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 206 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 15 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 2,758 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. 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.