NEWS.md
In mispitools: Missing Person Identification Tools

mispitools 2.0.0

This release adds an exact computational engine alongside the simulation workflow of the 1.x series. Where 1.x estimated likelihood-ratio distributions by Monte Carlo, the new layer computes them exactly from the pedigree and the allele frequencies, so quantities such as the expected weight of evidence or a tail probability no longer carry simulation error. The kernel is written in C++ and reached through Rcpp.

All functions from 1.x remain exported and continue to work.

marker_model() builds a validated marker model from a pedigree, a marker identifier, allele frequencies, and an optional mutation or linkage specification. It has print() and validation methods, so input errors surface at construction rather than mid-computation.
nongenetic_feature() gives non-genetic evidence (biological sex, pigmentation, age, birthdate) the same treatment as a marker: a population distribution, an observed value, and an error rate.
get_allele_freqs() converts the bundled population databases into the per-marker format the model layer expects.

lr_distribution() returns the full distribution of the profile log10 LR under both hypotheses, not a point estimate. method = "exact" performs a sparse convolution over the per-marker supports; method = "grid" projects onto a fixed lattice, which preserves total mass and the first moment exactly and keeps the computation bounded when the exact support would grow past what memory allows.
summary(), quantile() and plot() methods for the resulting object.
per_marker_kl() and per_marker_kl_profile() report the Kullback-Leibler divergence between the two hypotheses for each marker, which quantifies how much discriminating power each marker contributes before any data are observed.

Mutation models: equal, stepwise, and the asymmetric model of Dawid (2002).
Two-marker linked joint distributions computed by Elston-Stewart peeling, with recombination handled explicitly rather than assumed away.

Optimal threshold, false-positive and false-negative rates, ROC curve and AUC computed on the exact distribution.
concentration_index_positive(), leave_one_out(), calibrate_concentration_cutoff() and fragility_report() measure how much of the weight of evidence rests on a single marker, and produce a reportable statement for the case file.
belief_trajectory(), binary_belief_trajectory() and trajectory_metrics() follow the posterior as evidence accumulates.

The engine is checked against independent implementations rather than against itself: pedprobr and pedmut for genotype distributions under mutation, Familias and forrel for per-marker likelihood ratios, and analytic results where they exist. These cross-engine comparisons run as part of the test suite.

The kernel uses OpenMP where the toolchain provides it and falls back to single-threaded execution otherwise; results are identical either way. The exact engine enumerates joint genotype states, so its cost grows with pedigree size and with the number of alleles per marker. For large pedigrees or full profiles, use method = "grid" in lr_distribution().