Missing Person Identification Tools
Compute Conditioned Proportions for Pigmentation Traits
Compute Reference Population Proportions for Pigmentation Traits
Missing Person-Based Conditional Probability Table
Population-Based Conditional Probability Table
Compute Optimal Decision Threshold
Hair Color Error/Confusion Matrix
Get Allele Frequencies in pedtools Format
Bidirectional Kullback-Leibler Divergence for Genetic Markers
Multi-Population Kullback-Leibler Divergence Matrix
Kullback-Leibler Divergence for Probability Matrices
Likelihood Ratio for Age Variable
Likelihood Ratio for Birth Date
Combine Likelihood Ratios from Multiple Sources
Compute Likelihood Ratios for Pigmentation Traits
Likelihood Ratio for Hair Color
Simulate LR Distributions for Pigmentation Traits
Sensitivity Analysis for Likelihood Ratios
Likelihood Ratio for Biological Sex
Convert Genetic LR Simulations to Data Frame
Comprehensive Shiny App for Missing Person Identification
Deprecated functions in mispitools
mispitools: Missing Person Identification Tools
Plot Conditional Probability Tables Comparison
Plot Decision Curve (FPR vs FNR)
Plot Likelihood Ratio Distributions
Simulate Likelihood Ratios from Genetic Data
Simulate Likelihood Ratios from Preliminary Investigation Data
Simulate Preliminary Investigation Data for Missing Persons
Simulate Genetic Profiles for Persons of Interest
Simulate Preliminary Investigation Data for Persons of Interest
Simulate Posterior Odds Combining Genetic and Non-Genetic Evidence
Simulate Reference Population with Pigmentation Traits
Compute Error Rates at a Specific Threshold
A comprehensive toolkit for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. Computes likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color, and birthdate evidence. Provides decision analysis tools including optimal LR thresholds, error rate calculations, and ROC curve visualization. Includes interactive Shiny applications for exploring evidence combinations. For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.