multivariate pattern analysis with Python v. 2

 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.