glinternet: Learning Interactions via Hierarchical Group-Lasso Regularization

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <doi:10.1080/10618600.2014.938812>.

Version:	1.0.12
Published:	2021-09-03
DOI:	10.32614/CRAN.package.glinternet
Author:	Michael Lim, Trevor Hastie
Maintainer:	Michael Lim <michael626 at gmail.com>
License:	GPL-2
URL:	http://web.stanford.edu/~hastie/Papers/glinternet_jcgs.pdf
NeedsCompilation:	yes
CRAN checks:	glinternet results

Documentation:

Reference manual:

glinternet.pdf

Downloads:

Package source:	glinternet_1.0.12.tar.gz
Windows binaries:	r-devel: glinternet_1.0.12.zip, r-release: glinternet_1.0.12.zip, r-oldrel: glinternet_1.0.12.zip
macOS binaries:	r-release (arm64): glinternet_1.0.12.tgz, r-oldrel (arm64): glinternet_1.0.12.tgz, r-release (x86_64): glinternet_1.0.12.tgz, r-oldrel (x86_64): glinternet_1.0.12.tgz
Old sources:	glinternet archive

Reverse dependencies:

Reverse imports:

FindIt, modnets

Linking:

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