https://github.com/cran/kdevine
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Tip revision: 950ccc0c0c9d3f6c25ac1cfbddbf65faf191659d authored by Thomas Nagler on 17 December 2018, 15:00 UTC
version 0.4.2
Tip revision: 950ccc0
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](http://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)](http://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 `kdevincop()`.

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:

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

-   the latest development version:

    ``` 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* [\[preprint\]](http://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* [\[preprint\]](http://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)
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