R-Packages and Other Software
VineCopula: Statistical inference of vine copulas
This package provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package CDVine are provided.
CDVine: Statistical inference of C- and D-vine copulas
(Development of the package has been abandoned. Please consider using VineCopula.)
This package provides functions for statistical inference of canonical vine (C-vine) and D-vine copulas. It contains tools for bivariate exploratory data analysis and for bivariate as well as vine copula selection. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included.
- Final release on CRAN
- Presentation at the 4th Workshop on Vine Copula Distributions and Applications, TU München pdf
- Package vignette pdf
- Manual pdf
spcopula: Vine copulas in the spatial context
This package is intended to provide the power of copulas to the spatial and spatio-temporal context. It will offer tools and functions to perform spatial analysis exploiting the possibility to fully model the whole dependence structure with copulas.
It is an R-forge package, i.e. the package is still under construction and no support is provided.
kdecopula: Kernel smoothing for bivariate copula densities
This package provides fast implementations of kernel estimators for the copula density. Due to its several plotting options it is particularly useful for the exploratory analysis of copula data. It can be further used for accurate estimation of unusually shaped copula densities and resampling.
The "Uncertainty analysis with Correlations" (UNICORN) software tool, implementing staff research work on dependence modelling for high dimensional distributions. It uses dependence trees and regular vines with diagonal band, maximum entropy and elliptical copulae.
Homepage at TU Delft (Netherlands): http://risk2.ewi.tudelft.nl/oursoftware/3-unicorn