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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  4 months and 2 days ago 91 main
conda 370.8 kB | win-64/r-grpreg-3.5.0-r44h11b023d_0.conda  4 months and 2 days ago 78 main
conda 371.6 kB | osx-64/r-grpreg-3.5.0-r43h199b6f9_0.conda  4 months and 2 days ago 86 main
conda 376.5 kB | osx-64/r-grpreg-3.5.0-r44h199b6f9_0.conda  4 months and 2 days ago 79 main
conda 370.3 kB | linux-64/r-grpreg-3.5.0-r43h2b5f3a1_0.conda  4 months and 2 days ago 462 main
conda 374.7 kB | linux-64/r-grpreg-3.5.0-r44h2b5f3a1_0.conda  4 months and 2 days ago 470 main

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