Algorithms for Testing Models under Stress R package stressor version 0.2.0 Traditional model evaluation metrics fail to capture model performance under less than ideal conditions. This package employs techniques to evaluate models "under-stress". This includes testing models' extrapolation ability, or testing accuracy on specific sub-samples of the overall model space. Details describing stress-testing methods in this package are provided in Haycock (2023) <doi:10.26076/2am5-9f67>. The other primary contribution of this package is provided to R users access to the 'Python' library 'PyCaret' <https://pycaret.org/> for quick and easy access to auto-tuned machine learning models. Software Depends: R (>= 3.5) Imports: reticulate, stats, dplyr Suggests: knitr, rmarkdown, ggplot2, mlbench, testthat (>= 3.0.0) Sam Haycock <haycock.sam@outlook.com> Comprehensive R Archive Network (CRAN) Sam Haycock [aut, cre], Brennan Bean [aut], Utah State University [cph, fnd], Thermo Fisher Scientific Inc. [fnd] MIT + file LICENSE (https://CRAN.R-project.org/package=stressor/LICENSE) 2024-05-01 application/tgz https://CRAN.R-project.org/package=stressor doi:10.32614/CRAN.package.stressor