Statistical learning for neuroimaging in Python
Project 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
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
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