coroICA: 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 <>.

Getting started

Package details

AuthorNiklas Pfister and Sebastian Weichwald
MaintainerNiklas Pfister <>
Package repositoryView on CRAN
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coroICA documentation built on May 2, 2019, 6:33 a.m.