Package index • EDI

Package Overview

EDI-package EDI: Experimental Design and Inference

Experimental Designs: Fixed-Sample

Designs where the sample size n is fixed at construction and treatment assignment is drawn for all n subjects at once, either up front or via assign_w_to_all_subjects() once covariates are recorded.

DesignFixed: A Fixed Design
DesignFixedBernoulli: A Fixed-Sample-Size Bernoulli (Independent-Coin-Flip) Randomized Design
DesignFixediBCRD: A Fixed, Individually Balanced Completely Randomized Design (iBCRD)
DesignFixedFactorial: A Fixed, Balanced Two-Arm Factorial Design
DesignFixedBlocking: A Fixed, Stratified-Block Randomized Design
DesignFixedCluster: A Fixed, Unblocked Cluster Randomized Design
DesignFixedBlockedCluster: A Fixed, Blocked-and-Clustered Randomized Design
DesignFixedBinaryMatch: A Fixed, Non-Bipartite-Matched-Pair Design with Within-Pair Randomization
DesignFixedMatchingGreedyPairSwitching: A Fixed, Matched-Pair Design with Greedy Which-Member-Treated Optimization
DesignFixedGreedy: A Fixed, Covariate-Balanced Design via Greedy Pairwise-Swap Search
DesignFixedGreedyDOptimal: A Fixed, Model-Based Optimal Design via Greedy Pairwise-Exchange Search
DesignFixedOptimal: A Fixed, Deterministic Single-Allocation Optimal Design
DesignFixedOptimalBlocks: A Fixed, Covariate-Homogeneous-Block Randomized Design
DesignFixedRerandomization: A Fixed Rerandomization Design (Rejection-Sampled on Covariate Balance)

Experimental Designs: Sequential (One-by-One)

Designs where subjects arrive one at a time and treatment is assigned on arrival via add_one_subject_to_experiment_and_assign(), before the next subject’s covariates are known. This includes the KK “matching-on-the-fly” family, which builds matched pairs from the accruing subject stream.

DesignSeqOneByOne: Sequential One-by-One Experimental Design
DesignSeqOneByOneBernoulli: A Sequential Bernoulli (Independent-Coin-Flip) Randomized Design
DesignSeqOneByOneiBCRD: A Sequential Design Guaranteeing Exact Terminal Balance (Random Allocation Rule)
DesignSeqOneByOneUrn: Wei's (1977, 1978) Adaptive Urn Sequential Design, UD(\(\alpha\), \(\beta\))
DesignSeqOneByOneEfron: Efron's (1971) Biased Coin Sequential Design
DesignSeqOneByOneAtkinson: Atkinson's (1982) Covariate-Adjusted Biased Coin Sequential Design
DesignSeqOneByOnePocockSimon: Pocock and Simon's (1975) Minimization Sequential Design
DesignSeqOneByOneRandomBlockSize: A Sequential Permuted-Block Design with Randomly Varying Block Sizes
DesignSeqOneByOneSPBR: A Stratified Permuted-Block Sequential Design (SPBR) with Fixed Block Size
DesignSeqOneByOneKK14: Kapelner and Krieger's (2014) Sequential "Matching-on-the-Fly" Design
DesignSeqOneByOneKK21: Kapelner and Krieger's (2021) Outcome-Weighted Sequential Matching-on-the-Fly Design
DesignSeqOneByOneKK21stepwise: Stepwise Variant of the KK21 Outcome-Weighted Sequential Matching Design

Experimental Designs: Observational

Designs for already-observed (non-randomized) treatment assignments – the design object acts as a data container with matched-pair/blocking structure, but does not itself draw any randomization.

ObservationalDesign: A Fixed Observational (Non-Randomized) Design
ObservationalDesignBlocks: A Fixed Observational (Non-Randomized) Design With Blocks
ObservationalDesignMatching: A Fixed Observational (Non-Randomized) Matched-Pair Design

Design: Custom Extensions

Base classes for plugging in a user-defined assignment/drawing rule.

