Statistical learning for neuroimaging in Python

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  • License: OSI Approved (new BSD)
  • Maintainer: Gael Varoquaux
  • Requires: Python >=3.5
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Project description

Travis Build Status AppVeyor Build Status https://codecov.io/gh/nilearn/nilearn/branch/master/graph/badge.svg https://dev.azure.com/Parietal/Nilearn/_apis/build/status/nilearn.nilearn?branchName=master

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. Esteve and B. Cipollini.

Dependencies

The required dependencies to use the software are:

  • Python >= 3.5,

  • setuptools

  • Numpy >= 1.11

  • SciPy >= 0.19

  • Scikit-learn >= 0.19

  • Joblib >= 0.11

  • 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 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

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