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 the INRIA Parietal Project Team and the scikit-learn folks, among which P. Gervais, A. Abraham, V. Michel, A. Gramfort, G. Varoquaux, F. Pedregosa, B. Thirion, M. Eickenberg, C. F. Gorgolewski, D. Bzdok and L. Estève.
- Official source code repo: https://github.com/nilearn/nilearn/
- HTML documentation (stable release): http://nilearn.github.com/
The required dependencies to use the software are:
- Python >= 2.6,
- setuptools
- Numpy >= 1.6
- SciPy >= 0.9
- Scikit-learn >= 0.12.1
- Nibabel >= 1.1.0.
This configuration corresponds to versions about the end of 2012.
Running the examples requires matplotlib >= 1.2
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 --pre --user nilearn
Note that nilearn has been released as a beta so you need to use the
--pre command-line parameter only if your pip version is greater than 1.4.
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 git@github.com:nilearn/nilearn