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) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ2018017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Package details 


Author  PaulChristian Bürkner [aut, cre] 
Maintainer  PaulChristian Bürkner <paul.buerkner@gmail.com> 
License  GPL2 
Version  2.13.0 
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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