Statistical learning for neuroimaging in Python
Project description
nilearn
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.
Important links
Official source code repo: https://github.com/nilearn/nilearn/
HTML documentation (stable release): http://nilearn.github.io/
Dependencies
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.
Install
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.
Development
Detailed instructions on how to contribute are available at http://nilearn.github.io/contributing.html
Release history Release notifications | RSS feed
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