Tidy Drift Detection for Monitored Machine Learning Models R package deriva version 0.1.0 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>. Software Imports: cli, generics, rlang, stats, tibble, vctrs Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0) João Paulo Assis Bonifácio <jpab.27@hotmail.com> Comprehensive R Archive Network (CRAN) João Paulo Assis Bonifácio [aut, cre] (ORCID: <https://orcid.org/0009-0009-2335-8391>), Geraldo Magela da Cruz Pereira [aut] (ORCID: <https://orcid.org/0000-0001-6280-4870>), Pedro Mambelli Fernandes [aut] (ORCID: <https://orcid.org/0009-0001-1695-279X>) MIT + file LICENSE (https://CRAN.R-project.org/package=deriva/LICENSE) 2026-08-03 application/tgz https://CRAN.R-project.org/package=deriva doi:10.32614/CRAN.package.deriva