glinternet: Learning Interactions via Hierarchical Group-Lasso Regularization
Version 1.0.4

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) .

Getting started

Package details

AuthorMichael Lim, Trevor Hastie
Date of publication2017-06-15 13:39:57 UTC
MaintainerMichael Lim <michael626@gmail.com>
LicenseGPL-2
Version1.0.4
URL http://web.stanford.edu/~hastie/Papers/glinternet_jcgs.pdf
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("glinternet")

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glinternet documentation built on June 20, 2017, 9:04 a.m.