Changelog

Changelog#

Version 0.4.0 (2025-05-02)#

The 0.4.0 release introduces cross-entropy support, improves code packaging, and enhances documentation.

  • 📈 Cross-Entropy support:

    • Added cross-entropy for all approaches.

    • Integrated cross-entropy into the documentation with detailed explanations and examples.

    • Restricted the use of joint random variables (RVs) for cross-entropy to avoid ambiguity.

  • 📦 Code packaging:

    • 📦 Added tests to packaged tarball for testing in conda-forge.

    • 🔧 Updated deprecated license classifier.

    • 🔧 Added Zenodo integration and updated README.md with logo and badges.

    • 🔧 Added README.md formatting for logos and badges.

  • 🔧 Warnings handling: Handled warnings as errors in pytest and addressed warnings in the code.

  • 📚 Documentation:

    • 📚 Added benchmark demo page to documentation.

    • 📄 Added acknowledgments and funding information.

    • 🎨 Updated logo and icon design.

    • 🔧 Added favicon and polished documentation index page, including logo and dark mode support.

    • 🔧 Added demos for Gaussian data and Schreiber Article.

    • 📊 Changed Gaussian axis titles and corrected Schreiber Demo information unit.

    • 🔧 Changed links and reformatted documentation.

Version 0.3.3 (2025-04-16)#

The 0.3.3 release focuses on improving documentation, moving to Read the Docs, and polishing the project.

  • 📚 Improved documentation and moved to Read the Docs.

    • 📄 Added automodapi for estimators and sphinx-apidoc.

    • 📊 Added graphviz apt dependency and fixed requirement structure.

    • 📝 Added code examples and reworked guide pages.

    • 🔗 Changed URL and repository settings.

  • 📦 Updated project for publication.

  • ✨ Optimizations and bug fixes:

    • 🚀 Parallelized box and Gaussian kernel calculations.

    • 🔄 Reused parameters between p-value and t-score calculations.

    • 🔧 Fixed bootstrap resampling for inhomogeneous, higher-dimensional input data.

    • 🔧 Optimized kernel (C)TE calculations.

    • 🔧 Fixed calling t-score without p-value.

Version 0.3.0 (2025-04-01)#

The 0.3.0dev0 release focuses on performance improvements, feature enhancements, and API updates.

  • 🔧 Local values support: All approaches now support local values.

  • 🎯 Added two new composite measures:

    • Jensen-Shannon Divergence (JSD)

    • Kullback-Leibler Divergence (KLD)

  • ✨ Optimized algorithms for:

    • Mutual Information (MI) and Conditional Mutual Information (CMI) on discrete and ordinal data.

    • Transfer Entropy (TE) and Conditional Transfer Entropy (CTE).

  • ⚡ Major API refactoring to improve compatibility with arbitrary many random variables in MI and CMI.

  • 💡 Enhanced performance through optimizations in base.py.

  • 🔍 Added extensive testing for local values and tested manually with code notebooks.

  • ⬆️ Added Python 3.13 support.

Version 0.2.1 (2025-02-11)#

The 0.2.1dev0 release marks the first release, providing essential information measures and estimators like Entropy (H), Mutual Information (MI), and others. It includes a CI/CD pipeline, supports Python 3.10-3.12, and is licensed under AGPLv3+.

  • 📦 First release of the infomeasure package.

  • 🧩 Added essential information measure estimators:

    • Shannon entropy (H)

    • Mutual Information (MI)

    • Conditional Mutual Information (CMI)

    • Transfer Entropy (TE) and Conditional Transfer Entropy (CTE)

    • Jensen-Shannon Divergence (JSD)

    • Kullback-Leibler Divergence (KLD)

  • 🔄 Set up CI/CD pipeline with GitLLab CI.

  • 💻 Added support for Python 3.10+.

  • 📄 Updated documentation to include installation guide, package structure, and example use cases.

Version 0.0.0 (2024-06-06)#

  • Package setup

    • 🏗 Written pyproject.toml

    • 🔄 General project and test structure with CI/CD

    • 📚️ Documentation with sphinx, sphinxcontrib-bibtex and numpydoc

Contents