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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 | 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 | pymvpa.org Reviews

https://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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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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PyMVPA.org Data Server - - - - - - - - - - - This server distributes materials from PyMVPA team, which are interesting in the context of multivariate pattern analysis. All materials are distributed under free and open license terms. See corresponding README.rst for the details. Visit http:/ www.pymvpa.org for more information about PyMVPA project.

INTERNAL PAGES

pymvpa.org

1

PyMVPA User Manual — PyMVPA 2.6.0 documentation

http://www.pymvpa.org/manual.html

Multivariate Pattern Analysis in Python. What this Manual is NOT. A bit of History. How to cite PyMVPA. Where To Get Support. Tutorial Introduction to PyMVPA. Dataset basics and concepts. Getting data in shape. Classifiers – All Alike, Yet Different. Looking here and there – Searchlights. Classifiers that do more – Meta Classifiers. Classification Model Parameters – Sensitivity Analysis. Working with OpenFMRI.org data. WiP: The Earth Is Round – Significance Testing. Example Analyses and Scripts.

2

Tutorial Introduction to PyMVPA — PyMVPA 2.6.0 documentation

http://www.pymvpa.org/tutorial.html

Multivariate Pattern Analysis in Python. Tutorial Introduction to PyMVPA. In this tutorial we are going to take a look at all major parts of PyMVPA, introduce the most important concepts, and explore particular functionality in real-life analysis examples. This tutorial also serves as basic course material for workshops on introductions to MVPA. Please contact us, if you are interested in hosting a PyMVPA workshop at your institution. Throughout the tutorial we will analyze real BOLD fMRI data. If this c...

3

Documentation Overview — PyMVPA 2.6.0 documentation

http://www.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).

4

Installation — PyMVPA 2.6.0 documentation

http://www.pymvpa.org/installation.html

Multivariate Pattern Analysis in Python. This section covers the necessary steps to install and run PyMVPA. It contains a comprehensive list of software dependencies, as well as recommendation for additional software packages that further enhance the functionality provided by PyMVPA. If you don’t want to read this whole document, and you are on a Debian-based system, such as Ubuntu, all you need to know it:. This picture shows PyMVPA on an OpenMoko. Cell phone — running the. Example in an IPython. While ...

5

Dataset Database — PyMVPA 2.6.0 documentation

http://www.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).

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datalad.org

Articles - DataLad

http://datalad.org/category/articles.html

Git-annex-centric benchmark of the filesystems. For the DataLad project we will establish a large number of git-annex repositories. Majority of those git-annex repositories will not contain any data, but rather a lot of empty files and/or broken symlinks. Some data sharing projects which we are going to cover contain thousands and even . Mon 06 July 2015. Bits from DataLad #1. Summary report of recent activities. Sat 11 October 2014. Tue 16 September 2014. Working with big data without losing track.

datalad.org

About this Project - DataLad

http://datalad.org/pages/about.html

This project aims to adapt the model of open-source software (OSS) distributions to address the technical limitations of today's data-sharing and develop all components of a data distribution. The key concepts are: 1) Leverage - but do not replace - independent, existing, and future data hosting solutions to form a federated platform for data-sharing. 2) Employ software for data tracking and deployment logistics specialized for large data ( git-annex. Data sharing portals such as http:/ openfmri.org.

datalad.org

Working with big data without losing track - DataLad

http://datalad.org/gitannex_based_access.html

Working with big data without losing track. Mon 16 June 2014. Borrowed from the http:/ studyforrest.org. As a good use-case for demonstrating reasoning for use of git-annex by data providers. You can already fetch entire studyforrest dataset using git-annex directly. Here is e.g. a sample list of command to fetch all extracted into CSV timeseries:. Git clone http:/ psydata.ovgu.de/forrest gump/.git studyforrest cd studyforrest git annex get sub*/BOLD/task001 run*/*timeseries*.csv.gz. Thrown over the wall.

datalad.org

Bits from DataLad #1 - DataLad

http://datalad.org/bits_1.html

Bits from DataLad #1. Sat 11 October 2014. Etc We hope you will get to like the name, and we will come up with a beautiful project logo soonish. Meanwhile enjoy a functional colorful qrcode you might find associated with DataLad on those pages. Obtained integration with Travis-CI and coveralls.io. Apparently we are at 85% coverage of the codebase, not bad at all for starters. So you are welcome to submit pull requests without a fear of breaking our. A very important part of this project is also to identi...

cosmomvpa.org

Frequently Asked/Anticipated Questions — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/faq.html

How should I cite CoSMoMVPA? What is the history of CoSMoMVPA? What are the main features? Does it integrate with PyMVPA? Does it run on GNU Octave? How fast does it run? What should I use as input for MVPA? Who are the developers of CoSMoMVPA? Which classifiers are available? Which platforms does it support? What future features can be expected? How can I contact the developers directly? Is there a mailinglist? Why do you encourage balanced partitions? How do I . Get ECoG data in a CoSMoMVPA struct.

neurodebian.ovgu.de

Packages for Magnetic Reasonance Imaging — Debian Neuroscience Package Repository

http://neurodebian.ovgu.de/pkglists/toc_pkgs_for_field_mri.html

NeuroDebian “Derivatives”. Packages for Magnetic Reasonance Imaging. Toolkit for analyzing and visualizing functional MRI data. Miscellaneous scripts and data files for AFNI. Debug symbols for AFNI. Header and static libraries for AFNI plugin development. Computerized Anatomical Reconstruction and Editing Toolkit. Brain visualization, analysis and discovery tool. Brain visualization, analysis and discovery tool - debug symbols. Interactive Analysis and Visualization for MR Connectomics. Neuroimaging data...

neurodebian.ovgu.de

Frequently Asked Questions — Debian Neuroscience Package Repository

http://neurodebian.ovgu.de/faq.html

NeuroDebian “Derivatives”. In the course of our own research endeavors we. Have joined forces to develop PyMVPA – a Python framework for multivariate pattern analysis of neural data. To conveniently deploy PyMVPA for anyone using Debian-derived distributions this package repository was created initially. Besides the PyMVPA. Collaborate with the NiPy team. On various projects, such as NiBabel. Debian, Debian, Debian – What about Ubuntu? 8211; a repository of Debian packages rebuilt for a particular Ubuntu...

neurodebian.ovgu.de

NeuroDebian Virtual Machine — Debian Neuroscience Package Repository

http://neurodebian.ovgu.de/vm.html

NeuroDebian “Derivatives”. Working with the virtual machine. We find the approach taken by NeuroDebian and the progress it made so far very pleasant and promising. [.] The NeuroDebian team helped us to improve the quality of our software and of some of our processes by review, constructive suggestions and even software patches. The availability of the NeuroDebian Virtual Machine was instrumental for fast porting of the Psychtoolbox to a 64 bit Linux environment. Mdash;– Mario Kleiner. The following instr...

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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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