This is the first GitHub release of the infomeasure package, now versioned with Zenodo. The release includes essential information measures such as Entropy (H), Mutual Information (MI), Conditional Mutual Information (CMI), Transfer Entropy (TE), and more. Highlights include:
- Support for local values, optimized for discrete and ordinal data.
- New composite measures: Jensen-Shannon Divergence (JSD) and Kullback-Leibler Divergence (KLD).
- Performance improvements for TE, CTE, MI, and CMI estimators with vectorized implementations.
- Major API updates for compatibility with arbitrary random variables.
- Enhanced tests and validation, ensuring robust functionality.
To get to know more about infomeasure feel free to read the documentation, now hosted at Read the Docs.
The package supports Python 3.11-3.13, includes a CI/CD pipeline, and is licensed under AGPLv3+.
All Commits: https://github.com/cbueth/infomeasure/commits/0.3.3