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1.0.0AI-Driven Anomaly Detection for Data Quality
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4Kdownloads / year
27.9%test coverage
13/13checks pass
Overview
About
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.
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Slowest check: 2.5 min · r-devel-linux-x86_64-fedora-clang
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Last activity 2026-01-10
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1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/vikrant31/autoflagr on 2026-07-19.
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Package Timeline
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
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Package metadata
- First published
- 2026-01-15
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 761 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet