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.