Python module for fast and easy statistical learning on NeuroImaging data
Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. This work is made available by a community of people, amongst which the INRIA Parietal Project Team and the scikit-learn folks, in particular P. Gervais, A. Abraham, V. Michel, A. Gramfort, G. Varoquaux, F. Pedregosa, B. Thirion, M. Eickenberg, C. F. Gorgolewski, D. Bzdok, L. Esteve and B. Cipollini. Detailed documentation is available at http://nilearn.github.io/.
Release | Stable | Testing |
---|---|---|
Fedora Rawhide | 0.8.1-3.fc36 | - |
Fedora 35 | 0.7.1-3.fc35 | - |
Fedora 34 | 0.7.1-1.fc34 | - |
You can contact the maintainers of this package via email at
python-nilearn dash maintainers at fedoraproject dot org
.