cvms: Cross-Validation for Model Selection

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Version: 0.3.2
Depends: R (≥ 3.5)
Imports: data.table (≥ 1.12), dplyr, plyr, tidyr (≥ 0.8.3), ggplot2, purrr, tibble (≥ 2.1.1), caret (≥ 6.0-84), pROC (≥ 1.14.0), stats, lme4 (≥ 1.1-21), MuMIn (≥ 1.43.6), broom (≥ 0.5.2), stringr, mltools (≥ 0.3.5), rlang, utils, lifecycle
Suggests: knitr, groupdata2 (≥ 1.1.2), e1071 (≥ 1.7-2), rmarkdown, testthat (≥ 2.2.1), AUC, furrr, ModelMetrics (≥ 1.2.2), covr (≥ 3.3.1), nnet (≥ 7.3-12), randomForest (≥ 4.6-14)
Published: 2019-12-01
Author: Ludvig Renbo Olsen [aut, cre], Benjamin Hugh Zachariae [aut]
Maintainer: Ludvig Renbo Olsen <r-pkgs at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: cvms results


Reference manual: cvms.pdf
Vignettes: Introduction_to_cvms
Package source: cvms_0.3.2.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: cvms_0.3.2.tgz, r-oldrel: cvms_0.3.2.tgz
Old sources: cvms archive


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