Celeus engine validation report

This report is generated, not written by hand, and is byte-for-byte reproducible on the pinned engine (R 4.6.1 + a pinned dependency lockfile): it carries no timestamp, so re-running it and finding no diff is itself a demonstration of the determinism this product promises. Every dataset here is public and citable; no patient data is used anywhere.

What this is: evidence that the engine computes the right numbers and steers to a defendable test, on well-known public datasets, checked two independent ways. What this is NOT: a substitute for review by an independent statistician (the named next step, below), and NOT clinical decision support - Celeus is a research-statistics tool.

How to read the four tiers

Each result is checked at up to four levels of rigor:

1. Canonical agreement (all cases). The engine’s number equals an INDEPENDENT computation in base R / survival / lme4 / pROC, to a tolerance of 1e-06. This catches the class of bug that actually occurs - spec routing, coercion, NA handling, serialization - the statistics themselves are R’s.
2. Application-path agreement. The same equality through the full upload -> analyse -> report path, CI-gated in our automated test suite (not re-run here; asserted on every push).
3. Literature anchor (a subset). The engine’s number also equals a value published in a citable source (a paper, textbook, or the dataset’s own package vignette) - a checksum a script cannot accidentally regenerate wrong. Only some cases have a single citable published number; the rest are honestly labelled canonical-only, which is a real check, just not a literature one.
4. Certified reference value (a subset). The engine’s number agrees with a value certified by a standards body for that exact dataset: the NIST Statistical Reference Datasets for one-way analysis of variance. NIST certifies the degrees of freedom, sums of squares, mean squares, F statistic, R-squared and residual standard deviation; the engine is compared on the ones it reports (F, the degrees of freedom, and R-squared, which equals eta-squared in a one-way design). NIST does not certify a p-value, so a p-value is never shown as certified. Agreement is required to a stated number of significant digits per NIST difficulty level (lower 10, average 8, higher 3). The higher-difficulty data carry 13 constant leading digits, which use up most of the 15 to 16 significant digits a double-precision number holds, so any software can only agree there to about 3 or 4 digits.

Reproducibility stamp

field	value
R version	R version 4.6.1
pinned dependency lockfile md5	00dde6c5110046ae74c0cf51b3b69838
catalog version	c39-d3cd

Summary

• 168 cases across 39 catalog test types, 641 numeric comparisons total.
• 16 cases are additionally anchored to a published literature value and 11 carry a certified reference value (checked in this report when their data is present, otherwise in a separate external check whose last recorded run is shown below); 141 are canonical-only (labelled per-case below).
• 0 unexplained divergences (a non-zero count here fails the generator).
• Test selection: the correct test family was proposed in 129 of 150 evaluated cases (directly, or via a documented post-analysis follow-up - see below); the grader recommended a sound alternative in 64 of 150; the test actually used was itself graded “Not appropriate” in 0 of 150. (18 of 168 total cases skipped this evaluation - external-downloaded, fixture not present; see “Dataset sources” above.)
• Reproducibility is separately CI-gated, two ways. Over a 41-case builtin corpus: each analysis re-runs its emitted reproduce.R standalone, and re-analysing the package’s own bundled dataset must return every recorded number bit-for-bit. Across processes: the same analysis run in two independent operating-system processes must emit a byte-identical package - every file compared by hash, with nothing excluded. That proves reproducibility, not correctness - correctness is the tables below.

Dataset sources

Every case above draws on one of five source types:

source type	cases
bundled package (a CRAN/base-R package, ships with the pinned dependency set - no network)	128
published table (values entered from a table in the cited publication)	7
synthetic/illustrative (a documented worked example, NOT real patient data - honestly labelled)	15
externally downloaded (an opt-in network fetch, run weekly on a schedule - not part of this report’s own reproducibility check)	18
generated with a recorded seed (synthetic data for a large-sample case, labelled an independent computation on seeded synthetic data - never a published value)	0

External-sourced cases not run in THIS report

This report is generated network-free, to keep it byte-reproducible (see the reproducibility stamp above) - it never downloads external datasets. Those cases run on a separate weekly schedule instead:

case	dataset	method
heart-logistic_regression	UCI Heart Disease (Cleveland)	logistic_regression
cervical-hinselmann-diagnostic	UCI Cervical Cancer (Hinselmann vs Biopsy)	diagnostic_accuracy
cervical-schiller-diagnostic	UCI Cervical Cancer (Schiller vs Biopsy)	diagnostic_accuracy
cervical-citology-diagnostic	UCI Cervical Cancer (Citology vs Biopsy)	diagnostic_accuracy
cervical-hinselmann-vs-schiller-mcnemar	UCI Cervical Cancer (Hinselmann vs Schiller, two index tests on the same patients)	mcnemar
cervical-schiller-vs-citology-mcnemar	UCI Cervical Cancer (Schiller vs Citology, two index tests on the same patients)	mcnemar
power_plant-linear_regression	UCI Combined Cycle Power Plant	linear_regression
nist-SiRstv-one_way_anova	NIST StRD SiRstv (silicon resistivity, 5 instruments; 25 observations)	one_way_anova
nist-AtmWtAg-one_way_anova	NIST StRD AtmWtAg (atomic weight of silver, 2 instruments; 48 observations)	one_way_anova
nist-SmLs01-one_way_anova	NIST StRD SmLs01 (generated, 9 treatments; 189 observations)	one_way_anova
nist-SmLs02-one_way_anova	NIST StRD SmLs02 (generated, 9 treatments; 1809 observations)	one_way_anova
nist-SmLs03-one_way_anova	NIST StRD SmLs03 (generated, 9 treatments; 18009 observations)	one_way_anova
nist-SmLs04-one_way_anova	NIST StRD SmLs04 (generated, 9 treatments; 189 observations)	one_way_anova
nist-SmLs05-one_way_anova	NIST StRD SmLs05 (generated, 9 treatments; 1809 observations)	one_way_anova
nist-SmLs06-one_way_anova	NIST StRD SmLs06 (generated, 9 treatments; 18009 observations)	one_way_anova
nist-SmLs07-one_way_anova	NIST StRD SmLs07 (generated, 9 treatments; 189 observations)	one_way_anova
nist-SmLs08-one_way_anova	NIST StRD SmLs08 (generated, 9 treatments; 1809 observations)	one_way_anova
nist-SmLs09-one_way_anova	NIST StRD SmLs09 (generated, 9 treatments; 18009 observations)	one_way_anova

