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infomeasure: a comprehensive Python package for information theory measures and estimators
Carlson Moses Büth1, Kishor Acharya2, Massimiliano Zanin2
1Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, 07122, Palma de Mallorca, Spain. carlson@cbueth.de.
Scientific Reports
|August 11, 2025
Summary
Introducing infomeasure, an open-source Python package for robust information theory analysis. It simplifies calculating complex measures like entropy and mutual information, enhancing reproducibility in scientific studies.
Area of Science:
- Information theory
- Computational neuroscience
- Data analysis
Background:
- Information theory is crucial in modern science but often limited by computational complexity and lack of software.
- Low reproducibility is a common issue in applying information-theoretic measures to real-world data.
Purpose of the Study:
- Introduce infomeasure, an open-source Python package for information-theoretic analysis.
- Provide robust tools for calculating various information-theoretic measures.
- Enhance reproducibility and simplify practical implementation of these analyses.
Main Methods:
- infomeasure package implementation for discrete and continuous variables.
- Incorporation of state-of-the-art estimation techniques.
- Calculation of local measure values, p-values, and t-scores.
Main Results:
- infomeasure offers a unified framework for diverse information-theoretic measures.
- The package is validated against known analytical solutions.
- Demonstrated utility in analyzing human brain time series data.
Conclusions:
- infomeasure mitigates common pitfalls in information-theoretic analyses.
- The package ensures reproducibility and simplifies complex calculations.
- Facilitates broader application of information theory in scientific research.
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