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deriva

0.1.0

Tidy Drift Detection for Monitored Machine Learning Models

0packages depend
85downloads / year
74.9%test coverage
13/13checks pass

Overview

About
Maintained by João Paulo Assis BonifácioFirst published 2026-08-031 releasesCRAN page ↗GitHub ↗

Detects concept drift and data drift in streams produced by deployed machine learning models, using a tidy interface that composes with the 'tidymodels' ecosystem. Detectors are specified, fitted on a baseline period, and advanced over new batches of observations, returning tibbles annotated with warning and drift flags. A catalogue of 22 sequential drift detectors is provided. Error-based methods include the Drift Detection Method (DDM) of Gama et al. (2004) doi:10.1007/978-3-540-28645-5_29, the Early Drift Detection Method (EDDM) of Baena-Garcia et al. (2006), the Hoeffding's inequality based Drift Detection Methods (HDDM) of Frias-Blanco et al. (2015) doi:10.1109/TKDE.2014.2345382, and the Exponentially Weighted Moving Average (EWMA) chart of Ross et al. (2012) doi:10.1016/j.patrec.2011.08.019. Distribution-based methods include Adaptive Windowing (ADWIN) of Bifet and Gavalda (2007) doi:10.1137/1.9781611972771.42, Kolmogorov-Smirnov Windowing (KSWIN) of Raab et al. (2020) doi:10.1016/j.neucom.2019.11.111, and the Page-Hinkley test of Page (1954) doi:10.1093/biomet/41.1-2.100.

Install

Health

CRAN checks
13OK
Slowest check: 1.6 min · r-oldrel-windows-x86_64
Code health
Yes
Tests · ratio 4.14
74.9%
Coverage · measured lines
100%
Documentation · exports
6
Dependencies · direct
Check history
  • OK2026-08-04
    8 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 399 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
72%
Return-value docs
86%
References docs
0%

Downloads

85
CRAN downloads in the past year
Rank #24,725 · ~0/day · ~7/mo
deriva
Daily download trend is not available in this view yet.

Repository

Repository
0Stars
0Forks
0Open issues
0Open PRs
0Releases
Last activity 2026-08-03

Repository practices

Upstream repositoryBeta

4 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/bonijoao/deriva on 2026-08-09.

Continuous integration (1)
GitHub Actions
Reproducibility and dev environment (1)
data-raw/
CRAN release process (2)
cran-comments.mdCRAN-SUBMISSION
How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)
Depends (0)
none
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (3)
Maintainer (1)
Author, Maintainer
Authors (3)
Package Timeline

1 release. R releases are shown for context.

  • 0.1.0Latest
    2026-08-03 · current release
  • R
    R 4.6.0 released · 2026-04-24

Package metadata

First published
2026-08-03
Total releases
1 / 1 yrs
License
MIT + file LICENSE OSI
Download size
not tracked yet
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("deriva")
Bonifácio, J. P. A., Mambelli Fernandes, P., & Pereira, G. M. d. C. (2026). deriva: Tidy Drift Detection for Monitored Machine Learning Models (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.deriva

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for deriva version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-08-13. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-13, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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