Metadata-Version: 1.1
Name: nilearn
Version: 0.2.4
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: .. -*- 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. EstÃ¨ve 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 >= 2.6,
        * setuptools
        * Numpy >= 1.6.1
        * SciPy >= 0.9
        * Scikit-learn >= 0.13 (Some examples require 0.14 to run)
        * Nibabel >= 1.1.0
        
        If you are using nilearn plotting functionalities or running the
        examples, matplotlib >= 1.1.1 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 :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