DesignFixedCustom: Internal base for user-defined fixed-design extensions
DesignCustomSequential: Internal base for user-defined sequential-design extensions

Design: Infrastructure

Abstract bases, capability components, and shared machinery – not typically constructed directly. See vignette(“backend-contracts”) and fix_design_hierarchy.md for the capability/component model these implement.

Design: An Abstract Experimental Design

Inference: Continuous Outcomes

InferenceContinKKGLMM: Linear Mixed Model Inference for KK Designs with Continuous Response
InferenceContinKKOLSIVWC: OLS IVWC Compound Inference for KK Designs
InferenceContinKKOLSOneLik: OLS Combined-Likelihood Inference for KK Designs
InferenceContinKKQuantileRegrIVWC: Quantile Regression Compound Estimator for KK Matching-on-the-Fly Designs
InferenceContinKKQuantileRegrOneLik: Quantile Regression Combined-Likelihood Compound Estimator for KK Designs (Continuous)
InferenceContinKKRobustRegrIVWC: Robust-Regression IVWC Compound Inference for KK Designs
InferenceContinKKRobustRegrOneLik: Robust-Regression Combined-Likelihood Inference for KK Designs
InferenceContinLin: Lin (2013) Covariate-Adjusted OLS Inference for Continuous Responses
InferenceContinOLS: OLS Inference for Continuous Responses
InferenceContinQuantileRegr: Quantile Regression Inference for Continuous Responses
InferenceContinRobustRegr: Robust (M/MM-Estimator) Regression Inference for Continuous Responses
InferenceBaiAdjustedTKK14: Bai Adjusted-t Mean-Difference Inference for KK14 Designs
InferenceBaiAdjustedTKK21: Bai Adjusted-t Mean-Difference Inference for KK21 Designs

Inference: Incidence (Binary) Outcomes

InferenceIncidBinomialIdentityRiskDiff: Binomial Identity Risk Difference Inference for Incidence Responses
InferenceIncidCMH: CMH Blocked Incidence Inference
InferenceIncidExactBinomial: Exact Binomial (McNemar-Type) Incidence Inference for Matched-Pair Designs
InferenceIncidExactFisher: Exact Fisher (Conditional Hypergeometric) Incidence Inference
InferenceIncidExactZhang: Exact Zhang Combined-Test Incidence Inference
InferenceIncidExtendedRobins: Extended Robins Blocked Incidence Inference
InferenceIncidGCompRiskDiff: G-Computation Risk-Difference Inference for Binary Responses
InferenceIncidGCompRiskRatio: G-Computation Risk-Ratio Inference for Binary Responses
InferenceIncidKKCondLogitGLMMIVWC: Conditional Logistic Plus GLMM IVWC Inference for KK Designs
InferenceIncidKKCondLogitGLMMOneLik: Conditional Logistic Plus GLMM Combined-Likelihood Inference for KK Designs
InferenceIncidKKCondLogitIVWC: Conditional Logistic IVWC Inference (KK Designs, Binary Response)
InferenceIncidKKCondLogitOneLik: One-Likelihood Conditional-Logistic Inference for KK Binary Designs
InferenceIncidKKGCompRiskDiff: G-Computation Risk-Difference Inference for KK Designs with Binary Responses
InferenceIncidKKGCompRiskRatio: G-Computation Risk-Ratio Inference for KK Designs with Binary Responses
InferenceIncidKKGEE: GEE Inference for KK Designs with Binary Response
InferenceIncidKKModifiedPoisson: Modified-Poisson Inference for KK Designs with Binary Responses
KKNewcombeRiskDiffIVWCSource: KK Newcombe Risk-Difference IVWC Inference for Binary Responses
InferenceIncidLogBinomial: Log-Binomial Regression Inference for Incidence Responses
InferenceIncidLogRegr: Logistic Regression Inference for Incidence Responses
InferenceIncidMiettinenNurminenRiskDiff: Miettinen-Nurminen Risk-Difference Inference for Binary Responses
InferenceIncidModifiedPoisson: Modified Poisson Regression Inference for Incidence Responses
InferenceIncidNewcombeRiskDiff: Newcombe Risk-Difference Inference for Binary Responses
InferenceIncidProbitRegr: Probit Regression Inference for Incidence Responses
InferenceIncidRiskDiff: Risk Difference Inference for Incidence Responses
InferenceIncidWald: Wald Incidence Inference

