iTensor: ICA-Based Matrix/Tensor Decomposition

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub <>.

Version: 1.0.2
Depends: R (≥ 4.1.0)
Imports: MASS, methods, graphics, utils, stats, rTensor, jointDiag, mgcv, einsum, geigen, mixOmics, groupICA
Suggests: nnTensor, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-04-28
DOI: 10.32614/CRAN.package.iTensor
Author: Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <k.t.the-answer at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: iTensor results


Reference manual: iTensor.pdf
Vignettes: 1. Independent Component Analysis (ICA)
2. Multimodal Independent Component Analysis (MICA) and Group Independent Component Analysis (GroupICA)
3. Multilinear Independent Component Analysis (MultilinearICA)


Package source: iTensor_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): iTensor_1.0.2.tgz, r-oldrel (arm64): iTensor_1.0.2.tgz, r-release (x86_64): iTensor_1.0.2.tgz, r-oldrel (x86_64): iTensor_1.0.2.tgz
Old sources: iTensor archive

Reverse dependencies:

Reverse imports: mwTensor


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