datadriftR

datadriftR is an R package for detecting data drift in streaming data. It monitors when statistical properties of your data change over time, which is essential for maintaining machine learning model performance in production.

Available Methods

Method	Description
ddm	Drift Detection Method
eddm	Early Drift Detection Method
hddm_a	Hoeffding's bound with averaging
hddm_w	Hoeffding's bound with weighting
kswin	Kolmogorov-Smirnov Windowing
adwin	ADaptive WINdowing
page_hinkley	Page-Hinkley Test
kl_divergence	KL Divergence
profile_difference	Profile Difference

Installation

# Install from CRAN
install.packages("datadriftR")

# Or install the development version from GitHub with pak
# install.packages("pak")
pak::pak("ugurdar/datadriftR")

# Or install the development version from GitHub with remotes
# install.packages("remotes")
# remotes::install_github("ugurdar/datadriftR")

Documentation: https://ugurdar.github.io/datadriftR

Simple DDM Example

library(datadriftR)

# Create a stream with drift at position 501
set.seed(123)
stream <- c(

  sample(c(0, 1), 500, replace = TRUE, prob = c(0.7, 0.3)),
  sample(c(0, 1), 500, replace = TRUE, prob = c(0.3, 0.7))
)

# Detect drift
results <- detect_drift(stream, method = "ddm")
print(results)

Documentation

• Getting Started
• Reference

Citation

@article{dar2025datadriftr,
  title={datadriftR: Drift Detection Methods for Stream Data},
  author={Dar, Ugur and Cavus, Mustafa},
  journal={Journal of Open Source Software},
  year={2025}
}

Authors

• Ugur Dar - Eskisehir Technical University
• Mustafa Cavus - Eskisehir Technical University