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vines

Multivariate Dependence Modeling with Vines

Implementation of the vine graphical model for building high-dimensional probability distributions as a factorization of bivariate copulas and marginal density functions. This package provides S4 classes for vines (C-vines and D-vines) and methods for inference, goodness-of-fit tests, density/distribution function evaluation, and simulation.

Functions (15)

RVine-classes

Classes for Regular Vines

Vine

Create Vine Objects

Vine-class

Base Vine Class

Vine-distribution

Vine Distribution Functions

h-methods

Methods for the h-functions

hinverse-methods

Methods for the Inverse of the h-functions

vineFit

Vine Inference

vineFit-class

Class for the Results of Vine Inference

vineFitML-class

Class for the Results of Vine Inference by Maximum Likelihood

vineGoF

Vine Goodness-of-fit Tests

vineGoF-class

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

vineLogLik

Vine Log-likelihood Evaluation

vineOrder

Select an Order of the Variables

vinePIT-methods

Vine Probability Integral Transform Methods

vineParameters

Parameters of a Vine

vines

Multivariate Dependence Modeling with Vines

v1.1.5
GPL (>= 2)
Authors
Yasser Gonzalez-Fernandez [aut, cre], Marta Soto [aut], Joris Meys [ctb]
Initial release

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