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), Adaptive WINdowing
(ADWIN) 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. Kobylińska et al. (2023) <doi:10.48550/arXiv.2308.11446>,
S. Kullback & R.A. Leibler (1951) <doi:10.1214/aoms/1177729694>, Gama et al.
(2004) <doi:10.1007/978-3-540-28645-5_29>, Baena-Garcia et al. (2006)
<https://www.researchgate.net/publication/245999704_Early_Drift_Detection_Method>,
Frías-Blanco et al. (2014) <https://ieeexplore.ieee.org/document/6871418>,
Bifet and Gavalda (2007) <doi:10.1137/1.9781611972771>, 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) <https://jmlr.org/papers/volume19/18-251/18-251.pdf>.
| Version: |
1.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
R6, stats, fda.usc |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, pkgdown, dynaTree, ranger |
| Published: |
2026-04-22 |
| DOI: |
10.32614/CRAN.package.datadriftR |
| Author: |
Ugur Dar  [aut,
cre],
Mustafa Cavus
[aut] |
| Maintainer: |
Ugur Dar <ugurdarr at gmail.com> |
| BugReports: |
https://github.com/ugurdar/datadriftR/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://ugurdar.github.io/datadriftR/,
https://github.com/ugurdar/datadriftR |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
datadriftR results |
Documentation:
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