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Showing content from https://github.com/SnPM-toolbox/SnPM-devel below:

SnPM-toolbox/SnPM-devel: Statistical NonParametric Mapping, development version

SnPM: Statistical nonParametric Mapping

The Statistical non-Parametric Mapping (SnPM) toolbox provides an extensible framework for voxel level non-parametric permutation/randomisation tests of functional Neuroimaging experiments with independent observations.

The SnPM toolbox provides an alternative to the Statistics section of SPM. SnPM uses the General Linear Model to construct pseudo t-statistic images, which are then assessed for significance using a standard non-parametric multiple comparisons procedure based on randomisation/permutation testing. It is most suitable for single subject PET/SPECT analyses, or designs with low degrees of freedom available for variance estimation. In these situations the freedom to use weighted locally pooled variance estimates, or variance smoothing, makes the non-parametric approach considerably more powerful than conventional parametric approaches, as are implemented in SPM. Further, the non-parametric approach is always valid, given only minimal assumptions.

More information at: http://www.nisox.org/Software/SnPM

Download test data and set up test data directory (first time only)

The first time you will run the tests, clone the test data (from here) :

git clone git@github.com:SnPM-toolbox/SnPM_test_data.git

Then, fill in the testDataDir variable in snpm_test_config.m to point to the data you just downloaded. For example:

global testDataDir;
testDataDir = '~/snpm_test_data';

You can then untrack the configuration file in git (to avoid pushing your local configuration to the main repository):

git update-index --assume-unchanged test/snpm_test_config.m

Then, the tests can be started with:

run(test_oneSample, 'test_onesample_1')

Suggestion for citing SnPM Citation of the SnPM software can be made with reference to this URL: http://nisox.org/Software/SnPM13/; please also note the version (i.e. SnPM13) in any citation. Please also note the exact version (e.g. SnPM13.1.06) to maximise reproducibility. The full version number can be found from the snpm('ver') command. Concepts implemented in the SnPM software are best described in the Nichols & Holmes (2001) paper; see here. Alternatively, please see the Citation.cff file included in the root of this repository.

This section describes the bugs that have been reported, along with the appropriate fixes. Refer to https://github.com/SnPM-toolbox/SnPM-devel/releases for further information on bugs and fixes. Alternatively, to check for updates, type SnPM_update in the Matlab command line to verify that the current version of SnPM which you have installed is up to date (Note: this feature will not work for releases prior to SnPM 13.0.9 and requires an internet connection).

Updates from SnPM 13.1.03 Updates from SnPM 13.1.02 Updates from SnPM 13.1.01

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