grpreg: Regularization paths for regression models with grouped covariates

Efficient algorithms for fitting the regularization path of linear or logistic regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.

Version: 2.7-1
Depends: R (≥ 2.13.0), Matrix
Published: 2014-08-13
Author: Patrick Breheny
Maintainer: Patrick Breheny <patrick-breheny at uiowa.edu>
License: GPL-2
NeedsCompilation: yes
Citation: grpreg citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: grpreg results

Downloads:

Reference manual: grpreg.pdf
Package source: grpreg_2.7-1.tar.gz
Windows binaries: r-devel: grpreg_2.7-1.zip, r-release: grpreg_2.7-1.zip, r-oldrel: grpreg_2.7-1.zip
OS X Snow Leopard binaries: r-release: grpreg_2.7-1.tgz, r-oldrel: grpreg_2.7-1.tgz
OS X Mavericks binaries: r-release: grpreg_2.7-1.tgz
Old sources: grpreg archive