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) tools:::Rd_expr_doi("10.4310/sii.2009.v2.n3.a10"), Huang, Breheny, and Ma (2012) tools:::Rd_expr_doi("10.1214/12-sts392"), Breheny and Huang (2015) tools:::Rd_expr_doi("10.1007/s11222-013-9424-2"), and Breheny (2015) tools:::Rd_expr_doi("10.1111/biom.12300"), or visit the package homepage https://pbreheny.github.io/grpreg/.
Patrick Breheny
Yuan M and Lin Y. (2006) Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B, 68: 49-67. tools:::Rd_expr_doi("10.1111/j.1467-9868.2005.00532.x")
Huang J, Ma S, Xie H, and Zhang C. (2009) A group bridge approach for variable selection. Biometrika, 96: 339-355. tools:::Rd_expr_doi("10.1093/biomet/asp020")
Breheny P and Huang J. (2009) Penalized methods for bi-level variable selection. Statistics and its interface, 2: 369-380. tools:::Rd_expr_doi("10.4310/sii.2009.v2.n3.a10")
Huang J, Breheny P, and Ma S. (2012). A selective review of group selection in high dimensional models. Statistical Science, 27: 481-499. tools:::Rd_expr_doi("10.1214/12-sts392")
Breheny P and Huang J. (2015) Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing, 25: 173-187. tools:::Rd_expr_doi("10.1007/s11222-013-9424-2")
Breheny P. (2015) The group exponential lasso for bi-level variable selection. Biometrics, 71: 731-740. tools:::Rd_expr_doi("10.1111/biom.12300")
Useful links:
vignette("getting-started", package="grpreg")
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