@@ -38,25 +38,24 @@ You can view documentation for the `main` branch at
3838 You should be able to do a reasonably limited amount of
3939 customization on them. For the basic forecasters, we currently
4040 provide:
41- - Baseline flatline forecaster
42- - Autoregressive forecaster
43- - Autoregressive classifier
44- - CDC FluSight flatline forecaster
41+ - Baseline flatline forecaster
42+ - Autoregressive forecaster
43+ - Autoregressive classifier
44+ - CDC FluSight flatline forecaster
45452 . A framework for creating custom forecasters out of modular
4646 components. There are four types of components:
47- - Preprocessor: do things to the data before model training
48- - Trainer: train a model on data, resulting in a fitted model
49- object
50- - Predictor: make predictions, using a fitted model object
51- - Postprocessor: do things to the predictions before returning
47+ - Preprocessor: do things to the data before model training
48+ - Trainer: train a model on data, resulting in a fitted model object
49+ - Predictor: make predictions, using a fitted model object
50+ - Postprocessor: do things to the predictions before returning
5251
5352** Target audiences:**
5453
55- - Basic. Has data, calls forecaster with default arguments.
56- - Intermediate. Wants to examine changes to the arguments, take
57- advantage of built in flexibility.
58- - Advanced. Wants to write their own forecasters. Maybe willing to
59- build up from some components.
54+ - Basic. Has data, calls forecaster with default arguments.
55+ - Intermediate. Wants to examine changes to the arguments, take
56+ advantage of built in flexibility.
57+ - Advanced. Wants to write their own forecasters. Maybe willing to build
58+ up from some components.
6059
6160The Advanced user should find their task to be relatively easy. Examples
6261of these tasks are illustrated in the [ vignettes and
@@ -71,7 +70,7 @@ The package comes with some built-in historical data for illustration,
7170but up-to-date versions of this could be downloaded with the
7271[ ` {epidatr} ` package] ( https://cmu-delphi.github.io/epidatr/ ) and
7372processed using
74- [ ` {epiprocess} ` ] ( https://cmu-delphi.github.io/epiprocess/ ) .\[ 1 \ ]
73+ [ ` {epiprocess} ` ] ( https://cmu-delphi.github.io/epiprocess/ ) .[ ^ 1 ]
7574
7675``` r
7776library(epipredict )
@@ -114,7 +113,7 @@ two_week_ahead <- arx_forecaster(
114113two_week_ahead
115114# > ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
116115# >
117- # > This forecaster was fit on 2024-01-29 15:10:01 .
116+ # > This forecaster was fit on 2024-11-11 11:38:31 .
118117# >
119118# > Training data was an <epi_df> with:
120119# > • Geography: state,
@@ -207,7 +206,7 @@ through the end of 2021 for the 14th of January 2022. A prediction for
207206the death rate per 100K inhabitants is available for every state
208207(` geo_value ` ) along with a 90% predictive interval.
209208
210- 1 . Other epidemiological signals for non-Covid related illnesses are
209+ [ ^ 1 ] : Other epidemiological signals for non-Covid related illnesses are
211210 also available with
212211 [ ` {epidatr} ` ] ( https://github.com/cmu-delphi/epidatr ) which
213212 interfaces directly to Delphi’s [ Epidata
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