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:	0.1.4
Depends:	R (≥ 3.6.0)
Suggests:	knitr, rmarkdown, ggplot2, ggnewscale, reshape2, RColorBrewer, pryr
Published:	2024-09-03
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

Documentation:

Reference manual:	tensorEVD.pdf
Vignettes:	Documentation: A fast algorithm to factorize high-dimensional tensor product matrices (source, R code)

Downloads:

Package source:	tensorEVD_0.1.4.tar.gz
Windows binaries:	r-devel: tensorEVD_0.1.4.zip, r-release: tensorEVD_0.1.4.zip, r-oldrel: tensorEVD_0.1.4.zip
macOS binaries:	r-release (arm64): tensorEVD_0.1.4.tgz, r-oldrel (arm64): tensorEVD_0.1.4.tgz, r-release (x86_64): tensorEVD_0.1.4.tgz, r-oldrel (x86_64): tensorEVD_0.1.4.tgz
Old sources:	tensorEVD archive

Reverse dependencies:

Reverse imports:

SFSI

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