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>.
