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 talk, we will explore the main features of the library through a series of small examples and coding exercises.
https://us06web.zoom.us/j/88625149934?pwd=QFykLfnKavdAE8QJpgwA4IXCXmbAlK.1
Slides available at: https://carlson.pages.ifisc.uib-csic.es/infomeasure-introduction-presentation/lab/index.html
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