rrda
0.2.3Ridge Redundancy Analysis for High-Dimensional Omics Data
Overview
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.
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Health
- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 67%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
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Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.2.3Latest
- 0.1.12025-04-29
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-04-29
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 3) OSI
- Download size
- 544 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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