wideRhino: High-Dimensional Methods via Generalised Singular Decomposition

Construct a Canonical Variate Analysis Biplot via the Generalised Singular Value Decomposition, for cases when the number of samples is less than the number of variables. For more information on biplots, see Gower JC, Lubbe SG, Le Roux NJ (2011) <doi:10.1002/9780470973196> and for more information on the generalised singular value decomposition, see Edelman A, Wang Y (2020) <doi:10.1137/18M1234412>.

Version: 1.0.2
Depends: R (≥ 4.1.0)
Imports: geigen, Matrix, MASS, ggplot2, dplyr
Suggests: knitr, rmarkdown, testthat
Published: 2025-06-11
DOI: 10.32614/CRAN.package.wideRhino
Author: Raeesa Ganey ORCID iD [aut, cre]
Maintainer: Raeesa Ganey <Raeesa.ganey at wits.ac.za>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: wideRhino results

Documentation:

Reference manual: wideRhino.pdf

Downloads:

Package source: wideRhino_1.0.2.tar.gz
Windows binaries: r-devel: wideRhino_1.0.2.zip, r-release: wideRhino_1.0.2.zip, r-oldrel: wideRhino_1.0.2.zip
macOS binaries: r-release (arm64): wideRhino_1.0.2.tgz, r-oldrel (arm64): wideRhino_1.0.2.tgz, r-release (x86_64): wideRhino_1.0.2.tgz, r-oldrel (x86_64): wideRhino_1.0.2.tgz

Linking:

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