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.
News
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 & Contributors
The PyMVPA developers team currently consists of:
We are very grateful to the following people, who have contributed
valuable advice, code or documentation to PyMVPA:
- Greg Detre, Princeton University, USA
- Ingo Fründ, TU Berlin, Germany
- Scott Gorlin, 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
- Rajeev Raizada, Dartmouth College, USA
- Per B. Sederberg, Princeton University, USA
- Tiziano Zito, BCCN, Germany