Every registered test includes parity and
method metadata from ggpower_tests(). Tests
marked approximation use large-sample or proxy formulas.
Result objects include notes explaining the kernel
used.
tests <- ggpower_tests()
approx <- tests[tests$parity == "approximation", c("id", "method", "module")]
knitr::kable(approx, row.names = FALSE)
| exact_correlation |
Fisher Z with exact-kernel TODO |
workspace |
| z_tetrachoric |
large-sample tetrachoric z approximation |
workspace |
Workspace approximations
exact_correlation |
Fisher-Z; exact small-sample kernel planned |
z_tetrachoric |
Large-sample Fisher-Z on tetrachoric
|
exact_mcnemar |
Discordant-pair binomial proxy |
z_logistic, z_poisson
|
Wald normal approximation |
wilcoxon_signed,
wilcoxon_mann_whitney
|
ARE-scaled t tests |
Biomarker approximations
roc_auc_one, roc_auc_two
|
Hanley-McNeil / DeLong-style normal |
diagnostic_acc |
Binomial normal; power = min(sens, spec) |
survival_logrank, cox_regression
|
Equal follow-up / events simplification |
discovery_fdr |
Independent tests + simplified BH |
Clinical approximations
rct_noninferiority_binary,
rct_equivalence_proportion
|
Normal proportion approximation |
simon_two_stage |
Fixed design; no optimal design search |
When exact enumeration is used
exact_fisher and exact_binomial use
enumeration when the outcome grid is small enough; large tables fall
back to normal approximations.