SSGL

Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior introduced by Bai et al. (2022) doi:10.1080/01621459.2020.1765784 and extended to GLMs by Bai (2023) . This package supports fitting the SSGL model for the following GLMs with group sparsity: Gaussian linear regression, binary logistic regression, Poisson regression, negative binomial regression, and gamma regression. Stand-alone functions for group-regularized negative binomial regression and group-regularized gamma regression are also available, with the option of employing the group lasso penalty of Yuan and Lin (2006) doi:10.1111/j.1467-9868.2005.00532.x, the group minimax concave penalty (MCP) of Breheny and Huang doi:10.1007/s11222-013-9424-2, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) doi:10.1007/s11222-013-9424-2.