python-mvpa – multivariate pattern analysis with Python

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, GNB, 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.

Reference:
Michael Hanke, Yaroslav O. Halchenko, Per B. Sederberg, Stephen Jos{‘e} Hanson, James V. Haxby, Stefan Pollmann (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53. [Abstract] [DOI] [Pubmed]
Package availability chart
Distribution Base version Our version Architectures
Debian GNU/Linux 7.0 (wheezy) 0.4.8-1 0.4.8-1~nd70+1 i386, amd64, sparc, armel
Debian GNU/Linux 8.0 (jessie)   0.4.8-1~nd70+1 i386, amd64, sparc, armel
Debian unstable (sid)   0.4.8-1~nd+1 i386, amd64, sparc, armel
Ubuntu 12.04 LTS “Precise Pangolin” (precise) 0.4.7-2ubuntu1 0.4.8-1~nd11.10+1+nd12.04+1 i386, amd64, sparc
Ubuntu 14.04 “Trusty Tahr” (trusty) 0.4.8-3    

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