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)
- Version3.5.0
- R version≥ 3.1.0
- LicenseGPL-3
- Needs compilation?Yes
- grpreg citation info
- Last release09/03/2024
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Team
Patrick Breheny
Yaohui Zeng
Show author detailsRolesContributorRyan Kurth
Show author detailsRolesContributor
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