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,
zeroinflated, hurdle, and even selfdefined mixture models all in a
multilevel context. Further modeling options include nonlinear and
smooth terms, autocorrelation structures, censored data, metaanalytic
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 leaveoneout crossvalidation. References: Bürkner (2017)
Package details 


Author  PaulChristian Bürkner [aut, cre] 
Date of publication  20180916 16:40:03 UTC 
Maintainer  PaulChristian Bürkner <[email protected]> 
License  GPL (>= 3) 
Version  2.5.0 
URL  https://github.com/paulbuerkner/brms http://discourse.mcstan.org 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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