deriva: Tidy Drift Detection for Monitored Machine Learning Models
Detects concept drift and data drift in streams produced by
deployed machine learning models, using a tidy interface that composes
with the 'tidymodels' ecosystem. Detectors are specified, fitted on a
baseline period, and advanced over new batches of observations,
returning tibbles annotated with warning and drift flags. A catalogue
of 22 sequential drift detectors is provided. Error-based methods
include the Drift Detection Method (DDM) of Gama et al. (2004)
<doi:10.1007/978-3-540-28645-5_29>, the Early Drift Detection Method
(EDDM) of Baena-Garcia et al. (2006), the Hoeffding's inequality
based Drift Detection Methods (HDDM) of Frias-Blanco et al. (2015)
<doi:10.1109/TKDE.2014.2345382>, and the Exponentially Weighted
Moving Average (EWMA) chart of Ross et al. (2012)
<doi:10.1016/j.patrec.2011.08.019>. Distribution-based methods
include Adaptive Windowing (ADWIN) of Bifet and Gavalda (2007)
<doi:10.1137/1.9781611972771.42>, Kolmogorov-Smirnov Windowing
(KSWIN) of Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>,
and the Page-Hinkley test of Page (1954)
<doi:10.1093/biomet/41.1-2.100>.
| Version: |
0.1.0 |
| Imports: |
cli, generics, rlang, stats, tibble, vctrs |
| Suggests: |
ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-08-03 |
| DOI: |
10.32614/CRAN.package.deriva |
| Author: |
João Paulo Assis Bonifácio
 [aut, cre],
Geraldo Magela da Cruz Pereira
[aut],
Pedro Mambelli Fernandes
[aut] |
| Maintainer: |
João Paulo Assis Bonifácio <jpab.27 at hotmail.com> |
| BugReports: |
https://github.com/bonijoao/deriva/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/bonijoao/deriva |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
deriva results |
Documentation:
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