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. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run.
PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.
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News¶
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.
How to cite PyMVPA¶
Below is a list of publications about PyMVPA that have been published so far (in chronological order). If you use PyMVPA in your research please cite the one that matches best, and email use the reference so we could add it to our Who Is Using It? page.
Peer-reviewed publications¶
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53.
- First paper introducing fMRI data analysis with PyMVPA.
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. and Pollmann, S. (2009) PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.
- Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis.
- Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S. (2010) Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience. 4,1: 38-43
- Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research.
- Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416
- The Hyperalignment paper demonstrating its application to fMRI data in rich perceptual (movie) and categorization (monkey-dog) experiments.
Posters¶
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for machine-learning based data analysis.
- Poster emphasizing PyMVPA’s capabilities concerning multi-modal data analysis at the annual meeting of the Society for Neuroscience, Washington, 2008.
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for classifier-based data analysis.
- First presentation of PyMVPA at the conference Psychologie und Gehirn [Psychology and Brain], Magdeburg, 2008. This poster received the poster prize of the German Society for Psychophysiology and its Application.
Authors and Contributors¶
The PyMVPA developers team currently consists of:
- Michael Hanke, Dartmouth College, USA
- Yaroslav O. Halchenko, Dartmouth College, USA
- Emanuele Olivetti, Fondazione Bruno Kessler, Italy
We are very grateful to the following people, who have contributed valuable advice, code or documentation to PyMVPA:
- Florian Baumgartner, University of Magdeburg, Germany
- Michael W. Cole, Washington University in St. Louis, USA
- Ceyhun Çakar
- Reka Daniel, Princeton University, USA
- Greg Detre, Princeton University, USA
- Matthias Ekman, Donders Institute, Netherlands
- Ingo Fründ, TU Berlin, Germany
- Christoph Gohlke, University of California, Irvine, USA
- Scott Gorlin, MIT, USA
- Satrajit Ghosh, MIT, USA
- Jyothi Swaroop Guntupalli, Dartmouth College, USA
- Valentin Haenel, TU Berlin, Germany
- Stephen José Hanson, Rutgers University, USA
- James V. Haxby, Dartmouth College, USA
- James M. Hughes, Dartmouth College, USA
- James Kyle, UCLA, USA
- Russell Poldrack, University of Texas, USA
- Stefan Pollmann, University of Magdeburg, Germany
- Geethapriya Raghavan, University of Texas Austin, USA
- Rajeev Raizada, Dartmouth College, USA
- Per B. Sederberg, Princeton University, USA
- Tiziano Zito, BCCN, Germany
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 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
