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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 11 months ago 1056 main
conda 375.0 kB | win-64/r-grpreg-3.3.0-r36hda5aaf8_0.tar.bz2  5 years and 11 months ago 1066 main
conda 368.6 kB | osx-64/r-grpreg-3.3.0-r36h17f1fa6_0.tar.bz2  5 years and 11 months ago 348 main
conda 367.7 kB | osx-64/r-grpreg-3.3.0-r40h17f1fa6_0.tar.bz2  5 years and 11 months ago 354 main
conda 360.1 kB | linux-64/r-grpreg-3.3.0-r40hcdcec82_0.tar.bz2  5 years and 11 months ago 4030 main
conda 361.2 kB | linux-64/r-grpreg-3.3.0-r36hcdcec82_0.tar.bz2  5 years and 11 months ago 4019 main

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