gamlr: Gamma Lasso Regression

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <doi:10.48550/arXiv.1308.5623>.

Version:	1.13-9
Depends:	R (≥ 2.15), Matrix, methods, graphics, stats
Suggests:	parallel
Published:	2026-02-25
DOI:	10.32614/CRAN.package.gamlr
Author:	Matt Taddy [aut, cre]
Maintainer:	Matt Taddy <mataddy at gmail.com>
BugReports:	https://github.com/TaddyLab/gamlr/issues
License:	GPL-3
URL:	https://github.com/TaddyLab/gamlr
NeedsCompilation:	yes
Citation:	gamlr citation info
CRAN checks:	gamlr results

Documentation:

Reference manual:

gamlr.html , gamlr.pdf

Downloads:

Package source:	gamlr_1.13-9.tar.gz
Windows binaries:	r-devel: gamlr_1.13-9.zip, r-release: gamlr_1.13-9.zip, r-oldrel: gamlr_1.13-9.zip
macOS binaries:	r-release (arm64): gamlr_1.13-9.tgz, r-oldrel (arm64): gamlr_1.13-9.tgz, r-release (x86_64): gamlr_1.13-9.tgz, r-oldrel (x86_64): gamlr_1.13-9.tgz
Old sources:	gamlr archive

Reverse dependencies:

Reverse depends:	distrom, textir
Reverse imports:	bolasso

Linking:

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