
 #'   that `mode = "classification"` is enforced. The two typical trainers that
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 56034bffa..7ef609668 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -3,7 +3,7 @@
 #' This is an autoregressive forecasting model for
 #' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct"
 #' forecasting, meaning that it estimates a model for a particular target
-#' horizon of `outcome` based on the lags of the `predictors`. See the [Get
+#' horizon of the `outcome` based on the lags of the `predictors`. See the [Get
 #' started vignette](../articles/epipredict.html) for some worked examples and
 #' [Custom epi_workflows vignette](../articles/custom_epiworkflows.html) for a
 #' recreation using a custom `epi_workflow()`.
@@ -13,16 +13,15 @@
 #' @param outcome A character (scalar) specifying the outcome (in the `epi_df`).
 #' @param predictors A character vector giving column(s) of predictor variables.
 #'   This defaults to the `outcome`. However, if manually specified, only those
-#'   variables specifically mentioned will be used. (The `outcome` will not be
-#'   added.)  By default, equals the outcome. If manually specified, does not
-#'   add the outcome variable, so make sure to specify it.
+#'   variables specifically mentioned will be used, and the `outcome` will not be
+#'   added.
 #' @param trainer A `{parsnip}` model describing the type of estimation.  For
 #'   now, we enforce `mode = "regression"`.
 #' @param args_list A list of customization arguments to determine the type of
 #'   forecasting model. See [arx_args_list()].
 #'
 #' @return An `arx_fcast`, with the fields `predictions` and `epi_workflow`.
-#'   `predictions` is an `epi_df` of predicted values while `epi_workflow()` is
+#'   `predictions` is a `tibble` of predicted values while `epi_workflow()` is
 #'   the fit workflow used to make those predictions
 #' @export
 #' @seealso [arx_fcast_epi_workflow()], [arx_args_list()]
@@ -270,8 +269,7 @@ arx_fcast_epi_workflow <- function(
 #'   training residuals. A `NULL` value will result in point forecasts only.
 #' @param symmetrize Logical. The default `TRUE` calculates symmetric prediction
 #'   intervals. This argument only applies when residual quantiles are used. It
-#'   is not applicable with `trainer = quantile_reg()`, for example. This is
-#'   achieved by including both the residuals and their negation. Typically, one
+#'   is not applicable with `trainer = quantile_reg()`, for example. Typically, one
 #'   would only want non-symmetric quantiles when increasing trajectories are
 #'   quite different from decreasing ones, such as a strictly postive variable
 #'   near zero.
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 592fcccc0..1748f2103 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -134,12 +134,12 @@ climatological_forecaster <- function(epi_data,
   # get the distinct .idx for the target date(s)
   distinct_target_idx <- predictions$.idx %>% unique()
   # get all of the idx's within the window of the target .idxs
-  entries <- map(distinct_target_idx, \(idx) within_window(idx, window_size, modulus)) %>%
+  entries <- map(distinct_target_idx, function(idx) within_window(idx, window_size, modulus)) %>%
     do.call(c, .) %>%
     unique()
   # for the center, we need those within twice the window, since for each point
   # we're subtracting out the center to generate the quantiles
-  entries_double_window <- map(entries, \(idx) within_window(idx, window_size, modulus)) %>%
+  entries_double_window <- map(entries, function(idx) within_window(idx, window_size, modulus)) %>%
     do.call(c, .) %>%
     unique()
 
diff --git a/R/epi_recipe.R b/R/epi_recipe.R
index 3f90e40b1..02a463ed9 100644
--- a/R/epi_recipe.R
+++ b/R/epi_recipe.R
@@ -232,8 +232,7 @@ is_epi_recipe <- function(x) {
 
 
 
-#' Given an `epi_recipe`, add it to, remove it from, or update it in an
-#' `epi_workflow`
+#' Add/remove/update the `epi_recipe` of an `epi_workflow`
 #'
 #' @description
 #' - `add_recipe()` specifies the terms of the model and any preprocessing that
diff --git a/R/epi_workflow.R b/R/epi_workflow.R
index 6aac401b2..b17bd697b 100644
--- a/R/epi_workflow.R
+++ b/R/epi_workflow.R
@@ -113,14 +113,19 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' @description
 #' This is the `predict()` method for a fit epi_workflow object. The 3 steps that this implements are:
+#' - Preprocess `new_data` using the preprocessing method specified when the
+#'   workflow was created and fit. This is accomplished using
+#'   [hardhat::forge()], which will apply any formula preprocessing or call
+#'   [recipes::bake()] if a recipe was supplied.
 #'
 #' - Preprocessing `new_data` using the preprocessing method specified when the
 #'   epi_workflow was created and fit. This is accomplished using
-#'   `recipes::bake()` if a recipe was supplied. Note that this is a slightly
-#'   different `bake` operation than the one occuring during the fit. Any `step`
-#'   that has `skip = TRUE` isn't applied during prediction; for example in
-#'   `step_epi_naomit()`, `all_outcomes()` isn't `NA` omitted, since doing so
-#'   would drop the exact `time_values` we are trying to predict.
+#'   `hardhat::bake()` if a recipe was supplied (passing through
+#'   [hardhat::forge()], which is used for non-recipe preprocessors). Note that
+#'   this is a slightly different `bake` operation than the one occuring during
+#'   the fit. Any `step` that has `skip = TRUE` isn't applied during prediction;
+#'   for example in `step_epi_naomit()`, `all_outcomes()` isn't `NA` omitted,
+#'   since doing so would drop the exact `time_values` we are trying to predict.
 #'
 #' - Calling `parsnip::predict.model_fit()` for you using the underlying fit
 #'   parsnip model.
@@ -137,7 +142,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' @return
 #' A data frame of model predictions, with as many rows as `new_data` has.
-#' If `new_data` is an `epi_df()` or a data frame with `time_value` or
+#' If `new_data` is an `epiprocess::epi_df` or a data frame with `time_value` or
 #' `geo_value` columns, then the result will have those as well.
 #'
 #' @name predict-epi_workflow
@@ -234,7 +239,7 @@ print.epi_workflow <- function(x, ...) {
 }
 
 
-#' Produce a forecast from just an epi workflow
+#' Produce a forecast from an epi workflow and it's training data
 #'
 #' `forecast.epi_workflow` predicts by restricting the training data to the
 #' latest available data, and predicting on that. It binds together
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index ec68d8256..3736a2772 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -3,14 +3,15 @@
 #' This both interpolates between quantile levels already defined in `x` and
 #' extrapolates quantiles outside their bounds. The interpolation method is
 #' determined by the `quantile` argument `middle`, which can be either `"cubic"`
-#' for a (hyman) cubic spline interpolation, or `"linear"` for simple linear
+#' for a (Hyman) cubic spline interpolation, or `"linear"` for simple linear
 #' interpolation.
 #'
 #' There is only one extrapolation method for values greater than the largest
-#' known quantile level or smaller than the smallest known quantile level. It
-#' assumes a roughly exponential tail, whose decay rate and offset is derived
-#' from the slope of the two most extreme quantile levels on a logistic scale.
-#' See the internal function `tail_extrapolate()` for the exact implementation.
+#' available quantile level or smaller than the smallest available quantile
+#' level. It assumes a roughly exponential tail, whose decay rate and offset is
+#' derived from the slope of the two most extreme quantile levels on a logistic
+#' scale.  See the internal function `tail_extrapolate()` for the exact
+#' implementation.
 #'
 #' This function takes a `quantile_pred` vector and returns the same
 #' type of object, expanded to include
diff --git a/R/frosting.R b/R/frosting.R
index 65115cbcf..dca80392b 100644
--- a/R/frosting.R
+++ b/R/frosting.R
@@ -1,5 +1,4 @@
-#' Given a `frosting()`, add it to, remove it from, or update it in an
-#' `epi_workflow`
+#' Add/remove/update the `frosting` of an `epi_workflow`
 #'
 #' @param x A workflow
 #' @param frosting A frosting object created using `frosting()`.
diff --git a/R/get_test_data.R b/R/get_test_data.R
index fd01f10e2..174308242 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -1,9 +1,12 @@
 #' Get test data for prediction based on longest lag period
 #'
-#' If `predict()` is given the full training dataset, it will produce a forecast
-#' for every day which has enough data. For most cases, this is far more
-#' forecasts than is necessary. `get_test_data()` is designed to restrict the given dataset to the minimum amount needed to produce a forecast on the `forecast_date`.
-#' Primarily this is based on the longest lag period in the recipe.
+#' If `predict()` is given the full training dataset, it will produce a
+#' prediction for every `time_value` which has enough data. For most cases, this
+#' generates predictions for `time_values` where the `outcome` has already been
+#' observed.  `get_test_data()` is designed to restrict the given dataset to the
+#' minimum amount needed to produce a forecast on the `forecast_date` for future
+#' data, rather than a prediction on past `time_value`s.  Primarily this is
+#' based on the longest lag period in the recipe.
 #'
 #' The minimum required (recent) data to produce a forecast is equal to
 #' the maximum lag requested (on any predictor) plus the longest horizon
diff --git a/R/layer_population_scaling.R b/R/layer_population_scaling.R
index 91aae77c6..cc77bf03b 100644
--- a/R/layer_population_scaling.R
+++ b/R/layer_population_scaling.R
@@ -1,10 +1,10 @@
 #' Convert per-capita predictions to raw scale
 #'
 #' `layer_population_scaling` creates a specification of a frosting layer that
-#' will "undo" per-capita scaling done in `step_population_scaling()`. Typical
-#' usage would set `df` to be a dataset that contains state-level population,
-#' and use it to convert predictions made from a raw scale model to rate-scale
-#' by dividing by the population.
+#' will "undo" per-capita scaling done in `step_population_scaling()`.
+#' Typical usage would set `df` to be a dataset that contains a list of
+#' population for the `geo_value`s, and use it to convert predictions made from
+#' a raw scale model to rate-scale by dividing by the population.
 #' Although, it is worth noting that there is nothing special about
 #' "population", and  the function can be used to scale by any variable.
 #' Population is the standard use case in the epidemiology forecasting scenario.
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 779bf36b6..26ec9a184 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -1,9 +1,9 @@
 #' Creates predictions based on residual quantiles
 #'
-#' This function calculates quantiles based on the empirical quantiles of the
-#' model's residuals. If the model producing the forecast is distributional, it
-#' is recommended to use `layer_residual_quantiles()` instead, as those will be
-#' significantly more accurate.
+#' This function calculates predictive quantiles based on the empirical
+#' quantiles of the model's residuals. If the model producing the forecast is
+#' distributional, it is recommended to use `layer_residual_quantiles()`
+#' instead, as those will be more accurate.
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
@@ -11,7 +11,6 @@
 #'   referring to the desired quantile. Note that 0.5 will always be included
 #'   even if left out by the user.
 #' @param symmetrize logical. If `TRUE` then the interval will be symmetric.
-#'   This is achieved by including both the residuals and their negations.
 #'   Typically, one would only want non-symmetric quantiles when increasing
 #'   trajectories are quite different from decreasing ones, such as a strictly
 #'   postive variable near zero.
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 9e8d3bbae..1630ce629 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -6,11 +6,7 @@
 
