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. 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>, 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>.
