mispitools 2.0.1

Fixes a test failure reported by CRAN on macOS arm64 (r-release and r-oldrel) and on the M1mac additional check, and with it a genuine platform dependence in the size of the exact LR support.

Atoms of the LR distribution

A log10 LR atom is a real number that the C++ engine and the R reference implementation reach by different arithmetic routes. On a platform whose compiler contracts a * b + c into a fused multiply-add, which is the default on aarch64, one route rounds once where the other rounds twice, and two mathematically equal atoms end up differing in the last bits. Grouping them by IEEE equality, as 2.0.0 did, then split one atom into two, so the number of support points of lr_distribution() depended on the platform and the cross-check against the reference failed on arm64 while passing on x86.

• lr_distribution(), per_marker_lr_dist() and the evidence combination kernel now close an atom group by a relative tolerance (kAtomRelTol, 1e-12) rather than by exact equality. The measured separation justifies the constant: consecutive keys are either within one unit in the last place of each other or more than 1e-8 apart in relative terms, with nothing in between.
• The R reference aggregate_lr_dist() applies the same rule, so the two engines agree on the size of the support on every platform.
• The composed distribution is unchanged as a distribution. Its support is smaller because the duplicates that rounding had split are now merged: composing two nine-allele markers on a trio gives 98 415 atoms instead of 602 991, with the same total mass and an expected weight of evidence identical to twelve decimal places. Exact composition of two markers of 10 and 12 alleles drops from about 1.9 million support points to about 317 000, so it is correspondingly cheaper in time and memory.

Documentation

• The README opens the 2.0 section by carrying the tutorial case of Steps 1 to 6 through the four entry points of the exact engine, with the marker set and the frequency database used there.

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.

New: model layer

• 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.

New: exact likelihood-ratio distributions

• 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.

New: mutation and linkage

• 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.

New: decision quantities and fragility

• 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.

Verification

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.

Performance

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().

mispitools 1.4.0

See the CRAN release notes for the 1.x series.