Package: rrda
Title: Ridge Redundancy Analysis for High-Dimensional Omics Data
Version: 0.2.3
Authors@R: c(
    person("Hayato", "Yoshioka", , "yoshioka-hayato393@g.ecc.u-tokyo.ac.jp", role = "aut",
           comment = c(ORCID = "0000-0001-5383-2909")),
    person("Julie", "Aubert", , "julie.aubert@inrae.fr", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5203-5748")),
    person("Tristan", "Mary-Huard", , "tristan.mary-huard@agroparistech.fr", role = "aut",
           comment = c(ORCID = "0000-0002-3839-9067"))
  )
Description: Efficient framework for ridge redundancy analysis (rrda),
    tailored for high-dimensional omics datasets where the number of
    predictors exceeds the number of samples. The method leverages
    Singular Value Decomposition (SVD) to avoid direct inversion of the
    covariance matrix, enhancing scalability and performance. It also
    introduces a memory-efficient storage strategy for coefficient
    matrices, enabling practical use in large-scale applications. The
    package supports cross-validation for selecting regularization
    parameters and reduced-rank dimensions, making it a robust and
    flexible tool for multivariate analysis in omics research. Please
    refer to our article (Yoshioka et al., 2025) for more details.
License: GPL (>= 3)
Imports: dplyr, furrr, ggplot2, grDevices, MASS, pheatmap, reshape2,
        RSpectra, scales, stats
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-10-06 13:13:33 UTC; hayatoyoshioka
Author: Hayato Yoshioka [aut] (ORCID: <https://orcid.org/0000-0001-5383-2909>),
  Julie Aubert [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-5203-5748>),
  Tristan Mary-Huard [aut] (ORCID:
    <https://orcid.org/0000-0002-3839-9067>)
Maintainer: Julie Aubert <julie.aubert@inrae.fr>
Repository: CRAN
Date/Publication: 2025-10-15 12:20:02 UTC
Built: R 4.6.0; ; 2026-04-29 07:27:26 UTC; unix
