grpreg-package: Regularization paths for regression models with grouped...

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This package fits regularization paths for linear, logistic, and Cox regression models with grouped penalties, such as the group lasso, group MCP, group SCAD, group exponential lasso, and group bridge. The algorithms are based on the idea of either locally approximated coordinate descent or group descent, depending on the penalty. All of the algorithms (with the exception of group bridge) are stable and fast.


Given a design matrix X in which the features consist of non-overlapping groups and vector of responses y, grpreg solves the regularization path for a variety of penalties. The package also provides methods for plotting and cross-validation.

See the "Getting started" vignette for a brief overview of how the package works.

The following penalties are available:

The cMCP, gel, and gBridge penalties carry out bi-level selection, meaning that they carry out variable selection at the group level and at the level of individual covariates (i.e., they select important groups as well as important members of those groups). The grLasso, grMCP, and grSCAD penalties carry out group selection, meaning that within a group, coefficients will either all be zero or all nonzero. A variety of supporting methods for selecting lambda and plotting the paths are provided also.


Patrick Breheny



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grpreg documentation built on July 27, 2021, 1:08 a.m.