grpreg: Regularization Paths for Regression Models with Grouped
Covariates
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 |
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[aut, cre],
Yaohui Zeng [ctb],
Ryan Kurth [ctb]