PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, MDP, etc. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is free software and requires nothing but free-software to run.

PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.

Contributing¶ We welcome all kinds of contributions, and you do not need to be a programmer to contribute! If you have some feature in mind that is missing, some example use case that you want to share, you spotted a typo in the documentation, or you have an idea how to improve the user experience all together – do not hesitate and contact us. We will then figure out how your contribution can be best incorporated. Any contributor will be acknowledged and will appear in the list of people who have helped to develop PyMVPA on the front-page of the pymvpa.org.

License¶ PyMVPA is free-software (beer and speech) and covered by the MIT License. This applies to all source code, documentation, examples and snippets inside the source distribution (including this website). Please see the appendix of the manual for the copyright statement and the full text of the license.

Acknowledgements¶ We are greatful to the developers and contributers of NumPy, SciPy and IPython for providing an excellent Python-based computing environment. Additionally, as PyMVPA makes use of a lot of external software packages (e.g. classifier implementations), we want to acknowledge the authors of the respective tools and libraries (e.g. LIBSVM, MDP, scikit-learn, Shogun) and thank them for developing their packages as free and open source software. Finally, we would like to express our acknowledgements to the Debian project for providing us with hosting facilities for mailing lists and source code repositories. But most of all for developing the universal operating system. Grant support¶ PyMVPA development was supported, in part, by the following research grants. This list includes grants funding development of specific algorithm implementations in PyMVPA, as well as grants supporting individuals to work on PyMVPA: German Federal Ministry of Education and Research BMBF 01GQ11112 German federal state of Saxony-Anhalt Project: Center for Behavioral Brain Sciences German Academic Exchange Service PPP-USA D/05/504/7 McDonnel Foundation US National Institutes of Mental Health 5R01MH075706

F32MH085433-01A1 US National Science Foundation NSF 1129764