Skip to content
This repository has been archived by the owner on Aug 23, 2023. It is now read-only.
/ scikit-palm Public archive

Implementing permutation methods for multiview learning in python

License

Notifications You must be signed in to change notification settings

jameschapman19/scikit-palm

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Python package codecov Scrutinizer Code Quality

scikit-perm

Implementing the PALM — Permutation Analysis of Linear Models toolbox (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM) for Python. PALM a

Documentation

Further documentation is hosted at https://scikit-perm.readthedocs.io/en/latest/

Objectives

The goal is to have a flexible framework for statistically valid permutation testing when using linear models from https://github.com/scikit-learn/scikit-learn or otherwise.

The framework should also be adaptable to multiview models such as those in https://github.com/mvlearn/mvlearn

Project is open to contributions.

I am currently aware of some related repos e.g. https://github.com/danlurie/PyPALM https://github.com/statlab/permute but neither appear to contain the functionality for multi-level exchangeability block permuation.

  • quickperms
    • quickperms function and helpers
    • unit testing for quickperms
  • scikit-learn wrappers
    • regression/classification
      • adapt permutation_test_score() using permutations based on quickperms for multiblock exchangeability
    • canonical correlation analysis
      • wilks
      • lawley_hotelling
      • pillai
      • roy-ii
      • roy-iii

References

The authors of PALM https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM https://github.com/andersonwinkler/PALM give the following references:

The main reference for PALM:

  • Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 (Open Access)

For correction across contrasts:

  • Alberton BAV, Nichols TE, Gamba HR, Winkler AM. Multiple testing correction over contrasts for brain imaging. Neuroimage. 2020 Mar 19:116760. (Open Access)

For Non-Parametric Combination, classical multivariate tests (MANOVA, MANCOVA), assessed with permutations, and for correction over contrasts and/or modalities:

  • Winkler AM, Webster MA, Brooks JC, Tracey I, Smith SM, Nichols TE. Non-Parametric Combination and related permutation tests for neuroimaging. Hum Brain Mapp. 2016 Apr;37(4):1486-511. (Open Access)

For the multi-level exchangeability blocks:

  • Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM. Multi-level block permutation. Neuroimage. 2015;123:253-68. (Open Access)

For the accelerated permutation inference:

  • Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. Neuroimage. 2016 Jun 7;141:502-516. (Open Access)

For additional theory of permutation tests in neuroimaging:

  • Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002 Jan;15(1):1-25.
  • Holmes AP, Blair RC, Watson JD, Ford I. Nonparametric analysis of statistic images from functional mapping experiments. J Cereb Blood Flow Metab. 1996 Jan;16(1):7-22.

About

Implementing permutation methods for multiview learning in python

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages