cvms: Cross-Validation for Model Selection

Cross-validate one or multiple regression 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. Currently supports Gaussian and binomial regression. Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Version: 0.1.2
Depends: R (≥ 3.5)
Imports: data.table (≥ 1.12), dplyr, plyr, tidyr (≥ 0.7.0), ggplot2, purrr, tibble (≥ 2.1.1), caret (≥ 6.0-84), pROC (≥ 1.14.0), stats, lme4 (≥ 1.1-21), MuMIn (≥ 1.43.6), AICcmodavg (≥ 2.2-1), broom, stringr, mltools (≥ 0.3.5), rlang
Suggests: knitr, groupdata2 (≥ 1.1.1), e1071, rmarkdown, testthat, AUC, furrr, ModelMetrics, covr
Published: 2019-08-05
Author: Ludvig Renbo Olsen [aut, cre], Benjamin Hugh Zachariae [aut]
Maintainer: Ludvig Renbo Olsen <r-pkgs at ludvigolsen.dk>
BugReports: https://github.com/ludvigolsen/cvms/issues
License: MIT + file LICENSE
URL: https://github.com/ludvigolsen/cvms
NeedsCompilation: no
Materials: README NEWS
CRAN checks: cvms results

Downloads:

Reference manual: cvms.pdf
Vignettes: Introduction_to_cvms
Package source: cvms_0.1.2.tar.gz
Windows binaries: r-devel: cvms_0.1.2.zip, r-release: cvms_0.1.2.zip, r-oldrel: cvms_0.1.2.zip
OS X binaries: r-release: cvms_0.1.2.tgz, r-oldrel: cvms_0.1.2.tgz
Old sources: cvms archive

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