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datadriftR  

Concept Drift Detection Methods for Stream Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("datadriftR", "1.0.0")



Attach the package and use:
library("datadriftR")
Maintained by
Ugur Dar
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-06-13
Latest Update: 2025-01-09
Description:
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) <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>.
How to cite:
Ugur Dar (2024). datadriftR: Concept Drift Detection Methods for Stream Data. R package version 1.0.0, https://cran.r-project.org/web/packages/datadriftR. Accessed 06 Mar. 2026.
Previous versions and publish date:
0.0.1 (2024-06-13 20:10)
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