 #' @details
 #' Making case count predictions strictly positive is a typical example usage.
-#'   It can be called before or after the quantiles are created using
-#'   `layer_quantile_distn()` since the quantiles are an inherent part of the
-#'   result from `layer_predict()` for distributional models, but must be called
-#'   after `layer_residual_quantiles()`, since the quantiles for that case don't
-#'   exist until after that layer.
+#'   It must be called after there is a column containing quantiles. This means at earliest it can be called after `layer_predict()` for distributional models, or after `layer_residual_quantiles()` for point prediction models. Typical best practice will use `starts_with(".pred")` as the variables to threshold.
 #'
 #' @param frosting a `frosting` postprocessor
 #' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
@@ -41,7 +37,7 @@
 #'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
-#'   layer_threshold(.pred, lower = 0.180, upper = 0.310)
+#'   layer_threshold(starts_with(".pred"), lower = 0.180, upper = 0.310)
 #' wf <- wf %>% add_frosting(f)
 #' p <- forecast(wf)
 #' p
diff --git a/R/layer_unnest.R b/R/layer_unnest.R
index ffac480e9..e097ad465 100644
--- a/R/layer_unnest.R
+++ b/R/layer_unnest.R
@@ -2,7 +2,7 @@
 #'
 #' For any model that produces forecasts for multiple outcomes, such as multiple
 #' aheads, the resulting prediction is a list of forecasts inside a column of
-#' the prediction tibble, which is not an ideal format. This layer "lengthens"
+#' the prediction tibble, which is may not be desirable. This layer "lengthens"
 #' the result, moving each outcome to a separate row, in the same manner as
 #' `tidyr::unnest()` would. At the moment, the only such engine is
 #' `smooth_quantile_reg()`.
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 75fff6e3e..3534b4eed 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -32,9 +32,17 @@ nested_quantiles <- function(x) {
 }
 
 
-#' Pivot a column containing `quantile_pred` longer
+#' Pivot a column containing `quantile_pred` to explicit rows or columns
 #'
-#' Selected columns that contain `quantile_pred` will be "lengthened" with the
+#' Both functions expand a column of `quantile_pred`s into the separate
+#' quantiles. Since each consists of a set of names (quantiles) and values,
+#' these operate analogously with `pivot_wider` and `pivot_longer`.
+#'
+#' `piot_quantiles_wider` creates a new column for each `quantile_level`, with
+#' the values as the corresponding quantile values.  When pivoting multiple
+#' columns, the original column name will be used as a prefix.
+#'
+#' Similarly, `pivot_quantiles_longer` assigns the selected columns
 #' `quantile_level`s in one column and the `value`s in another. If multiple
 #' columns are selected, these will be prefixed with the column name.
 #'
@@ -47,6 +55,7 @@ nested_quantiles <- function(x) {
 #'
 #' @return An object of the same class as `.data`.
 #' @export
+#' @name pivot_quantiles
 #'
 #' @examples
 #' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
@@ -56,6 +65,15 @@ nested_quantiles <- function(x) {
 #' pivot_quantiles_longer(tib, "d1")
 #' pivot_quantiles_longer(tib, dplyr::ends_with("1"))
 #' pivot_quantiles_longer(tib, d2)
+#'
+#' pivot_quantiles_wider(tib, "d1")
+#' pivot_quantiles_wider(tib, dplyr::ends_with("2"))
+#' pivot_quantiles_wider(tib, d2)
+NULL
+
+
+#' @rdname pivot_quantiles
+#' @export
 pivot_quantiles_longer <- function(.data, ...) {
   col <- validate_pivot_quantiles(.data, ...)
   .data$.row <- seq_len(vctrs::vec_size(.data))
@@ -66,26 +84,8 @@ pivot_quantiles_longer <- function(.data, ...) {
     select(!.row)
 }
 
-#' Pivot a column containing `quantile_pred` wider
-#'
-#' Any selected columns that contain `quantile_pred` will be "widened" with the
-#' "taus" (quantile) serving as column names and the values in the corresponding
-#' column.  When pivoting multiple columns, the original column name will be
-#' used as a prefix.
-#'
-#' @inheritParams pivot_quantiles_longer
-#'
-#' @return An object of the same class as `.data`
+#' @rdname pivot_quantiles
 #' @export
-#'
-#' @examples
-#' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
-#' d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
-#' tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
-#'
-#' pivot_quantiles_wider(tib, "d1")
-#' pivot_quantiles_wider(tib, dplyr::ends_with("2"))
-#' pivot_quantiles_wider(tib, d2)
 pivot_quantiles_wider <- function(.data, ...) {
   col <- validate_pivot_quantiles(.data, ...)
   .data$.row <- seq_len(vctrs::vec_size(.data))
diff --git a/R/step_epi_shift.R b/R/step_epi_shift.R
index 862c224e5..376728752 100644
--- a/R/step_epi_shift.R
+++ b/R/step_epi_shift.R
@@ -1,11 +1,11 @@
 #' Create a shifted predictor
 #'
 #' `step_epi_lag` and `step_epi_ahead` create a *specification* of a recipe step
-#'   that will add new columns of shifted data. The `step_epi_lag` will created
+#'   that will add new columns of shifted data. The `step_epi_lag` will create
 #'   a lagged `predictor` column, while `step_epi_ahead` will create a leading
 #'   `outcome` column. Shifted data will by default include NA values where the
 #'   shift was induced.  These can be properly removed with [step_epi_naomit()],
-#'   or you may specify an alternative filler value with the `default` argument.
+#'   or you may specify an alternative value with the `default` argument.
 #'
 #'
 #' @param recipe A recipe object. The step will be added to the
@@ -29,8 +29,7 @@
 #' @param id A unique identifier for the step
 #' @template step-return
 #'
-#' @details The step assumes that the data's `time_value` column is already _in
-#'   the proper sequential order_ for shifting.
+#' @details
 #'
 #'  Our `lag/ahead` functions respect the `geo_value` and `other_keys` of the
 #'   `epi_df`, and allow for discontiguous `time_value`s. Both of these features
diff --git a/R/step_population_scaling.R b/R/step_population_scaling.R
index cd3a54f83..0721b30c0 100644
--- a/R/step_population_scaling.R
+++ b/R/step_population_scaling.R
@@ -2,9 +2,9 @@
 #'
 #' `step_population_scaling()` creates a specification of a recipe step that
 #' will perform per-capita scaling. Typical usage would set `df` to be a dataset
-#' that contains state-level population, and use it to convert predictions made
-#' from a raw scale model to rate-scale by dividing by the population.
-#' Although, it is worth noting that there is nothing special about
+#' that contains population for each `geo_value`, and use it to convert
+#' predictions made from a raw scale model to rate-scale by dividing by the
+#' population.  Although, it is worth noting that there is nothing special about
 #' "population", and the function can be used to scale by any variable.
 #' Population is the standard use case in the epidemiology forecasting scenario.
 #' Any value passed will *divide* the selected variables while the
@@ -13,10 +13,10 @@
 #' @inheritParams step_epi_lag
 #' @param role For model terms created by this step, what analysis role should
 #'   they be assigned?
-#' @param df a data frame containing the scaling data (such as population). The
+#' @param df a data frame containing the scaling data (typically population). The
 #'   target column is divided by the value in `df_pop_col`.
-#' @param by A (possibly named) character vector of variables to join `df` onto
-#'   the `epi_df` by.
+#' @param by A (possibly named) character vector of variables by which to join
+#'   `df` to the `epi_df`.
 #'
 #' If `NULL`, the default, the function will try to infer a reasonable set of
 #' columns. First, it will try to join by all variables in the training/test
diff --git a/R/step_training_window.R b/R/step_training_window.R
index 51361b4f9..dec380567 100644
--- a/R/step_training_window.R
+++ b/R/step_training_window.R
@@ -18,8 +18,8 @@
 #'   `step_epi_lag()`, or `step_epi_naomit()` steps. If `step_training_window()`
 #'   happens first, there will be less than `n_training` remaining examples,
 #'   since either leading or lagging will introduce `NA`'s later removed by
-#'   `step_epi_naomit()`. Typical usage will have this function applied after
-#'   every other step.
+#'   `step_epi_naomit()`. Typical usage will use this step last in an
+#'   `epi_recipe()`.
 #'
 #' @export
 #'
diff --git a/README.md b/README.md
index 15e14c693..97ee81219 100644
--- a/README.md
+++ b/README.md
@@ -235,7 +235,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-04-09 17:23:00.
+#> This forecaster was fit on 2025-05-22 11:56:44.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 32da5cae7..82ac5bf5e 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -128,9 +128,9 @@ reference:
 
   - subtitle: Utilities for quantile distribution processing
     contents:
+      - pivot_quantiles
       - dist_quantiles
       - contains("quantile_pred")
       - extrapolate_quantiles
       - nested_quantiles
       - weighted_interval_score
-      - starts_with("pivot_quantiles")
diff --git a/man/add_epi_recipe.Rd b/man/add_epi_recipe.Rd
index f5cb32a73..e750c72ba 100644
--- a/man/add_epi_recipe.Rd
+++ b/man/add_epi_recipe.Rd
@@ -4,8 +4,7 @@
 \alias{add_epi_recipe}
 \alias{remove_epi_recipe}
 \alias{update_epi_recipe}
-\title{Given an \code{epi_recipe}, add it to, remove it from, or update it in an
-\code{epi_workflow}}
+\title{Add/remove/update the \code{epi_recipe} of an \code{epi_workflow}}
 \usage{
 add_epi_recipe(x, recipe, ..., blueprint = default_epi_recipe_blueprint())
 
diff --git a/man/add_frosting.Rd b/man/add_frosting.Rd
index 6a38b359e..3e7be7647 100644
--- a/man/add_frosting.Rd
+++ b/man/add_frosting.Rd
@@ -4,8 +4,7 @@
 \alias{add_frosting}
 \alias{remove_frosting}
 \alias{update_frosting}
-\title{Given a \code{frosting()}, add it to, remove it from, or update it in an
-\code{epi_workflow}}
+\title{Add/remove/update the \code{frosting} of an \code{epi_workflow}}
 \usage{
 add_frosting(x, frosting, ...)
 
