grpreg: Regularization Paths for Regression Models with Grouped Covariates

Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox 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: 3.2-1
Depends: R (≥ 3.1.0)
Imports: Matrix
Suggests: grpregOverlap, knitr, survival
Published: 2019-02-26
Author: Patrick Breheny [aut, cre] (<https://orcid.org/000-0002-0650-1119>), Yaohui Zeng [ctb]
Maintainer: Patrick Breheny <patrick-breheny at uiowa.edu>
BugReports: http://github.com/pbreheny/grpreg/issues
License: GPL-3
URL: http://pbreheny.github.io/grpreg, https://github.com/pbreheny/grpreg
NeedsCompilation: yes
Citation: grpreg citation info
Materials: README NEWS
In views: MachineLearning
CRAN checks: grpreg results

Downloads:

Reference manual: grpreg.pdf
Vignettes: Getting started
Package source: grpreg_3.2-1.tar.gz
Windows binaries: r-devel: grpreg_3.2-1.zip, r-release: grpreg_3.2-1.zip, r-oldrel: grpreg_3.2-1.zip
OS X binaries: r-release: grpreg_3.2-1.tgz, r-oldrel: grpreg_3.2-1.tgz
Old sources: grpreg archive

Reverse dependencies:

Reverse depends: grpregOverlap
Reverse imports: bestglm, DMRnet, geoGAM, grpss, naivereg, refund

Linking:

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