python-mvpa2 – multivariate pattern analysis with Python v. 2¶
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PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun).
While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets.
This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package.
- Reference:
Hanke, Michael, Halchenko, Yaroslav O., Sederberg, Per B., Hanson, Stephen José, Haxby, James V., Pollmann, Stefan (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, . [Abstract] [DOI] [Pubmed]
Distribution |
Base version |
Our version |
Architectures |
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Debian GNU/Linux 10.0 (buster) |
2.6.5-1~nd100+1 |
i386, amd64, sparc, armel, ppc64el |
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Debian GNU/Linux 9.0 (stretch) |
2.6.0-1 |
2.6.5-1~nd90+1 |
i386, amd64, sparc, armel |
Debian unstable (sid) |
2.6.5-1~nd+1 |
i386, amd64, sparc, armel |
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Ubuntu 16.04 “Xenial Xerus” (xenial) |
2.4.1-1 |
2.6.5-1~nd16.04+1 |
i386, amd64, sparc, armel |
Ubuntu 18.04 “Bionic Beaver” (bionic) |
2.6.4-2 |
2.6.5-1~nd18.04+1 |
i386, amd64, sparc, armel |