Cross-Validating Regression Models R package cv version 2.0.6 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 Fox and Monette (2026) <doi:10.18637/jss.v116.i08>, and 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". Software Depends: R (>= 3.5.0), doParallel Imports: car, foreach, glmmTMB, graphics, grDevices, gtools, insight, lattice, lme4, MASS, methods, nlme, parallel, stats, utils Suggests: boot, carData, dplyr, effects, ISLR2, knitr, latticeExtra, leaps, Metrics, microbenchmark, nnet, rmarkdown, spelling, testthat Georges Monette <georges+cv@yorku.ca> Comprehensive R Archive Network (CRAN) John Fox [aut] (ORCID: <https://orcid.org/0000-0002-1196-8012>), Georges Monette [aut, cre] (ORCID: <https://orcid.org/0000-0003-0076-5532>) GPL (>= 2) 2026-06-09 application/tgz https://CRAN.R-project.org/package=cv doi:10.32614/CRAN.package.cv en-US