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. Estève 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 >= 2.6,
• setuptools
• Numpy >= 1.6.1
• SciPy >= 0.9
• Scikit-learn >= 0.13 (Some examples require 0.14 to run)
• Nibabel >= 1.1.0

If you are using nilearn plotting functionalities or running the examples, matplotlib >= 1.1.1 is required.

If you want to run the tests, you need nose >= 1.2.1 and coverage >= 3.6.

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.

You can check the latest sources with the command:

git clone git://github.com/nilearn/nilearn

or if you have write privileges:

git clone [email protected]:nilearn/nilearn