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
Author: Cedric Gouy-Pailler
Maintainer: Cedric Gouy-Pailler <cedric.gouypailler at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/gouypailler/jointDiag
NeedsCompilation: yes
Materials: README
CRAN checks: jointDiag results

Documentation:

Reference manual: jointDiag.pdf

Downloads:

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

Reverse dependencies:

Reverse imports: MMeM, morpheus
Reverse suggests: gmGeostats

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