2025/05/07 by Büth, Carlson Moses, Acharya, Kishor, Zanin, Massimiliano · 1 citation
#Computational Physics (physics.comp-ph) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2505.14696
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.