infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators

TL;DR

infomeasure is an open-source Python package that simplifies the computation of various information-theoretic measures, ensuring reproducibility and applicability to real-world data, including continuous and discrete variables.

Contribution

It introduces a unified, robust framework for calculating diverse information-theoretic measures with state-of-the-art estimation techniques in Python.

Findings

Validated against known analytical solutions.

Demonstrated in a human brain time series case study.

Provides local measure values, p-values, and t-scores.

Abstract

Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; implements state-of-the-art estimation techniques; and allows the calculation of local measure values, $p$-values and $t$-scores. By unifying these approaches under one consistent framework, infomeasure aims to mitigate common pitfalls, ensure reproducibility, and simplify the practical implementation of information-theoretic analyses. In this contribution, we explore the motivation and features of infomeasure; its validation, using known analytical solutions; and exemplify its utility in a case study involving the analysis of human brain time series.