loo (>= 0.1.6), shinystan (>= 2.2.1), lme4 (>= 1.1-11), Matrix
(>= 1.1.1), mgcv (>= 1.8-13), rstantools, bayesplot, coda,
abind, statmod, stats, CircStats, graphics, utils, parallel,
grDevices,

loo (>= 0.1.6), shinystan (>= 2.2.1), lme4 (>= 1.1-11), Matrix
(>= 1.1.1), mgcv (>= 1.8-13), rstantools, bayesplot, coda,
abind, statmod, stats, CircStats, RWiener, graphics, utils,
parallel, grDevices,
Fit Bayesian generalized (non-)linear multilevel models
using Stan for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit
linear, robust linear, binomial, Poisson, survival, response times, ordinal,
zero-inflated, hurdle, and even non-linear models all in a multilevel context.
Further modeling options include auto-correlation and smoothing terms,
user defined dependence 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.
In addition, model fit can easily be assessed and compared with
posterior predictive checks and leave-one-out cross-validation.

rstan (>= 2.13.2), loo (>= 0.1.6), shinystan (>= 2.2.1), lme4
(>= 1.1-11), Matrix (>= 1.1.1), mgcv (>= 1.8-13), rstantools,
bayesplot, coda, abind, statmod, stats, CircStats, RWiener,
graphics, utils, parallel, grDevices,

rstan (>= 2.14.1), loo (>= 0.1.6), shinystan (>= 2.2.1),
Matrix (>= 1.1.1), mgcv (>= 1.8-13), rstantools (>= 1.1.0),
bayesplot (>= 1.1.0), nlme, coda, abind, statmod, stats,
CircStats, RWiener, evd, graphics, utils, parallel, grDevices,
Fit Bayesian generalized (non-)linear 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, binomial, Poisson, survival, response times, ordinal,
zero-inflated, hurdle, and even non-linear models all in a
multilevel context. Further modeling options include auto-correlation
and smoothing terms, user defined dependence 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.
In addition, model fit can easily be assessed and compared with
posterior predictive checks and leave-one-out cross-validation.