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:	0.3.1
Depends:	R (≥ 3.0.0)
Imports:	qgraph, Matrix, glmnet
Suggests:	IsingSampler
Published:	2016-09-07
Author:	Claudia van Borkulo, Sacha Epskamp, with contributions from Alexander Robitzsch
Maintainer:	Claudia van Borkulo <cvborkulo at gmail.com>
License:	GPL-2
Copyright:	see file COPYRIGHTS
NeedsCompilation:	no
In views:	Psychometrics
CRAN checks:	IsingFit results

Documentation:

Reference manual:

IsingFit.pdf

Downloads:

Package source:	IsingFit_0.3.1.tar.gz
Windows binaries:	r-devel: IsingFit_0.3.1.zip, r-release: IsingFit_0.3.1.zip, r-oldrel: IsingFit_0.3.1.zip
macOS binaries:	r-release (arm64): IsingFit_0.3.1.tgz, r-oldrel (arm64): IsingFit_0.3.1.tgz, r-release (x86_64): IsingFit_0.3.1.tgz, r-oldrel (x86_64): IsingFit_0.3.1.tgz
Old sources:	IsingFit archive

Reverse dependencies:

Reverse imports:

bootnet, NetworkComparisonTest, NetworkToolbox

Linking:

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