kdevine

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 convolution.

• How to install
• Functionality
• References

How to install

You can install:

• the stable release on CRAN:

install.packages("kdevine")

Functionality

A detailed description of of all functions and options can be found in the API documentaion. 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]

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

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