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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.

Type Size Name Uploaded Downloads Labels
conda 358.8 kB | linux-64/r-grpreg-3.2_1-r36h96ca727_0.tar.bz2  5 years and 9 months ago 4 main
conda 367.9 kB | win-32/r-grpreg-3.2_1-r36h6115d3f_0.tar.bz2  5 years and 9 months ago 2 main
conda 372.6 kB | win-64/r-grpreg-3.2_1-r36h6115d3f_0.tar.bz2  5 years and 9 months ago 6 main
conda 360.0 kB | osx-64/r-grpreg-3.2_1-r36h46e59ec_0.tar.bz2  5 years and 9 months ago 3 main

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