tensorEVD: A Fast Algorithm to Factorize High-Dimensional Tensor Product Matrices

Here we provide tools for the computation and factorization of high-dimensional tensor products that are formed by smaller matrices. The methods are based on properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616). We evaluated this methodology by benchmark testing and illustrated its use in Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.

Version: 0.1.3
Depends: R (≥ 3.6.0)
Suggests: knitr, rmarkdown, ggplot2, ggnewscale, reshape2, RColorBrewer, pryr
Published: 2024-05-30
DOI: 10.32614/CRAN.package.tensorEVD
Author: Marco Lopez-Cruz [aut, cre], Gustavo de los Campos [aut], Paulino Perez-Rodriguez [aut]
Maintainer: Marco Lopez-Cruz <maraloc at gmail.com>
License: GPL-3
URL: https://github.com/MarcooLopez/tensorEVD
NeedsCompilation: yes
Citation: tensorEVD citation info
Materials: NEWS
CRAN checks: tensorEVD results


Reference manual: tensorEVD.pdf
Vignettes: Documentation: A fast algorithm to factorize high-dimensional tensor product matrices


Package source: tensorEVD_0.1.3.tar.gz
Windows binaries: r-devel: tensorEVD_0.1.3.zip, r-release: tensorEVD_0.1.3.zip, r-oldrel: tensorEVD_0.1.3.zip
macOS binaries: r-release (arm64): tensorEVD_0.1.3.tgz, r-oldrel (arm64): tensorEVD_0.1.3.tgz, r-release (x86_64): tensorEVD_0.1.3.tgz, r-oldrel (x86_64): tensorEVD_0.1.3.tgz
Old sources: tensorEVD archive

Reverse dependencies:

Reverse imports: SFSI


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