Introduction to mispitools

In mispitools: Missing Person Identification Tools

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Overview

The mispitools package provides tools for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. It implements likelihood ratio (LR) calculations for various types of evidence commonly used in forensic identification.

Key Features

• Genetic LR simulations: Simulate likelihood ratios from DNA profiles using pedigree-based kinship analysis
• Non-genetic LR calculations: Compute LRs for sex, age, hair color, and birthdate evidence
• Decision analysis: Evaluate error rates and optimal thresholds
• Visualization: Plot LR distributions, decision curves, and CPT heatmaps
• Interactive exploration: Shiny apps for parameter exploration

Installation

# Install from CRAN (when available)
install.packages("mispitools")

# Or install the development version from GitHub
# devtools::install_github("MarsicoFL/mispitools")

The Bayesian Framework

In missing person identification, we compare two hypotheses:

• H1: The person of interest (POI) is the missing person (MP)
• H2: The POI is unrelated to the MP

The likelihood ratio (LR) quantifies the relative support for H1 vs H2:

$$LR = \frac{P(Evidence | H1)}{P(Evidence | H2)}$$

Multiple pieces of evidence can be combined by multiplying their LRs:

$$LR_{total} = LR_{genetic} \times LR_{sex} \times LR_{age} \times LR_{color}$$

Quick Start

Non-Genetic Evidence

Calculate the LR for sex evidence when the missing person is female:

library(mispitools)

# LR for sex evidence
# H1: MP is female, POI observed as female
# eps = probability of sex observation error
lr_sex(LR = TRUE, H = 1, eps = 0.05)

Calculate the LR for age evidence:

# LR for age evidence
# MP age = 25, tolerance range = 5 years
# POI observed age falls within range
lr_age(LR = TRUE, H = 1, MPa = 25, MPr = 5, epa = 0.05)

Conditional Probability Tables

The package uses Conditional Probability Tables (CPTs) to model evidence under each hypothesis:

# CPT under H2 (population hypothesis)
cpt_h2 <- cpt_population(
  propS = c(0.5, 0.5),  # 50% female, 50% male
  MPa = 30,             # MP age
  MPr = 5,              # Age range
  propC = c(0.3, 0.25, 0.2, 0.15, 0.1)  # Hair color proportions
)

# CPT under H1 (MP hypothesis)
cpt_h1 <- cpt_missing_person(
  MPs = 1,    # Female
  MPc = 2,    # Hair color 2
  eps = 0.05, # Sex error
  epa = 0.05, # Age error
  epc = error_matrix_hair()  # Hair color error matrix
)

# View dimensions
dim(cpt_h1)

Visualizing CPTs

# Visualize both CPTs and LR heatmap
plot_cpt(cpt_h2, cpt_h1)

Available Functions

LR Calculation Functions

| Function | Description | |----------|-------------| | lr_sex() | LR for biological sex evidence | | lr_age() | LR for age evidence | | lr_hair_color() | LR for hair color evidence | | lr_birthdate() | LR for birthdate evidence (Dirichlet model) | | lr_pigmentation() | LR for multiple pigmentation traits |

Simulation Functions

| Function | Description | |----------|-------------| | sim_lr_genetic() | Simulate genetic LRs from pedigrees | | sim_lr_prelim() | Simulate non-genetic LRs | | sim_poi_prelim() | Generate random non-genetic data |

Decision Analysis

| Function | Description | |----------|-------------| | decision_threshold() | Find optimal LR threshold | | threshold_rates() | Calculate TPR/FPR at different thresholds | | lr_combine() | Combine genetic and non-genetic LRs |

Visualization

| Function | Description | |----------|-------------| | plot_lr_distribution() | Plot LR distributions under H1/H2 | | plot_decision_curve() | ROC-like decision curves | | plot_cpt() | Heatmap of CPT values |

Interactive Apps

Launch interactive Shiny applications:

# Basic CPT explorer
app_mispitools()

# Advanced LR comparison with ROC analysis
app_lr_comparison()

Population Frequency Databases

The package includes allele frequency databases for genetic LR calculations:

# Available databases
data(Argentina)
data(Europe)
data(USA)
data(Asia)
data(Austria)
data(BosniaHerz)
data(China)
data(Japan)

# View structure
dim(Argentina)
names(Argentina)[1:10]

Next Steps

See the "Complete Workflow" vignette for a full example combining genetic and non-genetic evidence in a missing person case.

References

Marsico FL, Vigeland MD, Egeland T, Herrera Pinero F (2021). "Making decisions in missing person identification cases with low statistical power." Forensic Science International: Genetics, 52, 102519. https://doi.org/10.1016/j.fsigen.2021.102519

Marsico FL, et al. (2023). "Likelihood ratios for non-genetic evidence in missing person cases." Forensic Science International: Genetics, 66, 102891. https://doi.org/10.1016/j.fsigen.2023.102891

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