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. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.
Documentation: Downloads: Reverse dependencies: Reverse depends: fsemipar Reverse imports: bestglm, DMRnet, fetwfe, geoGAM, HMC, kko, mixedLSR, MTAFT, MUGS, naivereg, NVCSSL, PCLassoReg, refund, SSGL Reverse suggests: riskRegression, spfda Linking:Please use the canonical form https://CRAN.R-project.org/package=grpreg to link to this page.
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