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vines (version 1.0.1)

Multivariate Dependence Modeling with Vines

Description

Vines are graphical models for pair-copula constructions that allows building high-dimensional distributions using bivariate copulas and marginal density functions. This package contains S4 classes for vines (C-vines and D-vines) and methods for inference, goodness-of-fit tests, density evaluation, distribution function evaluation, and simulation.

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Install

install.packages('vines')

Monthly Downloads

16

Version

1.0.1

License

GPL (>= 3)

Maintainer

Yasser GonzlezFernndez

Last Published

May 24th, 2011

Functions in vines (1.0.1)

RVine-class

Class for Regular Vines
Vine-class

Base Vine Class
Vine

Create Vine Objects
DVine-class

Class for D-vines
h-methods

Methods for the h-functions
vineFitML-class

Class for the Results of Vine Inference by Maximum Likelihood
vineParameters

Parameters of a Vine
hinverse-methods

Methods for the Inverse of the h functions
vineLogLik

Vine Log-likelihood Evaluation
vinePIT-methods

Vine Probability Integral Transform Methods
vineFit-class

Class for the Results of Vine Inference
vineGoF-class

Class for the Results of Vine Goodness-of-fit Tests
vineOrder

Select an Order of the Variables
CVine-class

Class for C-vines
Vine-distribution

Vine Distribution Functions
vineGoF

Vine Goodness-of-fit Tests
vineFit

Vine Inference