[Comp-neuro] PyMVPA 0.4.1: Multivariate Pattern Analysis in Python

[Yaroslav Halchenko]

PyMVPA 0.4.1: Multivariate Pattern Analysis in Python
-----------------------------------------------------
http://www.pymvpa.org

I would like to introduce PyMVPA to the neuroinformatics community.

PyMVPA is a Python module intended to ease multivariate pattern
classification analyses of large datasets.  In the neuroimaging
contexts such analysis techniques are also known as decoding or MVPA
analysis.

PyMVPA:

* provides high-level abstraction of typical processing steps and a
  number of implementations of some popular statistical learning
  algorithms, such as
  - classifiers: SVM, kNN, SMLR, etc
  - feature selection methods: RFE, IFS, etc

* is not limited to the neuroimaging domain, but is eminently suited
  for such datasets (e.g. transparent I/O for Nifti/Analyze data
  formats)

* allows researchers to compress complex analyses into a small amount
  of code.  This makes it possible to complement publications with the
  source code, which leads to an increase in scientific progress due
  to the superior accessibility of information and reproducibility of
  scientific results

* is truly a free software (MIT license) and additionally
  requires nothing but free-software to run

* is fully or partially supported on any platform supported by Python
  (depending on the availability of optional external dependencies)

* provides high-level "house-keeping" functionality done by the base
  classes, reducing the necessary amount of code needed to contribute
  a new fully-functional algorithm

* contains extensive user-manual with concrete and tested examples of the
  analysis pipelines


Besides all that, PyMVPA is a collaborative project, which was
initiated by Michael Hanke and Yaroslav O. Halchenko.  It welcomes new
contributors and users.  All the source materials (code, manuals,
website) are available for the collaborative development from a
distributed version control system (git).

The PyMVPA developers team currently consists of:

    * Michael Hanke, University of Magdeburg, Germany
    * Yaroslav O. Halchenko, Rutgers University Newark, USA
    * Per B. Sederberg, Princeton University, USA
    * Emanuele Olivetti, University of Trento, Italy

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

    * Greg Detre, Princeton University, USA
    * James M. Hughes, Dartmouth College, USA
    * Ingo Frund, University of Magdeburg, Germany
    * James Kyle, UCLA, USA

Following recently accepted papers provide high-level overview of
PyMVPA:

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Frund,
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.
http://dx.doi.org/10.3389/neuro.11.003.2009

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.
http://dx.doi.org/10.1007/s12021-008-9041-y

With best regards,
PyMVPA Team.

-- 
Yaroslav Halchenko
Research Assistant, Psychology Department, Rutgers-Newark
Student  Ph.D. @ CS Dept. NJIT
Office: (973) 353-1412 | FWD: 82823 | Fax: (973) 353-1171
        101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102
WWW:     http://www.linkedin.com/in/yarik        
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