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deriva  

Tidy Drift Detection for Monitored Machine Learning Models
View on CRAN: Click here


Download and install deriva package within the R console
Install from CRAN:
install.packages("deriva")

Install from Github:
library("remotes")
install_github("cran/deriva")

Install by package version:
library("remotes")
install_version("deriva", "0.2.0")



Attach the package and use:
library("deriva")
Maintained by
João Paulo Assis Bonifácio
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-03
Latest Update: 2026-08-03
Description:
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>.
How to cite:
João Paulo Assis Bonifácio (2026). deriva: Tidy Drift Detection for Monitored Machine Learning Models. R package version 0.2.0, https://cran.r-project.org/web/packages/deriva. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-09-20 19:50), 0.1.0 (2026-08-03 19:40)
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