EDI

New features

• InferenceCountQuasiPoisson now composes the marginal-estimand component (MarginalEstimand), like InferenceCountPoisson, so marginal (standardized) effect estimands are available for the quasi-Poisson class as well.

Bug fixes

• Reused-worker resampling reused a stale cached fit across draws for classes whose fit is cached under a custom guard key (first found in InferenceOrdinalGCompMeanDiff, whose cached_values$md guard survived the loader's narrow reset list). Every draw then returned the same number, the resampling distribution collapsed to a point mass, and compute_rand_two_sided_pval() sat at its floor regardless of the true effect (reject rate 1.0 at a true null). The worker loaders for randomization, non-parametric bootstrap, m-out-of-n bootstrap and randomization-bootstrap draws now reset the cached fit state between draws, and a structural regression test asserts that every class exposing a reusable-worker method returns a non-degenerate distribution.
• InferencePropGCompMeanDiff and InferenceSurvivalGLMMWeibullFrailtyLoggammaIVWC returned NA (or a stale value) in randomization inference: the first from a sample-usability gate applied to the wrong resampling context, the second from its estimate-only w_star pooling. Both are fixed and no longer listed as known-broken.
• The KK survival compound classes InferenceSurvivalKKLWACoxPHOneLik, InferenceSurvivalGLMMWeibullFrailtyLoggammaOneLik and InferenceSurvivalGLMMWeibullFrailtyLoggammaIVWC fed real NAs into the fitter for every censored subject during randomization inference: compute_treatment_estimate_during_randomization_inference() re-read y from the design and derived dead = as.numeric(!is.na(y)), but since the y/y_L/y_R migration the design's y uses NA to mark a censored observation. On simulated data with a strong effect the randomization p-value was 0.02 with no censoring and 0.97 with about 20% censoring. y/dead are now derived as Design$get_effective_time() / $get_effective_dead() do. A sweep of the other 16 survival classes found no further instance.
• InferenceOrdinalCloglogRegr's parametric-bootstrap p-value was badly over-rejecting (60% at a true null). One cause is fixed: the delta-constrained null refit started from a single cold start and could stall; it is now multi-start (the same fix InferenceOrdinalStereotypeLogitRegr received earlier). *A second cause, a sign mismatch in the shared bootstrap-data simulator, is root-caused but not fixed here.*
• The Efron biased-coin randomization null draws (generate_permutations) compared weighted arm counts (n_T * prob_T vs n_C * (1 - prob_T)) instead of the raw arm counts DesignSeqOneByOneEfron actually compares, so for any prob_T != 0.5 the randomization null did not follow the design's own assignment rule. It now uses the raw counts.
• fast_probit_regression's L-BFGS objective held a dangling Eigen::Ref to a temporary, giving non-deterministic fits and NaN negative log-likelihoods. The objective now owns its design matrix.
• fast_log_binomial_regression's weighted log-likelihood let zero-weight rows constrain the [0, 1] mean support although they are absent from the likelihood; zero-weight rows are now skipped, so the identity- and log-link binomial fits no longer reject valid data because of them.
• The robust-regression bootstrap kernel accepted any method string and would throw inside an OpenMP region, terminating R; method is now validated ("M" or "MM") before the parallel region.
• stable_signature() (used to key cached randomization state) hashed a strided sample of the serialized object, which collided on 0/1 integer permutation matrices; it now hashes the full serialization.
• InferenceCountPoisson's covariance falls back to the Fisher information when the kernel returns no X'WX; InferenceIncidLogRegr gained an estimand-aware standard error and degrees of freedom.
• Weibull and Cox fits with fixed (held) coefficients now compute the covariance from the free-parameter information block only and expand it back, so the free coefficients' variances are finite and the fixed parameters' rows and columns are NaN by convention; the Cox cluster-robust sandwich does the same.
• OpenMP's primary thread no longer polls R's interrupt machinery while worker threads are active in the Wilcoxon-Hodges-Lehmann kernels.
• fast_gaussian_lmm_gls_cpp() (the fixed-variance-component GLS solve used by the randomization and non-studentised bootstrap fast paths of InferenceContinKKGLMM) mixed X'X with a cross-term scaled by an extra 1/sigma_e^2, so it returned the wrong coefficient whenever the residual standard deviation was not exactly 1 (on a paired-plus-singleton fixture with sigma_e = 0.4 the slope was -0.53 instead of the correct 0.63; at sigma_e = 1 the two agreed). It now matches an explicit GLS to machine precision at every sigma_e.
• The KK21 stepwise weight selection for ordinal responses read a nonexistent field (ssq_b_2) from fast_ordinal_regression_with_var_cpp(); it now reads the returned ssq_b_j.
• fast_zero_one_inflated_beta_cpp() did not check the length of a supplied warm_start_params (or warm_start_fisher_info, or the row counts of X, X_zero_one and y), so a mis-sized start let the optimizer read and write past its parameter buffer (heap corruption, found under valgrind; it aborted R with free(): invalid next size). It now raises an ordinary error. InferencePropZeroOneInflatedBetaRegr always passed a correctly sized start and was not affected.
• fast_ordinal_clmm now errors on mismatched X/y/group_id lengths instead of reading out of bounds.
