| Title: | Lightweight Column Drift Detection for Tabular Data |
| Version: | 0.1.1 |
| Description: | Provides simple and efficient methods to detect column-level data drift between reference and target datasets. Designed for monitoring tabular data pipelines and machine learning inputs using statistical distance measures. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Imports: | stats |
| NeedsCompilation: | no |
| Packaged: | 2026-01-25 09:59:35 UTC; yash |
| Author: | Yash Auti [aut, cre] |
| Maintainer: | Yash Auti <autiyash97@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-01-29 18:50:06 UTC |
Detect Column-Level Data Drift
Description
Compares reference and target datasets to identify column-level drift using statistical distance measures.
Usage
detect_column_drift(reference, target, alpha = 0.05)
Arguments
reference |
A data.frame representing baseline data |
target |
A data.frame representing new incoming data |
alpha |
Significance level for drift detection |
Value
A data.frame with drift statistics per column
Examples
ref <- data.frame(
age = c(25, 30, 35, 40),
city = c("A", "B", "A", "C")
)
new <- data.frame(
age = c(26, 31, 36, 41),
city = c("A", "B", "C", "C")
)
detect_column_drift(ref, new)