hsstan

Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) doi:10.18637/jss.v076.i01). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) doi:10.1214/17-EJS1337SI), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) doi:10.1214/20-EJS1711).

Last 30 days

This package has been downloaded 302 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! 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 4,045 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 152 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.