Numeric agreement (all cases vs an independent computation)

case	dataset (field)	Celeus	independent R	match
sleep-paired_t	datasets::sleep (p_value)	0.00283289	0.00283289	yes
sleep-paired_t	datasets::sleep (estimate)	-1.58	-1.58	yes
sleep-wilcoxon	datasets::sleep (p_value)	0.00390625	0.00390625	yes
sleep-wilcoxon	datasets::sleep (estimate)	-1.3	-1.3	yes
sleep-wilcoxon	datasets::sleep (conf_int)	-2.7, -0.9	-2.7, -0.9	yes
sleep-wilcoxon	datasets::sleep (effect_size.value)	-1	-1	yes
ToothGrowth-welch_t	datasets::ToothGrowth (p_value)	0.0606345	0.0606345	yes
ToothGrowth-kruskal_wallis	datasets::ToothGrowth (p_value)	1.47521e-09	1.47521e-09	yes
ToothGrowth-kruskal_wallis	datasets::ToothGrowth (effect_size.value)	0.689304	0.689304	yes
iris-welch_anova	datasets::iris (p_value)	1.50506e-28	1.50506e-28	yes
mtcars-pearson	datasets::mtcars (p_value)	1.29396e-10	1.29396e-10	yes
mtcars-pearson	datasets::mtcars (estimate)	-0.867659	-0.867659	yes
mtcars-spearman	datasets::mtcars (p_value)	1.48759e-11	1.48759e-11	yes
mtcars-linear_regression	datasets::mtcars (estimate)	-3.87783	-3.87783	yes
mtcars-linear_regression	datasets::mtcars (p_value)	1.11965e-06	1.11965e-06	yes
mtcars-logistic_regression	datasets::mtcars (estimate)	-8.08348	-8.08348	yes
mtcars-logistic_regression	datasets::mtcars (p_value)	0.00843381	0.00843381	yes
sex2-firth_logistic	logistf::sex2 (estimate)	3.09601	3.09601	yes
sex2-firth_logistic	logistf::sex2 (p_value)	0.00495187	0.00495187	yes
sex2-firth_logistic	logistf::sex2 (conf_int)	0.774568, 8.030290	0.774568, 8.030290	yes
sex2-firth_logistic	logistf::sex2 (n)	239	239	yes
sex2-logistic_regression	logistf::sex2 (estimate)	16.7342	16.7342	yes
sex2-logistic_regression	logistf::sex2 (p_value)	0.983299	0.983299	yes
mtcars-fisher_exact	datasets::mtcars (p_value)	0.472697	0.472697	yes
mtcars-fisher_exact	datasets::mtcars (measures_2x2.odds_ratio.value)	2	2	yes
tea-tasting-fisher_exact	Fisher’s tea-tasting experiment (8 cups) (p_value)	0.485714	0.485714	yes
tea-tasting-fisher_exact	Fisher’s tea-tasting experiment (8 cups) (measures_2x2.odds_ratio.value)	6.40831	6.40831	yes
tea-tasting-fisher_exact	Fisher’s tea-tasting experiment (8 cups) (measures_2x2.odds_ratio.ci)	0.211733, 621.934000	0.211733, 621.934000	yes
titanic-chi_square	carData::TitanicSurvival (p_value)	4.58992e-81	4.58992e-81	yes
birthwt-logistic_regression	MASS::birthwt (estimate)	-0.0389946	-0.0389946	yes
birthwt-logistic_regression	MASS::birthwt (p_value)	0.23344	0.23344	yes
sleepstudy-mixed_model	lme4::sleepstudy (estimate)	10.4673	10.4673	yes
sleepstudy-mixed_model	lme4::sleepstudy (conf_int)	7.20595, 13.72860	7.20595, 13.72860	yes
sleepstudy-mixed_model	lme4::sleepstudy (variance_components.sd)	24.7407000, 5.9221400, 0.0655512, 25.5918000	24.7407000, 5.9221400, 0.0655512, 25.5918000	yes
sleepstudy-mixed_model	lme4::sleepstudy (n)	180	180	yes
sleepstudy-mixed_model	lme4::sleepstudy (statistic)	45.853	45.853	yes
sleepstudy-mixed_model	lme4::sleepstudy (p_value)	3.26381e-06	3.26381e-06	yes
sleepstudy-mixed_model	lme4::sleepstudy (term_tests.df_resid.1)	17	17	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (statistic)	169.401	169.401	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (p_value)	6.4126e-27	6.4126e-27	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (term_tests.df_resid.1)	161	161	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (conf_int)	8.8791, 12.0555	8.8791, 12.0555	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (effect_size.value)	0.589309	0.589309	yes
sleepstudy-intercept-mixed_model	lme4::sleepstudy (effect_size.ci)	0.412846, 0.765358	0.412846, 0.765358	yes
shoes-mixed_model-balanced	MASS::shoes (statistic)	11.215	11.215	yes
shoes-mixed_model-balanced	MASS::shoes (p_value)	0.00853878	0.00853878	yes
shoes-mixed_model-balanced	MASS::shoes (conf_int)	0.133046, 0.686954	0.133046, 0.686954	yes
shoes-mixed_model-imbalanced	MASS::shoes (statistic)	5.98932	5.98932	yes
shoes-mixed_model-imbalanced	MASS::shoes (p_value)	0.0441764	0.0441764	yes
shoes-mixed_model-imbalanced	MASS::shoes (term_tests.df_resid.1)	7.0219	7.0219	yes
ergoStool-mixed_model	nlme::ergoStool (statistic)	22.3556	22.3556	yes
ergoStool-mixed_model	nlme::ergoStool (p_value)	3.93456e-07	3.93456e-07	yes
ergoStool-mixed_model	nlme::ergoStool (parameter)	3,24	3,24	yes
ergoStool-mixed_model	nlme::ergoStool (posthoc.comparisons.group1)	T1, T1, T1, T2, T2, T3	T1, T1, T1, T2, T2, T3	yes
ergoStool-mixed_model	nlme::ergoStool (posthoc.comparisons.group2)	T2, T3, T4, T3, T4, T4	T2, T3, T4, T3, T4, T4	yes
orthodont-mixed_model-conf90	nlme::Orthodont (statistic)	9.2921	9.2921	yes
orthodont-mixed_model-conf90	nlme::Orthodont (p_value)	0.00537506	0.00537506	yes
orthodont-mixed_model-conf90	nlme::Orthodont (conf_int)	1.02042, 3.62163	1.02042, 3.62163	yes
orthodont-mixed_model-conf90	nlme::Orthodont (variance_components.variance)	3.26678, 2.04946	3.26678, 2.04946	yes
orthodont-mixed_model-conf90	nlme::Orthodont (predictor_levels.level)	between, within	between, within	yes
chickweight-mixed_model	datasets::ChickWeight (statistic)	6.27384	6.27384	yes
chickweight-mixed_model	datasets::ChickWeight (p_value)	0.00117822	0.00117822	yes
chickweight-mixed_model	datasets::ChickWeight (term_tests.df_resid.1)	45.5218	45.5218	yes
chickweight-mixed_model	datasets::ChickWeight (cluster_sizes.min)	2	2	yes
chickweight-mixed_model	datasets::ChickWeight (cluster_sizes.max)	12	12	yes
chickweight-mixed_model	datasets::ChickWeight (posthoc.comparisons.group1)	D1, D1, D1, D2, D2, D3	D1, D1, D1, D2, D2, D3	yes
