benfordClass: Benford's Law Analysis for Fraud Detection and Data...

In sbalci/ClinicoPathDescriptives: Descriptive Analysis Tools for Clinicopathological Research

Benford's Law Analysis for Fraud Detection and Data Validation

Description

Performs comprehensive Benford's Law analysis on numeric data to detect potential fraud, manipulation, or data quality issues. This implementation provides robust error handling, data validation, statistical testing, and detailed reporting for forensic data analysis.

Details

Benford's Law states that in many naturally occurring datasets, the leading digit d occurs with probability P(d) = log₁₀(1 + 1/d). This analysis:

• Validates data appropriateness for Benford analysis

• Performs chi-square goodness of fit testing

• Calculates Mean Absolute Deviation (MAD) for compliance assessment

• Identifies suspicious patterns and outliers

• Provides comprehensive statistical reporting

• Generates publication-ready visualizations

Value

A comprehensive results object containing:

• Statistical analysis with chi-square test and p-values

• Suspect identification with detailed risk assessment

• Data quality validation and compliance metrics

• Publication-ready visualization with theoretical vs. observed distributions

Super classes

jmvcore::Analysis -> ClinicoPathDescriptives::benfordBase -> benfordClass

Methods

Public methods

• benfordClass$clone()

Inherited methods

• jmvcore::Analysis$.createImage()
• jmvcore::Analysis$.createImages()
• jmvcore::Analysis$.createPlotObject()
• jmvcore::Analysis$.load()
• jmvcore::Analysis$.render()
• jmvcore::Analysis$.save()
• jmvcore::Analysis$.savePart()
• jmvcore::Analysis$.setCheckpoint()
• jmvcore::Analysis$.setParent()
• jmvcore::Analysis$.setReadDatasetHeaderSource()
• jmvcore::Analysis$.setReadDatasetSource()
• jmvcore::Analysis$.setResourcesPathSource()
• jmvcore::Analysis$.setStatePathSource()
• jmvcore::Analysis$addAddon()
• jmvcore::Analysis$asProtoBuf()
• jmvcore::Analysis$asSource()
• jmvcore::Analysis$check()
• jmvcore::Analysis$init()
• jmvcore::Analysis$optionsChangedHandler()
• jmvcore::Analysis$postInit()
• jmvcore::Analysis$print()
• jmvcore::Analysis$readDataset()
• jmvcore::Analysis$run()
• jmvcore::Analysis$serialize()
• jmvcore::Analysis$setError()
• jmvcore::Analysis$setStatus()
• jmvcore::Analysis$translate()
• ClinicoPathDescriptives::benfordBase$initialize()

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Method clone()

The objects of this class are cloneable with this method.

Usage

benfordClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Note

This function requires data with sufficient size (n ≥ 100 recommended) and appropriate distribution (positive numbers spanning multiple orders of magnitude). Small or constrained datasets may not follow Benford's Law naturally.

References

Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4), 551-572. Nigrini, M. J. (2012). Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection.

sbalci/ClinicoPathDescriptives documentation built on July 4, 2025, 5:25 p.m.