Ridge Redundancy Analysis for High-Dimensional Omics Data
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.