chickweight-mixed_model	datasets::ChickWeight (posthoc.comparisons.group2)	D2, D3, D4, D3, D4, D4	D2, D3, D4, D3, D4, D4	yes
sleepstudy-missing-mixed_model	lme4::sleepstudy (statistic)	161.918	161.918	yes
sleepstudy-missing-mixed_model	lme4::sleepstudy (p_value)	1.27852e-25	1.27852e-25	yes
sleepstudy-missing-mixed_model	lme4::sleepstudy (term_tests.df_resid.1)	150	150	yes
sleepstudy-missing-mixed_model	lme4::sleepstudy (n)	168	168	yes
airquality-mann_whitney-filtered	datasets::airquality (p_value)	6.10874e-05	6.10874e-05	yes
lung-kaplan_meier	survival::lung (status recoded 0/1) (p_value)	0.00131116	0.00131116	yes
lung-cox_ph	survival::lung (status recoded 0/1) (estimate)	1.01719	1.01719	yes
lung-cox_ph	survival::lung (status recoded 0/1) (p_value)	0.064591	0.064591	yes
lung-aft_weibull	survival::lung (status recoded 0/1) (estimate)	0.987818	0.987818	yes
lung-aft_weibull	survival::lung (status recoded 0/1) (p_value)	0.0781189	0.0781189	yes
mcnemar-disease-example	synthetic fixture from the McNemar’s test Wikipedia worked example (statistic)	20.6722	20.6722	yes
mcnemar-disease-example	synthetic fixture from the McNemar’s test Wikipedia worked example (p_value)	5.45009e-06	5.45009e-06	yes
respiratory-mcnemar	HSAUR::respiratory (baseline vs month-4 status) (statistic)	1.93939	1.93939	yes
respiratory-mcnemar	HSAUR::respiratory (baseline vs month-4 status) (p_value)	0.163734	0.163734	yes
haireye-chi_square	datasets::HairEyeColor (Hair x Eye, summed over Sex) (statistic)	138.29	138.29	yes
haireye-chi_square	datasets::HairEyeColor (Hair x Eye, summed over Sex) (parameter)	9	9	yes
haireye-chi_square	datasets::HairEyeColor (Hair x Eye, summed over Sex) (p_value)	2.32529e-25	2.32529e-25	yes
haireye-chi_square	datasets::HairEyeColor (Hair x Eye, summed over Sex) (effect_size.value)	0.279045	0.279045	yes
haireye-chi_square	datasets::HairEyeColor (Hair x Eye, summed over Sex) (effect_size.ci.1)	0.231149	0.231149	yes
haireye-chi_square-conf90	datasets::HairEyeColor (Hair x Eye, summed over Sex) (effect_size.value)	0.279045	0.279045	yes
haireye-chi_square-conf90	datasets::HairEyeColor (Hair x Eye, summed over Sex) (effect_size.ci.1)	0.239929	0.239929	yes
mtcars-chi_square-numeric-yates	datasets::mtcars (am, vs coded 0/1) (statistic)	0.347536	0.347536	yes
mtcars-chi_square-numeric-yates	datasets::mtcars (am, vs coded 0/1) (parameter)	1	1	yes
mtcars-chi_square-numeric-yates	datasets::mtcars (am, vs coded 0/1) (p_value)	0.555512	0.555512	yes
mtcars-chi_square-numeric-yates	datasets::mtcars (am, vs coded 0/1) (effect_size.value)	0.168345	0.168345	yes
mtcars-fisher_exact-cyl-am	datasets::mtcars (cyl 4/6/8 x am 0/1, numeric codes) (p_value)	0.0091047	0.0091047	yes
survey-fisher_exact-smoke-exer-missing	MASS::survey (Smoke x Exer, 1 answer missing) (p_value)	0.413845	0.413845	yes
survey-fisher_exact-smoke-exer-missing	MASS::survey (Smoke x Exer, 1 answer missing) (n)	236	236	yes
survey-fisher_exact-sex-hand-missing	MASS::survey (Sex x W.Hnd, answers missing) (p_value)	0.615836	0.615836	yes
survey-fisher_exact-sex-hand-missing	MASS::survey (Sex x W.Hnd, answers missing) (estimate)	0.688366	0.688366	yes
survey-fisher_exact-sex-hand-missing	MASS::survey (Sex x W.Hnd, answers missing) (conf_int.1)	0.214024	0.214024	yes
survey-fisher_exact-sex-hand-missing	MASS::survey (Sex x W.Hnd, answers missing) (conf_int.2)	2.08767	2.08767	yes
survey-fisher_exact-sex-hand-missing	MASS::survey (Sex x W.Hnd, answers missing) (n)	235	235	yes
survey-fisher_exact-sex-hand-conf99	MASS::survey (Sex x W.Hnd, answers missing) (p_value)	0.615836	0.615836	yes
survey-fisher_exact-sex-hand-conf99	MASS::survey (Sex x W.Hnd, answers missing) (estimate)	0.688366	0.688366	yes
survey-fisher_exact-sex-hand-conf99	MASS::survey (Sex x W.Hnd, answers missing) (conf_int.1)	0.149315	0.149315	yes
survey-fisher_exact-sex-hand-conf99	MASS::survey (Sex x W.Hnd, answers missing) (conf_int.2)	2.86022	2.86022	yes
survey-fisher_exact-sex-hand-conf99	MASS::survey (Sex x W.Hnd, answers missing) (effect_size.ci.1)	0	0	yes
tea-tasting-fisher_exact-greater	Fisher’s tea-tasting experiment (8 cups) (p_value)	0.242857	0.242857	yes
tea-tasting-fisher_exact-greater	Fisher’s tea-tasting experiment (8 cups) (estimate)	6.40831	6.40831	yes
tea-tasting-fisher_exact-greater	Fisher’s tea-tasting experiment (8 cups) (conf_int.1)	0.313569	0.313569	yes
convictions-fisher_exact-less	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (p_value)	0.000465181	0.000465181	yes
convictions-fisher_exact-less	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (estimate)	0.0469366	0.0469366	yes
convictions-fisher_exact-less	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (conf_int.1)	0	0	yes
convictions-fisher_exact-less	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (conf_int.2)	0.28496	0.28496	yes
convictions-fisher_exact-conf99	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (p_value)	0.000536724	0.000536724	yes
convictions-fisher_exact-conf99	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (estimate)	0.0469366	0.0469366	yes
convictions-fisher_exact-conf99	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (conf_int.1)	0.00138633	0.00138633	yes
convictions-fisher_exact-conf99	Fisher (1962) like-sex twins convictions, as printed in R’s ?fisher.test (conf_int.2)	0.578852	0.578852	yes
job-fisher_exact-rxc	Agresti (2002) job satisfaction by income, as printed in R’s ?fisher.test (p_value)	0.782685	0.782685	yes
job-fisher_exact-rxc	Agresti (2002) job satisfaction by income, as printed in R’s ?fisher.test (n)	96	96	yes
