grpreg (3.2-0)

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.

Maintainer: Patrick Breheny
Author(s): Patrick Breheny [aut, cre] (<>), Yaohui Zeng [ctb]

License: GPL-3

Uses: Matrix, survival, knitr, grpregOverlap
Reverse depends: grpregOverlap

Released about 1 year ago.