Easy-to-use Nearest Neighbor Estimation of Mutual Information
Project description
This package implements the estimation of mutual information (MI) between two continuous variables using a nearest-neighbor algorithm (Kraskov et al. 2004). Mutual information is an information-theoretical measure of dependency between two variables.
The interface is minimal and aimed at practical data analysis:
- Support for masking and time lags between variables
- Conditional MI with arbitrary-dimensional conditioning variables
- Normalization of MI to correlation coefficient scale
- Optional integration with
pandasdata frame types (no install-time dependency) - Optimized algorithm and parallel processing of multiple estimation tasks
This package depends only on NumPy. Support for Python 3.6+ on the latest macOS, Ubuntu and Windows versions is officially tested.
This project is still in alpha status and interface changes are possible. For more information on theoretical background and usage, please see the documentation. If you encounter any problems or have suggestions, please file an issue!
This package has been developed at Institute for Atmospheric and Earth System Research (INAR), University of Helsinki.
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