[pymvpa] PyMVPA 2.1.0 is out

[Yaroslav Halchenko]

Dear PyMVPA-ers,

We are glad to announce the availability of a new release addressing a
number of bugs and introducing new features and enhancements.

Sources:
  Tagged and pushed to http://github.com/PyMVPA/PyMVPA
  Tarballs available from https://github.com/PyMVPA/PyMVPA/tags
Debian/Ubuntu:
  Available from the http://neuro.debian.net
  and was uploaded to Debian unstable (i.e. sid)
Windows
  installer is available for Python 2.6 at
  https://github.com/PyMVPA/PyMVPA/downloads
  [although once again -- we recommend Windows/OSX users to have a 
   look at http://neuro.debian.net/vm.html]
  Hopefully soon http://www.lfd.uci.edu/~gohlke/pythonlibs collection by
  Christoph Gohlke would get 2.1.0 as well
Website: updated

And here is what would you are getting with the upgrade:

* 2.1.0 (Fri, June 29 2012)

  * Fixes

    - :func:`~mvpa2.misc.support.mask2slice` failed to convert an array of
      ``False`` values into ``slice(None, 0, None)`` (Fixes #56).
    - A number of fixes to the HDF5 IO code that ignored parts of an object's
      state when custom ``__reduce__()`` implementations were used (Fixes #42),
      and had problems storing metaclass types (Fixes #78).
    - Proper single quotes in documentation code snippets within PDFs.
    - Memory leak (model pointer) in LIBSVM bindings.

  * Enhancements

    - All searchlight implementations can now optionally store the IDs of all
      features for each generated ROI (conditional attr. ``roi_feature_ids``)
    - Add :func:`~mvpa2.misc.neighborhood.scatter_neighborhoods` to aid
      sparse sampling of spaces.
    - Add :class:`~mvpa2.clfs.transerror.ConfusionMatrixError` to compute
      confusion matrices with an error function interface (e.g. for
      ``CrossValidation(errorfx=...)``). This class existed for a long time, but
      was hidden in the unit tests.
    - Add :class:`~mvpa2.clfs.transerror.Confusion` to compute
      confusion matrices with a Node interface (e.g. for
      ``CrossValidation(postproc=...)``). This is useful if confusion matrices
      are necessary as an intermediate result and further processing with
      other nodes is desired.

  * New functionality

    - Add :class:`~mvpa2.clfs.transerror.BayesConfusionHypothesis` to perform
      Bayesian hypothesis testing of multi-class confusion statistics. This is
      useful to assess the likelihood of a particular (or all possible)
      grouping of classes being distinguishable.
    - Add :class:`~mvpa2.mappers.fxy.FxyMapper` to perform arbitrary
      computations involving two datasets.
    - Add :class:`~mvpa2.mappers.base.CombinedMapper` to run a dataset through
      a set of mappers and combine their outputs.
    - Add :class:`~mvpa2.measures.statsmodels_adaptor.UnivariateStatsModels` a wrapper
      for using models from the statsmodels_ package as a
      FeaturewiseMeasure.
    - Add :class:`~mvpa2.misc.dcov.dCOV` and
      :func:`~mvpa2.misc.dcov.dcorcoef` to quantify independence of
      (multivariate) signals.

  * API changes

    - Deprecating ``GLM`` that is now implemented with UnivariateStatsModels.
      This deprecated GLM class no longer supports the ``zstat`` calculation,
      and none of its previous conditional attributes are available anymore.

Enjoy!
-- 
=------------------------------------------------------------------=
Yaroslav O. Halchenko
Postdoctoral Fellow,   Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
WWW:   http://www.linkedin.com/in/yarik