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: 0.3.0
Depends: R (≥ 3.0.0)
Imports: qgraph, Matrix, glmnet
Suggests: IsingSampler
Published: 2014-10-23
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
CRAN checks: IsingFit results

Downloads:

Reference manual: IsingFit.pdf
Package source: IsingFit_0.3.0.tar.gz
Windows binaries: r-devel: IsingFit_0.3.0.zip, r-release: IsingFit_0.3.0.zip, r-oldrel: IsingFit_0.3.0.zip
OS X Snow Leopard binaries: r-release: IsingFit_0.3.0.tgz, r-oldrel: IsingFit_0.3.0.tgz
OS X Mavericks binaries: r-release: IsingFit_0.3.0.tgz
Old sources: IsingFit archive

Reverse dependencies:

Reverse imports: bootnet