mispitools_app: Comprehensive Shiny App for Missing Person Identification

In mispitools: Missing Person Identification Tools

Comprehensive Shiny App for Missing Person Identification

Description

Launches a comprehensive interactive Shiny application for calculating likelihood ratios (LRs) from non-genetic evidence in missing person cases. This unified app integrates all evidence types (sex, age, hair color, birthdate) with tutorials, visualizations, and decision analysis tools.

Usage

mispitools_app()

Details

This app provides a complete workflow for forensic identification using non-genetic evidence. It implements the Bayesian framework where:

• H1: The unidentified person IS the missing person

• H2: The unidentified person is NOT the missing person

• LR = P(Evidence | H1) / P(Evidence | H2)

Evidence types supported:

• Biological sex (male/female)

• Age (within expected range)

• Hair color (5 categories)

• Birth date (discrepancy analysis)

Value

A Shiny app object. When run interactively, launches a multi-tab web interface with:

• Welcome: Introduction to LR concepts

• Individual Evidence: Calculate LR for each evidence type

• CPT Analysis: Visualize conditional probability tables

• Distribution: Simulate and visualize LR distributions

• Combine Evidence: Combine multiple evidence types

• Decision Analysis: Threshold selection and error metrics

• Tutorial: Step-by-step educational content

References

Marsico FL, Caridi I (2023). "Incorporating non-genetic evidence in large scale missing person searches: A general approach beyond filtering." Forensic Science International: Genetics, 66, 102891. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fsigen.2023.102891")}

Marsico FL, Vigeland MD, et al. (2021). "Making decisions in missing person identification cases with low statistical power." Forensic Science International: Genetics, 52, 102519. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fsigen.2021.102519")}

See Also

lr_sex, lr_age, lr_hair_color, lr_birthdate for individual LR calculations, lr_combine for combining evidence, decision_threshold, threshold_rates for decision analysis.

Examples

if (interactive()) {
  mispitools_app()
}

mispitools documentation built on Aug. 26, 2026, 1:08 a.m.