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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.

copied from cf-post-staging / r-grpreg
Type Size Name Uploaded Downloads Labels
conda 374.1 kB | win-64/r-grpreg-3.3.0-r40hda5aaf8_0.tar.bz2  5 years and 9 months ago 1051 main
conda 375.0 kB | win-64/r-grpreg-3.3.0-r36hda5aaf8_0.tar.bz2  5 years and 9 months ago 1061 main
conda 368.6 kB | osx-64/r-grpreg-3.3.0-r36h17f1fa6_0.tar.bz2  5 years and 9 months ago 346 main
conda 367.7 kB | osx-64/r-grpreg-3.3.0-r40h17f1fa6_0.tar.bz2  5 years and 9 months ago 351 main
conda 360.1 kB | linux-64/r-grpreg-3.3.0-r40hcdcec82_0.tar.bz2  5 years and 9 months ago 3918 main
conda 361.2 kB | linux-64/r-grpreg-3.3.0-r36hcdcec82_0.tar.bz2  5 years and 9 months ago 3902 main

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