Overview#
How to install this package and run the first calculation.
Start your endeavour here!
Theoretic background of the library. See all estimation techniques with code snippets.
A collection of short demos showcasing the capabilities of this package.
E.g., analytical comparison and paper reproduction.
What is infomeasure?#
infomeasure is a Python library for computing information measures, such as entropy,
mutual information and conditional mutual information.
It provides a simple and efficient way to compute these measures on large datasets.
The Reference pages provide a comprehensive, theoretical background on the concepts behind these measures, while the Demos provide practical examples of how to use infomeasure in real-world applications.
Setup and use#
To set up infomeasure, see the Getting Started page, more on
the details of the inner workings can be found on the Reference pages.
Furthermore, you can also find the API documentation.
The introduction talk has been recorded and can be seen on
the IFISC YouTube channel and the
slides here.
How to cite#
If you use infomeasure in your research, please cite our pre-print (submitted).
You can also find citation information for this project in the CITATION.cff file in the repository and cite it accordingly.
Alternatively, if you’d like to cite the software itself or a specific version, find the Zenodo project page
for the specific version you are using and cite it accordingly.
Contributing#
If you want to contribute to the development of infomeasure, please read the
CONTRIBUTING.md
file.
Acknowledgments#
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 851255). This work was partially supported by the María de Maeztu project CEX2021-001164-M funded by the MICIU/AEI/10.13039/501100011033 and FEDER, EU.