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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 363.5 kB | win-64/r-grpreg-3.3.1-r41h6d2157b_0.tar.bz2  3 years and 7 months ago 790 main
conda 373.3 kB | osx-64/r-grpreg-3.3.1-r41h28b5c78_0.tar.bz2  3 years and 7 months ago 135 main
conda 369.1 kB | linux-64/r-grpreg-3.3.1-r41hcfec24a_0.tar.bz2  3 years and 7 months ago 2203 main
conda 380.5 kB | win-64/r-grpreg-3.3.1-r36h6d2157b_0.tar.bz2  3 years and 9 months ago 875 main
conda 379.4 kB | win-64/r-grpreg-3.3.1-r40h6d2157b_0.tar.bz2  3 years and 9 months ago 832 main
conda 372.6 kB | osx-64/r-grpreg-3.3.1-r40h28b5c78_0.tar.bz2  3 years and 9 months ago 228 main
conda 373.5 kB | osx-64/r-grpreg-3.3.1-r36h28b5c78_0.tar.bz2  3 years and 9 months ago 184 main
conda 369.5 kB | linux-64/r-grpreg-3.3.1-r40hcfec24a_0.tar.bz2  3 years and 9 months ago 2294 main
conda 370.3 kB | linux-64/r-grpreg-3.3.1-r36hcfec24a_0.tar.bz2  3 years and 9 months ago 2401 main

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