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VineCopula (version 1.2)

Statistical inference of vine copulas

Description

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.

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Version

Install

install.packages('VineCopula')

Monthly Downloads

2,950

Version

1.2

License

GPL (>= 2)

Maintainer

Ulf Schepsmeier

Last Published

November 11th, 2013

Functions in VineCopula (1.2)

Goodness-of-fit test for bivariate copulas

Chi-plot for bivariate copula data

Hessian matrix of the log-likelihood of an R-vine copula model

Transform D-vine to R-vine structure

BiCopPar2TailDep

Tail dependence coefficients of a bivariate copula

Blomqvist's beta values of an R-vine copula model

Selection and maximum likelihood estimation of bivariate copula families

AIC and BIC of an R-vine copula model

Goodness-of-fit tests for R-vine copula models

Blomqvist's beta value of a bivariate copula

Sequential copula selection and estimation of R-vine copula models

BiCopHfuncDeriv

Derivatives of the h-function of a bivariate copula

Parameter of a bivariate copula for a given Kendall's tau value

Probability integral transformation for R-vine copula models

VineCopula-package

Statistical inference of vine copulas

Log-likelihood of an R-vine copula model

Kendall's plot (K-plot) for bivariate copula data

Transform C-vine to R-vine structure

Bivariate copula family names

R-vine copula model in matrix notation

Independence test for bivariate copula data

Density of a bivariate copula

Matrix of empirical Blomqvist's beta values

Gradient of the log-likelihood of an R-vine copula model

Matrix of empirical Kendall's tau values

RVineMatrixNormalize

Permute the variables to achieve a natural ordering

BiCopHfuncDeriv2

Second derivatives of the h-function of a bivariate copula

correlations to partial correlations and vice versa for R-vines

Distribution function of a bivariate copula

Parameter estimation for bivariate copula data using inversion of Kendall's tau or maximum likelihood estimation

Conditional distribution function (h-function) of a bivariate copula

Sequential estimation of an R-vine copula model

BiCopMetaContour

Contour plot of bivariate meta distribution with different margins and copula (theoretical and empirical)

Derivatives of a bivariate copula density

RVineMatrixCheck

Vine Matrix validation

Kendall's tau values of an R-vine copula model

Major German Stocks

Vuong test comparing two R-vine copula models

RVineStructureSelect

Sequential specification of R- and C-vine copula models

Second derivatives of a bivariate copula density

Lambda-function (plot) for bivariate copula data

Simulation from a bivariate copula

Kendall's tau value of a bivariate copula

BiCopVuongClarke

Scoring goodness-of-fit test based on Vuong and Clarke tests for bivariate copula data

Maximum likelihood estimation of an R-vine copula model

RVineClarkeTest

Clarke test comparing two R-vine copula models

Plot function for R-vine trees

Simulation from an R-vine copula model