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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 363.5 kB | win-64/r-grpreg-3.3.1-r41h6d2157b_0.tar.bz2  4 years and 10 months ago 828 main
conda 373.3 kB | osx-64/r-grpreg-3.3.1-r41h28b5c78_0.tar.bz2  4 years and 10 months ago 156 main
conda 369.1 kB | linux-64/r-grpreg-3.3.1-r41hcfec24a_0.tar.bz2  4 years and 10 months ago 3177 main
conda 380.5 kB | win-64/r-grpreg-3.3.1-r36h6d2157b_0.tar.bz2  5 years and 7 days ago 920 main
conda 379.4 kB | win-64/r-grpreg-3.3.1-r40h6d2157b_0.tar.bz2  5 years and 7 days ago 869 main
conda 372.6 kB | osx-64/r-grpreg-3.3.1-r40h28b5c78_0.tar.bz2  5 years and 7 days ago 249 main
conda 373.5 kB | osx-64/r-grpreg-3.3.1-r36h28b5c78_0.tar.bz2  5 years and 7 days ago 206 main
conda 369.5 kB | linux-64/r-grpreg-3.3.1-r40hcfec24a_0.tar.bz2  5 years and 7 days ago 3259 main
conda 370.3 kB | linux-64/r-grpreg-3.3.1-r36hcfec24a_0.tar.bz2  5 years and 7 days ago 3389 main

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