% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dcvar-package.R
\docType{package}
\name{dcvar-package}
\alias{dcvar-package}
\title{dcvar: Copula VAR models for bivariate time series}
\description{
\code{dcvar} focuses on copula VAR(1) workflows for bivariate time series. Most
bundled models use Gaussian copulas. The core single-level Gaussian-copula
models \code{\link[=dcvar]{dcvar()}}, \code{\link[=dcvar_hmm]{dcvar_hmm()}}, and \code{\link[=dcvar_constant]{dcvar_constant()}} support normal,
exponential, skew-normal, and gamma margins. \code{\link[=dcvar_constant]{dcvar_constant()}} additionally
supports a Clayton copula baseline with normal margins.
}
\details{
Experimental extensions via \code{\link[=dcvar_multilevel]{dcvar_multilevel()}} and \code{\link[=dcvar_sem]{dcvar_sem()}} provide a
narrower diagnostic interface than the core models. The multilevel model
supports normal and exponential margins, while the SEM model supports normal
and exponential latent innovation margins plus a naive row-mean score method.

Estimation uses Stan through \code{rstan} by default, with optional \code{cmdstanr}
backend support. PIT diagnostics are approximate posterior-mean plug-in
diagnostics. PSIS-LOO model comparison is available for the supported
single-level models.
}
\seealso{
Useful links:
\itemize{
  \item \url{https://github.com/benlug/dcvar}
  \item Report bugs at \url{https://github.com/benlug/dcvar/issues}
}

}
\author{
\strong{Maintainer}: Benedikt Lugauer \email{benedikt.lugauer@uni-leipzig.de}

}
\keyword{internal}
