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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  15 days and 16 hours ago 24 main
conda 383.5 kB | osx-64/r-grpreg-3.6.0-r44h8eed41d_0.conda  15 days and 16 hours ago 24 main
conda 378.1 kB | win-64/r-grpreg-3.6.0-r45heceb674_0.conda  15 days and 16 hours ago 31 main
conda 378.5 kB | win-64/r-grpreg-3.6.0-r44heceb674_0.conda  15 days and 16 hours ago 33 main
conda 382.4 kB | linux-64/r-grpreg-3.6.0-r45h54b55ab_0.conda  15 days and 16 hours ago 72 main
conda 382.6 kB | linux-64/r-grpreg-3.6.0-r44h54b55ab_0.conda  15 days and 16 hours ago 68 main

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