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) doi:10.48550/arXiv.2007.07021. 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.

Last 30 days

This package has been downloaded 248 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,843 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Mar 28, 2025 with 54 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.