Introduction

This package contains a complete framework based on canonical vine copulas for modeling multivariate data that are partly discrete and partly continuous. The resulting multivariate distributions are flexible with rich dependence structures and marginals.

For continuous marginals, implementations of the normal and the gamma distributions are provided. For discrete marginals, Poisson, binomial and negative binomial distributions are provided. As bivariate copula building blocks, the Gaussian, Frank and Clayton families as well as rotation transformed families are provided. Additional marginal and pair-copula distributions can be added easily.

The package includes methods for sampling, likelihood calculation and inference, all of which have quadratic complexity. These procedures are combined to estimate entropy by means of Monte Carlo integration.