bamlss

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) doi:10.1080/10618600.2017.1407325 and the R package in Umlauf, Klein, Simon, Zeileis (2021) doi:10.18637/jss.v100.i04.

Last 30 days

This package has been downloaded 1,004 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 28 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 12,102 times in the last 365 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The day with the most downloads was Sep 26, 2024 with 121 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.