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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 368.0 kB | win-64/r-grpreg-3.2.2-r35hda5aaf8_0.tar.bz2  5 years and 7 months ago 1029 main cf202003
conda 370.4 kB | win-64/r-grpreg-3.2.2-r36hda5aaf8_0.tar.bz2  5 years and 7 months ago 1067 main cf202003
conda 362.0 kB | osx-64/r-grpreg-3.2.2-r35h17f1fa6_0.tar.bz2  5 years and 7 months ago 330 main cf202003
conda 364.2 kB | osx-64/r-grpreg-3.2.2-r36h17f1fa6_0.tar.bz2  5 years and 7 months ago 336 main cf202003
conda 355.7 kB | linux-64/r-grpreg-3.2.2-r35hcdcec82_0.tar.bz2  5 years and 7 months ago 3598 main cf202003
conda 357.4 kB | linux-64/r-grpreg-3.2.2-r36hcdcec82_0.tar.bz2  5 years and 7 months ago 3655 main cf202003

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