grpreg (3.12)
Regularization Paths for Regression Models with Grouped Covariates.
http://cran.rproject.org/web/packages/grpreg
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 bilevel 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: GPL3
Uses: Matrix, survival, knitr, grpregOverlap
Reverse depends: grpregOverlap
Released 3 months ago.
23 previous versions
 grpreg_3.11. Released 4 months ago.
 grpreg_3.10. Released 4 months ago.
 grpreg_3.02. Released about 1 year ago.
 grpreg_3.01. Released over 1 year ago.
 grpreg_3.00. Released over 1 year ago.
 grpreg_2.81. Released over 2 years ago.
 grpreg_2.80. Released almost 3 years ago.
 grpreg_2.71. Released about 3 years ago.
 grpreg_2.70. Released about 3 years ago.
 grpreg_2.60. Released over 3 years ago.
 grpreg_2.50. Released almost 4 years ago.
 grpreg_2.40. Released over 4 years ago.
 grpreg_2.30. Released over 4 years ago.
 grpreg_2.21. Released almost 5 years ago.
 grpreg_2.20. Released almost 5 years ago.
 grpreg_2.10. Released about 5 years ago.
 grpreg_2.01. Released about 5 years ago.
 grpreg_2.00. Released about 5 years ago.
 grpreg_1.22. Released about 5 years ago.
 grpreg_1.21. Released about 6 years ago.
 grpreg_1.20. Released over 6 years ago.
 grpreg_1.1. Released almost 8 years ago.
 grpreg_1.0. Released almost 9 years ago.
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