performance-mcnemar	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (statistic)	16.8178	16.8178	yes
performance-mcnemar	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (parameter)	1	1	yes
performance-mcnemar	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (p_value)	4.11456e-05	4.11456e-05	yes
performance-mcnemar	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (effect_size.value)	0.573333	0.573333	yes
performance-mcnemar	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (effect_size.ci)	0.434598, 0.752316	0.434598, 0.752316	yes
performance-mcnemar-conf90	Agresti (1990) presidential approval in two surveys, as printed in R’s ?mcnemar.test (effect_size.ci)	0.454029, 0.721548	0.454029, 0.721548	yes
anorexia-ancova	MASS::anorexia (statistic)	7.86808	7.86808	yes
anorexia-ancova	MASS::anorexia (parameter)	2,68	2,68	yes
anorexia-ancova	MASS::anorexia (p_value)	0.00084384	0.00084384	yes
anorexia-ancova	MASS::anorexia (effect_size.value)	0.187925	0.187925	yes
anorexia-ancova	MASS::anorexia (effect_size.ci)	0.0400448, 0.3403020	0.0400448, 0.3403020	yes
warpbreaks-poisson_regression	datasets::warpbreaks (estimate)	-0.205988	-0.205988	yes
warpbreaks-poisson_regression	datasets::warpbreaks (p_value)	6.48978e-05	6.48978e-05	yes
quine-negbin_regression	MASS::quine (estimate)	-0.569372	-0.569372	yes
quine-negbin_regression	MASS::quine (p_value)	0.00020458	0.00020458	yes
housing-ordinal_logistic	MASS::housing (expanded one row per Freq) (estimate)	0.566394	0.566394	yes
housing-ordinal_logistic	MASS::housing (expanded one row per Freq) (p_value)	6.22819e-08	6.22819e-08	yes
housing-ordinal_logistic	MASS::housing (expanded one row per Freq) (residual_deviance)	3479.15	3479.15	yes
warpbreaks-two_way_anova	datasets::warpbreaks (statistic)	4.18907	4.18907	yes
warpbreaks-two_way_anova	datasets::warpbreaks (parameter)	2,48	2,48	yes
warpbreaks-two_way_anova	datasets::warpbreaks (p_value)	0.0210442	0.0210442	yes
warpbreaks-two_way_anova	datasets::warpbreaks (term_tests.statistic)	3.76529, 8.49805, 4.18907	3.76529, 8.49805, 4.18907	yes
warpbreaks-two_way_anova	datasets::warpbreaks (term_tests.p_value)	0.058213000, 0.000692621, 0.021044200	0.058213000, 0.000692621, 0.021044200	yes
warpbreaks-two_way_anova	datasets::warpbreaks (term_tests.effect_size)	0.0727377, 0.2614940, 0.1486060	0.0727377, 0.2614940, 0.1486060	yes
warpbreaks-two_way_anova	datasets::warpbreaks (effect_size.ci)	0.00214424, 0.32360900	0.00214424, 0.32360900	yes
iris-multinomial_logistic	datasets::iris (estimate)	-9.74099	-9.74099	yes
iris-multinomial_logistic	datasets::iris (p_value)	0.411011	0.411011	yes
iris-multinomial_logistic	datasets::iris (residual_deviance)	23.8612	23.8612	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (estimate)	-0.205988	-0.205988	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (p_value)	6.48978e-05	6.48978e-05	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (conf_int)	-0.290952, -0.121272	-0.290951, -0.121272	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (effect_size.value)	0.813842	0.813842	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (effect_size.ci)	0.747552, 0.885793	0.747552, 0.885793	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (term_tests.statistic)	16.0388, 70.9416	16.0388, 70.9416	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (model_test.statistic)	86.9803	86.9803	yes
warpbreaks-poisson_regression-terms-conf90	datasets::warpbreaks (n)	54	54	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (estimate)	-0.456627	-0.456627	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (p_value)	1.23972e-08	1.23972e-08	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (conf_int)	-0.614809, -0.300297	-0.614808, -0.300296	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (effect_size.value)	0.633416	0.633416	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (effect_size.ci)	0.540744, 0.740598	0.540745, 0.740599	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (term_tests.statistic)	33.2735	33.2735	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (model_test.statistic)	33.2735	33.2735	yes
warpbreaks-L-poisson_regression-small	datasets::warpbreaks (tension L only) (n)	18	18	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (estimate)	-0.456627	-0.456627	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (p_value)	0.00834961	0.00834961	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (conf_int)	-0.796543, -0.117106	-0.796542, -0.117106	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (effect_size.value)	0.633416	0.633416	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (effect_size.ci)	0.450885, 0.889491	0.450885, 0.889491	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (term_tests.statistic)	6.93779	6.93779	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (model_test.statistic)	5.89443	5.89443	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (dispersion.theta)	9.43994	9.43994	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (dispersion.lr_statistic)	42.005	42.005	yes
warpbreaks-L-negbin_regression-small	datasets::warpbreaks (tension L only) (n)	18	18	yes
insectsprays-poisson_regression	datasets::InsectSprays (estimate)	0.0558805	0.0558805	yes
insectsprays-poisson_regression	datasets::InsectSprays (p_value)	0.597189	0.597189	yes
insectsprays-poisson_regression	datasets::InsectSprays (conf_int)	-0.151361, 0.263523	-0.151360, 0.263522	yes
insectsprays-poisson_regression	datasets::InsectSprays (effect_size.value)	1.05747	1.05747	yes
