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This page answers, for each important model family, “how do I know
this implementation actually computes what its documentation claims?” It
is an index into the test suite (R/EDI/tests/testthat/),
not a restatement of it — every row below names the specific test
file(s) that check the corresponding
fast_*/Inference* implementation against
independent evidence, so a reader auditing correctness (or a maintainer
touching a kernel) knows exactly which test to run or extend. All file
paths are relative to R/EDI/tests/testthat/.
Four kinds of evidence appear throughout the suite, matching
fix_documentation.md’s validation-evidence categories:
stats::glm, survival::coxph,
MASS::glm.nb, betareg::betareg,
VGAM::vglm, ordinal::clm,
lme4::glmer, glmmTMB,
pscl::hurdle, geepack/multgee,
copula, gamlss.dist) on the same simulated or
real data, usually to a tight numerical tolerance.numDeriv) approximation at the same parameter vector,
independent of any reference package.SimulationFramework’s own built-in exact-binomial
calibration test (see below), not just a single point comparison.| EDI implementation | Validated against | Test file |
|---|---|---|
fast_ols_with_var_cpp |
stats::lm |
test-rcpp-fitting-equivalence.R |
fast_ols_with_var_cpp (real data) |
stats::lm on MASS::Boston |
test-rcpp-fitting-real-data.R |
fast_robust_regression_cpp (M/MM) |
MASS::rlm |
test-rcpp-fitting-equivalence.R,
test-rcpp-fitting-real-data.R (mtcars) |
InferenceContinKKRobustRegrOneLik/IVWC
(use_rcpp path) |
MASS::rlm fallback, and Rcpp-vs.-fallback
bootstrap-weighted-estimate agreement |
test-kk-robust-regr-use-rcpp.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_logistic_regression_with_var_cpp |
stats::glm(family=binomial) |
test-rcpp-fitting-equivalence.R,
test-fast_glm_outputs.R |
fast_probit_regression_with_var_cpp |
stats::glm(family=binomial(link="probit")) |
test-rcpp-fitting-equivalence.R;
InferenceIncidProbitRegr vs. stats::glm in
test-incidence-probit.R |
fast_log_binomial_regression_with_var_cpp |
stats::glm(family=binomial(link="log")) |
test-rcpp-fitting-equivalence.R |
fast_identity_binomial_regression_with_var_cpp |
stats::glm(family=binomial(link="identity")) |
test-rcpp-fitting-equivalence.R |
| Real-data check | stats::glm on MASS::birthwt |
test-rcpp-fitting-real-data.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_poisson_regression_with_var_cpp |
stats::glm(family=poisson) |
test-rcpp-fitting-equivalence.R,
test-poisson-delta-eta-step-halving.R (IRLS internals
incl. step-halving) |
fast_quasipoisson_regression_with_var_cpp |
stats::glm(family=quasipoisson) |
test-rcpp-fitting-equivalence.R |
fast_neg_bin_with_var_cpp |
MASS::glm.nb |
test-rcpp-fitting-equivalence.R,
test-negbin-gemv-gradient.R (fit + analytic-vs.-numerical
gradient), test-negbin-weighted.R |
fast_neg_bin_weighted_cpp |
MASS::glm.nb with case weights |
test-negbin-weighted.R |
fast_truncated_negbin_count_cpp |
glmmTMB’s truncated_nbinom2 |
test-custom-implementation-canonical-reductions.R |
fast_zinb_cpp |
glmmTMB zero-inflated NB |
test-rcpp-fitting-equivalence.R, real-data check on
glmmTMB::Salamanders in
test-rcpp-fitting-real-data.R |
fast_zero_augmented_poisson_cpp (hurdle/ZIP) |
glmmTMB |
test-rcpp-fitting-equivalence.R, real-data check on
glmmTMB::Salamanders |
fast_hurdle_negbin_with_var_cpp |
pscl::hurdle(dist="negbin") |
test-rcpp-fitting-equivalence.R |
| Real-data check | stats::glm(family=poisson)/MASS::glm.nb on
MASS::quine |
test-rcpp-fitting-real-data.R |
fast_cpoisson_combined_with_var_cpp (matched-pair +
reservoir) |
reduces to canonical GLM fits in single-component cases | test-custom-implementation-canonical-reductions.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_ordinal_regression_with_var_cpp (proportional
odds) |
ordinal::clm |
test-rcpp-fitting-equivalence.R; real-data check on
ordinal::wine in
test-rcpp-fitting-real-data.R |
fast_ordinal_probit_regression_with_var_cpp |
ordinal::clm(link="probit") |
test-rcpp-fitting-equivalence.R |
fast_ordinal_cloglog_regression_with_var_cpp |
ordinal::clm(link="cloglog") |
test-rcpp-fitting-equivalence.R |
fast_ordinal_cauchit_regression_with_var_cpp |
ordinal::clm(link="cauchit") |
test-rcpp-fitting-equivalence.R |
fast_adjacent_category_logit_with_var_cpp |
VGAM::vglm(family=acat) |
test-rcpp-fitting-equivalence.R |
fast_continuation_ratio_regression_with_var_cpp |
VGAM::vglm(family=cratio) |
test-rcpp-fitting-equivalence.R |
fast_stereotype_logit_with_var_cpp |
K=2 reduces to stats::glm(binomial); K=3 checked
against score-at-MLE and finite-difference Hessian |
test-rcpp-fitting-equivalence.R |
fast_ordinal_clmm/fast_ordinal_glmm_cpp |
buffer-reuse/equivalence checks | test-ordinal-glmm-alpha-buf.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_coxph_regression_cpp |
survival::coxph |
test-rcpp-fitting-equivalence.R; real-data check on
survival::lung in
test-rcpp-fitting-real-data.R; component-composition
regression in test-cox-component-composition.R |
fast_stratified_coxph_regression_cpp |
survival::coxph with strata() |
test-rcpp-fitting-equivalence.R |
| Cluster-robust Cox covariance | survival::coxph’s cluster-robust vcov |
test-coxph-robust-vcov.R |
