Package: mispitools 2.0.1

mispitools: Missing Person Identification Tools

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>.

Authors:Franco Marsico [aut, cre], Suisei Nakagawa [aut]

mispitools_2.0.1.tar.gz
mispitools_2.0.1.zip(r-4.7-x86_64)mispitools_2.0.1.zip(r-4.7-arm64)mispitools_2.0.1.zip(r-4.6-x86_64)mispitools_2.0.1.zip(r-4.6-arm64)mispitools_2.0.1.zip(r-4.5-x86_64)
mispitools_2.0.1.tgz(r-4.6-x86_64)mispitools_2.0.1.tgz(r-4.6-arm64)mispitools_2.0.1.tgz(r-4.5-x86_64)mispitools_2.0.1.tgz(r-4.5-arm64)
mispitools_2.0.1.tar.gz(r-4.7-arm64)mispitools_2.0.1.tar.gz(r-4.7-x86_64)mispitools_2.0.1.tar.gz(r-4.6-arm64)mispitools_2.0.1.tar.gz(r-4.6-x86_64)
mispitools_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mispitools/json (API)

# Install 'mispitools' in R:
install.packages('mispitools', repos = c('https://marsicofl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/marsicofl/mispitools/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • Argentina - STR Allele Frequencies from Argentina
  • Asia - STR Allele Frequencies from Asian Populations
  • Austria - STR Allele Frequencies from Austria
  • BosniaHerz - STR Allele Frequencies from Bosnia and Herzegovina
  • China - STR Allele Frequencies from China
  • Europe - STR Allele Frequencies from Europe
  • Japan - STR Allele Frequencies from Japan
  • USA - STR Allele Frequencies from United States

On CRAN:

Conda:

bayesian-inferencekinshipcppopenmp

8.51 score 38 stars 37 scripts 465 downloads 73 exports 68 dependencies

Last updated from:8326981f84. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK191
linux-devel-x86_64OK277
source / vignettesOK415
linux-release-arm64OK179
linux-release-x86_64OK240
macos-release-arm64OK198
macos-release-x86_64OK292
macos-oldrel-arm64OK202
macos-oldrel-x86_64OK444
windows-devel-arm64OK309
windows-devel-x86_64OK228
windows-release-arm64OK292
windows-release-x86_64OK209
windows-oldrel-x86_64OK200
wasm-releaseOK151

Exports:app_lr_comparisonapp_mispitoolsas_lr_distbelief_trajectorybinary_belief_trajectorycalibrate_concentration_cutoffCmodelcombLRcompute_conditioned_propcompute_LRs_colorscompute_reference_propconcentration_indexconcentration_index_positiveconditionedPropCondPlotcpt_missing_personCPT_MPCPT_POPcpt_populationdecision_thresholddeplotDeTentropy_log10error_matrix_hairfamilias_trajectoryfragility_reportget_allele_freqsgetfreqsherfindahl_indexkl_divergence_log10leave_one_outlr_agelr_birthdatelr_combinelr_compute_pigmentationlr_distributionlr_hair_colorlr_pigmentationlr_sensitivitylr_sexlr_to_dataframeLRageLRcolLRcolorslrComparisonAppLRdateLRdistLRsexmakeMPprelimmakePOIprelimmarker_modelmispiAppmispitools_appnongenetic_featureper_marker_klper_marker_kl_profileplot_cptplot_decision_curveplot_lr_distributionrefPropshannon_concentrationsim_lr_geneticsim_lr_prelimsim_mp_prelimsim_poi_prelimsim_reference_popsimLR2dataframesimLRgensimLRprelimsimRefthreshold_ratestrajectory_metricsTrates

Dependencies:base64encbslibcachemclicommonmarkcpp11digestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshttpuvisobandjquerylibjsonlitekinship2knitrlabelinglaterlatticelifecyclelpSolvemagrittrMatrixmemoisemimeotelpatchworkpedmutpedtoolspillarpkgconfigplyrpromisespurrrquadprogR6rappdirsRColorBrewerRcppRcppArmadilloreshape2rlangS7sassscalesshinysourcetoolsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxfunxtableyaml

Belief Dynamics in Missing-Person Identification
What this vignette is about | 1. Pedigree setup | 2. Per-marker LR computation | 3. Belief trajectory | 4. Fragility diagnostics | 5. Pedigree-specific calibration | Calibration and reporting helpers | 6. Combining genetic and non-genetic evidence | 7. Decision thresholds | Further reading
Rendered frombelief_dynamics.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2026-08-24

Introduction to mispitools
Overview | Key Features | Installation | The Bayesian Framework | Quick Start | Non-Genetic Evidence | Conditional Probability Tables | Visualizing CPTs | Available Functions | LR Calculation Functions | Simulation Functions | Decision Analysis | Visualization | Interactive Apps | Population Frequency Databases | Next Steps | References
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2026-01-08

