Package: grpreg 3.5.0.1

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/>.

Authors:Patrick Breheny [aut, cre], Yaohui Zeng [ctb], Ryan Kurth [ctb]

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Bug tracker:https://github.com/pbreheny/grpreg/issues

On CRAN:

10 exports 33 stars 5.33 score 2 dependencies 32 dependents 19 mentions 159 scripts 3.8k downloads

Last updated 15 days agofrom:6d914cb777. Checks:OK: 1 ERROR: 8. Indexed: yes.

Exports:AUCcv.grpregcv.grpsurvexpand_splinegBridgegen_nonlinear_datagrpreggrpsurvplot_splineselect

Dependencies:latticeMatrix