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grpreg: Regularization Paths for Regression Models with GroupedCovariates

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. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.

Version:	3.5.0
Depends:	R (≥ 3.1.0)
Imports:	Matrix
Suggests:	knitr,rmarkdown, splines,survival,tinytest
Published:	2024-09-03
DOI:	10.32614/CRAN.package.grpreg
Author:	Patrick Breheny [aut, cre], Yaohui Zeng [ctb], Ryan Kurth [ctb]
Maintainer:	Patrick Breheny <patrick-breheny at uiowa.edu>
BugReports:	https://github.com/pbreheny/grpreg/issues
License:	GPL-3
URL:	https://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

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