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.
Robin (2023). GCMI: Gaussian copula mutual information (https://github.com/robince/gcmi), GitHub. Retrieved .
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