deriva
0.1.0Tidy Drift Detection for Monitored Machine Learning Models
Overview
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.
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- OK2026-08-048 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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1 release. R releases are shown for context.
- 0.1.0Latest2026-08-03 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-03
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- not tracked yet
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- With dependencies
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