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 < https://sweichwald.de/coroICA/>.


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Reference manual

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install.packages("coroICA")

1.0.1 by Niklas Pfister, 4 months ago


https://github.com/sweichwald/coroICA-R


Report a bug at https://github.com/sweichwald/coroICA-R/issues


Browse source code at https://github.com/cran/coroICA


Authors: Niklas Pfister and Sebastian Weichwald


Documentation:   PDF Manual  


AGPL-3 license


Imports stats, MASS


See at CRAN