grpreg: Regularization Paths for Regression Models with Grouped Covariates
Version 3.1-2

Efficient algorithms for fitting the regularization path of linear or logistic 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.

Package details

AuthorPatrick Breheny [aut, cre], Yaohui Zeng [ctb]
Date of publication2017-07-06 10:19:31 UTC
MaintainerPatrick Breheny <[email protected]>
Package repositoryView on CRAN
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grpreg documentation built on July 6, 2017, 5:02 p.m.