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Jun 9, 2023
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Feat small changes #141

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8ad5e44
adding headers to the read me for better optics/ structure
LeonieHagitte Dec 25, 2022
38a07e5
forgot to capitalise one letter
LeonieHagitte Dec 25, 2022
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8 changes: 6 additions & 2 deletions README.md
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Expand Up @@ -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*.
Expand All @@ -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)
Expand All @@ -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?
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