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.
- Official source code repo: https://github.com/nilearn/nilearn/
- HTML documentation (stable release): http://nilearn.github.io/
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