Complete Workflow: Combining Genetic and Non-Genetic Evidence
Introduction | The Case Scenario | Setup | Part 1: Non-Genetic Evidence | Step 1.1: Define Error Rates | Step 1.2: Calculate Individual LRs | Step 1.3: Combined Non-Genetic LR | Step 1.4: Visualize the CPTs | Part 2: Genetic Evidence (Simulation) | Step 2.1: Create Pedigree | Step 2.2: Simulate Genetic LRs | Step 2.3: Visualize Genetic LR Distributions | Part 3: Combining All Evidence | Step 3.1: Prior Probability | Step 3.2: Posterior Odds Calculation | Step 3.3: Decision Threshold Analysis | Part 4: Interpretation | Strength of Evidence Categories | Our Case Results | Part 5: Interactive Exploration | Conclusion | Session Information | References
Rendered fromworkflow.Rmdusingknitr::rmarkdown

Last update: 2026-01-08
Started: 2026-01-08

Readme and manuals

Help Manual

Help pageTopics
mispitools: Missing Person Identification Toolsmispitools-package mispitools
STR Allele Frequencies from ArgentinaArgentina
Coerce to a Likelihood-Ratio Distribution Objectas_lr_dist
STR Allele Frequencies from Asian PopulationsAsia
STR Allele Frequencies from AustriaAustria
Compute a belief trajectory from sequential evidencebelief_trajectory
Compute a binary belief trajectory from per-marker likelihood ratiosbinary_belief_trajectory
STR Allele Frequencies from Bosnia and HerzegovinaBosniaHerz
Calibrate pedigree-specific concentration cutoffcalibrate_concentration_cutoff
STR Allele Frequencies from ChinaChina
Compute Conditioned Proportions for Pigmentation Traitscompute_conditioned_prop
Compute Reference Population Proportions for Pigmentation Traitscompute_reference_prop
Concentration index of per-step evidence contributionsconcentration_index
Inclusion-fragility concentration index (positive weights only)concentration_index_positive
Missing Person-Based Conditional Probability Tablecpt_missing_person
Population-Based Conditional Probability Tablecpt_population
Compute Optimal Decision Thresholddecision_threshold
Shannon entropy in base-10 (bans)entropy_log10
Hair Color Error/Confusion Matrixerror_matrix_hair
STR Allele Frequencies from EuropeEurope
Belief trajectory metrics from a 'Familias::FamiliasPosterior' resultfamilias_trajectory
Per-case fragility reportfragility_report
Get Allele Frequencies in pedtools Formatget_allele_freqs
Herfindahl-Hirschman concentration of evidence contributionsherfindahl_index
STR Allele Frequencies from JapanJapan
Kullback-Leibler divergence in base-10 (bans)kl_divergence_log10
Leave-one-out fragility analysis of evidence contributionsleave_one_out
Likelihood Ratio for Age Variablelr_age
Likelihood Ratio for Birth Datelr_birthdate
Combine Likelihood Ratios from Multiple Sourceslr_combine
Compute Likelihood Ratios for Pigmentation Traitslr_compute_pigmentation
Composed likelihood-ratio distribution over a marker profilelr_distribution
Likelihood Ratio for Hair Colorlr_hair_color
Simulate LR Distributions for Pigmentation Traitslr_pigmentation
Sensitivity Analysis for Likelihood Ratioslr_sensitivity
Likelihood Ratio for Biological Sexlr_sex
Convert Genetic LR Simulations to Data Framelr_to_dataframe
Marker model (S3)marker_model
Comprehensive Shiny App for Missing Person Identificationmispitools_app
Non-genetic feature model (S3)nongenetic_feature
Per-marker bidirectional Kullback-Leibler divergence and expected log10 LRper_marker_kl
Per-marker bidirectional KL across a marker profileper_marker_kl_profile
Plot Conditional Probability Tables Comparisonplot_cpt
Plot Decision Curve (FPR vs FNR)plot_decision_curve
Plot Likelihood Ratio Distributionsplot_lr_distribution
Plot a Likelihood-Ratio Distributionplot.lr_dist
Print method for marker_modelprint.marker_model
Print method for nongenetic_featureprint.nongenetic_feature
Quantiles of a likelihood-ratio distributionquantile.lr_dist
Shannon-entropy-based concentration of evidence contributionsshannon_concentration
Simulate Likelihood Ratios from Genetic Datasim_lr_genetic
Simulate Likelihood Ratios from Preliminary Investigation Datasim_lr_prelim
Simulate Preliminary Investigation Data for Missing Personssim_mp_prelim
Simulate Preliminary Investigation Data for Persons of Interestsim_poi_prelim
Simulate Reference Population with Pigmentation Traitssim_reference_pop
Summarise a Likelihood-Ratio Distributionsummary.lr_dist
Compute Error Rates at a Specific Thresholdthreshold_rates
Trajectory metrics from a belief trajectory matrixtrajectory_metrics
STR Allele Frequencies from United StatesUSA