% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kdevine-package.R
\docType{package}
\name{kdevine-package}
\alias{kdevine-package}
\title{Kernel Smoothing for Bivariate Copula Densities}
\description{
This package implements a vine copula based kernel density estimator. The
estimator does not suffer from the curse of dimensionality and is therefore
well suited for high-dimensional applications (see, Nagler and Czado, 2016).
}
\details{
The multivariate kernel density estimators is implemented by the
\code{\link{kdevine}} function. It combines a kernel density estimator for
the margins (\code{\link{kde1d}}) and a kernel estimator of the vine copula
density (\code{\link{kdevinecop}}). The package is built on top of the copula
density estimators in the \link[kdecopula:kdecopula]{kdecopula::kdecopula-package} and let's you
choose from all its implemented methods. Optionally, the vine copula can be
estimated parameterically (only the margins are nonparametric).
}
\references{
Nagler, T., Czado, C. (2016) \cr \emph{Evading the curse of
dimensionality in nonparametric density estimation with simplified vine
copulas.} \cr \emph{Journal of Multivariate Analysis 151, 69-89
(doi:10.1016/j.jmva.2016.07.003)} \cr

Nagler, T., Schellhase, C. and Czado, C. (2017) \cr \emph{Nonparametric
estimation of simplified vine copula models: comparison of methods}
arXiv:1701.00845

Nagler, T. (2017) \cr
\emph{A generic approach to nonparametric function
estimation with mixed data.} \cr
\href{https://arxiv.org/abs/1704.07457}{arXiv:1704.07457}
}
\author{
Thomas Nagler
}
\keyword{package}