funresMech 1.1.0

This version replaces the simulation and optimisation core. The model is the same (Okuyama 2012, 2026); what changes is how it is computed and how the results are summarised. It was validated against Okuyama (2026, Table S1) with the datasets D2 and D3 of FoRAGE, and with simulated data (parameter recovery).

New simulation engine

• The stochastic foraging process now runs in C++ through ‘Rcpp’ (src/motor.cpp); with the same seed it reproduces exactly the pure-R reference implementation and the simulator of Okuyama (2026). It is about 40-65 times faster.
• Each trial is stopped once all hosts have been parasitised. The response is the number of distinct parasitised hosts, so this does not change the simulated distribution, but it removes pathological run times when the handling time and the Gamma shape approach zero.
• No random seed is set inside the package: set.seed() of the user controls every result, including the parallel profile.

Changes that alter results

• s is now the standard deviation of the handling time on the natural scale (same units as h), as in Okuyama (2026), instead of the standard deviation on the log scale. The Lognormal still has mean h; the equivalent log-scale value is sqrt(log(1 + (s / h)^2)). Values of s from version 1.0.4 are not comparable.
• The 95% interval of z uses the likelihood-ratio threshold NLL_min + qchisq(0.95, 1) / 2 (+1.92). Version 1.0.4 used +3.84, which gives an interval of about 99.5%.
• Optimisation on the log scale. The search is carried out on log(a * H_ref^z), log(h), log(k) and sqrt(s), with wide bounds (1.0.4 used fixed bounds that excluded the real values of the validation datasets: h <= 0.5, k >= 0.5, z <= 3). A warning is shown when a parameter ends on a bound.
• z_hat is the minimum of the likelihood profile, and the AIC of the model with free z uses its NLL (previously a separate five-parameter fit, which could be worse than the profile because of Monte Carlo noise).
• The default z grid is 0.5-5 in steps of 0.25 (it was 0.5-1.5).

New features

• The profile is computed in parallel over the grid of z (future); the previous plan(multisession) had no effect on the fit.
• If the upper limit of the interval of z is open, the grid is extended (z = 8, 10, 15, 20) and the limit is reported as “not identified” when the profile stays flat (typical when k is very small). When a limit falls in a bracket wider than the grid step, extra values of z are evaluated inside it so that the interpolated limit is as precise as the rest of the grid.
• Diagnostics tab and report section: small k, parameters on a bound, open intervals and noisy likelihood.
• Data screening for atypical trials before the fit (leave-one-out Beta-Binomial, Bonferroni threshold). Data are never removed: flagged trials should be checked and the analysis repeated without them.
• If the parallel workers cannot load this version of the package (for example, an older version is installed), the profile is evaluated sequentially with a message instead of failing.
• The NLL is re-evaluated at each optimum with fresh random numbers and reported alongside the optimiser’s value (nll_reeval).

Internal

• New dependency: ‘Rcpp’ (Imports and LinkingTo).
• fit_full() was replaced by fit_profile() and fit_z_fixed().
• Package help page (?funresMech) and extended ?run_app.
• New tests: engine against the R reference, saturation cut-off, reparameterisation, likelihood-ratio interval, profile fit and screening.