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News — PyMVPA 2.4.0 documentation

Multivariate Pattern Analysis in Python. PyMVPA is a Python. Package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn. PyMVPA stands for M. We welcome all kinds of contributions, and you do not need to be a programmer. How to cite PyMVPA.

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News — PyMVPA 2.4.0 documentation | dev.pymvpa.org Reviews

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Multivariate Pattern Analysis in Python. PyMVPA is a Python. Package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn. PyMVPA stands for M. We welcome all kinds of contributions, and you do not need to be a programmer. How to cite PyMVPA.

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News — PyMVPA 2.4.0 documentation | dev.pymvpa.org Reviews

https://dev.pymvpa.org

Multivariate Pattern Analysis in Python. PyMVPA is a Python. Package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn. PyMVPA stands for M. We welcome all kinds of contributions, and you do not need to be a programmer. How to cite PyMVPA.

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Who Is Using It? — PyMVPA 2.4.0 documentation

http://dev.pymvpa.org/whoisusingit.html

Multivariate Pattern Analysis in Python. Who Is Using It? If you are using PyMVPA or have published a study employing it, please leave a comment at the bottom of this page, if you want to be listed here as well. Institutions Where PyMVPA Is Known To Be Used. Center for Mind/Brain Sciences, University of Trento. Department of Psychological and Brain Sciences, Dartmouth College. Thayer School of Engineering, Dartmouth College. Department of Psychology and Neuroscience, Duke University. Characterizing the a...

2

Where To Get Support — PyMVPA 2.4.0 documentation

http://dev.pymvpa.org/support.html

Multivariate Pattern Analysis in Python. Where To Get Support. If you have problems installing the software or questions about usage, documentation or something else related to PyMVPA, you can post to the PyMVPA mailing list (preferred) or contact the authors on IRC:. Mailing list archive search. Located in the sidebar of the PyMVPA home page. If you think you have discovered a bug, please report it. Only known bugs can be fixed. If you have a GitHub. Which summarizes the details of your system.

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Documentation Overview — PyMVPA 2.4.0 documentation

http://dev.pymvpa.org/docoverview.html

Multivariate Pattern Analysis in Python. Where to obtain it, and how to get it running). Example Analyses and Scripts. Short answers to common problems). References to interesting literature). See what has changed). Online and offline full-text search). Information for people contributing code). Achievements of the first PyMVPA Extravaganza. At Dartmouth College (Nov 30 – Dec 4 2009). Enter search terms or a module, class or function name.

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Dataset Database — PyMVPA 2.4.0 documentation

http://dev.pymvpa.org/datadb.html

Multivariate Pattern Analysis in Python. Provided collection of datasets is not aiming to replace other data repositories available online. Rather it is a selection of datasets which was repeatedly analyzed by PyMVPA team and found to be useful for demonstration of concrete analysis methodologies especially in the context of neural data analysis. Tutorial Data: Block-design fMRI. Eye-tracking Data: Face inversion. Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI).

5

mvpa2.algorithms.hyperalignment.Hyperalignment — PyMVPA 2.4.0 documentation

http://dev.pymvpa.org/generated/mvpa2.algorithms.hyperalignment.Hyperalignment.html

Multivariate Pattern Analysis in Python. Align the features across multiple datasets into a common feature space. This is a three-level algorithm. In the first level, a series of input datasets is projected into a common feature space using a configurable mapper. The common space is initially defined by a chosen exemplar from the list of input datasets, but is subsequently refined by iteratively combining the common space with the projected input datasets. Level 1 and 2 are performed by the. The default ...

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Andy's Computer Vision and Machine Learning Blog. Monday, November 8, 2010. Wow So much to read today. While following link upon link, I found so many great toolkits that I think it is worth listing them here. One of the greatest sources was the GNU/Linux AI and Alife HOWTO. Edit] It's been a while since I wrote this blog post but still many people seem to find it, so here a quick update. After looking into many libraries, I started using scikit-learn. Maximum Entropy Modeling Toolkit for Python and C.

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News — PyMVPA 2.4.0 documentation

Multivariate Pattern Analysis in Python. PyMVPA is a Python. Package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn. PyMVPA stands for M. We welcome all kinds of contributions, and you do not need to be a programmer. How to cite PyMVPA.

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