Metadata-Version: 1.1
Name: nilearn
Version: 0.1b1
Summary: Statistical learning for neuroimaging in Python
Home-page: http://nilearn.github.com
Author: Gael Varoquaux
Author-email: gael.varoquaux@normalesup.org
License: new BSD
Download-URL: http://nilearn.github.com
Description: .. -*- mode: rst -*-
        
        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 the INRIA Parietal Project Team and the
        scikit-learn folks, among which P. Gervais, A. Abraham, V. Michel, A.
        Gramfort, G. Varoquaux, F. Pedregosa, B. Thirion, M. Eickenberg, C. F. Gorgolewski,
        D. Bzdok and L. Estève.
        
        Important links
        ===============
        
        - Official source code repo: https://github.com/nilearn/nilearn/
        - HTML documentation (stable release): http://nilearn.github.com/
        
        Dependencies
        ============
        
        The required dependencies to use the software are:
        
        * Python >= 2.6,
        * setuptools
        * Numpy >= 1.3
        * SciPy >= 0.7
        * Scikit-learn >= 0.12.1
        * Nibabel >= 1.1.0.
        This configuration almost matches the Ubuntu 10.04 LTS release from
        April 2010, except for scikit-learn, which must be installed separately.
        
        Running the examples requires matplotlib >= 0.99.1
        
        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 --pre --user nilearn
        
        Note that nilearn has been released as a beta so you need to use the
        ``--pre`` command-line parameter only if your pip version is greater than 1.4.
        
        More detailed instructions are available at
        http://nilearn.github.io/introduction.html#installation.
        
        Development
        ===========
        
        Build Status
        ------------
        .. |travis-master| image:: https://travis-ci.org/nilearn/nilearn.svg?branch=master
           :target: https://travis-ci.org/nilearn/nilearn
           :alt: Build Status
        
        |travis-master|
        
        Code
        ----
        
        GIT
        ~~~
        
        You can check the latest sources with the command::
        
            git clone git://github.com/nilearn/nilearn
        
        or if you have write privileges::
        
            git clone git@github.com:nilearn/nilearn
        
        
        
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
