Metadata-Version: 1.2
Name: nilearn
Version: 0.7.1
Summary: Statistical learning for neuroimaging in Python
Home-page: http://nilearn.github.io
Maintainer: Gael Varoquaux
Maintainer-email: gael.varoquaux@normalesup.org
License: new BSD
Download-URL: http://nilearn.github.io
Description: 	.. -*- mode: rst -*-
        
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        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 <http://scikit-learn.org>`_ 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 >= 3.5,
        * setuptools
        * Numpy >= 1.11
        * SciPy >= 0.19
        * Scikit-learn >= 0.19
        * Joblib >= 0.12
        * 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 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
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