insectsprays-poisson_regression	datasets::InsectSprays (effect_size.ci)	0.859537, 1.301510	0.859538, 1.301510	yes
insectsprays-poisson_regression	datasets::InsectSprays (term_tests.statistic)	310.713	310.713	yes
insectsprays-poisson_regression	datasets::InsectSprays (model_test.statistic)	310.713	310.713	yes
insectsprays-poisson_regression	datasets::InsectSprays (n)	72	72	yes
quine-negbin_regression-terms-conf90	MASS::quine (estimate)	-0.569372	-0.569372	yes
quine-negbin_regression-terms-conf90	MASS::quine (p_value)	0.00020458	0.00020458	yes
quine-negbin_regression-terms-conf90	MASS::quine (conf_int)	-0.829430, -0.310456	-0.829429, -0.310456	yes
quine-negbin_regression-terms-conf90	MASS::quine (effect_size.value)	0.565881	0.565881	yes
quine-negbin_regression-terms-conf90	MASS::quine (effect_size.ci)	0.436298, 0.733112	0.436299, 0.733112	yes
quine-negbin_regression-terms-conf90	MASS::quine (term_tests.statistic)	13.05860, 0.25004, 11.97920, 2.52498	13.05860, 0.25004, 11.97920, 2.52498	yes
quine-negbin_regression-terms-conf90	MASS::quine (model_test.statistic)	25.1159	25.1159	yes
quine-negbin_regression-terms-conf90	MASS::quine (dispersion.theta)	1.27489	1.27489	yes
quine-negbin_regression-terms-conf90	MASS::quine (dispersion.lr_statistic)	1192.03	1192.03	yes
quine-negbin_regression-terms-conf90	MASS::quine (n)	146	146	yes
insurance-poisson_regression	MASS::Insurance (estimate)	-0.438219	-0.438219	yes
insurance-poisson_regression	MASS::Insurance (p_value)	2.02014e-24	2.02014e-24	yes
insurance-poisson_regression	MASS::Insurance (conf_int)	-0.522707, -0.354241	-0.522707, -0.354241	yes
insurance-poisson_regression	MASS::Insurance (effect_size.value)	0.645185	0.645185	yes
insurance-poisson_regression	MASS::Insurance (effect_size.ci)	0.592913, 0.701706	0.592914, 0.701706	yes
insurance-poisson_regression	MASS::Insurance (term_tests.statistic)	803.439, 938.502, 2373.430	803.439, 938.502, 2373.430	yes
insurance-poisson_regression	MASS::Insurance (model_test.statistic)	4115.37	4115.37	yes
insurance-poisson_regression	MASS::Insurance (n)	64	64	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (estimate)	0.00414617	0.00414617	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (p_value)	0.185332	0.185332	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (conf_int)	-0.00198351, 0.01027520	-0.00198351, 0.01027520	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (effect_size.value)	1.00415	1.00415	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (effect_size.ci)	0.998018, 1.010330	0.998018, 1.010330	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (term_tests.statistic)	1.75771, 178.60700	1.75771, 178.60700	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (model_test.statistic)	179.596	179.596	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (dispersion.theta)	0.642025	0.642025	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (dispersion.lr_statistic)	8647900	8647900	yes
txhousing-negbin_regression-missing	ggplot2::txhousing (n)	8034	8034	yes
wine-ordinal_logistic	ordinal::wine (estimate)	2.50307	2.5031	yes
wine-ordinal_logistic	ordinal::wine (p_value)	2.19493e-06	2.19461e-06	yes
wine-ordinal_logistic	ordinal::wine (conf_int)	1.50976, 3.59521	1.50976, 3.59523	yes
wine-ordinal_logistic	ordinal::wine (effect_size.value)	12.22	12.2203	yes
wine-ordinal_logistic	ordinal::wine (effect_size.ci)	4.52565, 36.42340	4.52566, 36.42390	yes
wine-ordinal_logistic	ordinal::wine (term_tests.statistic)	26.928, 11.043	26.928, 11.043	yes
wine-ordinal_logistic	ordinal::wine (model_test.statistic)	34.4543	34.4543	yes
wine-ordinal_logistic	ordinal::wine (thresholds.estimate)	-1.34437, 1.25080, 3.46687, 5.00639	-1.34438, 1.25081, 3.46689, 5.00640	yes
wine-ordinal_logistic	ordinal::wine (n)	72	72	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (estimate)	2.50307	2.5031	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (p_value)	2.19493e-06	2.19461e-06	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (conf_int)	1.50976, 3.59521	1.50976, 3.59523	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (effect_size.value)	12.22	12.2203	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (effect_size.ci)	4.52565, 36.42340	4.52566, 36.42390	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (term_tests.statistic)	26.928, 11.043	26.928, 11.043	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (model_test.statistic)	34.4543	34.4543	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (thresholds.estimate)	-1.34437, 1.25080, 3.46687, 5.00639	-1.34438, 1.25081, 3.46689, 5.00640	yes
wine-ordinal_logistic-integer-outcome	ordinal::wine (rating as integers 1-5) (n)	72	72	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (estimate)	2.50307	2.5031	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (p_value)	2.19493e-06	2.19461e-06	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (conf_int)	1.50976, 3.59521	1.50976, 3.59523	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (effect_size.value)	12.22	12.2203	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (effect_size.ci)	4.52565, 36.42340	4.52566, 36.42390	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (term_tests.statistic)	26.928, 11.043	26.928, 11.043	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (model_test.statistic)	34.4543	34.4543	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (thresholds.estimate)	-1.34437, 1.25080, 3.46687, 5.00639	-1.34438, 1.25081, 3.46689, 5.00640	yes
