Changelog

Source: NEWS.md

EDI 1.0.0

Initial release of EDI (Experimental Design and Inference): a framework that pairs randomized experimental designs — fixed-sample and sequential — with inference procedures matched to each design and response type, so that estimation and testing always reflect how the data were generated.

Experimental designs

• Fixed-sample designs (all n subjects assigned at once via assign_w_to_all_subjects()): DesignFixedBernoulli, DesignFixediBCRD, DesignFixedFactorial, DesignFixedBlocking, DesignFixedCluster, DesignFixedBlockedCluster, DesignFixedBinaryMatch, DesignFixedMatchingGreedyPairSwitching, DesignFixedGreedy, DesignFixedGreedyDOptimal, DesignFixedOptimal and DesignFixedOptimalBlocks (mixed-integer-programming optimal designs via ompr/GLPK, with simulated-annealing and greedy alternatives), and DesignFixedRerandomization.
• Sequential one-by-one designs (each subject assigned on arrival via add_one_subject_to_experiment_and_assign(), maintaining covariate balance): DesignSeqOneByOneBernoulli, DesignSeqOneByOneiBCRD, DesignSeqOneByOneUrn, DesignSeqOneByOneEfron (biased coin), DesignSeqOneByOneAtkinson, DesignSeqOneByOnePocockSimon (minimization), DesignSeqOneByOneRandomBlockSize, DesignSeqOneByOneSPBR (stratified permuted block), and the Kapelner-Krieger matching-on-the-fly family that builds matched pairs from the accruing subject stream: DesignSeqOneByOneKK14, DesignSeqOneByOneKK21, DesignSeqOneByOneKK21stepwise.
• Observational designs (containers for already-observed, non-randomized assignments with blocking/matching structure): ObservationalDesign, ObservationalDesignBlocks, ObservationalDesignMatching.
• Custom-design extension bases for user-defined assignment rules: DesignFixedCustom, DesignCustomSequential.
• Unequal allocation supported; missing covariate data imputed automatically (missRanger/missForest).

Response types

Six response types, each with its own matched inference classes: continuous, incidence (binary), count, proportion (values in [0, 1]), ordinal, and survival — the survival response supporting exact, left-censored, right-censored, and interval-censored observations through one y/y_L/y_R interface.

Inference

• Continuous: OLS (InferenceContinOLS), Lin’s covariate-interacted OLS, quantile regression, robust (Huber) regression, and for matched (KK) designs GLMM (InferenceContinKKGLMM), OLS/quantile/robust variants in both IVWC (inverse-variance-weighted combination) and combined-likelihood pooling, plus the Bai adjusted-t estimators (InferenceBaiAdjustedTKK14, InferenceBaiAdjustedTKK21).
• Incidence: logistic, probit, log-binomial, and identity-link binomial regression, Wald and exact binomial tests, Fisher’s exact test, CMH, Newcombe and Miettinen-Nurminen risk-difference intervals, extended-Robins and Zhang (2026) exact test-inversion randomization CIs, g-computation marginal effects, modified Poisson, and conditional-logit / GLMM classes for matched designs.
• Count: Poisson, quasi-Poisson, robust Poisson, negative binomial, zero-inflated and hurdle models, composite likelihood, conditional Poisson, and GEE classes for matched designs.
• Proportion: beta regression, fractional logit, zero-one-inflated beta, quantile regression, g-computation, and matched-design GEE/quantile variants.
• Ordinal: proportional odds, partial proportional odds, adjacent-category logit, continuation-ratio, stereotype logit, ordered probit, cauchit and complementary-log-log links, g-computation, ridit scoring, Jonckheere-Terpstra, paired sign test, and CLMM-based matched-design classes; randomization confidence intervals for ordinal GLM effects are obtained by inverting the exact permutation test (following the Wang-Rosenberger approach).
• Survival: Cox proportional hazards (plain and stratified), Weibull AFT (with full censoring support), restricted mean survival time, log-rank and Gehan-Wilcoxon tests, Kaplan-Meier survival differences, Weibull frailty GLMMs (log-gamma and normal frailties), Clayton-copula and dependent-censoring-transform estimators, and LWA/rank-regression classes for matched designs.
• Cross-cutting estimators usable across response types: InferenceAllSimpleAverageDiff, InferenceAllSimpleMeanDiffPooledVar, InferenceAllSimpleWilcox, InferenceAllKKMeanDiffIVWC, InferenceAllKKWilcoxIVWC.
• Every applicable procedure at once: InferenceSuite runs all inference classes valid for a given design/response combination and reports a single Cauchy-combined p-value alongside the individual results.
• Custom-inference extension bases: InferenceCustomAsymp, InferenceCustomBoot, InferenceCustomRand.
• Where a specialized engine is best-in-class, estimation delegates to it — glmmTMB (GLMMs), fixest (fast GLMs), aftgee (rank-based AFT), Rfit (R-estimation), survival — with EDI’s own C++ kernels used everywhere else.

