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GCMI: Gaussian copula mutual information

version 1.0.0.0 (54.1 KB) by Robin
Calculating mutual information and other quantities using a parametric Gaussian copula.

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Updated Mon, 27 Jul 2020 07:05:06 +0000

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Functions for calculating mutual information and other information theoretic quantities using a parametric Gaussian copula.
This provides a robust rank based statistic that can handle multidimensional, continuous and discrete variables in a unified way with a meaningful effect size on a common scale (bits).
Higher order quantities such as conditional mutual information and interaction information quantify statistical relationships between multiple variables.

If you use this code for analysis that is published in an indexed journal or repository, please cite the following article:

RAA Ince, BL Giordano, C Kayser, GA Rousselet, J Gross and PG Schyns
"A statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula"
Human Brain Mapping doi:10.1002/hbm.23471

For journals with supplementary information that may not be indexed for citations, please place the citation in the indexed main manuscript.

The matlab_examples directory contains tutorial example scripts reproducing the analyses from that paper.

Cite As

Robin (2022). GCMI: Gaussian copula mutual information (https://github.com/robince/gcmi), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.