Inference: Count Outcomes

InferenceCountHurdleNegBin: Hurdle Negative Binomial Regression Inference for Count Responses
InferenceCountHurdlePoisson: Hurdle Poisson Regression Inference for Count Responses
InferenceCountKKCondPoissonOneLik: One-Likelihood Conditional-Poisson Inference for KK Count Designs
InferenceCountKKGLMM: GLMM Inference for KK Designs with Count Response
InferenceCountKKHurdlePoissonIVWC: KK Hurdle Poisson IVWC Inference for Count Responses
InferenceCountKKHurdlePoissonOneLik: KK Hurdle-Poisson Combined-Likelihood Inference for Count Responses
InferenceCountNegBin: Negative Binomial Regression Inference for Count Responses
InferenceCountPoisson: Poisson Regression Inference for Count Responses
InferenceCountPoissonKKGEE: GEE Inference for KK Designs with Count Response
InferenceCountQuasiPoisson: Quasi-Poisson Regression Inference for Count Responses
InferenceCountRobustPoisson: Robust (Sandwich-Variance) Poisson Regression Inference for Count Responses
InferenceCountZeroInflatedNegBin: Zero-Inflated Negative Binomial Regression Inference for Count Responses
InferenceCountZeroInflatedPoisson: Zero-Inflated Poisson Regression Inference for Count Responses

Inference: Proportion Outcomes

InferencePropBetaRegr: Beta Regression Inference for Proportion Responses
InferencePropFractionalLogit: Fractional Logit Inference for Proportion Responses
InferencePropGCompMeanDiff: G-Computation Mean-Difference Inference for Proportion Responses
InferencePropKKGEE: GEE Inference for KK Designs with Proportion Response
InferencePropKKGLMM: KK GLMM Inference for Proportion Responses
InferencePropKKQuantileRegrIVWC: Quantile Regression Compound Estimator for KK Matching-on-the-Fly Designs (Proportion Outcomes)
InferencePropKKQuantileRegrOneLik: Quantile Regression Combined-Likelihood Compound Estimator for KK Designs (Proportion)
InferencePropQuantileRegr: Quantile Regression Inference for Proportion Responses
InferencePropZeroOneInflatedBetaRegr: Zero/One-Inflated Beta Inference for Proportion Responses

Inference: Ordinal Outcomes

InferenceOrdinalAdjCatLogitRegr: Adjacent Category Logit Regression Inference for Ordinal Responses
InferenceOrdinalCauchitRegr: Cauchit Regression Inference for Ordinal Responses
InferenceOrdinalCloglogRegr: Cumulative Cloglog Inference for Ordinal Responses
InferenceOrdinalContRatioRegr: Continuation Ratio Regression Inference for Ordinal Responses
InferenceOrdinalGCompMeanDiff: G-Computation Mean-Difference Inference for Ordinal Responses
InferenceOrdinalJonckheereTerpstraTest: Jonckheere-Terpstra (JT) Test for Ordinal Responses
InferenceOrdinalKKCLMM: Ordinal KK CLMM (Proportional Odds / logit link)
InferenceOrdinalKKCLMMCauchit: Ordinal KK CLMM (Cauchit link)
InferenceOrdinalKKCLMMCloglog: Ordinal KK CLMM (Complementary log-log link)
InferenceOrdinalKKCLMMProbit: Ordinal KK CLMM (Probit link)
InferenceOrdinalKKCondAdjCatLogitRegr: Adjacent Category Logit Inference for KK Matching-on-the-fly Designs
InferenceOrdinalKKGEE: GEE Inference for KK Designs with Ordinal Response
InferenceOrdinalKKGLMM: GLMM Inference for KK Designs with Ordinal Response
InferenceOrdinalOrderedProbitRegr: Ordered Probit Regression Inference for Ordinal Responses
InferenceOrdinalPairedSignTest: Paired Sign Test Inference for KK Designs with Ordinal Response
InferenceOrdinalPartialProportionalOddsRegr: Partial Proportional-Odds Regression Inference for Ordinal Responses
InferenceOrdinalPropOddsRegr: Proportional Odds Regression Inference for Ordinal Responses
InferenceOrdinalRidit: Ridit Analysis for Ordinal Responses
InferenceOrdinalStereotypeLogitRegr: Stereotype Logit Regression Inference for Ordinal Responses