wine-ordinal_logistic-text-level-orders	ordinal::wine (rating as text labels) (n)	72	72	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (estimate)	2.80486	2.80485	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (p_value)	0.0139945	0.0139946	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (conf_int)	0.750324, 5.344450	0.750135, 5.344360	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (effect_size.value)	16.5247	16.5246	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (effect_size.ci)	2.11769, 209.44200	2.11729, 209.42300	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (term_tests.statistic)	7.49456	7.49456	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (model_test.statistic)	7.49456	7.49456	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (thresholds.estimate)	-1.17623, 0.18171, 1.66304, 3.40810	-1.176210, 0.181715, 1.663040, 3.408100	yes
wine-ordinal_logistic-small	ordinal::wine (judges 1 and 2) (n)	16	16	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (estimate)	2.40768	2.40767	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (p_value)	6.03737e-06	6.03773e-06	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (conf_int)	1.40669, 3.50552	1.40669, 3.50554	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (effect_size.value)	11.1082	11.1081	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (effect_size.ci)	4.08244, 33.29880	4.08242, 33.29950	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (term_tests.statistic)	24.2764, 10.8245	24.2764, 10.8245	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (model_test.statistic)	31.4648	31.4648	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (thresholds.estimate)	-1.23001, 1.13719, 3.38253, 4.92260	-1.23002, 1.13719, 3.38253, 4.92259	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (n)	69	69	yes
wine-ordinal_logistic-missing	ordinal::wine (rating removed in rows 1, 10 and 20) (rows_dropped)	3	3	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (estimate)	0.566394	0.566394	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (p_value)	6.22819e-08	6.22818e-08	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (conf_int)	0.394516, 0.738841	0.394516, 0.738841	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (effect_size.value)	1.7619	1.7619	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (effect_size.ci)	1.48367, 2.09351	1.48367, 2.09351	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (term_tests.statistic)	108.2390, 55.9101, 14.3062	108.2390, 55.9101, 14.3062	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (model_test.statistic)	169.728	169.728	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (thresholds.estimate)	-0.496135, 0.690708	-0.496135, 0.690708	yes
housing-ordinal_logistic-terms-conf90	MASS::housing (expanded one row per Freq) (n)	1681	1681	yes
wvs-ordinal_logistic-po-violated	carData::WVS (estimate)	0.176369	0.17637	yes
wvs-ordinal_logistic-po-violated	carData::WVS (p_value)	0.000870167	0.000870094	yes
wvs-ordinal_logistic-po-violated	carData::WVS (conf_int)	0.0725805, 0.2802400	0.0725805, 0.2802400	yes
wvs-ordinal_logistic-po-violated	carData::WVS (effect_size.value)	1.19288	1.19288	yes
wvs-ordinal_logistic-po-violated	carData::WVS (effect_size.ci)	1.07528, 1.32345	1.07528, 1.32345	yes
wvs-ordinal_logistic-po-violated	carData::WVS (term_tests.statistic)	11.09630, 5.43355, 4.51792, 250.88100, 51.11980	11.09630, 5.43355, 4.51792, 250.88100, 51.11980	yes
wvs-ordinal_logistic-po-violated	carData::WVS (model_test.statistic)	337.784	337.784	yes
wvs-ordinal_logistic-po-violated	carData::WVS (thresholds.estimate)	0.729764, 2.532480	0.729769, 2.532480	yes
wvs-ordinal_logistic-po-violated	carData::WVS (n)	5381	5381	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (estimate)	-9.74099	-9.74099	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (p_value)	0.411011	0.411011	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (conf_int)	-29.23030, 9.74834	-29.23030, 9.74834	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (effect_size.value)	5.88224e-05	5.88224e-05	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (effect_size.ci)	2.02039e-13, 1.71258e+04	2.02039e-13, 1.71258e+04	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (term_tests.statistic)	9.62472, 158.20700	9.62472, 158.20700	yes
iris-multinomial_logistic-terms-conf90	datasets::iris (residual_deviance)	23.8612	23.8612	yes
wvs-multinomial_logistic	carData::WVS (estimate)	0.24345	0.24345	yes
wvs-multinomial_logistic	carData::WVS (p_value)	0.00231626	0.00231626	yes
wvs-multinomial_logistic	carData::WVS (conf_int)	0.0868198, 0.4000810	0.0868198, 0.4000810	yes
wvs-multinomial_logistic	carData::WVS (effect_size.value)	1.27564	1.27564	yes
wvs-multinomial_logistic	carData::WVS (effect_size.ci)	1.09070, 1.49195	1.09070, 1.49195	yes
wvs-multinomial_logistic	carData::WVS (term_tests.statistic)	605.9050, 30.9372	605.9050, 30.9372	yes
wvs-multinomial_logistic	carData::WVS (residual_deviance)	13935.6	13935.6	yes
mtcars-factors_continuous	datasets::mtcars (estimate)	-3.38279	-3.38279	yes
mtcars-factors_continuous	datasets::mtcars (p_value)	0.00277724	0.00277724	yes
birthwt-factors_binary	MASS::birthwt (race as a factor) (estimate)	-0.029549	-0.029549	yes
birthwt-factors_binary	MASS::birthwt (race as a factor) (p_value)	0.424895	0.424895	yes
ovarian-factors_survival	survival::ovarian (estimate)	1.13294	1.13294	yes
ovarian-factors_survival	survival::ovarian (p_value)	0.00777225	0.00777225	yes
PlantGrowth-one_way_anova	datasets::PlantGrowth (statistic)	4.84609	4.84609	yes
