Statistical learning for neuroimaging in Python
Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & friendly community.
It supports general linear model (GLM) based analysis and 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.
The required dependencies to use the software are:
- Python >= 3.5,
- Numpy >= 1.11
- SciPy >= 0.19
- Scikit-learn >= 0.19
- Joblib >= 0.12
- Nibabel >= 2.0.2
If you are using nilearn plotting functionalities or running the examples, matplotlib >= 1.5.1 is required.
If you want to run the tests, you need pytest >= 3.9 and pytest-cov for coverage reporting.
First make sure you have installed all the dependencies listed above. Then you can install nilearn by running the following command in a command prompt:
pip install -U --user nilearn
More detailed instructions are available at http://nilearn.github.io/introduction.html#installation.
Detailed instructions on how to contribute are available at http://nilearn.github.io/development.html
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