StructuralEquationModels

A fast and flexible Structural Equation Modelling Framework

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

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.4%) to scientific vocabulary

Keywords

psychometrics regularization statistics structural-equation-modelling structural-equation-models
Last synced: 6 months ago · JSON representation ·

Repository

A fast and flexible Structural Equation Modelling Framework

Basic Info
Statistics
  • Stars: 50
  • Watchers: 1
  • Forks: 6
  • Open Issues: 36
  • Releases: 7
Topics
psychometrics regularization statistics structural-equation-modelling structural-equation-models
Created about 6 years ago · Last pushed 9 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

StructuralEquationModels.jl

| Documentation | Build Status | Citation | |:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:| | Stable Dev | Project Status: Active – The project has reached a stable, usable state and is being actively developed. Github Action CI codecov | DOI |

[!NOTE]
Check out our preprint 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. Please observe our code of conduct.

Owner

  • Name: StructuralEquationModels
  • Login: StructuralEquationModels
  • Kind: organization

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: StructuralEquationModels.jl
message: >-
  Please cite StructuralEquationModels.jl if you use
  it for your research.
type: software
authors:
  - family-names: Ernst
    given-names: Maximilian Stefan
    email: ernst@mpib-berlin.mpg.de
    affiliation: Max Planck Institute for Human Development
    orcid: 'https://orcid.org/0000-0003-2237-6255'
  - given-names: Aaron
    family-names: Peikert
    email: peikert@mpib-berlin.mpg.de
    affiliation: Max Planck Institute for Human Development
    orcid: 'https://orcid.org/0000-0001-7813-818X'
repository-code: >-
  https://github.com/StructuralEquationModels/StructuralEquationModels.jl
license: MIT
doi: 10.5281/zenodo.6719626

GitHub Events

Total
  • Create event: 23
  • Release event: 3
  • Issues event: 53
  • Watch event: 5
  • Delete event: 2
  • Issue comment event: 71
  • Push event: 77
  • Pull request review event: 53
  • Pull request review comment event: 49
  • Pull request event: 59
Last Year
  • Create event: 23
  • Release event: 3
  • Issues event: 53
  • Watch event: 5
  • Delete event: 2
  • Issue comment event: 71
  • Push event: 77
  • Pull request review event: 53
  • Pull request review comment event: 49
  • Pull request event: 59

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 740
  • Total Committers: 4
  • Avg Commits per committer: 185.0
  • Development Distribution Score (DDS): 0.082
Top Committers
Name Email Commits
maximilian m****t@g****m 679
Aaron Peikert a****t@p****e 35
Maximilian-Stefan-Ernst 3****t@u****m 25
nickhaf 8****f@u****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 117
  • Total pull requests: 120
  • Average time to close issues: 9 months
  • Average time to close pull requests: 15 days
  • Total issue authors: 11
  • Total pull request authors: 7
  • Average comments per issue: 0.8
  • Average comments per pull request: 1.08
  • Merged pull requests: 93
  • Bot issues: 0
  • Bot pull requests: 9
Past Year
  • Issues: 18
  • Pull requests: 46
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 3 days
  • Issue authors: 5
  • Pull request authors: 4
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.63
  • Merged pull requests: 36
  • Bot issues: 0
  • Bot pull requests: 3
Top Authors
Issue Authors
  • Maximilian-Stefan-Ernst (89)
  • aaronpeikert (11)
  • nickhaf (6)
  • brandmaier (3)
  • alyst (3)
  • anggoran (1)
  • gdalle (1)
  • dsryu0822 (1)
  • dependabot[bot] (1)
  • veroyatnost (1)
  • JuliaTagBot (1)
  • moritzketzer (1)
Pull Request Authors
  • Maximilian-Stefan-Ernst (89)
  • alyst (30)
  • dependabot[bot] (15)
  • aaronpeikert (14)
  • brandmaier (2)
  • LeonieHagitte (1)
  • nickhaf (1)
Top Labels
Issue Labels
enhancement (17) documentation (12) test (12) bug (11) question (5) dependencies (1)
Pull Request Labels
dependencies (15) github_actions (3) documentation (2)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 6 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
juliahub.com: StructuralEquationModels

A fast and flexible Structural Equation Modelling Framework

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 6 Total
Rankings
Dependent repos count: 9.9%
Stargazers count: 16.8%
Average: 22.6%
Forks count: 24.5%
Dependent packages count: 38.9%
Last synced: 6 months ago

Dependencies

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