[pymvpa] PyMVPA 2.1.0 is out
debian at onerussian.com
Sat Jun 30 15:31:08 UTC 2012
We are glad to announce the availability of a new release addressing a
number of bugs and introducing new features and enhancements.
Tagged and pushed to http://github.com/PyMVPA/PyMVPA
Tarballs available from https://github.com/PyMVPA/PyMVPA/tags
Available from the http://neuro.debian.net
and was uploaded to Debian unstable (i.e. sid)
installer is available for Python 2.6 at
[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
And here is what would you are getting with the upgrade:
* 2.1.0 (Fri, June 29 2012)
- :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.
- 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
- Add :class:`~mvpa2.misc.dcov.dCOV` and
:func:`~mvpa2.misc.dcov.dcorcoef` to quantify independence of
* 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.
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
More information about the Pkg-ExpPsy-PyMVPA