Package

r-brms

Description

Bayesian Regression Models using 'Stan'. 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 non-linear and smooth terms, auto-
correlation structures, censored data, 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 beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: Burkner (2017)
<doi:10.18637/jss.v080.i01>; Burkner (2018) <doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Note