jointDiag: Joint Approximate Diagonalization of a Set of Square Matrices

Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.

Version: 0.4
Published: 2020-10-27
DOI: 10.32614/CRAN.package.jointDiag
Author: Cedric Gouy-Pailler
Maintainer: Cedric Gouy-Pailler <cedric.gouypailler at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README
CRAN checks: jointDiag results


Reference manual: jointDiag.pdf


Package source: jointDiag_0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): jointDiag_0.4.tgz, r-oldrel (arm64): jointDiag_0.4.tgz, r-release (x86_64): jointDiag_0.4.tgz, r-oldrel (x86_64): jointDiag_0.4.tgz
Old sources: jointDiag archive

Reverse dependencies:

Reverse imports: HDTSA, iTensor, MMeM, morpheus
Reverse suggests: gmGeostats


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