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https://github.com/asnelt/mixedvinetoolbox

Matlab toolbox for canonical vine copula trees with mixed continuous and discrete marginals
https://github.com/asnelt/mixedvinetoolbox

c-vines copula copula-models copulae copulas data-analysis dependency-analysis dependency-modeling matlab modeling regular-vines statistics

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Matlab toolbox for canonical vine copula trees with mixed continuous and discrete marginals

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README 

        
Mixed Vine Toolbox for Matlab

=============================



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.



Description

-----------



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.



Demonstration

-------------



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.



Functions

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* 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



Acknowledgements

----------------



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.



License

-------



```text

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

your option) any later version.



This program is distributed in the hope that it will be useful, but

WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General

Public License for more details.



You should have received a copy of the GNU General Public License along

with this program; if not, see .

```