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rrda  

Ridge Redundancy Analysis for High-Dimensional Omics Data
View on CRAN: Click here


Download and install rrda package within the R console
Install from CRAN:
install.packages("rrda")

Install from Github:
library("remotes")
install_github("cran/rrda")

Install by package version:
library("remotes")
install_version("rrda", "0.2.3")



Attach the package and use:
library("rrda")
Maintained by
Julie Aubert
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-04-29
Latest Update: 2025-04-29
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.
How to cite:
Julie Aubert (2025). rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data. R package version 0.2.3, https://cran.r-project.org/web/packages/rrda. Accessed 25 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:53), 0.1.1 (2025-04-29 11:10)
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Complete documentation for rrda
Functions, R codes and Examples using the rrda R package
Full rrda package functions and examples
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