Resampling and randomization machinery

• Non-parametric, parametric, and Bayesian bootstrap; BCa intervals; jackknife; m-out-of-n bootstrap and Politis-Romano-Wolf subsampling; exchangeable-resampling-unit handling for blocked/matched/cluster structures; minimum-volatility CI selection.
• Randomization tests with sequential Monte Carlo p-values, custom randomization statistics, quantile randomization CIs, and randomization/bootstrap confidence intervals via parallel bisection test-inversion.

Simulation framework

• SimulationFramework runs Monte Carlo power, size, and operating-characteristic studies across designs, response types, and inference procedures, with coverage_pval/size_pval calibration diagnostics; SimulationFrameworkReport renders results.
• Helpers generate_covariate_dataset() and transform_cont_y_based_on_response_type(); optional parallelization via mirai (set_num_cores()/unset_num_cores()).

Performance

• All model-fitting and variance kernels implemented in C++ (Rcpp, RcppEigen/Eigen, RcppNumerical, LBFGS++, IRLS, OpenMP), exposed as documented fast_* functions — typically one to three orders of magnitude faster than the corresponding pure-R fits (see the shipped benchmark comparisons against each canonical R baseline).
• Machine-specific tuning: tune_EDI_for_this_machine() benchmarks the local machine across four axes and persists tuned performance-policy defaults (get_local_EDI_optimization(), clear_local_EDI_optimization()).
• Runtime-tunable dispatch policies for optimizer choice, cold/warm-start heuristics, and parallel/serial execution: get_optimization_dispatch_policy()/set_optimization_dispatch_policy() and the corresponding *_cold_start_, *_warm_start_, and *_parallel_dispatch_policy() pairs, plus get_bootstrap_dispatch_policy().
• Install-time build configuration via environment variables (EDI_PORTABLE, EDI_NATIVE_SPEED, EDI_NATIVE_LTO, EDI_UNITY, EDI_DISABLE_VECTORIZATION, EDI_DEBUG_SYMBOLS): a tuned -march=native unity build by default locally, and a fully portable, warning-free build for CRAN/CI (auto-selected on r-universe builders).
• Runtime argument-checking can be disabled for production speed with toggle_asserts().

Documentation and extensibility

• Five concept vignettes: notation glossary; reproducibility (RNG and seed conventions); backend contracts (fast_*/C++ kernel conventions); validation evidence; and extending EDI with your own design and inference classes (backed by the design/inference class registries and the DesignFixedCustom/DesignCustomSequential/InferenceCustom* bases).
• Utilities including create_model_matrix_from_features(), robust_negbinreg(), robust_survreg(), and robust_survreg_with_surv_object().

Companion Python package

• The same C++ kernels are published separately for Python as edi_kernels (PyPI; pybind11, no R dependency).