Metadata-Version: 1.1
Name: nilearn
Version: 0.5.0a0
Summary: Statistical learning for neuroimaging in Python
Home-page: http://nilearn.github.io
Author: Gael Varoquaux
Author-email: gael.varoquaux@normalesup.org
License: new BSD
Download-URL: http://nilearn.github.io
Description-Content-Type: UNKNOWN
Description: .. -*- mode: rst -*-
        
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        nilearn
        =======
        
        Nilearn is a Python module for fast and easy statistical learning on
        NeuroImaging data.
        
        It leverages the `scikit-learn <http://scikit-learn.org>`_ 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.
        
        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 >= 2.7,
        * setuptools
        * Numpy >= 1.11
        * SciPy >= 0.17
        * Scikit-learn >= 0.18
        * 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
        
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 :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
