% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brms-package.R
\docType{package}
\name{brms-package}
\alias{brms-package}
\alias{brms}
\title{Bayesian Regression Models using 'Stan'}
\description{
\if{html}{
   \figure{stanlogo.png}{options: width="50px" alt="http://mc-stan.org/about/logo/"}
   \emph{Stan Development Team}
}

The \pkg{brms} package provides an interface to fit Bayesian generalized
multivariate (non-)linear multilevel models using \pkg{Stan}, which is a 
C++ package for obtaining full Bayesian inference (see \url{http://mc-stan.org/}).
The formula syntax is an extended version of the syntax applied in the 
\pkg{lme4} package to provide a familiar and simple interface for 
performing regression analyses.
}
\details{
The main function of \pkg{brms} is \code{\link{brm}}, which uses
formula syntax to specify a wide range of complex Bayesian models 
(see \code{\link{brmsformula}} for details). Based on the supplied 
formulas, data, and additional information, it writes the Stan code 
on the fly via \code{\link{make_stancode}}, prepares the data via 
\code{\link{make_standata}}, and fits the model using 
\pkg{\link[rstan:rstan]{Stan}}.

Subsequently, a large number of post-processing methods can be applied: 
To get an overview on the estimated parameters, 
\code{\link[brms:summary.brmsfit]{summary}} or 
\code{\link[brms:marginal_effects.brmsfit]{marginal_effects}} 
are perfectly suited. Detailed visual analyses can be performed by applying 
the \code{\link{pp_check}} and \code{\link{stanplot}} methods, which both
rely on the \pkg{\link[bayesplot:bayesplot-package]{bayesplot}} package.
Model comparsions can be done via \code{\link{loo}} and \code{\link{waic}}, 
which make use of the \pkg{\link[loo:loo-package]{loo}} package as well as
via \code{\link{bayes_factor}} which relies on the \pkg{bridgesampling} package. 
For a full list of methods to apply, type \code{methods(class = "brmsfit")}.

Because \pkg{brms} is based on \pkg{Stan}, a C++ compiler is required. 
The program Rtools (available on \url{https://cran.r-project.org/bin/windows/Rtools/}) 
comes with a C++ compiler for Windows. On Mac, you should use Xcode.
For further instructions on how to get the compilers running, see the
prerequisites section at the
\href{https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started}{RStan-Getting-Started}
page.

When comparing other packages fitting multilevel models to \pkg{brms}, keep 
in mind that the latter needs to compile models before actually fitting them, 
which will require between 20 and 40 seconds depending on your machine, 
operating system and overall model complexity.

Thus, fitting smaller models may be relatively slow as compilation time makes
up the majority of the whole running time. For larger / more complex
models however, fitting my take several minutes or even hours, so that the
compilation time won't make much of a difference for these models.

See \code{vignette("brms_overview")} and \code{vignette("brms_multilevel")} 
for a general introduction and overview of \pkg{brms}. For a full list of 
available vignettes, type \code{vignette(package = "brms")}.
}
\references{
Paul-Christian Buerkner (2017). brms: An R Package for Bayesian Multilevel 
Models Using Stan. \emph{Journal of Statistical Software}, 80(1), 1-28. 
\code{doi:10.18637/jss.v080.i01}

Paul-Christian Buerkner (in press). Advanced Bayesian Multilevel Modeling 
with the R Package brms. \emph{The R Journal}.

The Stan Development Team. \emph{Stan Modeling Language User's Guide and
Reference Manual}. \url{http://mc-stan.org/users/documentation}.

Stan Development Team (2018). RStan: the R interface to Stan. R package
version 2.18.1. \url{http://mc-stan.org}
}
\seealso{
\code{\link{brm}}, 
\code{\link{brmsformula}}, 
\code{\link{brmsfamily}},
\code{\link{brmsfit}}
}