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. Alternatively, evaluate predictions from an external model. Currently supports linear regression, logistic regression and (some functions only) multiclass classification. Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).


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install.packages("cvms")

0.2.0 by Ludvig Renbo Olsen, 11 days ago


https://github.com/ludvigolsen/cvms


Report a bug at https://github.com/ludvigolsen/cvms/issues


Browse source code at https://github.com/cran/cvms


Authors: Ludvig Renbo Olsen [aut, cre] , Benjamin Hugh Zachariae [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports data.table, dplyr, plyr, tidyr, ggplot2, purrr, tibble, caret, pROC, stats, lme4, MuMIn, AICcmodavg, broom, stringr, mltools, rlang, utils

Suggests knitr, groupdata2, e1071, rmarkdown, testthat, AUC, furrr, ModelMetrics, covr, nnet


See at CRAN