Inference: Survival Outcomes

InferenceSurvivalCoxPHRegr: Cox Proportional Hazards Regression Inference for Survival Responses
InferenceSurvivalDepCensTransformRegr: Dependent-Censoring Transformation Inference for Survival Responses
InferenceSurvivalGLMMWeibullFrailtyLoggammaIVWC: Clayton Copula / Standard Weibull Compound Inference for KK Designs
InferenceSurvivalGLMMWeibullFrailtyLoggammaOneLik: One-Likelihood Clayton-Copula Weibull AFT Inference for KK Survival Designs
InferenceSurvivalGLMMWeibullFrailtyNormalIVWC: Weibull Frailty IVWC Inference for KK Designs
InferenceSurvivalGLMMWeibullFrailtyNormalOneLik: Weibull Frailty Combined-Likelihood Inference for KK Designs
InferenceSurvivalGehanWilcox: Gehan-Wilcoxon (Peto-Prentice) Inference for Survival Data with Censoring
InferenceSurvivalKKLWACoxPHIVWC: LWA-style Marginal Cox IVWC Compound Inference for KK Designs
InferenceSurvivalKKLWACoxPHOneLik: LWA-style Marginal Cox Combined-Likelihood Inference for KK Designs
InferenceSurvivalKKRankRegrIVWC: Rank Regression Inference for Survival Responses under KK Designs
InferenceSurvivalKKStratCoxPHIVWC: Stratified Cox / Standard Cox Compound Inference for KK Designs
InferenceSurvivalKKStratCoxPHOneLik: Stratified Cox Combined-Likelihood Compound Inference for KK Designs
SurvivalKKWeibullMarginalSource: Marginal (Cluster-Robust) Weibull Inference for KK Matched-Pair Survival Designs
InferenceSurvivalKMDiff: Kaplan-Meier Median-Difference Inference for Survival Responses
InferenceSurvivalLogRank: Log-Rank Inference for Survival Data with Censoring
InferenceSurvivalRestrictedMeanDiff: Restricted Mean Survival Time (RMST) Difference Inference for Survival Responses
InferenceSurvivalStratCoxPHRegr: Stratified Cox PH Inference for Survival Responses
InferenceSurvivalWeibullRegr: Weibull AFT Inference for Survival Responses

Inference: Cross-Cutting / General

Estimators and coordinators that work across more than one response type, rather than being tied to a single outcome family.

InferenceAllKKMeanDiffIVWC: Mean-Difference IVWC Inference for KK Matching-on-the-Fly Designs
InferenceAllKKWilcoxIVWC: Non-parametric Wilcoxon-based Compound Inference for KK Matching-on-the-Fly Designs
InferenceAllSimpleAverageDiff: Simple Mean-Difference Inference for Continuous Responses
InferenceAllSimpleMeanDiffPooledVar: Simple Mean-Difference Inference with Pooled Variance
InferenceAllSimpleWilcox: Simple Wilcoxon Rank-Sum (Hodges-Lehmann) Inference
InferenceSuite: Inference Suite: Discover and Bundle Every Applicable Inference Class for a Design
print(<EDIInferenceSuiteResults>): Prints the results table from an InferenceSuite run_all_inference() call – the same table screen = TRUE prints during the call itself, so a user who assigned the return value and later types its name (or calls print() on it) sees a readable table rather than a raw nested list dump. The table itself is rendered by run_all_inference_format_pretty_table(): rows sorted by estimand, with a double rule under the header and a single rule between estimand groups and at the bottom, class names and estimand values shortened for display (never the underlying results_table values), and a cov_model letter-key legend appended when applicable – see that function's own documentation for the exact column-by-column rendering rules.
summary(<EDIInferenceSuiteResults>) print(<summary.EDIInferenceSuiteResults>): Summarizes an InferenceSuite run_all_inference() result: counts by status, the estimate range across status == "ok" classes, and how many reject at alpha.
EDI_COMPREHENSIVE_SLOW_PATHS: Comprehensive-test slow-path registry

Inference: Custom Extensions

Base classes for plugging in a user-defined asymptotic/bootstrap/randomization estimator.

