autoFlagR: AI-Driven Anomaly Detection for Data Quality

Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000) <doi:10.1145/342009.335388>.

Version: 1.0.0
Imports: isotree, dbscan, dplyr, ggplot2, pROC, PRROC, knitr, gt, scales, rmarkdown (≥ 2.0)
Suggests: testthat, pkgdown, ggnewscale
Published: 2026-01-15
DOI: 10.32614/CRAN.package.autoFlagR (may not be active yet)
Author: Vikrant Dev Rathore [aut, cre]
Maintainer: Vikrant Dev Rathore <rathore.vikrant at gmail.com>
BugReports: https://github.com/vikrant31/autoFlagR/issues
License: MIT + file LICENSE
URL: https://github.com/vikrant31/autoFlagR, https://vikrant31.github.io/autoFlagR/
NeedsCompilation: no
Citation: autoFlagR citation info
Materials: NEWS
CRAN checks: autoFlagR results

Documentation:

Reference manual: autoFlagR.html , autoFlagR.pdf
Vignettes: Benchmarking Anomaly Detection Performance (source, R code)
Getting Started with autoFlagR (source, R code)
Healthcare Data Quality Example (source, R code)

Downloads:

Package source: autoFlagR_1.0.0.tar.gz
Windows binaries: r-devel: autoFlagR_1.0.0.zip, r-release: not available, r-oldrel: autoFlagR_1.0.0.zip
macOS binaries: r-release (arm64): autoFlagR_1.0.0.tgz, r-oldrel (arm64): autoFlagR_1.0.0.tgz, r-release (x86_64): autoFlagR_1.0.0.tgz, r-oldrel (x86_64): autoFlagR_1.0.0.tgz

Linking:

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