# kdevine

**The kdevine package is no longer actively developed.**
Consider using

- the kde1d package for
marginal estimation,

- the functions `vine()`

and `vinecop()`

from the
rvinecopulib
package as replacements for `kdevine()`

and
`kdevinecop()`

.

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). The package is built on top of the copula density
estimators in kdecopula and let’s you
choose from all its implemented methods. The package can handle discrete
and categorical data via continuous
convolution.

## How to install

You can install:

- the stable release on CRAN:

`install.packages("kdevine")`

## Functionality

A detailed description of of all functions and options can be found
in the API
documentaion. In short, the package provides the following
functionality:

Class `kdevine`

and its methods:

`kdevine()`

: Multivariate kernel density estimation
based on vine copulas. Implements the estimator of (see, Nagler and
Czado, 2016).

`dkdevine()`

, `rkdevine()`

: Density and
simulation functions.

Class `kdevinecop`

and its methods:

`kdevinecop()`

: Kernel estimator for the vine copula
density (see, Nagler and Czado, 2016).

`dkdevinecop()`

, `rkdevinecop()`

: Density
and simulation functions.

`contour.kdevinecop()`

: Matrix of contour plots of all
pair-copulas.

Class `kde1d`

and its methods:

`kde1d()`

: Univariate kernel density estimation for
bounded and unbounded support.

`dke1d()`

, `pkde1d()`

,
`rkde1d()`

: Density, cdf, and simulation functions.

`plot.kde1d()`

, `lines.kde1d()`

: Plots the
estimated density.

## References

Nagler, T., Czado, C. (2016)

Evading the curse of dimensionality in nonparametric density estimation
with simplified vine copulas

*Journal of Multivariate Analysis 151, 69-89* [preprint]

Nagler, T., Schellhase, C. and Czado, C. (2017)

Nonparametric estimation of simplified vine copula models: comparison of
methods

*Dependence Modeling, 5:99-120* [preprint]

Nagler, T. (2018)

A generic approach to nonparametric function estimation with mixed
data

*Statistics & Probability Letters, 137:326–330* [preprint]