Statistical learning for neuroimaging in Python
Project description
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
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