brms

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit – among others – linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) doi:10.18637/jss.v080.i01; Bürkner (2018) doi:10.32614/RJ-2018-017; Bürkner (2021) doi:10.18637/jss.v100.i05; Carpenter et al. (2017) doi:10.18637/jss.v076.i01.

Last 30 days

This package has been downloaded 27,456 times in the last 30 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1,223 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 275,596 times in the last 365 days. Practically an academic rockstar! That's enough downloads to cause murmurs at international conferences. The day with the most downloads was Sep 24, 2024 with 1,446 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.