pbreheny/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. 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/>.

Getting started

Package details

Maintainer
LicenseGPL-3
Version3.4.0.1
URL https://pbreheny.github.io/grpreg/ https://github.com/pbreheny/grpreg
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("pbreheny/grpreg")
pbreheny/grpreg documentation built on Aug. 14, 2021, 5:12 a.m.