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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 384.3 kB | osx-64/r-grpreg-3.6.0-r45h8eed41d_0.conda  2 months and 1 day ago 33 main
conda 383.5 kB | osx-64/r-grpreg-3.6.0-r44h8eed41d_0.conda  2 months and 1 day ago 33 main
conda 378.1 kB | win-64/r-grpreg-3.6.0-r45heceb674_0.conda  2 months and 1 day ago 41 main
conda 378.5 kB | win-64/r-grpreg-3.6.0-r44heceb674_0.conda  2 months and 1 day ago 45 main
conda 382.4 kB | linux-64/r-grpreg-3.6.0-r45h54b55ab_0.conda  2 months and 1 day ago 208 main
conda 382.6 kB | linux-64/r-grpreg-3.6.0-r44h54b55ab_0.conda  2 months and 1 day ago 186 main

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