infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911105965621248 |
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| author | Büth, Carlson Moses Acharya, Kishor Zanin, Massimiliano |
| author_facet | Büth, Carlson Moses Acharya, Kishor Zanin, Massimiliano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators Büth, Carlson Moses Acharya, Kishor Zanin, Massimiliano Physics and Society Information Theory Computational Physics Data Analysis, Statistics and Probability 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. |
| title | infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators |
| topic | Physics and Society Information Theory Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2505.14696 |