PlantGrowth-one_way_anova	datasets::PlantGrowth (parameter)	2,27	2,27	yes
PlantGrowth-one_way_anova	datasets::PlantGrowth (p_value)	0.01591	0.01591	yes
PlantGrowth-one_way_anova	datasets::PlantGrowth (effect_size.value)	0.264148	0.264148	yes
ToothGrowth-student_t	datasets::ToothGrowth (statistic)	1.91527	1.91527	yes
ToothGrowth-student_t	datasets::ToothGrowth (p_value)	0.0603934	0.0603934	yes
ToothGrowth-student_t	datasets::ToothGrowth (estimate)	3.7	3.7	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (statistic)	1.91527	1.91527	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (p_value)	0.0303173	0.0303173	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (estimate)	3.7	3.7	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (parameter)	55.3094	55.3094	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (conf_int.1)	0.468269	0.468269	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (effect_size.value)	0.49452	0.49452	yes
ToothGrowth-welch_t-greater	datasets::ToothGrowth (effect_size.ci.1)	0.0610971	0.0610971	yes
ToothGrowth-welch_t-conf90	datasets::ToothGrowth (statistic)	1.91527	1.91527	yes
ToothGrowth-welch_t-conf90	datasets::ToothGrowth (p_value)	0.0606345	0.0606345	yes
ToothGrowth-welch_t-conf90	datasets::ToothGrowth (estimate)	3.7	3.7	yes
ToothGrowth-welch_t-conf90	datasets::ToothGrowth (parameter)	55.3094	55.3094	yes
ToothGrowth-welch_t-conf90	datasets::ToothGrowth (conf_int)	0.468269, 6.931730	0.468269, 6.931730	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (statistic)	1.91527	1.91527	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (p_value)	0.0603934	0.0603934	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (estimate)	3.7	3.7	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (parameter)	58	58	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (conf_int)	0.47082, 6.92918	0.47082, 6.92918	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (effect_size.value)	0.49452	0.49452	yes
ToothGrowth-student_t-cohens_d	datasets::ToothGrowth (effect_size.ci)	0.0610971, 0.9237640	0.0610971, 0.9237640	yes
ToothGrowth-student_t-less	datasets::ToothGrowth (statistic)	1.91527	1.91527	yes
ToothGrowth-student_t-less	datasets::ToothGrowth (p_value)	0.969803	0.969803	yes
ToothGrowth-student_t-less	datasets::ToothGrowth (estimate)	3.7	3.7	yes
ToothGrowth-student_t-less	datasets::ToothGrowth (parameter)	58	58	yes
ToothGrowth-student_t-less	datasets::ToothGrowth (conf_int.2)	6.92918	6.92918	yes
sleep-paired_t-less	datasets::sleep (statistic)	-4.06213	-4.06213	yes
sleep-paired_t-less	datasets::sleep (p_value)	0.00141645	0.00141645	yes
sleep-paired_t-less	datasets::sleep (estimate)	-1.58	-1.58	yes
sleep-paired_t-less	datasets::sleep (parameter)	9	9	yes
sleep-paired_t-less	datasets::sleep (conf_int.2)	-0.866995	-0.866995	yes
sleep-paired_t-less	datasets::sleep (effect_size.value)	-1.28456	-1.28456	yes
sleep-paired_t-less	datasets::sleep (effect_size.ci.2)	-0.54464	-0.54464	yes
sleep-paired_t-conf99	datasets::sleep (statistic)	-4.06213	-4.06213	yes
sleep-paired_t-conf99	datasets::sleep (p_value)	0.00283289	0.00283289	yes
sleep-paired_t-conf99	datasets::sleep (estimate)	-1.58	-1.58	yes
sleep-paired_t-conf99	datasets::sleep (parameter)	9	9	yes
sleep-paired_t-conf99	datasets::sleep (conf_int)	-2.844050, -0.315948	-2.844050, -0.315948	yes
sleep-paired_t-conf99	datasets::sleep (effect_size.value)	-1.28456	-1.28456	yes
sleep-paired_t-conf99	datasets::sleep (effect_size.ci)	-2.401070, -0.164067	-2.401070, -0.164067	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (statistic)	-4.27218	-4.27218	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (p_value)	0.00207365	0.00207365	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (estimate)	-1.71667	-1.71667	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (parameter)	9	9	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (conf_int)	-2.625660, -0.807676	-2.625660, -0.807676	yes
sleep-paired_t-mean-imputed	datasets::sleep (one value removed) (n)	10	10	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (statistic)	-3.71673	-3.71673	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (p_value)	0.00589906	0.00589906	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (estimate)	-1.61111	-1.61111	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (parameter)	8	8	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (conf_int)	-2.610710, -0.611514	-2.610710, -0.611514	yes
sleep-paired_t-missing	datasets::sleep (one value removed) (n)	9	9	yes
sleep-wilcoxon-conf90	datasets::sleep (statistic)	0	0	yes
sleep-wilcoxon-conf90	datasets::sleep (p_value)	0.00390625	0.00390625	yes
sleep-wilcoxon-conf90	datasets::sleep (estimate)	-1.3	-1.3	yes
sleep-wilcoxon-conf90	datasets::sleep (conf_int)	-2.1, -1.0	-2.1, -1.0	yes
sleep-wilcoxon-conf90	datasets::sleep (n_zero_differences)	1	1	yes
depression-wilcoxon_signed_rank-greater	Hamilton depression scale, 9 patients (stats::wilcox.test help example) (statistic)	40	40	yes
depression-wilcoxon_signed_rank-greater	Hamilton depression scale, 9 patients (stats::wilcox.test help example) (p_value)	0.0195312	0.0195312	yes
depression-wilcoxon_signed_rank-greater	Hamilton depression scale, 9 patients (stats::wilcox.test help example) (estimate)	0.46	0.46	yes
depression-wilcoxon_signed_rank-greater	Hamilton depression scale, 9 patients (stats::wilcox.test help example) (conf_int.1)	0.175	0.175	yes
permeability-mann_whitney-greater	chorioamnion permeability, 10 vs 5 (stats::wilcox.test help example) (statistic)	35	35	yes
