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 file COPYRIGHTS
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 form https://CRAN.R-project.org/package=IsingFit to link to this page.