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 |
| Reference manual: | tensorEVD.pdf |
| Vignettes: |
Hadamard Kronecker Documentation: A fast algorithm to factorize high-dimensional tensor product matrices tensorEVD |
| Package source: | tensorEVD_0.1.1.tar.gz |
| Windows binaries: | r-devel: tensorEVD_0.1.1.zip, r-release: tensorEVD_0.1.1.zip, r-oldrel: tensorEVD_0.1.1.zip |
| macOS binaries: | r-release (arm64): tensorEVD_0.1.1.tgz, r-oldrel (arm64): tensorEVD_0.1.1.tgz, r-release (x86_64): tensorEVD_0.1.1.tgz, r-oldrel (x86_64): tensorEVD_0.1.1.tgz |
| Old sources: | tensorEVD archive |
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