• InferenceIncidRiskDiff, InferenceCountRobustPoisson, and the zero-inflated/hurdle Poisson classes reported a confident (often zero-width) confidence interval and p-value on a perfectly or near-perfectly fit response (e.g. y fully separated by w, or constant), because the Huber-White/model-based standard error collapses to a numerically-zero-but-positive value there. Found via a raw comprehensive_tests results audit's new adversarial-data/fault-injection sweep. robust_sandwich_variance() and the affected classes' standard-error paths now treat a variance below .Machine$double.eps as non-estimable (NA), not as precision.
• InferenceCountHurdlePoisson/InferenceCountZeroInflatedPoisson reported a confident estimate and p-value on a hurdle fit that has no MLE by construction (every positive count equal to 1, so the zero-truncated Poisson likelihood only improves as lambda -> 0). Non-estimability is now decided from the data alone (not from what the optimizer/glmmTMB happens to return), so the result is NA on every platform.
• InferenceRandCustom$compute_rand_confidence_interval()'s fast path ignored the trial delta shift during the randomization search, so the returned interval was in the custom statistic's own scale (e.g. centred on a Welch-t value of ~2-4) rather than the response's scale. The comprehensive_tests harness's own custom-statistic randomization-CI check also fed it a non-translation-equivariant statistic (Welch t), which cannot give a meaningful CI under this or any correct implementation; it now uses a difference-in-means statistic for that check.
• InferenceAllSimpleWilcox$compute_asymp_confidence_interval(alpha) cached and returned the 95% Wilcoxon interval for every alpha, so a 90% or 99% interval silently came back at the wrong nominal level.
• InferenceOrdinalAdjCatLogitRegr's parametric-bootstrap confidence interval could be wildly wrong (e.g. [-30, -11] around an estimate of -0.09) because each simulated replicate's treatment coefficient was read from the wrong field of the refit object (fit$b, slopes only) when the anchor fit's index into its own coefficient vector (fit$params, thresholds + slopes) pointed somewhere else entirely on fit$b. Every parametric-bootstrap replicate now reads the same field the anchor fit used.
• InferenceOrdinalContRatioRegr (and, via the same hardened QR-column-dropping helper, 8 other ordinal threshold-model classes) could silently fit from a garbage cold start and "converge" after one iteration when the design matrix contained columns collinear with the model's *implicit* per-stage intercept (e.g. a stratified design's two complementary factor dummies) — qr() alone, with no intercept column to check against, reported the matrix as full rank. The column-dropping helper now optionally includes that implicit intercept in its rank check.
• A likelihood-ratio confidence interval whose Newton/bisection inversion converges to within a small tolerance of the point estimate — without landing on it exactly — was reported as a zero-width interval instead of falling back to the Wald interval (or NA), because the "failed inversion" sentinel check required bit-identical bounds. InferenceOrdinalStereotypeLogitRegr's compute_lik_ratio_confidence_interval() hit this on ~6% of one audited sample. Its compute_lik_ratio_bootstrap_confidence_interval() had a related bug: a bootstrap p-value below alpha *at the point estimate itself* (which should be ~1, since the LR statistic there is 0) was silently treated as a legitimate zero-width interval rather than a failed constrained-fit refit; it now reports NA.
• compute_rand_bootstrap_confidence_interval(type = "smoothed") and compute_rand_bootstrap_two_sided_pval(type = "smoothed") added raw-scale Gaussian kernel noise to count responses, so a resampled zero could become a slightly negative non-integer. Under the CI inversion's multiplicative count shift that became a large negative integer, the Rcpp Poisson-GLMM fit gave up, and the glmmTMB fallback rejected every such draw (GLMM FIT ERROR: negative values not allowed for the 'Poisson' family), corrupting the null distribution and yielding a degenerate conservative bound (e.g. InferenceCountKKGLMM). With use_rcpp = FALSE every draw failed and the p-value was NA. Kernel noise on count responses is now rounded and floored at zero so the resampled draw stays on the non-negative integer support (the same convention the count shift already uses); other response types are unchanged.
• InferenceCountHurdlePoisson/InferenceCountHurdleNegBin/ InferenceCountZeroInflatedPoisson/InferenceCountZeroInflatedNegBin's standard error could fall through to the generic information-matrix-inverse fallback on a hurdle fit with no MLE by construction (every positive count equal to 1), and whether that generic inversion happened to return a spurious finite value or correctly fail depended on the LAPACK/BLAS backend (confirmed via CI to differ between this environment, where it correctly returned non-estimable, and Windows). The data-driven non-estimability check that already gated the estimate and p-value now also runs before this generic fallback, so the standard error is NA on every platform.
• SimulationFramework's internal design/inference-combination builder unconditionally disabled package assertions and never restored them; a caller running with turn_off_asserts_for_speed = FALSE (assertions intentionally kept on) had assertions silently disabled for the rest of the R session after this method ran once, which could mask unrelated argument-validation bugs in later, unrelated code in the same session. It now restores whatever assert state was in effect on entry.