Package: brms
Encoding: UTF-8
Type: Package
Title: Bayesian Regression Models using Stan
Version: 1.4.0
Date: 2017-01-25
Authors@R: person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com",
  role = c("aut", "cre"))
Depends: R (>= 3.2.0), Rcpp (>= 0.12.0), ggplot2 (>= 2.0.0), methods
Imports: 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,
Suggests: testthat (>= 0.9.1), arm, mvtnorm, KernSmooth, R.rsp, knitr,
        rmarkdown
Description: 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.
LazyData: true
NeedsCompilation: no
License: GPL (>= 3)
URL: https://github.com/paul-buerkner/brms
BugReports: https://github.com/paul-buerkner/brms/issues
VignetteBuilder: knitr, R.rsp
RoxygenNote: 5.0.1
Packaged: 2017-01-27 10:55:36 UTC; paulb
Author: Paul-Christian Bürkner [aut, cre]
Maintainer: Paul-Christian Bürkner <paul.buerkner@gmail.com>
Repository: CRAN
Date/Publication: 2017-01-27 18:47:28