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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.
Version: | 0.1.1 |
Imports: | dplyr, furrr, ggplot2, MASS, reshape2, RSpectra, scales, stats |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2025-04-29 |
DOI: | 10.32614/CRAN.package.rrda |
Author: | Hayato Yoshioka |
Maintainer: | Julie Aubert <julie.aubert at inrae.fr> |
License: | GPL (≥ 3) |
NeedsCompilation: | no |
CRAN checks: | rrda results |
Reference manual: | rrda.pdf |
Package source: | rrda_0.1.1.tar.gz |
Windows binaries: | r-devel: rrda_0.1.1.zip, r-release: rrda_0.1.1.zip, r-oldrel: not available |
macOS binaries: | r-release (arm64): rrda_0.1.1.tgz, r-oldrel (arm64): rrda_0.1.1.tgz, r-release (x86_64): rrda_0.1.1.tgz, r-oldrel (x86_64): rrda_0.1.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.