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: 0.1.0
Depends: R (≥ 3.5)
Imports: reticulate, stats, dplyr
Suggests: knitr, rmarkdown, ggplot2, mlbench, testthat (≥ 3.0.0)
Published: 2024-01-31
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_0.1.0.tar.gz
Windows binaries: r-prerel: stressor_0.1.0.zip, r-release: stressor_0.1.0.zip, r-oldrel: stressor_0.1.0.zip
macOS binaries: r-prerel (arm64): stressor_0.1.0.tgz, r-release (arm64): stressor_0.1.0.tgz, r-oldrel (arm64): stressor_0.1.0.tgz, r-prerel (x86_64): stressor_0.1.0.tgz, r-release (x86_64): stressor_0.1.0.tgz

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