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Changelog
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Version 0.4.0 (2025-05-02)
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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)
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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 <https://infomeasure.readthedocs.io/>`_.

  - 📄 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)
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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)
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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)
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* Package setup

  - 🏗 Written `pyproject.toml`
  - 🔄 General project and test structure with CI/CD
  - 📚️ Documentation with `sphinx`, `sphinxcontrib-bibtex` and `numpydoc`