abind, statmod, stats, graphics, utils, parallel, grDevices,
Suggests: testthat (>= 0.9.1), arm, mvtnorm, KernSmooth, R.rsp, knitr,
Description: Fit Bayesian generalized (non-)linear mixed models using Stan for full

Imports: methods, stats, abind, parallel (>= 3.1.3), coda (>= 0.17-1),
Description: Fit Bayesian generalized linear and ordinal mixed models using Stan for full Bayesian inference.

Imports: methods, stats, grDevices, abind, parallel (>= 3.1.3), coda
Description: Fit Bayesian generalized linear mixed models using Stan for full Bayesian inference.

Imports: methods, stats, grDevices, abind, reshape2, parallel (>=

abind, methods, stats, parallel, grDevices, grid

abind, statmod, methods, stats, utils, parallel, grDevices,
Description: Fit Bayesian generalized linear mixed models using Stan for full

lme4 (>= 1.1-8), nlme (>= 3.1-123), abind, statmod, methods,
stats, graphics, utils, parallel, grDevices, grid
Suggests: testthat (>= 0.9.1), mvtnorm, KernSmooth, R.rsp, knitr,

lme4 (>= 1.1-11), Matrix (>= 1.1.1), coda, abind, statmod,

abind, statmod, stats, CircStats, graphics, utils, parallel,

Description: Fit Bayesian generalized (non-)linear multilevel models

Authors@R: person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com",
1.8-13), rstantools (>= 1.3.0), bayesplot (>= 1.3.0), shinystan
(>= 2.4.0), matrixStats, bridgesampling, nlme, coda, abind,
Suggests: testthat (>= 0.9.1), RWiener, future, arm, spdep, mnormt,
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
Author: Paul-Christian Bürkner [aut, cre]