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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)