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 Advances in Neural Information Processing Systems 29 (NIPS 2016).
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
Copyright (C) 2016 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
your option) any later version.
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