nilearn 0.8.0

Statistical learning for neuroimaging in Python

nilearn

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.

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 >= 3.6,

• setuptools

• Numpy >= 1.16

• SciPy >= 1.2

• Scikit-learn >= 0.21

• Joblib >= 0.12

• Nibabel >= 2.5

• Pandas >= 0.24

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.

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/development.html