cv: Cross-Validating Regression Models

Cross-validation methods of regression models that exploit features of various modeling functions to improve speed. Some of the methods implemented in the package are novel, as described in the package vignettes; for general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition".

Version: 2.0.0
Depends: R (≥ 3.5.0), doParallel
Imports: car, foreach, glmmTMB, gtools, insight, lme4, MASS, methods, nlme
Suggests: boot, carData, dplyr, effects, ISLR2, knitr, lattice, latticeExtra, leaps, Metrics, microbenchmark, nnet, rmarkdown, spelling, testthat
Published: 2024-04-29
DOI: 10.32614/
Author: John Fox ORCID iD [aut], Georges Monette [aut, cre]
Maintainer: Georges Monette <georges+cv at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: cv results


Reference manual: cv.pdf
Vignettes: Extending the cv package
Cross-validating mixed-effects models
Computational and technical notes on cross-validating regression models
Cross-validating model selection
Cross-validating regression models


Package source: cv_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): cv_2.0.0.tgz, r-oldrel (arm64): cv_2.0.0.tgz, r-release (x86_64): cv_2.0.0.tgz, r-oldrel (x86_64): cv_2.0.0.tgz
Old sources: cv archive


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