https://github.com/cran/kdevine
Raw File
Tip revision: 06a935fcd0daca8842f0d3138ff354753033c0ec authored by Thomas Nagler on 11 May 2021, 23:50 UTC
version 0.4.3
Tip revision: 06a935f
README.md

# kdevine

[![Build status
Linux](https://travis-ci.org/tnagler/kdevine.svg?branch=master)](https://travis-ci.org/tnagler/kdevine)
[![Build status
Windows](https://ci.appveyor.com/api/projects/status/epfs987wspjqkwlk/branch/master?svg=true)](https://ci.appveyor.com/project/tnagler/kdevine)
[![CRAN
version](https://www.r-pkg.org/badges/version/kdevine)](https://cran.r-project.org/package=kdevine)
[![License: GPL
v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

> **The kdevine package is no longer actively developed.** Consider
> using  
> \- the [kde1d](https://github.com/tnagler/kde1d) package for marginal
> estimation,  
> \- the functions `vine()` and `vinecop()` from the
> [rvinecopulib](https://github.com/vinecopulib/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](https://github.com/tnagler/kdecopula) and
let’s you choose from all its implemented methods. The package can
handle discrete and categorical data via [continuous
convolution](https://github.com/tnagler/cctools).

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

-----

## How to install

You can install:

  - the stable release on CRAN:

<!-- end list -->

``` r
install.packages("kdevine")
```

  - the latest development version:

<!-- end list -->

``` r
devtools::install_github("tnagler/kdevine")
```

## Functionality

A detailed description of of all functions and options can be found in
the [API
documentaion](https://tnagler.github.io/kdevine/reference/index.html).
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*
[\[preprint\]](https://arxiv.org/abs/1503.03305)

Nagler, T., Schellhase, C. and Czado, C. (2017)  
Nonparametric estimation of simplified vine copula models: comparison of
methods  
*Dependence Modeling, 5:99-120*
[\[preprint\]](https://arxiv.org/abs/1701.00845)

Nagler, T. (2018)  
A generic approach to nonparametric function estimation with mixed
data  
*Statistics & Probability Letters, 137:326–330*
[\[preprint\]](https://arxiv.org/abs/1704.07457)
back to top