Confounding Robust Independent Component Analysis for Noisy and Grouped Data

Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <>.


Reference manual

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1.0.2 by Niklas Pfister, a year ago

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Authors: Niklas Pfister and Sebastian Weichwald

Documentation:   PDF Manual  

AGPL-3 license

Imports stats, MASS

See at CRAN