@@ -24,8 +23,7 @@ update_frosting(x, frosting, ...)
 \code{x}, updated with a new frosting postprocessor
 }
 \description{
-Given a \code{frosting()}, add it to, remove it from, or update it in an
-\code{epi_workflow}
+Add/remove/update the \code{frosting} of an \code{epi_workflow}
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index ca00bcc5c..ce4d57fce 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -68,8 +68,7 @@ training residuals. A \code{NULL} value will result in point forecasts only.}
 
 \item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
 intervals. This argument only applies when residual quantiles are used. It
-is not applicable with \code{trainer = quantile_reg()}, for example. This is
-achieved by including both the residuals and their negation. Typically, one
+is not applicable with \code{trainer = quantile_reg()}, for example. Typically, one
 would only want non-symmetric quantiles when increasing trajectories are
 quite different from decreasing ones, such as a strictly postive variable
 near zero.}
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index be4ecedef..aec29a286 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -24,9 +24,8 @@ code up a classifier from scratch using \code{\link[=epi_recipe]{epi_recipe()}}.
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
 This defaults to the \code{outcome}. However, if manually specified, only those
-variables specifically mentioned will be used. (The \code{outcome} will not be
-added.)  By default, equals the outcome. If manually specified, does not
-add the outcome variable, so make sure to specify it.}
+variables specifically mentioned will be used, and the \code{outcome} will not be
+added.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation. For
 now, we enforce \code{mode = "classification"}. Typical values are
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index 61cb8db8c..48ed72fcc 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -24,9 +24,8 @@ code up a classifier from scratch using \code{\link[=epi_recipe]{epi_recipe()}}.
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
 This defaults to the \code{outcome}. However, if manually specified, only those
-variables specifically mentioned will be used. (The \code{outcome} will not be
-added.)  By default, equals the outcome. If manually specified, does not
-add the outcome variable, so make sure to specify it.}
+variables specifically mentioned will be used, and the \code{outcome} will not be
+added.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation.
 For now, we enforce \code{mode = "classification"}. Typical values are
@@ -82,17 +81,15 @@ out$predictions
 }\if{html}{\out{</div>}}
 
 The key takeaway from the predictions is that there are two prediction
-classes: \verb{(-Inf, 0.25]} and \verb{(0.25, Inf)}. This is because for our goal of
-classification the classes must be discrete. The discretization of the
-real-valued outcome is controlled by the \code{breaks} argument, which defaults
-to \code{0.25}. Such breaks will be automatically extended to cover the entire
-real line. For example, the default break of \code{0.25} is silently extended to
-\code{breaks = c(-Inf, .25, Inf)} and, therefore, results in two classes:
-\verb{[-Inf, 0.25]} and \verb{(0.25, Inf)}. These two classes are used to discretize
-the outcome. The conversion of the outcome to such classes is handled
-internally. So if discrete classes already exist for the outcome in the
-\code{epi_df}, then we recommend to code a classifier from scratch using the
-\code{epi_workflow} framework for more control.
+classes: \verb{(-Inf, 0.25]} and \verb{(0.25, Inf)}: the classes to predict must be
+discrete. The discretization of the real-valued outcome is controlled by
+the \code{breaks} argument, which defaults to \code{0.25}. Such breaks will be
+automatically extended to cover the entire real line. For example, the
+default break of \code{0.25} is silently extended to \code{breaks = c(-Inf, .25, Inf)} and, therefore, results in two classes: \verb{[-Inf, 0.25]} and \verb{(0.25, Inf)}. These two classes are used to discretize the outcome. The conversion
+of the outcome to such classes is handled internally. So if discrete
+classes already exist for the outcome in the \code{epi_df}, then we recommend to
+code a classifier from scratch using the \code{epi_workflow} framework for more
+control.
 
 The \code{trainer} is a \code{parsnip} model describing the type of estimation such
 that \code{mode = "classification"} is enforced. The two typical trainers that
diff --git a/man/arx_fcast_epi_workflow.Rd b/man/arx_fcast_epi_workflow.Rd
index 4aa97ff45..97fa16273 100644
--- a/man/arx_fcast_epi_workflow.Rd
+++ b/man/arx_fcast_epi_workflow.Rd
@@ -19,9 +19,8 @@ arx_fcast_epi_workflow(
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
 This defaults to the \code{outcome}. However, if manually specified, only those
-variables specifically mentioned will be used. (The \code{outcome} will not be
-added.)  By default, equals the outcome. If manually specified, does not
-add the outcome variable, so make sure to specify it.}
+variables specifically mentioned will be used, and the \code{outcome} will not be
+added.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation. For
 now, we enforce \code{mode = "regression"}. May be \code{NULL} if you'd like to
diff --git a/man/arx_forecaster.Rd b/man/arx_forecaster.Rd
index 85c25c115..63b5cfb8c 100644
--- a/man/arx_forecaster.Rd
+++ b/man/arx_forecaster.Rd
@@ -19,9 +19,8 @@ arx_forecaster(
 
 \item{predictors}{A character vector giving column(s) of predictor variables.
 This defaults to the \code{outcome}. However, if manually specified, only those
-variables specifically mentioned will be used. (The \code{outcome} will not be
-added.)  By default, equals the outcome. If manually specified, does not
-add the outcome variable, so make sure to specify it.}
+variables specifically mentioned will be used, and the \code{outcome} will not be
+added.}
 
 \item{trainer}{A \code{{parsnip}} model describing the type of estimation.  For
 now, we enforce \code{mode = "regression"}.}
@@ -31,14 +30,14 @@ forecasting model. See \code{\link[=arx_args_list]{arx_args_list()}}.}
 }
 \value{
 An \code{arx_fcast}, with the fields \code{predictions} and \code{epi_workflow}.
-\code{predictions} is an \code{epi_df} of predicted values while \code{epi_workflow()} is
+\code{predictions} is a \code{tibble} of predicted values while \code{epi_workflow()} is
 the fit workflow used to make those predictions
 }
 \description{
 This is an autoregressive forecasting model for
 \link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct"
 forecasting, meaning that it estimates a model for a particular target
-horizon of \code{outcome} based on the lags of the \code{predictors}. See the \href{../articles/epipredict.html}{Get started vignette} for some worked examples and
+horizon of the \code{outcome} based on the lags of the \code{predictors}. See the \href{../articles/epipredict.html}{Get started vignette} for some worked examples and
 \href{../articles/custom_epiworkflows.html}{Custom epi_workflows vignette} for a
 recreation using a custom \code{epi_workflow()}.
 }
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 2c557e5dc..a837bd7a0 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -53,8 +53,7 @@ in linear interpolation on the X scale. This is achieved with
 
 \item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
 intervals. This argument only applies when residual quantiles are used. It
-is not applicable with \code{trainer = quantile_reg()}, for example. This is
-achieved by including both the residuals and their negation. Typically, one
+is not applicable with \code{trainer = quantile_reg()}, for example. Typically, one
 would only want non-symmetric quantiles when increasing trajectories are
 quite different from decreasing ones, such as a strictly postive variable
 near zero.}
diff --git a/man/climate_args_list.Rd b/man/climate_args_list.Rd
index bb1ef7eab..b2b91061b 100644
--- a/man/climate_args_list.Rd
+++ b/man/climate_args_list.Rd
@@ -48,8 +48,7 @@ training residuals. A \code{NULL} value will result in point forecasts only.}
 
 \item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
 intervals. This argument only applies when residual quantiles are used. It
-is not applicable with \code{trainer = quantile_reg()}, for example. This is
-achieved by including both the residuals and their negation. Typically, one
+is not applicable with \code{trainer = quantile_reg()}, for example. Typically, one
 would only want non-symmetric quantiles when increasing trajectories are
 quite different from decreasing ones, such as a strictly postive variable
 near zero.}
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index 5b6b97ff0..68def9b2e 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -25,15 +25,16 @@ of the original \code{quantile_values} (the union of those and \code{probs}).
 This both interpolates between quantile levels already defined in \code{x} and
 extrapolates quantiles outside their bounds. The interpolation method is
 determined by the \code{quantile} argument \code{middle}, which can be either \code{"cubic"}
-for a (hyman) cubic spline interpolation, or \code{"linear"} for simple linear
+for a (Hyman) cubic spline interpolation, or \code{"linear"} for simple linear
 interpolation.
 }
 \details{
 There is only one extrapolation method for values greater than the largest
-known quantile level or smaller than the smallest known quantile level. It
-assumes a roughly exponential tail, whose decay rate and offset is derived
-from the slope of the two most extreme quantile levels on a logistic scale.
-See the internal function \code{tail_extrapolate()} for the exact implementation.
+available quantile level or smaller than the smallest available quantile
+level. It assumes a roughly exponential tail, whose decay rate and offset is
+derived from the slope of the two most extreme quantile levels on a logistic
+scale.  See the internal function \code{tail_extrapolate()} for the exact
+implementation.
 
 This function takes a \code{quantile_pred} vector and returns the same
 type of object, expanded to include
diff --git a/man/figures/README-show-single-forecast-1.png b/man/figures/README-show-single-forecast-1.png
index 8756795ded01f722cf332964b5adea6c1f08a16b..8ec4f90a113bf10555c23973c41b352fff4bfd20 100644
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diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 7c9bb0399..368b8d141 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -46,8 +46,7 @@ training residuals. A \code{NULL} value will result in point forecasts only.}
 
 \item{symmetrize}{Logical. The default \code{TRUE} calculates symmetric prediction
 intervals. This argument only applies when residual quantiles are used. It
-is not applicable with \code{trainer = quantile_reg()}, for example. This is
-achieved by including both the residuals and their negation. Typically, one
+is not applicable with \code{trainer = quantile_reg()}, for example. Typically, one
 would only want non-symmetric quantiles when increasing trajectories are
 quite different from decreasing ones, such as a strictly postive variable
 near zero.}
diff --git a/man/forecast.epi_workflow.Rd b/man/forecast.epi_workflow.Rd
index feae32f68..1792fe1f9 100644
--- a/man/forecast.epi_workflow.Rd
+++ b/man/forecast.epi_workflow.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/epi_workflow.R
 \name{forecast.epi_workflow}
 \alias{forecast.epi_workflow}
-\title{Produce a forecast from just an epi workflow}
+\title{Produce a forecast from an epi workflow and it's training data}
 \usage{
 \method{forecast}{epi_workflow}(object, ...)
 }
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index 3c3317067..8d99a50a7 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -17,10 +17,13 @@ An object of the same type as \code{x} with columns \code{geo_value}, \code{time
 keys, as well other variables in the original dataset.
 }
 \description{
-If \code{predict()} is given the full training dataset, it will produce a forecast
-for every day which has enough data. For most cases, this is far more
-forecasts than is necessary. \code{get_test_data()} is designed to restrict the given dataset to the minimum amount needed to produce a forecast on the \code{forecast_date}.
-Primarily this is based on the longest lag period in the recipe.
+If \code{predict()} is given the full training dataset, it will produce a
+prediction for every \code{time_value} which has enough data. For most cases, this
+generates predictions for \code{time_values} where the \code{outcome} has already been
+observed.  \code{get_test_data()} is designed to restrict the given dataset to the
+minimum amount needed to produce a forecast on the \code{forecast_date} for future
+data, rather than a prediction on past \code{time_value}s.  Primarily this is
+based on the longest lag period in the recipe.
 }
 \details{
 The minimum required (recent) data to produce a forecast is equal to
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index ce40b6845..2e4b8bcbb 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -52,8 +52,8 @@ details, see \href{https://grf-labs.github.io/grf/articles/categorical_inputs.ht
 #> Model fit template:
 #> grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~integer(1), 
 #>     x), num.trees = integer(1), min.node.size = min_rows(~integer(1), 
-#>     x), quantiles = c(0.1, 0.5, 0.9), num.threads = 1L, seed = stats::runif(1, 
-#>     0, .Machine$integer.max))
+#>     x), quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95), 
+#>     num.threads = 1L, seed = stats::runif(1, 0, .Machine$integer.max))
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/layer_population_scaling.Rd b/man/layer_population_scaling.Rd
index 25164bbd9..6a08f57cd 100644
--- a/man/layer_population_scaling.Rd
+++ b/man/layer_population_scaling.Rd
@@ -69,10 +69,10 @@ an updated \code{frosting} postprocessor
 }
 \description{
 \code{layer_population_scaling} creates a specification of a frosting layer that
-will "undo" per-capita scaling done in \code{step_population_scaling()}. Typical
-usage would set \code{df} to be a dataset that contains state-level population,
-and use it to convert predictions made from a raw scale model to rate-scale
-by dividing by the population.
+will "undo" per-capita scaling done in \code{step_population_scaling()}.
+Typical usage would set \code{df} to be a dataset that contains a list of
+population for the \code{geo_value}s, and use it to convert predictions made from
+a raw scale model to rate-scale by dividing by the population.
 Although, it is worth noting that there is nothing special about
 "population", and  the function can be used to scale by any variable.
 Population is the standard use case in the epidemiology forecasting scenario.
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index e879677f3..ab19a9511 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -24,7 +24,6 @@ referring to the desired quantile. Note that 0.5 will always be included
 even if left out by the user.}
 
