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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.

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Summary
This summary is machine-generated.

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.

Keywords:
EntropyInformation theoryMutual informationSoftware packageTransfer entropy

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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.