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rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data

The R package rrda provides functions for performing ridge redundancy analysis (rrda) for high-dimensional datasets. It is useful for modeling the relationship between a matrix of response variables (Y; n × q ) and a matrix of explanatory variables (X; n × p ) with ridge penalty and rank restraint. The method is designed to handle high-dimensional data, allowing efficient computation and storage optimization.

🌴 Overview

This repository provides the implementation of the R package rrda, designed for ridge redundancy analysis in high-dimensional data.

Installation

rrda is available on CRAN. The development version is available on Gitlab.

Installing rrda from the CRAN

For the development version, use the gitlab install

# install.packages("remotes")
#remotes::install_gitlab(
#  repo = "mia-ps/rrdaS@main",
#  host = "forge.inrae.fr"
#)

Issues and Support

If you encounter any bugs or have suggestions for improvement, please use the issue tracker.

Cite us

If you use rrda in your research or applications, please cite the package AND the associated publication and the package :

Yoshioka, H., Aubert, J., Iwata, H., and Mary-Huard, T., 2025. Ridge Redundancy Analysis for High-Dimensional Omics Data. bioRxiv, doi: 10.1101/2025.04.16.649138

@article {Yoshioka2025.04.16.649138,
    author = {Yoshioka, Hayato and Aubert, Julie and Iwata, Hiroyoshi and Mary-Huard, Tristan},
    title = {Ridge Redundancy Analysis for High-Dimensional Omics Data},
    elocation-id = {2025.04.16.649138},
    year = {2025},
    doi = {10.1101/2025.04.16.649138},
    URL = {https://www.biorxiv.org/content/early/2025/09/10/2025.04.16.649138},
    eprint = {https://www.biorxiv.org/content/early/2025/09/10/2025.04.16.649138.full.pdf},
    journal = {bioRxiv}
}

Code of Conduct

Please note that the rrda project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.