 \item{symmetrize}{logical. If \code{TRUE} then the interval will be symmetric.
-This is achieved by including both the residuals and their negations.
 Typically, one would only want non-symmetric quantiles when increasing
 trajectories are quite different from decreasing ones, such as a strictly
 postive variable near zero.}
@@ -41,10 +40,10 @@ an updated \code{frosting} postprocessor with additional columns of the
 residual quantiles added to the prediction
 }
 \description{
-This function calculates quantiles based on the empirical quantiles of the
-model's residuals. If the model producing the forecast is distributional, it
-is recommended to use \code{layer_residual_quantiles()} instead, as those will be
-significantly more accurate.
+This function calculates predictive quantiles based on the empirical
+quantiles of the model's residuals. If the model producing the forecast is
+distributional, it is recommended to use \code{layer_residual_quantiles()}
+instead, as those will be more accurate.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
diff --git a/man/layer_threshold.Rd b/man/layer_threshold.Rd
index 77f0a9aa2..e89347d9d 100644
--- a/man/layer_threshold.Rd
+++ b/man/layer_threshold.Rd
@@ -41,11 +41,7 @@ threshold values.
 }
 \details{
 Making case count predictions strictly positive is a typical example usage.
-It can be called before or after the quantiles are created using
-\code{layer_quantile_distn()} since the quantiles are an inherent part of the
-result from \code{layer_predict()} for distributional models, but must be called
-after \code{layer_residual_quantiles()}, since the quantiles for that case don't
-exist until after that layer.
+It must be called after there is a column containing quantiles. This means at earliest it can be called after \code{layer_predict()} for distributional models, or after \code{layer_residual_quantiles()} for point prediction models. Typical best practice will use \code{starts_with(".pred")} as the variables to threshold.
 }
 \examples{
 jhu <- covid_case_death_rates \%>\%
@@ -59,7 +55,7 @@ wf <- epi_workflow(r, linear_reg()) \%>\% fit(jhu)
 
 f <- frosting() \%>\%
   layer_predict() \%>\%
-  layer_threshold(.pred, lower = 0.180, upper = 0.310)
+  layer_threshold(starts_with(".pred"), lower = 0.180, upper = 0.310)
 wf <- wf \%>\% add_frosting(f)
 p <- forecast(wf)
 p
diff --git a/man/layer_unnest.Rd b/man/layer_unnest.Rd
index dfa1f4dc1..f50ce6ccd 100644
--- a/man/layer_unnest.Rd
+++ b/man/layer_unnest.Rd
@@ -22,7 +22,7 @@ an updated \code{frosting} postprocessor
 \description{
 For any model that produces forecasts for multiple outcomes, such as multiple
 aheads, the resulting prediction is a list of forecasts inside a column of
-the prediction tibble, which is not an ideal format. This layer "lengthens"
+the prediction tibble, which is may not be desirable. This layer "lengthens"
 the result, moving each outcome to a separate row, in the same manner as
 \code{tidyr::unnest()} would. At the moment, the only such engine is
 \code{smooth_quantile_reg()}.
diff --git a/man/pivot_quantiles_longer.Rd b/man/pivot_quantiles.Rd
similarity index 54%
rename from man/pivot_quantiles_longer.Rd
rename to man/pivot_quantiles.Rd
index 3c112b12f..fcf8ded67 100644
--- a/man/pivot_quantiles_longer.Rd
+++ b/man/pivot_quantiles.Rd
@@ -1,10 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/pivot_quantiles.R
-\name{pivot_quantiles_longer}
+\name{pivot_quantiles}
+\alias{pivot_quantiles}
 \alias{pivot_quantiles_longer}
-\title{Pivot a column containing \code{quantile_pred} longer}
+\alias{pivot_quantiles_wider}
+\title{Pivot a column containing \code{quantile_pred} to explicit rows or columns}
 \usage{
 pivot_quantiles_longer(.data, ...)
+
+pivot_quantiles_wider(.data, ...)
 }
 \arguments{
 \item{.data}{A data frame, or a data frame extension such as a tibble or
@@ -19,7 +23,16 @@ can be selected for this operation.}
 An object of the same class as \code{.data}.
 }
 \description{
-Selected columns that contain \code{quantile_pred} will be "lengthened" with the
+Both functions expand a column of \code{quantile_pred}s into the separate
+quantiles. Since each consists of a set of names (quantiles) and values,
+these operate analogously with \code{pivot_wider} and \code{pivot_longer}.
+}
+\details{
+\code{piot_quantiles_wider} creates a new column for each \code{quantile_level}, with
+the values as the corresponding quantile values.  When pivoting multiple
+columns, the original column name will be used as a prefix.
+
+Similarly, \code{pivot_quantiles_longer} assigns the selected columns
 \code{quantile_level}s in one column and the \code{value}s in another. If multiple
 columns are selected, these will be prefixed with the column name.
 }
@@ -31,4 +44,8 @@ tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 pivot_quantiles_longer(tib, "d1")
 pivot_quantiles_longer(tib, dplyr::ends_with("1"))
 pivot_quantiles_longer(tib, d2)
+
+pivot_quantiles_wider(tib, "d1")
+pivot_quantiles_wider(tib, dplyr::ends_with("2"))
+pivot_quantiles_wider(tib, d2)
 }
diff --git a/man/pivot_quantiles_wider.Rd b/man/pivot_quantiles_wider.Rd
deleted file mode 100644
index bba08e292..000000000
--- a/man/pivot_quantiles_wider.Rd
+++ /dev/null
@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/pivot_quantiles.R
-\name{pivot_quantiles_wider}
-\alias{pivot_quantiles_wider}
-\title{Pivot a column containing \code{quantile_pred} wider}
-\usage{
-pivot_quantiles_wider(.data, ...)
-}
-\arguments{
-\item{.data}{A data frame, or a data frame extension such as a tibble or
-epi_df.}
-
-\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One unquoted
-expressions separated by commas. Variable names can be used as if they
-were positions in the data frame. Note that only one variable
-can be selected for this operation.}
-}
-\value{
-An object of the same class as \code{.data}
-}
-\description{
-Any selected columns that contain \code{quantile_pred} will be "widened" with the
-"taus" (quantile) serving as column names and the values in the corresponding
-column.  When pivoting multiple columns, the original column name will be
-used as a prefix.
-}
-\examples{
-d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
-d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
-tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
-
-pivot_quantiles_wider(tib, "d1")
-pivot_quantiles_wider(tib, dplyr::ends_with("2"))
-pivot_quantiles_wider(tib, d2)
-}
diff --git a/man/predict-epi_workflow.Rd b/man/predict-epi_workflow.Rd
index c04097c47..577b20abb 100644
--- a/man/predict-epi_workflow.Rd
+++ b/man/predict-epi_workflow.Rd
@@ -47,19 +47,24 @@ time points at which the survival probability or hazard is estimated.
 }
 \value{
 A data frame of model predictions, with as many rows as \code{new_data} has.
-If \code{new_data} is an \code{epi_df()} or a data frame with \code{time_value} or
+If \code{new_data} is an \code{epiprocess::epi_df} or a data frame with \code{time_value} or
 \code{geo_value} columns, then the result will have those as well.
 }
 \description{
 This is the \code{predict()} method for a fit epi_workflow object. The 3 steps that this implements are:
 \itemize{
+\item Preprocess \code{new_data} using the preprocessing method specified when the
+workflow was created and fit. This is accomplished using
+\code{\link[hardhat:forge]{hardhat::forge()}}, which will apply any formula preprocessing or call
+\code{\link[recipes:bake]{recipes::bake()}} if a recipe was supplied.
 \item Preprocessing \code{new_data} using the preprocessing method specified when the
 epi_workflow was created and fit. This is accomplished using
-\code{recipes::bake()} if a recipe was supplied. Note that this is a slightly
-different \code{bake} operation than the one occuring during the fit. Any \code{step}
-that has \code{skip = TRUE} isn't applied during prediction; for example in
-\code{step_epi_naomit()}, \code{all_outcomes()} isn't \code{NA} omitted, since doing so
-would drop the exact \code{time_values} we are trying to predict.
+\code{hardhat::bake()} if a recipe was supplied (passing through
+\code{\link[hardhat:forge]{hardhat::forge()}}, which is used for non-recipe preprocessors). Note that
+this is a slightly different \code{bake} operation than the one occuring during
+the fit. Any \code{step} that has \code{skip = TRUE} isn't applied during prediction;
+for example in \code{step_epi_naomit()}, \code{all_outcomes()} isn't \code{NA} omitted,
+since doing so would drop the exact \code{time_values} we are trying to predict.
 \item Calling \code{parsnip::predict.model_fit()} for you using the underlying fit
 parsnip model.
 \item \code{slather()} any frosting that has been included in the \code{epi_workflow}.
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 93cc72502..437811b39 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -151,7 +151,8 @@ toy_recipe \%>\%
 #> 4 ca        2015-01-14   103    10
 #> 5 ma        2015-01-11    20     6
 #> 6 ma        2015-01-12    23     6
-#> # i 2 more rows
+#> 7 ma        2015-01-13    25     6
+#> 8 ma        2015-01-14    25     6
 }\if{html}{\out{</div>}}
 }
 
