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Efficient implementations of cross-validation techniques for linear and ridge regression models, leveraging 'C++' code with 'Rcpp', 'RcppParallel', and 'Eigen' libraries. It supports leave-one-out, generalized, and K-fold cross-validation methods, utilizing 'Eigen' matrices for high performance. Methodology references: Hastie, Tibshirani, and Friedman (2009) <doi:10.1007/978-0-387-84858-7>.
| Version: | 1.0.4 |
| Imports: | stats, Rcpp (≥ 1.0.13), RcppParallel (≥ 5.1.8) |
| LinkingTo: | Rcpp, RcppParallel, RcppEigen |
| Published: | 2024-08-01 |
| DOI: | 10.32614/CRAN.package.cvLM |
| Author: | Philip Nye [aut, cre] |
| Maintainer: | Philip Nye <phipnye at proton.me> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| Materials: | README |
| CRAN checks: | cvLM results |
| Reference manual: | cvLM.pdf |
| Package source: | cvLM_1.0.4.tar.gz |
| Windows binaries: | r-devel: cvLM_1.0.4.zip, r-release: cvLM_1.0.4.zip, r-oldrel: cvLM_1.0.4.zip |
| macOS binaries: | r-release (arm64): cvLM_1.0.4.tgz, r-oldrel (arm64): cvLM_1.0.4.tgz, r-release (x86_64): cvLM_1.0.4.tgz, r-oldrel (x86_64): cvLM_1.0.4.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.