Implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via 'RcppArmadillo' with optional parallelization using 'RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations.
| Version: | 2.0.0 |
| Imports: | stats, Rcpp (≥ 1.0.13), RcppParallel (≥ 5.1.8) |
| LinkingTo: | Rcpp, RcppArmadillo, RcppParallel |
| Suggests: | boot, RhpcBLASctl, testthat (≥ 3.0.0) |
| Published: | 2026-02-03 |
| DOI: | 10.32614/CRAN.package.cvLM |
| Author: | Philip Nye [aut, cre] |
| Maintainer: | Philip Nye <phipnye at proton.me> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/phipnye/CV-LM |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make, C++17 |
| Materials: | README, NEWS |
| CRAN checks: | cvLM results |
| Reference manual: | cvLM.html , cvLM.pdf |
| Package source: | cvLM_2.0.0.tar.gz |
| Windows binaries: | r-devel: cvLM_2.0.0.zip, r-release: cvLM_2.0.0.zip, r-oldrel: cvLM_2.0.0.zip |
| macOS binaries: | r-release (arm64): cvLM_2.0.0.tgz, r-oldrel (arm64): cvLM_2.0.0.tgz, r-release (x86_64): cvLM_2.0.0.tgz, r-oldrel (x86_64): cvLM_2.0.0.tgz |
| Old sources: | cvLM archive |
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