@@ -178,15 +179,19 @@ toy_recipe \%>\%
 #> * as_of     = 2015-01-14
 #> 
 #> # A tibble: 21 x 7
-#>   geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
-#>   <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
-#> 1 ca        2015-01-10    NA    NA      NA      NA       100
-#> 2 ca        2015-01-11   100     5      NA      NA       103
-#> 3 ca        2015-01-12   103    10      NA      NA        NA
-#> 4 ca        2015-01-13    NA    NA      NA      NA        NA
-#> 5 ca        2015-01-14    NA    NA     100      NA        NA
-#> 6 ca        2015-01-15    NA    NA     103       5        NA
-#> # i 15 more rows
+#>    geo_value time_value     a     b lag_3_a lag_4_b ahead_1_a
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <dbl>     <dbl>
+#>  1 ca        2015-01-10    NA    NA      NA      NA       100
+#>  2 ca        2015-01-11   100     5      NA      NA       103
+#>  3 ca        2015-01-12   103    10      NA      NA        NA
+#>  4 ca        2015-01-13    NA    NA      NA      NA        NA
+#>  5 ca        2015-01-14    NA    NA     100      NA        NA
+#>  6 ca        2015-01-15    NA    NA     103       5        NA
+#>  7 ca        2015-01-16    NA    NA      NA      10        NA
+#>  8 ca        2015-01-17    NA    NA      NA      NA        NA
+#>  9 ca        2015-01-18    NA    NA      NA      NA        NA
+#> 10 ca        2015-01-19    NA    NA      NA      NA        NA
+#> # i 11 more rows
 }\if{html}{\out{</div>}}
 
 The maximum latency in column \code{a} is 2 days, so the lag is increased to 3,
@@ -222,15 +227,18 @@ toy_recipe \%>\%
 #> * as_of     = 2015-01-14
 #> 
 #> # A tibble: 10 x 6
-#>   geo_value time_value     a     b lag_0_a ahead_3_a
-#>   <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
-#> 1 ca        2015-01-08    NA    NA      NA       100
-#> 2 ca        2015-01-09    NA    NA      NA       103
-#> 3 ca        2015-01-11   100     5     100        NA
-#> 4 ca        2015-01-12   103    10     103        NA
-#> 5 ma        2015-01-08    NA    NA      NA        20
-#> 6 ma        2015-01-09    NA    NA      NA        23
-#> # i 4 more rows
+#>    geo_value time_value     a     b lag_0_a ahead_3_a
+#>    <chr>     <date>     <dbl> <dbl>   <dbl>     <dbl>
+#>  1 ca        2015-01-08    NA    NA      NA       100
+#>  2 ca        2015-01-09    NA    NA      NA       103
+#>  3 ca        2015-01-11   100     5     100        NA
+#>  4 ca        2015-01-12   103    10     103        NA
+#>  5 ma        2015-01-08    NA    NA      NA        20
+#>  6 ma        2015-01-09    NA    NA      NA        23
+#>  7 ma        2015-01-10    NA    NA      NA        25
+#>  8 ma        2015-01-11    20     6      20        NA
+#>  9 ma        2015-01-12    23    NA      23        NA
+#> 10 ma        2015-01-13    25    NA      25        NA
 }\if{html}{\out{</div>}}
 
 Even though we're doing a 1 day ahead forecast, because our worst latency
diff --git a/man/step_epi_shift.Rd b/man/step_epi_shift.Rd
index 3291d95dd..e53c94a07 100644
--- a/man/step_epi_shift.Rd
+++ b/man/step_epi_shift.Rd
@@ -61,16 +61,13 @@ sequence of any existing operations.
 }
 \description{
 \code{step_epi_lag} and \code{step_epi_ahead} create a \emph{specification} of a recipe step
-that will add new columns of shifted data. The \code{step_epi_lag} will created
+that will add new columns of shifted data. The \code{step_epi_lag} will create
 a lagged \code{predictor} column, while \code{step_epi_ahead} will create a leading
 \code{outcome} column. Shifted data will by default include NA values where the
 shift was induced.  These can be properly removed with \code{\link[=step_epi_naomit]{step_epi_naomit()}},
-or you may specify an alternative filler value with the \code{default} argument.
+or you may specify an alternative value with the \code{default} argument.
 }
 \details{
-The step assumes that the data's \code{time_value} column is already \emph{in
-the proper sequential order} for shifting.
-
 Our \code{lag/ahead} functions respect the \code{geo_value} and \code{other_keys} of the
 \code{epi_df}, and allow for discontiguous \code{time_value}s. Both of these features
 are noticably lacking from \code{recipe::step_lag()}.
diff --git a/man/step_population_scaling.Rd b/man/step_population_scaling.Rd
index 7340762ac..733e1a9f8 100644
--- a/man/step_population_scaling.Rd
+++ b/man/step_population_scaling.Rd
@@ -28,11 +28,11 @@ for this step. See \code{\link[recipes:selections]{recipes::selections()}} for m
 \item{role}{For model terms created by this step, what analysis role should
 they be assigned?}
 
-\item{df}{a data frame containing the scaling data (such as population). The
+\item{df}{a data frame containing the scaling data (typically population). The
 target column is divided by the value in \code{df_pop_col}.}
 
-\item{by}{A (possibly named) character vector of variables to join \code{df} onto
-the \code{epi_df} by.
+\item{by}{A (possibly named) character vector of variables by which to join
+\code{df} to the \code{epi_df}.
 
 If \code{NULL}, the default, the function will try to infer a reasonable set of
 columns. First, it will try to join by all variables in the training/test
@@ -83,9 +83,9 @@ Scales raw data by the population
 \description{
 \code{step_population_scaling()} creates a specification of a recipe step that
 will perform per-capita scaling. Typical usage would set \code{df} to be a dataset
-that contains state-level population, and use it to convert predictions made
-from a raw scale model to rate-scale by dividing by the population.
-Although, it is worth noting that there is nothing special about
+that contains population for each \code{geo_value}, and use it to convert
+predictions made from a raw scale model to rate-scale by dividing by the
+population.  Although, it is worth noting that there is nothing special about
 "population", and the function can be used to scale by any variable.
 Population is the standard use case in the epidemiology forecasting scenario.
 Any value passed will \emph{divide} the selected variables while the
diff --git a/man/step_training_window.Rd b/man/step_training_window.Rd
index 86c9a2952..d10b0e41c 100644
--- a/man/step_training_window.Rd
+++ b/man/step_training_window.Rd
@@ -44,8 +44,8 @@ It is recommended to do this after any \code{step_epi_ahead()},
 \code{step_epi_lag()}, or \code{step_epi_naomit()} steps. If \code{step_training_window()}
 happens first, there will be less than \code{n_training} remaining examples,
 since either leading or lagging will introduce \code{NA}'s later removed by
-\code{step_epi_naomit()}. Typical usage will have this function applied after
-every other step.
+\code{step_epi_naomit()}. Typical usage will use this step last in an
+\code{epi_recipe()}.
 }
 \examples{
 tib <- tibble(
diff --git a/man/within_window.Rd b/man/within_window.Rd
new file mode 100644
index 000000000..b0b824690
--- /dev/null
+++ b/man/within_window.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{within_window}
+\alias{within_window}
+\title{generate the idx values within \code{window_size} of \code{target_idx} given that our
+time value is of the type matching modulus}
+\usage{
+within_window(target_idx, window_size, modulus)
+}
+\arguments{
+\item{target_idx}{the time index which we're drawing the window around}
+
+\item{window_size}{the size of the window on one side of \code{target_idx}}
+
+\item{modulus}{the number of days/weeks/months in the year, not including any leap days/weeks}
+}
+\description{
+generate the idx values within \code{window_size} of \code{target_idx} given that our
+time value is of the type matching modulus
+}
+\keyword{internal}
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 058d6a5b6..775fd0bf6 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -24,7 +24,7 @@ new data is constantly being collected and added to the dataset, and old data po
 Training and making
 predictions only on finalized data can lead to overly optimistic estimates of accuracy
 (see, for example, [McDonald et al.
-(2021)](https://www.pnas.org/content/118/51/e2111453118/) and the references
+2021](https://www.pnas.org/content/118/51/e2111453118/) and the references
 therein).
 
 In the `{epiprocess}` package, we provide the function `epix_slide()` to help conviently perform version-faithful forecasting by only using the data as
@@ -46,6 +46,7 @@ library(tidyr)
 library(ggplot2)
 library(magrittr)
 library(purrr)
+library(lubridate)
 ```
 
 First, we create an `epi_archive()` to store the version history of the
@@ -76,12 +77,12 @@ doctor_visits <- pub_covidcast(
   issues = epirange(20200601, 20211201)
 ) |>
   # The version date column is called `issue` in the Epidata API. Rename it.
-  rename(version = issue, percent_cli = value) |>
+  select(version = issue, geo_value, time_value, percent_cli = value) |>
   as_epi_archive(compactify = TRUE)
 ```
 
 In the interest of computational speed, we limit the dataset to 4 states and 
-2020-2021, but the full archive can be used in the same way and has performed
+2020–2021, but the full archive can be used in the same way and has performed
 well in the past.
 
 We choose this dataset in particular partly because it is revision heavy; for
@@ -89,7 +90,7 @@ example, here is a plot that compares monthly snapshots of the data.
 
 <details>
 <summary>Code for plotting</summary>
-```{r plot_revision_example, warn = FALSE}
+```{r plot_revision_example, warn = FALSE, message = FALSE}
 geo_choose <- "ca"
 forecast_dates <- seq(from = as.Date("2020-08-01"), to = as.Date("2021-11-01"), by = "1 month")
 percent_cli_data <- bind_rows(
@@ -106,23 +107,22 @@ percent_cli_data <- bind_rows(
   doctor_visits |>
     epix_as_of(doctor_visits$versions_end) |>
     mutate(version_faithful = "Version un-faithful")
-)
-
-p0 <-
-  ggplot(data = percent_cli_data |> filter(geo_value == geo_choose)) +
-  geom_vline(aes(color = factor(version), xintercept = version), lty = 2) +
-  geom_line(
-    aes(x = time_value, y = percent_cli, color = factor(version)),
-    inherit.aes = FALSE, na.rm = TRUE
-  ) +
-  scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
-  scale_y_continuous(expand = expansion(c(0, 0.05))) +
-  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+) |> as_tibble()
+p0 <- autoplot(
+  archive_cases_dv_subset, percent_cli, 
+  .versions = seq(ymd("2020-07-01"), ymd("2021-11-30"), by = "1 month"), 
+  .mark_versions = TRUE,
+  .facet_filter = (geo_value == "ca")
+) +
+  scale_x_date(minor_breaks = "month", date_labels = "%b %Y") +
+  labs(x = "", y = "% of doctor's visits with\n Covid-like illness") + 
+  scale_color_viridis_c(option = "turbo", guide = guide_legend(reverse=TRUE), direction = -1) +
+  scale_y_continuous(limits = c(0, NA), expand = expansion(c(0, 0.05))) +
   theme(legend.position = "none")
 ```
 </details>
 
-```{r plot_just_revisioning, echo = FALSE, warn = FALSE, message = FALSE}
+```{r plot_just_revisioning, echo = FALSE, warning = FALSE, warn = FALSE, message = FALSE}
 p0
 ```
 
@@ -166,8 +166,8 @@ Much of that functionality is built into `arx_forecaster()` using the parameter
 
 # Backtesting a simple autoregressive forecaster
 
-One of the most common use cases of `epiprocess::epi_archive()` object is for
-accurate model back-testing.
+In addition to outlier detection and nowcasting, a common use case of `epiprocess::epi_archive()` object is for accurate model
+back-testing.
 
