VineCopula: Statistical Inference of Vine Copulas

Tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction are provided. Vine copula models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are 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.

Version: 1.6
Depends: R (≥ 2.11.0)
Imports: graphics, grDevices, stats, utils, MASS, mvtnorm, igraph (≥ 1.0.0), methods, copula, ADGofTest, lattice
Suggests: CDVine, TSP
Published: 2015-07-22
Author: Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler, Thomas Nagler, Tobias Erhardt
Maintainer: Tobias Erhardt <tobias.erhardt at tum.de>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: ChangeLog
In views: Distributions
CRAN checks: VineCopula results

Downloads:

Reference manual: VineCopula.pdf
Package source: VineCopula_1.6.tar.gz
Windows binaries: r-devel: VineCopula_1.6.zip, r-release: VineCopula_1.6.zip, r-oldrel: VineCopula_1.6.zip
OS X Snow Leopard binaries: r-release: VineCopula_1.6.tgz, r-oldrel: VineCopula_1.4.tgz
OS X Mavericks binaries: r-release: VineCopula_1.6.tgz
Old sources: VineCopula archive

Reverse dependencies:

Reverse depends: CopulaRegression
Reverse imports: kdecopula, SemiParBIVProbit