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confounds: deconfounding library to properly handle confounds  #90

@raamana

Description

@raamana

Title

confounds: deconfounding library to properly handle confounds

Short description and the goals for the OHBM BrainHack

Develop a python library of methods to handle confounds in various neuroscientific analyses, esp. statistics and predictive modeling. More info and slides here: https://crossinvalidation.com/2020/03/04/conquering-confounds-and-covariates-in-machine-learning/

Link to the Project

https://github.com/raamana/confounds

Image for the OHBM brainhack website

No response

Project lead

Pradeep Reddy Raamana @raamana

Main Hub

Glasgow

Other Hub covered by the leaders

  • Glasgow
  • Asia / Pacific
  • Europe / Middle East / Africa
  • Americas

Skills

  • python programming (preferably intermediate or better, but can work with basic skills)
  • some statistics
  • documentation ability

Recommended tutorials for new contributors

Good first issues

  • Add a simple test to target a specific issue or behaviour of ComBat
  • Implement metrics to quantify confound to target relationships
  • Add tutorial notebooks, with few example use-cases
  • Implement metrics to quantify the level of confounding in a given a sample
  • Add tests for Residualize() with non-linear models
  • Add tests for DummyDeconfounding() method

Twitter summary

Python library to handle #confounds/covariates in #machinelearning and #neuroscience, contribute to a great #openscience cause!
github.com/raamana/confounds
Pradeep Reddy Raamana @raamana_

Short name for the Discord chat channel (~15 chars)

confounds

Please read and follow the OHBM Code of Conduct

  • I agree to follow the OHBM Code of Conduct during the hackathon

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