 To start, let's use a simple autoregressive forecaster to predict `percent_cli`, the percentage
 of doctor's hospital visits associated with COVID-like illness, 14
@@ -178,7 +178,7 @@ For increased accuracy we will use quantile regression.
 
 As a sanity check before we backtest the _entire_ dataset, let's 
 forecast a single day in the middle of the dataset. 
-We can do this by setting the `.version` argument in `epix_slide()`:
+We can do this by setting the `.versions` argument in `epix_slide()`:
 
 ```{r single_version, warn = FALSE}
 forecast_date <- as.Date("2021-04-06")
@@ -199,7 +199,7 @@ We need truth data to compare our forecast against. We can construct it by using
 the archive at the last available date[^1].
 
 _Note:_ We always want to compare our forecasts to actual (most recently reported) values because that is the outcome we care about.
-`as_of` data is useful for understanding why we're getting the forecasts we're getting, but `as_of` values are not the real outcome
+`as_of` data is useful for understanding why we're getting the forecasts we're getting, but `as_of` values are only preliminary outcomes.
 Therefore, it's not meaningful to use them for evaluating the performance of a forecast.
 Unfortunately, it's not uncommon for revisions to cause poor (final) performance of a forecaster that was decent at the time of the forecast.
 
@@ -241,8 +241,8 @@ We will also create the helper function `forecast_wrapper()` to let us easily ma
 
 ```{r arx-kweek-preliminaries, warning = FALSE}
 forecast_wrapper <- function(
-    epi_data, aheads, outcome, predictors,
-    process_data = identity) {
+    epi_data, aheads, outcome, predictors, process_data = identity
+    ) {
   map(
     aheads,
     \(ahead) {
@@ -253,11 +253,9 @@ forecast_wrapper <- function(
           lags = c(0:7, 14, 21),
           adjust_latency = "extend_ahead"
         )
-      )$predictions |>
-        pivot_quantiles_wider(.pred_distn)
+      )$predictions |> pivot_quantiles_wider(.pred_distn)
     }
-  ) |>
-    bind_rows()
+  ) |> bind_rows()
 }
 ```
 
@@ -323,33 +321,35 @@ forecasts_filtered <- forecasts |>
 # we need to add the ground truth data to the version faithful plot as well
 plotting_data <- bind_rows(
   percent_cli_data,
-  percent_cli_data %>%
-    filter(version_faithful == "Version un-faithful") %>%
+  percent_cli_data |>
+    filter(version_faithful == "Version un-faithful") |>
+    mutate(version = max(percent_cli_data$version)) |>
     mutate(version_faithful = "Version faithful")
 )
-
-p1 <- # first plotting the forecasts as bands, lines and points
-  ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
-  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
-  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
+p1 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
+  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)), alpha = 0.4) +
+  geom_line(aes(y = .pred, color = (time_value)), linetype = 2L) +
+  geom_point(aes(y = .pred, color = (time_value)), size = 0.75) +
   # the forecast date
   geom_vline(
     data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
-    aes(color = factor(version), xintercept = version),
+    aes(color = version, xintercept = version, group = version),
     lty = 2
   ) +
   # the underlying data
   geom_line(
     data = plotting_data |> filter(geo_value == geo_choose),
-    aes(x = time_value, y = percent_cli, color = factor(version)),
+    aes(x = time_value, y = percent_cli, color = (version), group = version),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
   facet_grid(version_faithful ~ geo_value, scales = "free") +
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  scale_color_viridis_c(option = "turbo", direction = -1) +
+  scale_fill_viridis_c(option = "turbo", direction = -1) +
   theme(legend.position = "none")
+p1
 ```
 
 ```{r plot_fl_forecasts, warning = FALSE}
@@ -358,25 +358,32 @@ forecasts_filtered <- forecasts |>
   filter(geo_value == geo_choose) |>
   mutate(time_value = version)
 
+forecasts_filtered %>% names
 p2 <-
   ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = factor(time_value)), alpha = 0.4) +
-  geom_line(aes(y = .pred, color = factor(time_value)), linetype = 2L) +
-  geom_point(aes(y = .pred, color = factor(time_value)), size = 0.75) +
+  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)), alpha = 0.4) +
+  geom_line(aes(y = .pred, color = (time_value)), linetype = 2L) +
+  geom_point(aes(y = .pred, color = (time_value)), size = 0.75) +
+  # the forecast date
   geom_vline(
     data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
-    aes(color = factor(version), xintercept = version), lty = 2
+    aes(color = version, xintercept = version, group = version),
+    lty = 2
   ) +
+  # the underlying data
   geom_line(
     data = plotting_data |> filter(geo_value == geo_choose),
-    aes(x = time_value, y = percent_cli, color = factor(version)),
+    aes(x = time_value, y = percent_cli, color = (version), group = version),
     inherit.aes = FALSE, na.rm = TRUE
   ) +
   facet_grid(version_faithful ~ geo_value, scales = "free") +
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  scale_color_viridis_c(option = "turbo", direction = -1) +
+  scale_fill_viridis_c(option = "turbo", direction = -1) +
   theme(legend.position = "none")
+p2
 ```
 </details>
 
@@ -384,7 +391,7 @@ p2 <-
 p1
 ```
 
-The version faithful and un-faithful forecasts look moderately similar except for the 1 day horizons  
+The version faithful and un-faithful forecasts look moderately similar except for the 1 day horizons
 (although neither approach produces amazingly accurate forecasts).
 
 In the version faithful case for California, the March 2021 forecast (turquoise)
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 6419504bd..0bf49bdab 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -58,7 +58,7 @@ All `epi_workflows`, including simple and canned workflows, consist of 3 compone
 
 ### Preprocessor
 
-A preprocessor (also called a recipe) transforms the data before model training and prediction.
+A preprocessor (in the context of epipredict this means a `{recipe}`) transforms the data before model training and prediction.
 Transformations can include converting counts to rates, applying a running average 
 to columns, or [any of the `step`s found in `{recipes}`](https://recipes.tidymodels.org/reference/index.html).
 
@@ -75,8 +75,8 @@ In general, there are 2 broad classes of transformation that `{recipes}` `step`s
   Examples include taking the log of a variable, leading or lagging columns,
   filtering out rows, handling dummy variables, calculating growth rates,
   etc.
-- Operations that rely on stored information (parameters fit during training) to modify train and test data.
-  Examples include centering by the mean, and normalizing the variance (whitening).
+- Operations that rely on stored information (parameters estimated during training) to modify both train and test data.
+  Examples include centering by the mean, and normalizing the variance to be one (whitening).
 
 We differentiate between these types of transformations because the second type can result in information leakage if not done properly.
 Information leakage or data leakage happens when a system has access to information that would not have been available at prediction time and could change our evaluation of the model's real-world performance.
@@ -86,12 +86,12 @@ the training data and use that value on the prediction data, rather than
 using the mean of the test predictor for centering or including test data in the mean calculation.
 
 A major benefit of `{recipes}` is that it prevents information leakage. 
-However, the _main_ mechanism we rely on to prevent data leakage is proper
-[backtesting](backtesting.html).
+However, the _main_ mechanism we rely on to prevent data leakage in the context
+of epidemiological forecasting is proper [backtesting](backtesting.html).
 
 ### Trainer
 
-A trainer (aso called a model or engine) fits a `{parsnip}` model on data, and outputs a fitted model object.
+A trainer (aso called a model or engine) fits a `{parsnip}` model to training data, and outputs a fitted model object.
 Examples include linear regression, quantile regression, or [any `{parsnip}` engine](https://www.tidymodels.org/find/parsnip/).
 The `{parsnip}` front-end abstracts away the differences in interface between a wide collection of statistical models.
 
@@ -99,14 +99,17 @@ All workflows must include a model.
 
 ### Postprocessor
 
-Postprocessors are unique to `{epipredict}`.
-A postprocessor (also known as frosting) modifies and formats the prediction after a model has been fit.
+Generally  a postprocessor modifies and formats the prediction after a model has
+been fit.
+An `{epipredict}` postprocessor is called a `frosting()`; there are alternatives
+such as [tailor](https://tailor.tidymodels.org/) which performs calibration.
 
 The postprocessor is _optional_.
 It only needs to be included in a workflow if you need to process the model output.
 
-Each operation within a postprocessor is called a "layer" (functions are named `layer_*`), and the stack of layers is known as `frosting()`,
-continuing the metaphor of baking a cake established in `{recipes}`.
+Each operation within a postprocessor is called a "layer" (functions are named
+`layer_*`), and the stack of layers is known as `frosting()`, continuing the
+metaphor of baking a cake established in `{recipes}`.
 Some example operations include:
 
 - generating quantiles from purely point-prediction models
@@ -137,7 +140,7 @@ hardhat::extract_recipe(four_week_ahead$epi_workflow)
 ```
 
 There are 6 steps we will need to recreate.
-Note that all steps in the extracted recipe are marked as already been
+Note that all steps in the extracted recipe are marked as already having been
 `Trained`. For steps such as `recipes::step_BoxCox()` that have parameters that change their behavior, this means that their
 parameters have already been calculated based on the training data set.
 
@@ -241,7 +244,7 @@ four_week_layers <- frosting() |>
   layer_threshold()
 ```
 
-`layer_predict()` needs to be included in every postprocessor to actually train the model.
+`layer_predict()` needs to be included in every postprocessor to actually predict on the prediction data.
 
 Most layers work with any engine or `step`s.
 There are a couple of layers, however, that depend on whether the engine predicts quantiles or point estimates.
@@ -249,8 +252,8 @@ There are a couple of layers, however, that depend on whether the engine predict
 The following layers are only supported by point estimate engines, such as
 `linear_reg()`:
 
-- `layer_residual_quantiles()`: the preferred method of generating quantiles for
-  models that don't generate quantiles themselves.
+- `layer_residual_quantiles()`: for models that don't generate quantiles, the
+  preferred method of generating quantiles.
   This function uses the error residuals of the engine to calculate quantiles. 
   This will work for most `{parsnip}` engines.
 - `layer_predictive_distn()`: alternate method of generating quantiles using
@@ -263,8 +266,9 @@ output quantiles, such as `quantile_reg()`:
 - `layer_quantile_distn()`: adds the specified quantiles.
   If the user-requested quantile levels differ from the ones actually fit, they will be interpolated and/or
   extrapolated.
-- `layer_point_from_distn()`: this adds the middle quantile (median) as a point estimate,
-  and, if used, should be included after `layer_quantile_distn()`. 
+- `layer_point_from_distn()`: this generates a point estimate from a
+  distribution (either median or mean), and, if used, should be included after
+  `layer_quantile_distn()`. 
 
 ## Fitting an `epi_workflow()`
 
@@ -339,7 +343,7 @@ There are many ways we could modify `four_week_ahead`. We might consider:
 
 - Converting from rates to counts
 - Including a growth rate estimate as a predictor
-- Including a time component as a predictor -- useful if we
+- Including a time component as a predictor --- useful if we
 expect there to be a strong seasonal component to the outcome
 - Scaling by a factor
 
@@ -356,12 +360,12 @@ growth_rate_recipe <- epi_recipe(
   covid_case_death_rates |>
     filter(time_value <= forecast_date, geo_value %in% used_locations)
 ) |>
+  # Calculate growth rate from death rate column.
+  step_growth_rate(death_rate) |>
   step_epi_lag(case_rate, lag = c(0, 1, 2, 3, 7, 14)) |>
   step_epi_lag(death_rate, lag = c(0, 7, 14)) |>
   step_epi_ahead(death_rate, ahead = 4 * 7) |>
   step_epi_naomit() |>
-  # Calculate growth rate from death rate column.
-  step_growth_rate(death_rate) |>
   step_training_window()
 ```
 
@@ -462,8 +466,7 @@ count_layers <-
   layer_predict() |>
   layer_residual_quantiles(quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)) |>
   layer_population_scaling(
-    .pred,
-    .pred_distn,
+    starts_with(".pred"),
     # `df` contains scaling values for all regions; in this case it is the state populations
     df = epidatasets::state_census,
     df_pop_col = "pop",
@@ -490,6 +493,8 @@ count_predictions <- count_workflow |>
 count_predictions
 ```
 
+note that we've used `tidyselect::starts_with(".pred")` here, which will apply the function to both the `.pred` and `.pred_distn` columns.
+
 # Custom classifier workflow
 
 Let's work through an example of a more complicated kind of pipeline you can build using
@@ -521,19 +526,9 @@ classifier_recipe <- epi_recipe(training_data) |>
   # Create and lag `case_rate` growth rate
   step_growth_rate(case_rate, role = "none", prefix = "gr_") |>
   step_epi_lag(starts_with("gr_"), lag = c(0, 7, 14)) |>
-  step_epi_ahead(starts_with("gr_"), ahead = 7, role = "none") |>
-  # Divide growth rate into 3 bins, and label as outcome variable
-  # Note `recipes::step_cut()` has a bug that prevents us from using it here
-  step_mutate(
-    response = cut(
-      ahead_7_gr_7_rel_change_case_rate,
-      # Define bin thresholds.
-      # Divide by 7 to create weekly values.
-      breaks = c(-Inf, -0.2, 0.25, Inf) / 7,
-      labels = c("down", "flat", "up")
-    ),
-    role = "outcome"
-  ) |>
+  step_epi_ahead(starts_with("gr_"), ahead = 7) |>
+  # divide growth rate into 3 bins
+  step_cut(ahead_7_gr_7_rel_change_case_rate, breaks = c(-Inf, -0.2, 0.25, Inf) / 7) |>
   # Drop unused columns based on role assignments. This is not strictly
   # necessary, as columns with roles unused in the model will be ignored anyway.
   step_rm(has_role("none"), has_role("raw")) |>
@@ -571,7 +566,7 @@ Afterwards, `step_rm()` drops the temporary columns, along with the original `ro
 because their roles have been reassigned.
 
 
-To fit a classification model like this, we will need to use a `{parsnip}` model
+To fit a 3-class classification model like this, we will need to use a `{parsnip}` model
 that has `mode = "classification"`.
 The simplest example of a `{parsnip}` `classification`-`mode` model is `multinomial_reg()`.
 The needed layers are more or less the same as the `linear_reg()` regression layers, with the addition that we need to remove some `NA` values:
@@ -580,8 +575,7 @@ The needed layers are more or less the same as the `linear_reg()` regression lay
 frost <- frosting() |>
   layer_naomit(starts_with(".pred")) |>
   layer_add_forecast_date() |>
-  layer_add_target_date() |>
-  layer_threshold()
+  layer_add_target_date()
 ```
 
 ```{r, warning=FALSE}
@@ -606,7 +600,7 @@ growth_rates <- covid_case_death_rates |>
   ) |>
   ungroup()
 
-growth_rates |> filter(time_value == "2021-08-01")
+growth_rates |> filter(time_value == "2021-08-01") |> select(-death_rate)
 ```
 
 we see that they're all significantly higher than 25% per week (36%-62%),
@@ -627,3 +621,4 @@ See the [tooling book](https://cmu-delphi.github.io/delphi-tooling-book/preproce
     Academy of Sciences 118.51 (2021): e2111453118. doi:10.1073/pnas.2111453118
 
 [^4]: Note that `prep()` and `bake()` are standard `{recipes}` functions, so any discussion of them there applies just as well here. For example in the [guide to creating a new step](https://www.tidymodels.org/learn/develop/recipes/#create-the-prep-method).
+

From f6a311efd86b044232fb41f6a6c07c31866aba26 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Fri, 20 Jun 2025 12:11:17 -0500
Subject: [PATCH 385/387] lightswitch customization is hard

---
 _pkgdown.yml               | 1 -
 man/step_adjust_latency.Rd | 4 ++--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 82ac5bf5e..cd37dc3fd 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -4,7 +4,6 @@ development:
   mode: devel
 
 template:
-  light-switch: true
   package: delphidocs
 
 navbar:
diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index 437811b39..c909452eb 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -268,8 +268,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't work with
-#> modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous
+#> `step_epi_lag`s won't work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as

From 6047d4f8baf7348670c9dea933a6ce3e3dc66351 Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 23 Jun 2025 16:04:04 -0500
Subject: [PATCH 386/387] final pass

---
 man/step_adjust_latency.Rd        |   4 +-
 vignettes/backtesting.Rmd         |  49 +++++++-----
 vignettes/custom_epiworkflows.Rmd |   6 +-
 vignettes/epipredict.Rmd          | 120 +++++++++++++++++-------------
 4 files changed, 103 insertions(+), 76 deletions(-)

diff --git a/man/step_adjust_latency.Rd b/man/step_adjust_latency.Rd
index c909452eb..019434409 100644
--- a/man/step_adjust_latency.Rd
+++ b/man/step_adjust_latency.Rd
@@ -268,8 +268,8 @@ while this will not:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{toy_recipe <- epi_recipe(toy_df) \%>\%
    step_epi_lag(a, lag=0) \%>\%
    step_adjust_latency(a, method = "extend_lags")
-#> Warning: If `method` is "extend_lags" or "locf", then the previous
-#> `step_epi_lag`s won't work with modified data.
+#> Warning: If `method` is "extend_lags" or "locf", then the previous `step_epi_lag`s won't
+#> work with modified data.
 }\if{html}{\out{</div>}}
 
 If you create columns that you then apply lags to (such as
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index 775fd0bf6..f355f5516 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -92,7 +92,10 @@ example, here is a plot that compares monthly snapshots of the data.
 <summary>Code for plotting</summary>
 ```{r plot_revision_example, warn = FALSE, message = FALSE}
 geo_choose <- "ca"
-forecast_dates <- seq(from = as.Date("2020-08-01"), to = as.Date("2021-11-01"), by = "1 month")
+forecast_dates <- seq(
+  from = as.Date("2020-08-01"),
+  to = as.Date("2021-11-01"),
+  by = "1 month")
 percent_cli_data <- bind_rows(
   # Snapshotted data for the version-faithful forecasts
   map(
@@ -110,13 +113,15 @@ percent_cli_data <- bind_rows(
 ) |> as_tibble()
 p0 <- autoplot(
   archive_cases_dv_subset, percent_cli, 
-  .versions = seq(ymd("2020-07-01"), ymd("2021-11-30"), by = "1 month"), 
+  .versions = forecast_dates, 
   .mark_versions = TRUE,
   .facet_filter = (geo_value == "ca")
 ) +
   scale_x_date(minor_breaks = "month", date_labels = "%b %Y") +
   labs(x = "", y = "% of doctor's visits with\n Covid-like illness") + 
-  scale_color_viridis_c(option = "turbo", guide = guide_legend(reverse=TRUE), direction = -1) +
+  scale_color_viridis_c(
+    option = "viridis",
+    guide = guide_legend(reverse=TRUE), direction = -1) +
   scale_y_continuous(limits = c(0, NA), expand = expansion(c(0, 0.05))) +
   theme(legend.position = "none")
 ```
@@ -133,11 +138,11 @@ For example, the snapshot on March 1st, 2021 is aquamarine, and increases to
 slightly over 10.
 Every series is necessarily to the left of the snapshot date (since all known
 values must happen before the snapshot is taken[^4]).
-The grey line overlaying the various snapshots represents the "final
+The black line overlaying the various snapshots represents the "final
 value", which is just the snapshot at the last version in the archive (the
 `versions_end`).
 
-Comparing with the grey line tells us how much the value at the time of the
+Comparing with the black line tells us how much the value at the time of the
 snapshot differs with what was eventually reported.
 The drop in January 2021 in the snapshot on `2021-02-01` was initially reported
 as much steeper than it eventually turned out to be, while in the period after
@@ -145,7 +150,7 @@ that the values were initially reported as higher than they actually were.
 
 Handling data latency is important in both real-time forecasting and retrospective
 forecasting.
-Looking at the very first snapshot, `2020-08-01` (the red dotted
+Looking at the very first snapshot, `2020-08-01` (the purple dotted
 vertical line), there is a noticeable gap between the forecast date and the end
 of the red time-series to its left.
 In fact, if we take a snapshot and get the last `time_value`,
@@ -308,7 +313,7 @@ To see how the version faithful and un-faithful predictions compare, let's plot
 rates, using the same versioned plotting method as above.
 Note that even though we fit the model on four states (California, Texas, Florida, and
 New York), we'll just display the results for two states, California (CA) and Florida
-(FL), to get a sense of the model performance while keeping the graphic simple.
+(FL), to get a sense of the model performance while keeping the graphic simpler.
 
 <details>
 <summary>Code for plotting</summary>
@@ -326,13 +331,19 @@ plotting_data <- bind_rows(
     mutate(version = max(percent_cli_data$version)) |>
     mutate(version_faithful = "Version faithful")
 )
-p1 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)), alpha = 0.4) +
+
+p1 <- ggplot(data = forecasts_filtered,
+             aes(x = target_date, group = time_value)) +
+  geom_ribbon(
+    aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)),
+    alpha = 0.4) +
   geom_line(aes(y = .pred, color = (time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = (time_value)), size = 0.75) +
   # the forecast date
   geom_vline(
-    data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
+    data = percent_cli_data |>
+      filter(geo_value == geo_choose) |>
+      select(-version_faithful),
     aes(color = version, xintercept = version, group = version),
     lty = 2
   ) +
@@ -345,11 +356,11 @@ p1 <- ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)
   facet_grid(version_faithful ~ geo_value, scales = "free") +
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
-  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
-  scale_color_viridis_c(option = "turbo", direction = -1) +
-  scale_fill_viridis_c(option = "turbo", direction = -1) +
+  labs(x = "Date",
+       y = "smoothed, day of week adjusted covid-like doctors visits") +
+  scale_color_viridis_c(option = "viridis", direction = -1) +
+  scale_fill_viridis_c(option = "viridis", direction = -1) +
   theme(legend.position = "none")
-p1
 ```
 
 ```{r plot_fl_forecasts, warning = FALSE}
@@ -380,8 +391,8 @@ p2 <-
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
   labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
-  scale_color_viridis_c(option = "turbo", direction = -1) +
-  scale_fill_viridis_c(option = "turbo", direction = -1) +
+  scale_color_viridis_c(option = "viridis", direction = -1) +
+  scale_fill_viridis_c(option = "viridis", direction = -1) +
   theme(legend.position = "none")
 p2
 ```
@@ -391,17 +402,17 @@ p2
 p1
 ```
 
-The version faithful and un-faithful forecasts look moderately similar except for the 1 day horizons
-(although neither approach produces amazingly accurate forecasts).
+There are some weeks when the forecasts are somewhat similar, and others when they are wildly different, although neither approach produces amazingly accurate forecasts.
 