InferenceCustomAsymp: Internal base for user-defined asymptotic inference extensions
InferenceCustomBoot: Internal base for user-defined bootstrap inference extensions
InferenceCustomRand: Internal base for user-defined randomization inference extensions

Inference: Infrastructure

Abstract bases, resampling/likelihood mixins, and matched-design (KK) family bases – not typically constructed directly. See vignette(“reproducibility”) for the resampling mixins’ RNG/seed conventions and vignette(“notation-glossary”) for shared symbols.

Inference: Inference for A Sequential Design
InferenceAsymp: Asymptotic Inference
InferenceAsympLik: Likelihood-Backed Asymptotic Inference
InferenceRand: Randomization-based Inference
InferenceRandBootstrap: Bootstrap Randomization Test Inference
InferenceRandBootstrapCI: Bootstrap Randomization Confidence Intervals
InferenceNonParamBootstrap: Bootstrap-based Inference
InferenceParamBootstrap: Parametric-Bootstrap-Capable Likelihood Inference
InferenceBayesianBootstrap: Bayesian Bootstrap-capable Inference
InferenceJackknife: Jackknife-based Inference
CountLikelihoodPlumbingSource: Count-Specific Likelihood Inference
kk_passthrough_compound_host_public: Internal Base Class for KK Matching-on-the-Fly Designs
InferenceMLEorKMSummaryTable: Inference for A Sequential Design
InferenceAbstractQuantileRandCI: Abstract mixin: Zhang combined randomisation CI for quantile regression
InferenceAbstractKKCondLogitGLMM: Abstract Conditional Logistic GLMM Inference
InferenceAbstractKKCondLogitGLMMOneLik: Abstract class for Conditional Logistic Combined-Likelihood Combined Inference
InferenceAbstractKKMarginalIncid: Abstract class for all-subject marginal incidence inference in KK designs
InferenceAbstractKKModifiedPoisson: Abstract class for all-subject modified-Poisson inference in KK designs
InferenceAbstractKKOrdinalCLMM: Abstract class for ordinal CLMM-based Inference in KK designs

Simulation Framework

Monte Carlo simulation for comparing designs and inference methods, and for computing the coverage_pval/size_pval calibration diagnostics documented in vignette(“validation-evidence”).

SimulationFramework: Simulation Framework for Experimental Designs and Inference Methods
SimulationFrameworkReport: Reporting class for SimulationFramework results
generate_covariate_dataset(): Generate Synthetic Simulation Covariates and Continuous Response
transform_cont_y_based_on_response_type(): Transform continuous latent signal to the response type scale

Backend: Continuous & Robust Regression Kernels

See vignette(“backend-contracts”) for the shared conventions these C++ kernels follow.

fast_ols_cpp(): Fast Ordinary Least Squares (OLS) Regression, Estimate-Only (C++ Backend)
fast_ols_with_var_cpp(): Fast Ordinary Least Squares (OLS) Regression with Variance (C++ Backend)
ols_hc2_post_fit_cpp(): Export of C++ function ols_hc2_post_fit_cpp

Backend: Binary/Incidence Regression Kernels

fast_logistic_regression(): Fast Logistic Regression, Estimate Only (R Wrapper)
fast_logistic_regression_cpp(): Fast Logistic Regression, Estimate Only (C++ Backend)
fast_logistic_regression_weighted_cpp(): Fast Weighted Logistic Regression, Estimate Only (C++ Backend)
fast_logistic_regression_with_var(): Fast Logistic Regression with Variance, Auto-Retrying on Separation (R Wrapper)
fast_logistic_regression_with_var_cpp(): Fast Logistic Regression with Targeted Variance (C++ Backend)
get_identity_binomial_regression_hessian_cpp(): Identity-Link (Risk-Difference) Binomial Regression Hessian, Standalone (C++)
get_identity_binomial_regression_score_cpp(): Identity-Link (Risk-Difference) Binomial Regression Score, Standalone (C++)
get_identity_binomial_regression_weighted_hessian_cpp(): Weighted Identity-Link (Risk-Difference) Binomial Regression Hessian, Standalone (C++)
get_identity_binomial_regression_weighted_score_cpp(): Weighted Identity-Link (Risk-Difference) Binomial Regression Score, Standalone (C++)
get_log_binomial_regression_hessian_cpp(): Log-Link (Relative-Risk) Binomial Regression Hessian, Standalone (C++)
get_log_binomial_regression_score_cpp(): Log-Link (Relative-Risk) Binomial Regression Score, Standalone (C++)
get_log_binomial_regression_weighted_hessian_cpp(): Weighted Log-Link (Relative-Risk) Binomial Regression Hessian, Standalone (C++)
get_log_binomial_regression_weighted_score_cpp(): Weighted Log-Link (Relative-Risk) Binomial Regression Score, Standalone (C++)
gcomp_logistic_point_estimate_cpp(): Fast G-Computation (Standardization) Point Estimate for Logistic Regression (C++)
gcomp_logistic_post_fit_cpp(): Export of C++ function gcomp_logistic_post_fit_cpp
gcomp_fractional_logit_point_estimate_cpp(): Fast G-Computation (Standardization) Point Estimate for a Logit-Link Model (C++)
mn_pvalue_cpp(): Export of C++ function mn_pvalue_cpp
newcombe_independent_ci_cpp(): Export of C++ function newcombe_independent_ci_cpp

Backend: Count Regression Kernels

fast_poisson_regression_cpp(): Fast Poisson Regression, Estimate-Only (C++ Backend)
fast_poisson_regression_weighted_cpp(): Fast Weighted Poisson Regression (C++ Backend)
fast_poisson_regression_with_var_cpp(): Fast Poisson Regression with Variance Calculation (C++ Backend)
fast_quasipoisson_regression_with_var_cpp(): Fast Quasi-Poisson Regression with Variance Calculation (C++ Backend)
fast_negbin_regression(): Fast Negative Binomial Regression, Estimate-Only (R Wrapper)
fast_negbin_regression_with_var(): Fast Negative Binomial Regression with Variance Calculation (R Wrapper)
fast_cpoisson_combined_with_var_cpp(): Fast Combined Conditional-Poisson + Poisson Regression for KK Matched-Pair/ Reservoir Designs, with Variance (C++ Backend)
get_cpoisson_combined_hessian_cpp(): Combined Conditional-Poisson/Poisson Hessian, Standalone (C++)
get_cpoisson_combined_score_cpp(): Combined Conditional-Poisson/Poisson Score, Standalone (C++)
get_negbin_regression_hessian_cpp(): Negative Binomial Regression Hessian, Standalone (C++)
get_negbin_regression_score_cpp(): Compute Negative Binomial Regression Score

Backend: Ordinal Regression Kernels

expand_adjacent_category_data_cpp(): Expand Ordinal Data into Stacked Binary Comparisons for Adjacent-Category Logit Regression (C++ Backend)
expand_continuation_ratio_data_cpp(): Expand Ordinal Data into Stacked Binary Comparisons for Continuation-Ratio Regression (C++ Backend)
exact_jonckheere_terpstra_pval_cpp(): Exact Two-Group Jonckheere-Terpstra Test via Full Randomization Enumeration (C++ Backend)
gcomp_ordinal_proportional_odds_post_fit_cpp(): Export of C++ function gcomp_ordinal_proportional_odds_post_fit_cpp
ordinal_gcomp_post_fit_cpp(): Fast G-Computation (Standardization) Point Estimate and Model-Based Inference for a Proportional-Odds Ordinal Model (C++)
get_ordinal_regression_hessian_cpp(): Proportional-Odds Ordinal Regression Hessian, Standalone (C++)
get_ordinal_regression_score_cpp(): Proportional-Odds Ordinal Regression Score, Standalone (C++)
get_stereotype_logit_hessian_cpp(): Stereotype Logit Regression Hessian, Standalone (C++)
get_stereotype_logit_score_cpp(): Compute Stereotype Logit Score

Backend: Survival Regression Kernels

fast_coxph_regression(): Fast Cox Proportional Hazards Regression (R Wrapper)
fast_coxph_regression_cpp(): Fast Cox Proportional Hazards Regression, One-Shot Fit (C++ Backend)
fast_coxph_regression_prebuilt_cpp(): Fast Cox Proportional Hazards Regression, Cache-Reusing Fit (C++ Backend)
fast_weibull_regression(): Fast Weibull AFT Regression (R Wrapper: Rcpp Backend or survival)
build_cox_data_cache_cpp(): Build a Reusable Unstratified Cox Data Cache (C++ Backend)
build_stratified_cox_data_cache_cpp(): Build a Reusable Stratified Cox Data Cache (C++ Backend)
compute_coxph_rand_bootstrap_cpp(): Randomization/Bootstrap Reference Distribution of the Treatment Log-Hazard-Ratio for a Treatment-Only Cox PH Model (C++ Backend, Single-Covariate)
get_weibull_regression_general_hessian_cpp(): Compute Weibull Regression Hessian (General Censoring)
get_weibull_regression_general_score_cpp(): Compute Weibull Regression Score (General Censoring)

Backend: Proportion/Beta Regression Kernels

fast_beta_regression(): Fast Beta Regression (R Wrapper)
fast_beta_regression_with_var(): Fast Beta Regression with Variance Calculation (R Wrapper)
get_beta_regression_hessian_cpp(): Beta Regression Hessian, Standalone (C++)
get_beta_regression_score_cpp(): Compute Beta Regression Score

Backend: Matched-Design (Pocock-Simon) Assignment

Backend: Math/Numeric Utilities

Scalar/vectorized special-function kernels used throughout the package’s likelihoods; see vignette(“backend-contracts”).

logit(): Logit (Log-Odds) Transform
inv_logit(): Inverse Logit (Logistic) Function
sample_mode(): Sample Mode
summary_glm_lean(): Lean GLM Summary (Skips Deviance Residual Quantiles)

Dispatch Policy Configuration

Runtime-tunable policies governing optimizer choice, cold/warm-start heuristics, and parallel/serial dispatch across inference classes. See each get_/set_ pair’s own documentation for the built-in defaults and override mechanism.

get_bootstrap_dispatch_policy(): Get the default bootstrap dispatch policy
get_cold_start_dispatch_policy(): Get the default cold-start dispatch policy
get_optimization_dispatch_policy(): Get the default optimization dispatch policy
get_parallel_dispatch_policy(): Get the default parallel dispatch policy
get_warm_start_dispatch_policy(): Get the default warm-start dispatch policy
set_cold_start_dispatch_policy(): Update the cold-start dispatch policy
set_optimization_dispatch_policy(): Update the optimization dispatch policy
set_parallel_dispatch_policy(): Update the parallel dispatch policy
set_warm_start_dispatch_policy(): Update the warm-start dispatch policy
set_num_cores(): Set the number of cores for parallelization
unset_num_cores(): Unset the number of cores and stop parallel clusters

Local Machine Tuning

Benchmark this machine and persist machine-specific overrides for the performance-policy defaults above. See vignette(“reproducibility”)’s “Machine-dependent performance defaults” section.

tune_EDI_for_this_machine(): Benchmark this machine and tune EDI's performance-policy defaults to it
get_local_EDI_optimization(): Show this machine's saved EDI tuning, if any
clear_local_EDI_optimization(): Delete this machine's saved EDI tuning and return to shipped defaults

Miscellaneous Utilities

check_package_installed(): Check Whether a Suggested Package Is Installed (Memoized)
create_model_matrix_from_features(): Build an Intercept-Free, Full-Rank Covariate Design Matrix from a Formula
edi_build_info_cpp(): Return EDI Build Information (C++ Backend)
robust_negbinreg(): Robust Negative Binomial Regression with Backward Column-Dropping Fallback
robust_survreg(): Robust Parametric Survival Regression from Response/Censoring Vectors
robust_survreg_with_surv_object(): Robust Parametric Survival Regression (AFT) with Warm-Start and Random-Restart Fallback
toggle_asserts(): Toggle the execution of assertions throughout the package