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IsingFit: Fitting Ising Models Using the ELasso Method

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

Version:0.4
Depends:R (≥ 3.0.0)
Imports:qgraph,Matrix,glmnet
Suggests:IsingSampler
Published:2023-10-03
DOI:10.32614/CRAN.package.IsingFit
Author:Claudia van Borkulo, Sacha Epskamp; with contributions from Alexander Robitzsch and Mihai Alexandru Constantin
Maintainer:Sacha Epskamp <mail at sachaepskamp.com>
License:GPL-2
Copyright:see fileCOPYRIGHTS
NeedsCompilation:no
Materials:README,NEWS
In views:Psychometrics
CRAN checks:IsingFit results

Documentation:

Reference manual:IsingFit.html ,IsingFit.pdf

Downloads:

Package source: IsingFit_0.4.tar.gz
Windows binaries: r-devel:IsingFit_0.4.zip, r-release:IsingFit_0.4.zip, r-oldrel:IsingFit_0.4.zip
macOS binaries: r-release (arm64):IsingFit_0.4.tgz, r-oldrel (arm64):IsingFit_0.4.tgz, r-release (x86_64):IsingFit_0.4.tgz, r-oldrel (x86_64):IsingFit_0.4.tgz
Old sources: IsingFit archive

Reverse dependencies:

Reverse imports:bootnet,NetworkComparisonTest,NetworkToolbox
Reverse suggests:Isinglandr

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=IsingFitto link to this page.


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