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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.
| 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 |
# 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
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)@article{dar2025datadriftr,
title={datadriftR: Drift Detection Methods for Stream Data},
author={Dar, Ugur and Cavus, Mustafa},
journal={Journal of Open Source Software},
year={2025}
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.