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Statistical learning for neuroimaging in Python

Project Description

nilearn

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data.

It leverages the scikit-learn 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.

Dependencies

The required dependencies to use the software are:

  • Python >= 2.7,
  • setuptools
  • Numpy >= 1.6.1
  • SciPy >= 0.9
  • Scikit-learn >= 0.15
  • Nibabel >= 1.2.0

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

Release History

Release History

This version
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0.3.1

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0.3.0

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0.3.0b1

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0.2.6

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0.2.5.1

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0.2.5

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0.2.4

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0.2.3

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0.2.2

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0.2.1

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0.2.0

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0.1.4.post1

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0.1.4

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0.1.3

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0.1.2

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0.1.1

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0.1

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0.1b1

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0.1a1

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
nilearn-0.3.1.tar.gz (844.2 kB) Copy SHA256 Checksum SHA256 Source Jun 20, 2017

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