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>
License: GPL-2
Copyright: see file COPYRIGHTS
NeedsCompilation: no
Materials: README NEWS
In views: Psychometrics
CRAN checks: IsingFit results


Reference manual: IsingFit.pdf


Package source: IsingFit_0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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