grpreg

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/.

Last 30 days

This package has been downloaded 6,010 times in the last 30 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 236 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 67,127 times in the last 365 days. An impressive feat! Enough downloads to make even seasoned academics take note. The day with the most downloads was Sep 04, 2024 with 466 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.