News PyMVPA 2.6.5.dev1 documentation

Time 2021-11-22 07:12:47

Web Name: News PyMVPA 2.6.5.dev1 documentation

WebSite: http://www.pymvpa.org

ID:245021

Keywords:

PyMVPA,News,documentation,mvpa,pymvpa,python,classifier,machinelearning,neurosci

Description:

keywords:mvpa, pymvpa, python, classifier, machine learning, neuroscience
description:

Multivariate Pattern Analysis in Python Navigation index modules | next | PyMVPA Home | Sitemap

PyMVPA is a Python package intended to ease statistical learning analyses oflarge datasets. It offers an extensible framework with a high-level interfaceto a broad range of algorithms for classification, regression, featureselection, data import and export. It is designed to integrate well withrelated software packages, such as scikit-learn, shogun, MDP, etc. While it is notlimited 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.

Installation Tutorial Documentation Support News¶Tweets by @pymvpaContributing¶

We welcome all kinds of contributions, and you do not need to be aprogrammer 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 thedocumentation, or you have an idea how to improve the user experience alltogether do not hesitate and contact us. We will thenfigure out how your contribution can be best incorporated. Any contributor willbe acknowledged and will appear in the list of people who have helped todevelop 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 insidethe source distribution (including this website). Please see theappendix of the manual for the copyright statement and thefull text of the license.

How to cite PyMVPA¶

Below is a list of publications about PyMVPA that have been publishedso far (in chronological order). If you use PyMVPA in your researchplease cite the one that matches best, and email use the reference sowe 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 ormodality-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: 38-43Focused review article emphasizing the role of transparency to facilitateadoption and evaluation of statistical learning techniques in neuroimagingresearch.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, 404–416The Hyperalignment paperdemonstrating its application to fMRI data in rich perceptual (movie) andcategorization (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 PyMVPAs capabilities concerning multi-modal data analysisat 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 posterprize of the German Society for Psychophysiology and its Application.Authors and Contributors¶

The PyMVPA developers team currently consists of:

Michael Hanke, University of Magdeburg, GermanyYaroslav O. Halchenko, Dartmouth College, USANikolaas N. Oosterhof, University of Trento, Italy

We are very grateful to the following people, who have contributedvaluable advice, code or documentation to PyMVPA:

Florian Baumgartner, University of Magdeburg, GermanySven Buchholz, University of Magdeburg, GermanyAndrew C. Connolly, Dartmouth College, USAMichael W. Cole, Washington University in St. Louis, USACeyhun ÇakarReka Daniel, Princeton University, USAGreg Detre, Princeton University, USAMatthias Ekman, Donders Institute, NetherlandsIngo Fründ, TU Berlin, GermanyChristoph Gohlke, University of California, Irvine, USAScott Gorlin, MIT, USASatrajit Ghosh, MIT, USAJyothi Swaroop Guntupalli, Dartmouth College, USAValentin Haenel, TU Berlin, GermanyStephen José Hanson, Rutgers University, USAJames V. Haxby, Dartmouth College, USAJames M. Hughes, Dartmouth College, USAJames Kyle, UCLA, USAEmanuele Olivetti, Fondazione Bruno Kessler, ItalyRussell Poldrack, University of Texas, USAStefan Pollmann, University of Magdeburg, GermanyGeethapriya Raghavan, University of Texas Austin, USARajeev Raizada, Dartmouth College, USAPer B. Sederberg, Princeton University, USATiziano Zito, BCCN, GermanyAcknowledgements¶

We are greatful to the developers and contributers of NumPy, SciPy andIPython for providing an excellent Python-based computing environment.

Additionally, as PyMVPA makes use of a lot of external softwarepackages (e.g. classifier implementations), we want to acknowledgethe authors of the respective tools and libraries (e.g. LIBSVM, MDP,scikit-learn, Shogun) and thank them for developing their packagesas free and open source software.

Finally, we would like to express our acknowledgements to the Debianproject for providing us with hosting facilities for mailing listsand source code repositories. But most of all for developing theuniversal operating system.

Grant support¶

PyMVPA development was supported, in part, by the following research grants.This list includes grants funding development of specific algorithmimplementations in PyMVPA, as well as grants supporting individuals to work onPyMVPA:

German Federal Ministry of Education and ResearchBMBF 01GQ11112German federal state of Saxony-AnhaltProject: Center for Behavioral Brain SciencesGerman Academic Exchange ServicePPP-USA D/05/504/7

McDonnel Foundation

US National Institutes of Mental Health5R01MH075706F32MH085433-01A1US National Science FoundationNSF 1129764Similar or Related Projects¶

There are a number other projects with in comparison toPyMVPA partially overlapping features or a similar purpose.Some of their functionality is already available through andwithin the PyMVPA framework. Only free software projects are listedhere.

3dsvm: AFNI plugin to apply support vector machine classifiers to fMRI data.CoSMoMVPA: Matlab/Octave toolbox designed after PyMVPA and with goodinteroperability with PyMVPA.Elefant: Efficient Learning, Large-scale Inference, and OptimizationToolkit. Multi-purpose open source library for machine learning.MDP:Python data processing framework. MDP provides various algorithms.PyMVPA makes use of MDPs PCA and ICA implementations.MVPA Toolbox: Matlab-based toolbox to facilitate multi-voxel patternanalysis of fMRI neuroimaging data.nilearn: scikit-learn based Python module for fast and easy statisticallearning on NeuroImaging data.NiPy: Project with growing functionality to analyze brain imaging data. NiPyis heavily connected to SciPy and lots of functionality developed withinNiPy becomes part of SciPy.OpenMEEG: Software package for low-frequency bio-electromagnetismsolving forward problems in the field of EEG and MEG.OpenMEEG includes Python bindings.Orange: Powerful general-purpose data mining software. Orange also has Pythonbindings.PROBID: Matlab-based GUI pattern recognition toolbox for MRI data.PyMGH/PyFSIO: Python IO library to for FreeSurfers mgh data format.PyML: Interactive object oriented framework for machine learningwritten in Python. PyML focuses on SVMs and other kernel methods.NiBabel_: Read and write NIfTI images from within Python.PyMVPA uses NiBabel to access MRI datasets.scikit-learn: Python module integrating classic machine learningalgorithms in the tightly-knit world of scientific Python packages.Shogun: Comprehensive machine learning toolbox with bindings to variousprogramming languages.PyMVPA can optionally use implementations of Support Vector Machines fromShogun. Table Of Contents NewsContributingLicenseHow to cite PyMVPAPeer-reviewed publicationsPostersAuthors and ContributorsAcknowledgementsGrant supportSimilar or Related Projects Next topic

PyMVPA User Manual

Quick linksSource downloadCode repositoryBug trackerMailing list archiveWho is using PyMVPA?Dataset ArchivePyMVPA@MLOSS.orgPyMVPA@INCF

Search mailing list archive Quick search

Navigation index modules | next | PyMVPA Home | Sitemap Copyright 2006-2016, PyMVPA Authors. Created using Sphinx 1.4.9.

TAGS:PyMVPA News documentation mvpa pymvpa python classifier machinelearning neurosci

<<< Thank you for your visit >>>

Hot Websites