autoFlagR1.0.0 package

AI-Driven Anomaly Detection for Data Quality

calculate_benchmark_metrics

Calculate Benchmarking Metrics

calculate_feature_importance

Calculate Feature Importance for Anomalies

create_rmd_template

Create R Markdown Template

extract_benchmark_metrics

Extract Benchmark Metrics from Scored Data

flag_top_anomalies

Flag Top Anomalies Based on Score Threshold

generate_audit_report

Generate Automated Data Quality Audit Report

get_top_anomalies

Get Top Anomalous Records

prep_for_anomaly

Prepare Data for Anomaly Detection

score_anomaly

Score Anomalies Using Unsupervised Machine Learning

score_iforest

Score anomalies using Isolation Forest

score_lof

Score anomalies using Local Outlier Factor

Download source packageRead PDF manual

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>.

  • Maintainer: Vikrant Dev Rathore
  • License: MIT + file LICENSE
  • Last published: 2026-01-15

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