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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.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 stressor_0.2.0.tar.gz 290.7 KiB
0.2.0 rolling linux/noble R-4.5 stressor_0.2.0.tar.gz 290.8 KiB
0.2.0 rolling source/ R- stressor_0.2.0.tar.gz 446.9 KiB
0.2.0 latest linux/jammy R-4.5 stressor_0.2.0.tar.gz 290.7 KiB
0.2.0 latest linux/noble R-4.5 stressor_0.2.0.tar.gz 290.8 KiB
0.2.0 latest source/ R- stressor_0.2.0.tar.gz 446.9 KiB
0.2.0 2026-04-26 source/ R- stressor_0.2.0.tar.gz 446.9 KiB
0.2.0 2026-04-23 source/ R- stressor_0.2.0.tar.gz 446.9 KiB
0.2.0 2026-04-09 windows/windows R-4.5 stressor_0.2.0.zip 295.6 KiB
0.2.0 2025-04-20 source/ R- stressor_0.2.0.tar.gz 446.9 KiB

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