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: not available, r-release: not available, r-oldrel: not available
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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