Regularization Paths for Regression Models with Grouped Covariates R package grpreg version 3.6.0 CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) 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/>. Software Depends: R (>= 3.1.0) Imports: Matrix Suggests: knitr, rmarkdown, splines, survival, tinytest Patrick Breheny <patrick-breheny@uiowa.edu> Comprehensive R Archive Network (CRAN) Patrick Breheny [aut, cre] (ORCID: <https://orcid.org/0000-0002-0650-1119>), Yaohui Zeng [ctb], Ryan Miller [aut] (ORCID: <https://orcid.org/0000-0003-0446-9992>), Ryan Kurth [ctb] GPL-3 2026-03-26 application/tgz https://CRAN.R-project.org/package=grpreg doi:10.32614/CRAN.package.grpreg