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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "rrda" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data'
version: 0.2.3
doi: 10.32614/CRAN.package.rrda
abstract: 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.
authors:
- family-names: Yoshioka
  given-names: Hayato
  email: yoshioka-hayato393@g.ecc.u-tokyo.ac.jp
  orcid: https://orcid.org/0000-0001-5383-2909
- family-names: Aubert
  given-names: Julie
  email: julie.aubert@inrae.fr
  orcid: https://orcid.org/0000-0001-5203-5748
- family-names: Mary-Huard
  given-names: Tristan
  email: tristan.mary-huard@agroparistech.fr
  orcid: https://orcid.org/0000-0002-3839-9067
repository: https://cran.r-universe.dev
commit: 8a9e05eed40966fb3b243a284a43718873c93a84
date-released: '2025-10-15'
contact:
- family-names: Aubert
  given-names: Julie
  email: julie.aubert@inrae.fr
  orcid: https://orcid.org/0000-0001-5203-5748