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Tip revision: 06a935fcd0daca8842f0d3138ff354753033c0ec authored by Thomas Nagler on 11 May 2021, 23:50:12 UTC
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Tip revision: 06a935f

# kdevine

[![Build status
[![Build status
[![License: GPL

> **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

  - [How to install](#how-to-install)
  - [Functionality](#functionality)
  - [References](#references)


## How to install

You can install:

  - the stable release on CRAN:

<!-- end list -->

``` r

  - the latest development version:

<!-- end list -->

``` r

## Functionality

A detailed description of of all functions and options can be found in
the [API
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
      - `contour.kdevinecop()`: Matrix of contour plots of all

  - Class `kde1d` and its methods:
      - `kde1d()`: Univariate kernel density estimation for bounded and
        unbounded support.
      - `dke1d()`, `pkde1d()`, `rkde1d()`: Density, cdf, and simulation
      - `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*

Nagler, T., Schellhase, C. and Czado, C. (2017)  
Nonparametric estimation of simplified vine copula models: comparison of
*Dependence Modeling, 5:99-120*

Nagler, T. (2018)  
A generic approach to nonparametric function estimation with mixed
*Statistics & Probability Letters, 137:326–330*
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