stressor: Algorithms for Testing Models under Stress

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.

Version:	0.2.0
Depends:	R (≥ 3.5)
Imports:	reticulate, stats, dplyr
Suggests:	knitr, rmarkdown, ggplot2, mlbench, testthat (≥ 3.0.0)
Published:	2024-05-01
DOI:	10.32614/CRAN.package.stressor
Author:	Sam Haycock [aut, cre], Brennan Bean [aut], Utah State University [cph, fnd], Thermo Fisher Scientific Inc. [fnd]
Maintainer:	Sam Haycock <haycock.sam at outlook.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
SystemRequirements:	python(>=3.8.10)
Materials:	README
CRAN checks:	stressor results

Documentation:

Reference manual:	stressor.pdf
Vignettes:	stressor

Downloads:

Package source:	stressor_0.2.0.tar.gz
Windows binaries:	r-devel: stressor_0.2.0.zip, r-release: stressor_0.2.0.zip, r-oldrel: stressor_0.2.0.zip
macOS binaries:	r-release (arm64): stressor_0.2.0.tgz, r-oldrel (arm64): stressor_0.2.0.tgz, r-release (x86_64): stressor_0.2.0.tgz, r-oldrel (x86_64): stressor_0.2.0.tgz
Old sources:	stressor archive

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