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Package: brms
Encoding: UTF-8
Type: Package
Title: Bayesian Regression Models using Stan
Version: 1.3.1.9000
Date: 2016-12-22
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