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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>.

Versions across snapshots

VersionRepositoryFileSize
1.0.0 rolling linux/jammy R-4.5 autoFlagR_1.0.0.tar.gz 813.9 KiB
1.0.0 rolling linux/noble R-4.5 autoFlagR_1.0.0.tar.gz 813.9 KiB
1.0.0 rolling source/ R- autoFlagR_1.0.0.tar.gz 760.7 KiB
1.0.0 latest linux/jammy R-4.5 autoFlagR_1.0.0.tar.gz 813.9 KiB
1.0.0 latest linux/noble R-4.5 autoFlagR_1.0.0.tar.gz 813.9 KiB
1.0.0 latest source/ R- autoFlagR_1.0.0.tar.gz 760.7 KiB
1.0.0 2026-04-26 source/ R- autoFlagR_1.0.0.tar.gz 760.7 KiB
1.0.0 2026-04-23 source/ R- autoFlagR_1.0.0.tar.gz 760.7 KiB
1.0.0 2026-04-09 windows/windows R-4.5 autoFlagR_1.0.0.zip 823.9 KiB

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