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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-staging / r-grpreg
Type Size Name Uploaded Downloads Labels
conda 366.7 kB | win-64/r-grpreg-3.5.0-r43h11b023d_0.conda  11 months and 17 days ago 328 main
conda 370.8 kB | win-64/r-grpreg-3.5.0-r44h11b023d_0.conda  11 months and 17 days ago 340 main
conda 371.6 kB | osx-64/r-grpreg-3.5.0-r43h199b6f9_0.conda  11 months and 17 days ago 269 main
conda 376.5 kB | osx-64/r-grpreg-3.5.0-r44h199b6f9_0.conda  11 months and 17 days ago 267 main
conda 370.3 kB | linux-64/r-grpreg-3.5.0-r43h2b5f3a1_0.conda  11 months and 17 days ago 1146 main
conda 374.7 kB | linux-64/r-grpreg-3.5.0-r44h2b5f3a1_0.conda  11 months and 17 days ago 1253 main

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