fast_weibull_regression_general_cpp |
survival::survreg |
test-rcpp-fitting-equivalence.R,
test-weibull-general-censoring.R; real-data check on
survival::lung in
test-rcpp-fitting-real-data.R |
compute_weibull_rand_bootstrap_parallel_cpp |
reproduces survreg on the same bootstrap resamples |
test-brt-weibull-kernel-matches-reference.R |
InferenceSurvivalKKWeibullMarginal |
survreg with cluster-robust / no-covariate fits |
test-weibull-marginal.R |
| Weibull frailty | analytic score vs. numerical gradient; log-likelihood collapses to
plain survreg Weibull log-likelihood as the frailty SD
-> 0 |
test-weibull-frailty.R |
fast_gehan_wilcox_stats/martingale-residual kernel |
survival::survdiff(rho=1); canonical Peto-Prentice
weighted martingale residuals |
test-gehan-wilcox-fused-martingale.R; end-to-end
InferenceSurvivalGehanWilcox check in the same file |
fast_logrank_stats/martingale-residual kernel |
survival::survdiff; coxph martingale
residuals |
test-logrank-fused-martingale.R |
| Log-rank/Gehan-Wilcoxon under general censoring | consistency checks across censoring patterns | test-logrank-gehan-wilcox-general-censoring.R |
get_survival_stat_for_group/get_survival_stat_diff
(KM median) |
canonical survfit median, including exact-crossing,
tie, and non-estimable (returns NA, not Inf)
edge cases |
test-km-median-canonical.R |
| KM/RMST under general censoring | test-km-rmst-general-censoring.R |
|
fast_dep_cens_transform_optim_cpp |
rho=0 score matches two independent lognormal survreg
fits |
test-custom-implementation-canonical-reductions.R |
fast_clayton_weibull_aft_optim_cpp |
singleton-only case matches plain survreg Weibull; pair
score matches the copula package’s reference
likelihood |
test-custom-implementation-canonical-reductions.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_beta_regression_with_var_cpp/fast_beta_regression_mle |
betareg::betareg |
test-rcpp-fitting-equivalence.R; real-data check on
betareg::ReadingSkills in
test-rcpp-fitting-real-data.R |
fast_zero_one_inflated_beta_cpp |
factors into a betareg continuous submodel plus a
nnet::multinom inflation submodel; likelihood matches
gamlss.dist::dBEINF |
test-custom-implementation-canonical-reductions.R |
| EDI implementation | Validated against | Test file |
|---|---|---|
fast_log1pexp |
closed-form/limiting behavior, precision at extreme arguments | test-fast-log1pexp.R |
| Bartlett likelihood-ratio approximation | smoke-tested across families (InferenceCountPoisson,
InferenceCountNegBin,
InferenceContinKKOLSOneLik,
InferenceSurvivalWeibullRegr,
InferenceOrdinalPropOddsRegr,
InferenceCountZeroInflatedNegBin/Poisson,
InferenceCountHurdlePoisson) |
test-bartlett-lr-approx-smoke-families.R,
test-bartlett-lr-plumbing.R,
test-bartlett-lr-logit.R,
test-bartlett-lr-ols-exact.R |
Design-side
BlockingStructure/ClusterStructure
bootstrap-index generalization |
byte-identical (identical(), matched seeds) against
each real class’s pre-generalization output |
test-design-blocking-structure-bootstrap-golden.R,
test-design-cluster-structure-golden.R |
Merged DesignFixedGreedyDOptimal |
behavior-preservation against the pre-merge
DesignFixedAOptimal/DesignFixedDOptimal
classes |
test-greedy-d-optimal-merged.R |
Point-estimate-vs-reference-package equivalence (above) confirms a
single fit is numerically correct; it does not by itself confirm an
inference procedure’s coverage or type-I
error are correct, since a subtly wrong standard-error formula
can still pass an equivalence test on the point estimate alone.
SimulationFrameworkReport$summarize() closes that gap: for
any (design, inference) pair it reports
coverage_pval/size_pval — the exact two-sided
binomial-test p-value of “true coverage = 1 - alpha” (respectively “true
size = alpha”) over Nrep_W * Nrep_Y_w Monte Carlo
replications — so a calibration claim is itself a hypothesis test with a
controlled false-alarm rate, not an eyeballed point estimate (see
vignette("reproducibility")’s “Monte Carlo error” section
for why a single observed coverage rate near but not exactly at the
nominal level is expected, and how many replications are enough to
distinguish that from genuine miscalibration). Running
SimulationFramework$new(...)$run() followed by
SimulationFrameworkReport$new(sim)$summarize() for a given
(design_classes_and_params, inference_classes_and_params, response_type)
combination is the package’s built-in mechanism for producing this
evidence for a specific method on demand; no single pre-computed report
is checked into the repository as of this writing (unlike the
point-estimate equivalence tests above, which run on every
R CMD check).
This page indexes what the test suite already demonstrates; it is not
a claim that every documented method has independent package-to-package
validation evidence. Custom/composite estimators without an external
single-package analogue (e.g. the matched-pair-plus-reservoir combined
kernels, fast_cpoisson_combined_with_var_cpp and
fast_clogit_plus_glmm_cpp) are instead validated by the
closed-form-reduction and numerical-derivative methods described above,
since no independent reference package implements the exact combined
model to compare against directly.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.