dsos: Dataset Shift with Outlier Scores

Test for no adverse shift in two-sample comparison when we have a training set, the reference distribution, and a test set. The approach is flexible and relies on a robust and powerful test statistic, the weighted AUC. Technical details are in Kamulete, V. M. (2021) <doi:10.48550/arXiv.1908.04000>. Modern notions of outlyingness such as trust scores and prediction uncertainty can be used as the underlying scores for example.

Version: 0.1.2
Imports: data.table (≥ 1.14.6), future.apply (≥ 1.10.0), ggplot2 (≥ 3.4.0), scales (≥ 1.2.1), simctest (≥ 2.6), stats (≥ 4.2.1)
Suggests: fdrtool (≥ 1.2.17), knitr (≥ 1.42), rmarkdown (≥ 2.20), testthat (≥ 3.1.6)
Published: 2023-02-19
Author: Vathy M. Kamulete ORCID iD [aut, cre], Royal Bank of Canada (RBC) [cph] (Research supported and funded by RBC)
Maintainer: Vathy M. Kamulete <vathymut at gmail.com>
BugReports: https://github.com/vathymut/dsos/issues
License: GPL (≥ 3)
URL: https://github.com/vathymut/dsos
NeedsCompilation: no
Language: en-US
Materials: NEWS
CRAN checks: dsos results

Documentation:

Reference manual: dsos.pdf
Vignettes: Acknowledgements
Bring Your Own Scores
A 10-minute Crash Course

Downloads:

Package source: dsos_0.1.2.tar.gz
Windows binaries: r-devel: dsos_0.1.2.zip, r-release: dsos_0.1.2.zip, r-oldrel: dsos_0.1.2.zip
macOS binaries: r-release (arm64): dsos_0.1.2.tgz, r-oldrel (arm64): dsos_0.1.2.tgz, r-release (x86_64): dsos_0.1.2.tgz
Old sources: dsos archive

Linking:

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