https://github.com/cran/kdevine
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Tip revision: ffe65e8c4bf0dedb1b77175620f104742f65d711 authored by Thomas Nagler on 19 May 2017, 20:26 UTC
version 0.4.1
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README.md

kdevine
=======

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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* ([doi:10.1016/j.jmva.2016.07.003](http://dx.doi.org/10.1016/j.jmva.2016.07.003), [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
[arXiv:1701.00845](http://arxiv.org/abs/1701.00845)

Nagler, T. (2017)
A generic approach to nonparametric function estimation with mixed data
[arXiv:1704.07457](https://arxiv.org/abs/1704.07457)
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