CRAN/E | grpreg

grpreg

Regularization Paths for Regression Models with Grouped Covariates

Installation

About

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 .

Citation grpreg citation info
pbreheny.github.io/grpreg/
github.com/pbreheny/grpreg
Bug report File report

Key Metrics

Version 3.4.0
R ≥ 3.1.0
Published 2021-07-26 1008 days ago
Needs compilation? yes
License GPL-3
CRAN checks grpreg results

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Maintainer

Maintainer

Patrick Breheny

patrick-breheny@uiowa.edu

Authors

Patrick Breheny

aut / cre

Yaohui Zeng

ctb

Ryan Kurth

ctb

Material

README
NEWS
Reference manual
Package source

In Views

MachineLearning

Vignettes

Getting started

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

grpreg archive

Depends

R ≥ 3.1.0

Imports

Matrix

Suggests

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Reverse Imports

bestglm
DMRnet
kko
naivereg
NVCSSL
PCLassoReg
refund
sparseGAM

Reverse Suggests

spfda