 In the version faithful case for California, the March 2021 forecast (turquoise)
 starts at a value just above 10, which is very well lined up with reported values leading up to that forecast.
 The measured and forecasted trends are also concordant (both increasingly moderately fast).
 
 Because the data for this time period was later adjusted down with a decreasing trend, the March 2021 forecast looks quite bad compared to finalized data.
-
 The equivalent version un-faithful forecast starts at a value of 5, which is in line with the finalized data but would have been out of place compared to the version data.
 
+The October 2021 forecast for the version faithful case floors out at zero, whereas the un-faithful is much closer to the finalized data.
+
 ```{r show-plot2, warning = FALSE, echo=FALSE}
 p2
 ```
diff --git a/vignettes/custom_epiworkflows.Rmd b/vignettes/custom_epiworkflows.Rmd
index 0bf49bdab..9d2d35093 100644
--- a/vignettes/custom_epiworkflows.Rmd
+++ b/vignettes/custom_epiworkflows.Rmd
@@ -347,7 +347,7 @@ There are many ways we could modify `four_week_ahead`. We might consider:
 expect there to be a strong seasonal component to the outcome
 - Scaling by a factor
 
-We will demo a couple of these modifications below.
+We will demonstrate a couple of these modifications below.
 
 ## Growth rate
 
@@ -456,9 +456,9 @@ result_plot
 
 ## Population scaling
 
-Suppose we want to modify our predictions to apply to counts, rather than rates.
+Suppose we want to modify our predictions to return a rate prediction, rather than the count prediction.
 To do that, we can adjust _just_ the `frosting` to perform post-processing on our existing rates forecaster.
-Since rates are calculated as counts per 100 000 people, we will convert back to counts by multiplying rates by the factor $\frac{regional \text{ } population}{100000}$.
+Since rates are calculated as counts per 100 000 people, we will convert back to counts by multiplying rates by the factor $\frac{ \text{regional population} }{100,000}$.
 
 ```{r rate_scale}
 count_layers <-
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 6288594f8..3786bc356 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -325,8 +325,18 @@ Instead, we'll use the fluview ILI dataset, which is weekly influenza like illne
 We'll predict the 2023/24 season using all previous data, including 2020-2022, the two years where there was approximately no seasonal flu, forecasting from the start of the season, `2023-10-08`:
 
 ```{r make-climatological-forecast, warning=FALSE}
-fluview_hhs <- pub_fluview(regions = paste0("hhs", 1:10), epiweeks = epirange(100001,222201))
-fluview <- fluview_hhs %>% select(geo_value = region, time_value = epiweek, issue, ili) %>% as_epi_archive() %>% epix_as_of_current()
+fluview_hhs <- pub_fluview(
+  regions = paste0("hhs", 1:10),
+  epiweeks = epirange(100001,222201)
+)
+fluview <- fluview_hhs %>%
+  select(
+    geo_value = region,
+    time_value = epiweek,
+    issue,
+    ili) %>%
+  as_epi_archive() %>%
+  epix_as_of_current()
 
 all_climate <- climatological_forecaster(
   fluview %>% filter(time_value < "2023-10-08"),
@@ -343,7 +353,9 @@ results <- all_climate$predictions
 autoplot(
   object = workflow,
   predictions = results,
-  observed_response = fluview %>% filter(time_value >= "2023-10-08", time_value < "2024-05-01") %>% mutate(geo_value = factor(geo_value, levels = paste0("hhs", 1:10)))
+  observed_response = fluview %>%
+    filter(time_value >= "2023-10-08", time_value < "2024-05-01") %>%
+    mutate(geo_value = factor(geo_value, levels = paste0("hhs", 1:10)))
 )
 ```
 
@@ -412,11 +424,10 @@ The accuracy is 50%, since all 4 states were predicted to be in the interval
 
 If multiple keys are set in the `epi_df` as `other_keys`, `arx_forecaster` will
 automatically group by those in addition to the required geographic key.
-For example, predicting the number of graduates in each of the categories in
+For example, predicting the number of graduates in a subset of the categories in
 `grad_employ_subset` from above:
 
 ```{r multi_key_forecast, warning=FALSE}
-# only fitting a subset, otherwise there are ~550 distinct pairs, which is bad for plotting
 edu_quals <- c("Undergraduate degree", "Professional degree")
 geo_values <- c("Quebec", "British Columbia")
 
@@ -478,7 +489,7 @@ all_fits |>
   list_rbind()
 ```
 
-Estimating separate models for each geography is both 56 times slower[^7] than geo-pooling, and uses far less data for each estimate.
+Estimating separate models for each geography uses far less data for each estimate than geo-pooling and is 56 times slower[^7].
 If a dataset contains relatively few observations for each geography, fitting a geo-pooled model is likely to produce better, more stable results.
 However, geo-pooling can only be used if values are comparable in meaning and scale across geographies or can be made comparable, for example by normalization.
 
@@ -488,7 +499,56 @@ workflow](custom_epiworkflows) with geography as a factor.
 
 # Anatomy of a canned forecaster
 
-This section describes the resulting object from `arx_forecaster()`, an `arx_fcast` object, along with a fairly minimal description of the actual mathematical model used for `arx_forecaster()`.
+This section describes the resulting object from `arx_forecaster()`, a fairly minimal description of the mathematical model used, and a description of an `arx_fcast` object.
+
+## Mathematical description
+
+Let's look at the mathematical details of the model in more detail, using a minimal version of
+`four_week_ahead`:
+
+```{r, four_week_again}
+four_week_small <- arx_forecaster(
+  covid_case_death_rates |> filter(time_value <= forecast_date),
+  outcome = "death_rate",
+  predictors = c("case_rate", "death_rate"),
+  args_list = arx_args_list(
+    lags = list(c(0, 7, 14), c(0, 7, 14)),
+    ahead = 4 * 7,
+    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
+  )
+)
+hardhat::extract_fit_engine(four_week_small$epi_workflow)
+```
+
+If $d_{t,j}$ is the death rate on day $t$ at location $j$ and $c_{t,j}$ is the
+associated case rate, then the corresponding model is:
+
+$$
+\begin{aligned}
+d_{t+28, j} = & a_0 + a_1 d_{t,j} + a_2 d_{t-7,j} + a_3 d_{t-14, j} +\\
+     & a_4 c_{t, j} + a_5 c_{t-7, j} + a_6 c_{t-14, j} + \varepsilon_{t,j}.
+\end{aligned}
+$$
+
+For example, $a_1$ is `lag_0_death_rate` above, with a value of 
+`r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"],3)`,
+while $a_5$ is 
+`r round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"],4) `.
+Note that unlike `d_{t,j}` or `c_{t,j}`, these *don't* depend on either the time
+$t$ or the location $j$.
+This is what make it a geo-pooled model.
+
+
+The training data for estimating the parameters of this linear model is
+constructed within the `arx_forecaster()` function by shifting a series of
+columns the appropriate amount -- based on the requested `lags`.
+Each row containing no `NA` values in the predictors is used as a training
+observation to fit the coefficients $a_0,\ldots, a_6$.
+
+The equation above is only an accurate description of the model for a linear
+engine like `quantile_reg()` or `linear_reg()`; a nonlinear model like
+`rand_forest(mode = "regression")` will use the same input variables and
+training data, but fit the appropriate model for them.
 
 ## Code object
 Let's dissect the forecaster we trained back on the [landing
@@ -551,50 +611,6 @@ An `epi_workflow()` consists of 3 parts:
 See the [Custom Epiworkflows vignette](custom_epiworkflows) for recreating and then
 extending `four_week_ahead` using the custom forecaster framework.
 
-## Mathematical description
-
-Let's look at the mathematical details of the model in more detail, using a minimal version of
-`four_week_ahead`:
-
-```{r, four_week_again}
-four_week_small <- arx_forecaster(
-  covid_case_death_rates |> filter(time_value <= forecast_date),
-  outcome = "death_rate",
-  predictors = c("case_rate", "death_rate"),
-  args_list = arx_args_list(
-    lags = list(c(0, 7, 14), c(0, 7, 14)),
-    ahead = 4 * 7,
-    quantile_levels = c(0.1, 0.25, 0.5, 0.75, 0.9)
-  )
-)
-hardhat::extract_fit_engine(four_week_small$epi_workflow)
-```
-
-If $d_{t,j}$ is the death rate on day $t$ at location $j$ and $c_{t,j}$ is the
-associated case rate, then the corresponding model is:
-
-$$
-\begin{aligned}
-d_{t+28, j} = & a_0 + a_1 d_{t,j} + a_2 d_{t-7,j} + a_3 d_{t-14, j} +\\
-     & a_4 c_{t, j} + a_5 c_{t-7, j} + a_6 c_{t-14, j} + \varepsilon_{t,j}.
-\end{aligned}
-$$
-
-For example, $a_1$ is `lag_0_death_rate` above, with a value of `r
-round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_0_death_rate"],
-3)`,
-while $a_5$ is `r
-round(hardhat::extract_fit_engine(four_week_small$epi_workflow)$coefficients["lag_7_case_rate"],
-4) `.
-Note that unlike `d_{t,j}` or `c_{t,j}`, these *don't* depend on either the time
-$t$ or the location $j$.
-This is what make it a geo-pooled model.
-
-The training data for estimating the parameters of this linear model is
-constructed within the `arx_forecaster()` function by shifting a series of
-columns the appropriate amount -- based on the requested `lags`.
-Each row containing no `NA` values in the predictors is used as a training observation to fit the
-coefficients $a_0,\ldots, a_6$.
 
 [^4]: in the case of a `{parsnip}` engine which doesn't explicitly predict
     quantiles, these quantiles are created using `layer_residual_quantiles()`,
@@ -611,4 +627,4 @@ coefficients $a_0,\ldots, a_6$.
 
 [^8]: It has only a year of data, which is barely enough to run the method without errors, let alone get a meaningful prediction.
 
-[^9]: Though not 28 weeks into the future! Such a forecast will be an absurd extrapolation.
+[^9]: Though not 28 weeks into the future! Such a forecast will likely be absurdly low or high.

From f8b48ea6c4cc8686189e8e44398be4c7c385746c Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Mon, 23 Jun 2025 18:09:11 -0500
Subject: [PATCH 387/387] bump version to 0.2.0

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 0fcae7990..1d04a3b2a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.15
+Version: 0.2.0
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),