https://github.com/structuralequationmodels/structuralequationmodels.jl

A fast and flexible Structural Equation Modelling Framework
https://github.com/structuralequationmodels/structuralequationmodels.jl

psychometrics regularization statistics structural-equation-modelling structural-equation-models

Last synced: 21 days ago
JSON representation

A fast and flexible Structural Equation Modelling Framework

• Host: GitHub
• URL: https://github.com/structuralequationmodels/structuralequationmodels.jl
• Owner: StructuralEquationModels
• License: mit
• Created: 2019-12-17T15:48:20.000Z (about 6 years ago)
• Default Branch: main
• Last Pushed: 2026-02-20T17:50:57.000Z (21 days ago)
• Last Synced: 2026-02-20T22:14:32.564Z (21 days ago)
• Topics: psychometrics, regularization, statistics, structural-equation-modelling, structural-equation-models
• Language: Julia
• Homepage: https://structuralequationmodels.github.io/StructuralEquationModels.jl/dev/
• Size: 3.91 MB
• Stars: 50
• Watchers: 1
• Forks: 6
• Open Issues: 51
• Metadata Files:
  • Readme: README.md
  • Contributing: contributing.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

Awesome Lists containing this project

README

          
#  StructuralEquationModels.jl

| **Documentation**                                                               | **Build Status**                                                                                | Citation                                                                                        |

|:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|

| [![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) |

> [!NOTE]  

> Check out our [preprint](https://doi.org/10.31234/osf.io/zwe8g_v1) on the package!

# What is this Package for?

This is a package for Structural Equation Modeling.

It is still *in development*.

Models you can fit include

- Linear SEM that can be specified in RAM (or LISREL) notation

- ML, GLS and FIML estimation

- Regularized SEM (Ridge, Lasso, L0, ...)

- Multigroup SEM

- Sums of arbitrary loss functions (everything the optimizer can handle).

# What are the merits?

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 want to extend SEM (e.g. add a new objective function) and need an extensible 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)

- you are planning a study and would like to do power simulations

The package makes use of

- Symbolics.jl for symbolically precomputing parts of the objective and gradients to generate fast, specialized functions.

- SparseArrays.jl to speed up symbolic computations.

- Optim.jl and NLopt.jl to provide a range of different Optimizers/Linesearches.

- ProximalAlgorithms.jl for regularization.

- FiniteDiff.jl and to provide gradient approximations for user-defined loss functions.

# At the moment, we are still working on:

- optimizing performance for big models (with hundreds of parameters)

# Questions?

If you have questions you may ask them here in the [issues](https://github.com/StructuralEquationModels/StructuralEquationModels.jl/issues/new).

Please observe our [code of conduct](/CODE_OF_CONDUCT.md).