Merge pull request #141 from LeonieHagitte/feat-small-changes

Feat small changes

Author: Maximilian-Stefan-Ernst

README.md

@@ -4,6 +4,7 @@
 |:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|
 | [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://structuralequationmodels.github.io/StructuralEquationModels.jl/) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://structuralequationmodels.github.io/StructuralEquationModels.jl/dev/) | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Github Action CI](https://github.com/StructuralEquationModels/StructuralEquationModels.jl/workflows/CI_extended/badge.svg)](https://github.com/StructuralEquationModels/StructuralEquationModels.jl/actions/) [![codecov](https://codecov.io/gh/StructuralEquationModels/StructuralEquationModels.jl/branch/main/graph/badge.svg?token=P2kjzpvM4V)](https://codecov.io/gh/StructuralEquationModels/StructuralEquationModels.jl) | [![DOI](https://zenodo.org/badge/228649704.svg)](https://zenodo.org/badge/latestdoi/228649704) |
 
+# What is this Package for?
 
 This is a package for Structural Equation Modeling.
 It is still *in development*.
@@ -14,14 +15,17 @@ Models you can fit include
 - Multigroup SEM
 - Sums of arbitrary loss functions (everything the optimizer can handle).
 
+# What are the merrits?
+
 We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof.
 As a user, you can easily define custom loss functions.
 For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation.
 You can choose to mix and match loss functions natively found in this package and those you provide.
 In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge).
 This mix and match strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mix analytical and automatic differentiation.
 
-You may consider using this package if:
+# You may consider using this package if:
+
 - you want to extend SEM (e.g. add a new objective function) and need an extendable framework
 - you want to extend SEM, and your implementation needs to be fast (because you want to do a simulation, for example)
 - you want to fit the same model(s) to many datasets (bootstrapping, simulation studies)
@@ -33,7 +37,7 @@ The package makes use of
 - Optim.jl and NLopt.jl to provide a range of different Optimizers/Linesearches.
 - FiniteDiff.jl and ForwardDiff.jl to provide gradients for user-defined loss functions.
 
-At the moment, we are still working on
+# At the moment, we are still working on:
 - optimizing performance for big models (with hundreds of parameters)
 
 # Questions?