gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) <doi:10.1007/s11222-014-9498-5>.

Version:	1.6
Imports:	methods
Suggests:	testthat, knitr, rmarkdown
Published:	2025-05-08
DOI:	10.32614/CRAN.package.gglasso
Author:	Yi Yang [aut, cre] (http://www.math.mcgill.ca/yyang/), Hui Zou [aut] (http://users.stat.umn.edu/~zouxx019/), Sahir Bhatnagar [aut] (http://sahirbhatnagar.com/)
Maintainer:	Yi Yang <yi.yang6 at mcgill.ca>
BugReports:	https://github.com/archer-yang-lab/gglasso/issues
License:	GPL-2
URL:	https://github.com/archer-yang-lab/gglasso
NeedsCompilation:	yes
Materials:	README, ChangeLog
CRAN checks:	gglasso results

Documentation:

Reference manual:

gglasso.html , gglasso.pdf

Downloads:

Package source:	gglasso_1.6.tar.gz
Windows binaries:	r-devel: gglasso_1.6.zip, r-release: gglasso_1.6.zip, r-oldrel: gglasso_1.6.zip
macOS binaries:	r-release (arm64): gglasso_1.6.tgz, r-oldrel (arm64): gglasso_1.6.tgz, r-release (x86_64): gglasso_1.6.tgz, r-oldrel (x86_64): gglasso_1.6.tgz
Old sources:	gglasso archive

Reverse dependencies:

Reverse depends:	PIE
Reverse imports:	changepoints, ICBioMark, MLGL, PhylogeneticEM
Reverse suggests:	dfr, fdaSP, sgs, sharp, sparsegl, tidyfit

Linking:

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