Toolbox for canonical vine copula trees with mixed continuous and discrete margins. If you use this toolbox, then please cite: A. Onken and S. Panzeri (2016). Mixed vine copulas as joint models of spike counts and local field potentials. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon and R. Garnett, editors, Advances in Neural Information Processing Systems 29 (NIPS 2016), pages 1325–1333.

In this toolbox, we implemented a complete framework based on canonical vine copulas for modelling multivariate data that are partly discrete and partly continuous. The resulting multivariate distributions are flexible with rich dependence structures and arbitrary margins. For continuous margins, we provide implementations of the normal and the gamma distributions. For discrete margins, we provide the Poisson, binomial and negative binomial distributions. As bivariate copula building blocks, we provide the Gaussian, student and Clayton families as well as rotation transformed Clayton families. The toolbox includes methods for sampling, likelihood calculation and inference, all of which have quadratic complexity. These procedures are combined to estimate entropy and mutual information by means of Monte Carlo integration.

The script demo.m demonstrates how to apply the Mixed Vine Toolbox. It constructs a 4D mixed canonical vine with normal, gamma, Poisson and binomial margins and builds the vine tree from Gaussian, Student, Clayton and rotated Clayton copula families. It calculates and plots multivariate marginal probability densities, samples from the distribution, estimates the model from the samples and calculates entropy.

• mixedvinefit - Mixed copula vine estimates
• mixedvinepdf - Mixed copula vine probability density function
• mixedvinernd - Mixed copula vine random numbers
• mixedvineentropy - Mixed copula vine entropy estimate
• mixedvineinfo - Mixed copula vine mutual information estimate
• mixedgaussfit - Mixed copula vine estimates with Gaussian copula
• marginfit - Univariate margin estimates
• marginpdf - Univariate margin probability density function
• margincdf - Univariate margin cumulative distribution function
• margininv - Inverse of univariate margin CDF
• copulafit - Copula parameter estimates
• copulapdf - Copula probability density function
• copulacdf - Copula cumulative distribution function
• copulaccdf - Copula conditional cumulative distribution function
• copulaccdfinv - Inverse of copula conditional CDF
• copularnd - Copula random numbers

Funded by a Marie Sklodowska-Curie Action: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 659227.

Copyright (C) 2016, 2017 Arno Onken

This file is part of the Mixed Vine Toolbox.

The Mixed Vine Toolbox is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as published
by the Free Software Foundation; either version 3 of the License, or (at
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