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: 0.1.1
Depends: R (≥ 3.6.0)
Suggests: knitr, rmarkdown, ggplot2, ggnewscale, reshape2, RColorBrewer, pryr
Published: 2024-02-08
Author: Marco Lopez-Cruz [aut, cre], Gustavo de los Campos [aut], Paulino Perez-Rodriguez [aut]
Maintainer: Marco Lopez-Cruz <lopezcru at msu.edu>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: tensorEVD results

Documentation:

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

Downloads:

Package source: tensorEVD_0.1.1.tar.gz
Windows binaries: r-prerel: tensorEVD_0.1.1.zip, r-release: tensorEVD_0.1.1.zip, r-oldrel: tensorEVD_0.1.1.zip
macOS binaries: r-prerel (arm64): tensorEVD_0.1.1.tgz, r-release (arm64): tensorEVD_0.1.1.tgz, r-oldrel (arm64): tensorEVD_0.1.1.tgz, r-prerel (x86_64): tensorEVD_0.1.1.tgz, r-release (x86_64): tensorEVD_0.1.1.tgz
Old sources: tensorEVD archive

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