The HiDimStat package provides statistical inference methods to solve the problem of variable importance evaluation in the context of predictive model using high-dimensional and spatially structured data.
If you like the package, spread the word and ⭐ our official repository!
Visit our website, https://hidimstat.github.io/, for more information.
Find your important variables in your data with the help of our examples.
If you have any problems, please report them to the GitHub issue tracker or contribute to the library by opening a pull request.
HiDimStat requires:
- Python (>= 3.9)
- joblib (>= 1.2)
- NumPy (>= 1.25)
- Pandas (>= 2.0)
- Scikit-learn (>= 1.4)
- SciPy (>= 1.6)
HiDimStat's plotting capabilities require Matplotlib (>= 3.9.0).
To run the examples, Matplotlib (>= 3.9.0) and seaborn (>= 0.9.0) are required.
HiDimStat can easily be installed via pip. For more installation information,
see the installation instructions.
pip install -U hidimstat
The best way to support the development of HiDimStat is to spread the word!
HiDimStat aims to be supported by an active community, and we welcome contributions to our code and documentation.
For bug reports, feature requests, documentation improvements, or other issues, you can create a GitHub issue.
If you want to contribute directly to the library, check the how to contribute page on the website for more information.
Currently, this library is supported by the INRIA
team MIND.
If you want to report a problem or suggest an enhancement, we would love for you
to open an issue at
this GitHub repository so we can address it quickly.
For less formal discussions or to exchange ideas, you can contact the main
contributors:
| Lionel Kusch | Bertrand Thirion | Joseph Paillard | Angel Reyero Lobo |
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If you use a HiDimStat method for your research, you'll find the associated reference paper in the method description, and we recommend that you cite it.
If you publish a paper using HiDimStat, please contact us or open an issue! We would love to hear about your work and help you promote it.
This project has been funded by Labex DigiCosme (ANR-11-LABEX-0045-DIGICOSME) as part of the program Investissement d’Avenir (ANR-11-IDEX-0003-02), by the Fast Big project (ANR-17-CE23-0011), by the KARAIB AI Chair (ANR-20-CHIA-0025-01), and by the VITE project (ANR-23-CE23-0016). This study has also been supported by the European Union’s Horizon 2020 research and innovation program as part of the program Human Brain Project SGA3 (Grant Agreement No. 945539) and EBRAIN-Health (Grant Agreement No. 101058516).