PyMVPA is a Python module intended to ease pattern classification analyzes of large datasets. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms.

While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truely free software (in every respect) and additionally requires nothing but free-software to run.

PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.

What's New in This Release: [ read full changelog ]

· states -> ca (for conditional attributes). All attributes stored in collections (parameters for Classifiers in params, states in ca) should be accessed not at top level of the object but through a collection.
· Dataset: behaves more like a NumPy array. No specialized Dataset classes, but constructors
· MaskedDataset -> dataset_wizard
· NiftiDataset -> fmri_dataset
· ERNiftiDataset -> fmri_dataset + eventrelated_dataset (see event-related analysis example)
· MRI volumes: 3,4D volumes (and coordinates) are exposed with following order of axes: t,x,y,z. Previously we followed a convention of t,z,y,x order of axis in volume data (to be consistent with PyNIfTI).
· Masks (mask_mapper)

· now [1,1,0] is not the same as [True, True, False]

· We have weird (but consistent) conventions now - classes are CamelCased - factory functions (even for whatever might have been before a class) are in pythonic_style
· detrend -> poly_detrend
· perchunk=bool (in zscore/detrend) got refactored into chunks_attr=None or string...