regnet

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) doi:10.1186/s12863-017-0495-5 and Ren et al.(2019) doi:10.1002/gepi.22194). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

Last 30 days

This package has been downloaded 198 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 12 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 2,753 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 Feb 12, 2025 with 50 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.