datadriftR: Concept Drift Detection Methods for Stream Data

A system designed for detecting concept drift in streaming datasets. It offers a comprehensive suite of statistical methods to detect concept drift, including methods for monitoring changes in data distributions over time. The package supports several tests, such as Drift Detection Method (DDM), Early Drift Detection Method (EDDM), Hoeffding Drift Detection Methods (HDDM_A, HDDM_W), Kolmogorov-Smirnov test-based Windowing (KSWIN) and Page Hinkley (PH) tests. The methods implemented in this package are based on established research and have been demonstrated to be effective in real-time data analysis. For more details on the methods, please check to the following sources. Gama et al. (2004) <doi:10.1007/978-3-540-28645-5_29>, Baena-Garcia et al. (2006) <>, Frías-Blanco et al. (2014) <>, Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Montiel et al. (2018) <>.

Version: 0.0.1
Depends: R (≥ 3.5.2)
Imports: R6
Published: 2024-06-13
DOI: 10.32614/CRAN.package.datadriftR
Author: Ugur Dar [aut, cre], Mustafa Cavus [ctb, ths]
Maintainer: Ugur Dar <ugurdarr at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: datadriftR results


Reference manual: datadriftR.pdf


Package source: datadriftR_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): datadriftR_0.0.1.tgz, r-oldrel (arm64): datadriftR_0.0.1.tgz, r-release (x86_64): datadriftR_0.0.1.tgz, r-oldrel (x86_64): datadriftR_0.0.1.tgz


Please use the canonical form to link to this page.