cvms (0.3.2)

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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).

Maintainer: Ludvig Renbo Olsen
Author(s): Ludvig Renbo Olsen [aut, cre], Benjamin Hugh Zachariae [aut]

License: MIT + file LICENSE

Uses: broom, caret, data.table, dplyr, ggplot2, lifecycle, lme4, mltools, MuMIn, plyr, pROC, purrr, rlang, stringr, tibble, tidyr, e1071, randomForest, nnet, testthat, knitr, AUC, rmarkdown, covr, ModelMetrics, groupdata2, furrr

Released 4 months ago.

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