permeability-mann_whitney-greater	chorioamnion permeability, 10 vs 5 (stats::wilcox.test help example) (p_value)	0.127206	0.127206	yes
permeability-mann_whitney-greater	chorioamnion permeability, 10 vs 5 (stats::wilcox.test help example) (estimate)	0.305	0.305	yes
permeability-mann_whitney-greater	chorioamnion permeability, 10 vs 5 (stats::wilcox.test help example) (conf_int.1)	-0.08	-0.08	yes
mtcars-welch_t-numeric-group	datasets::mtcars (am coded 0/1) (statistic)	-3.76712	-3.76712	yes
mtcars-welch_t-numeric-group	datasets::mtcars (am coded 0/1) (p_value)	0.00137364	0.00137364	yes
mtcars-welch_t-numeric-group	datasets::mtcars (am coded 0/1) (estimate)	-7.24494	-7.24494	yes
mtcars-welch_t-numeric-group	datasets::mtcars (am coded 0/1) (parameter)	18.3323	18.3323	yes
mtcars-welch_t-numeric-group	datasets::mtcars (am coded 0/1) (conf_int)	-11.28020, -3.20968	-11.28020, -3.20968	yes
mtcars-mann_whitney-ties	datasets::mtcars (am coded 0/1) (statistic)	42	42	yes
mtcars-mann_whitney-ties	datasets::mtcars (am coded 0/1) (p_value)	0.00115901	0.00115901	yes
mtcars-mann_whitney-ties	datasets::mtcars (am coded 0/1) (estimate)	-6.8	-6.8	yes
mtcars-mann_whitney-ties	datasets::mtcars (am coded 0/1) (conf_int)	-12.6, -1.7	-12.6, -1.7	yes
chickwts-mann_whitney-hl	datasets::chickwts (horsebean vs linseed) (statistic)	20	20	yes
chickwts-mann_whitney-hl	datasets::chickwts (horsebean vs linseed) (p_value)	0.00714456	0.00714456	yes
chickwts-mann_whitney-hl	datasets::chickwts (horsebean vs linseed) (estimate)	-60.5	-60.5	yes
chickwts-mann_whitney-hl	datasets::chickwts (horsebean vs linseed) (conf_int)	-105, -12	-105, -12	yes
chickwts-mann_whitney-hl-conf99	datasets::chickwts (horsebean vs linseed) (statistic)	20	20	yes
chickwts-mann_whitney-hl-conf99	datasets::chickwts (horsebean vs linseed) (p_value)	0.00714456	0.00714456	yes
chickwts-mann_whitney-hl-conf99	datasets::chickwts (horsebean vs linseed) (estimate)	-60.5	-60.5	yes
chickwts-mann_whitney-hl-conf99	datasets::chickwts (horsebean vs linseed) (conf_int)	-120, -1	-120, -1	yes
chickwts-mann_whitney-hl-conf99	datasets::chickwts (horsebean vs linseed) (conf_level_achieved)	0.991043	0.991043	yes
anorexia-FT-paired_t	MASS::anorexia (family therapy arm) (statistic)	-4.18491	-4.18491	yes
anorexia-FT-paired_t	MASS::anorexia (family therapy arm) (p_value)	0.000700253	0.000700253	yes
anorexia-FT-paired_t	MASS::anorexia (family therapy arm) (estimate)	-7.26471	-7.26471	yes
anorexia-FT-paired_t	MASS::anorexia (family therapy arm) (parameter)	16	16	yes
anorexia-FT-paired_t	MASS::anorexia (family therapy arm) (conf_int)	-10.9447, -3.5847	-10.9447, -3.5847	yes
anorexia-FT-wilcoxon_signed_rank	MASS::anorexia (family therapy arm) (statistic)	11	11	yes
anorexia-FT-wilcoxon_signed_rank	MASS::anorexia (family therapy arm) (p_value)	0.000839233	0.000839233	yes
anorexia-FT-wilcoxon_signed_rank	MASS::anorexia (family therapy arm) (estimate)	-7.65	-7.65	yes
anorexia-FT-wilcoxon_signed_rank	MASS::anorexia (family therapy arm) (conf_int)	-11.20, -3.45	-11.20, -3.45	yes
diamonds-welch_t-large	ggplot2::diamonds (Ideal vs Premium cut) (statistic)	-24.9179	-24.9179	yes
diamonds-welch_t-large	ggplot2::diamonds (Ideal vs Premium cut) (p_value)	1.7189e-135	1.7189e-135	yes
diamonds-welch_t-large	ggplot2::diamonds (Ideal vs Premium cut) (estimate)	-1126.72	-1126.72	yes
diamonds-welch_t-large	ggplot2::diamonds (Ideal vs Premium cut) (parameter)	26552.2	26552.2	yes
diamonds-welch_t-large	ggplot2::diamonds (Ideal vs Premium cut) (conf_int)	-1215.34, -1038.09	-1215.34, -1038.09	yes
VerbAgg-mixed_logistic	lme4::VerbAgg (estimate)	matches refit (not frozen)	(independent refit)	yes
VerbAgg-mixed_logistic	lme4::VerbAgg (p_value)	matches refit (not frozen)	(independent refit)	yes
VerbAgg-mixed_logistic	lme4::VerbAgg (conf_int)	matches refit (not frozen)	(independent refit)	yes
VerbAgg-mixed_logistic	lme4::VerbAgg (effect_size.ci)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (estimate)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (p_value)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (conf_int)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (effect_size.ci)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (term_tests.statistic)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (posthoc.comparisons.group1)	matches refit (not frozen)	(independent refit)	yes
cbpp-mixed_logistic-conf99	lme4::cbpp (posthoc.comparisons.group2)	matches refit (not frozen)	(independent refit)	yes
bacteria-slope-mixed_logistic	MASS::bacteria (estimate)	matches refit (not frozen)	(independent refit)	yes
bacteria-slope-mixed_logistic	MASS::bacteria (p_value)	matches refit (not frozen)	(independent refit)	yes
epil-slope-mixed_poisson-conf90	MASS::epil (estimate)	matches refit (not frozen)	(independent refit)	yes
epil-slope-mixed_poisson-conf90	MASS::epil (p_value)	matches refit (not frozen)	(independent refit)	yes
epil-slope-mixed_poisson-conf90	MASS::epil (conf_int)	matches refit (not frozen)	(independent refit)	yes
epil-slope-mixed_poisson-conf90	MASS::epil (effect_size.ci)	matches refit (not frozen)	(independent refit)	yes
epil-mixed_poisson	MASS::epil (estimate)	matches refit (not frozen)	(independent refit)	yes
epil-mixed_poisson	MASS::epil (p_value)	matches refit (not frozen)	(independent refit)	yes
ergoStool-rm_anova	nlme::ergoStool (statistic)	22.3556	22.3556	yes
ergoStool-rm_anova	nlme::ergoStool (parameter)	1.94,15.5	1.94,15.5	yes
ergoStool-rm_anova	nlme::ergoStool (p_value)	3.00556e-05	3.00556e-05	yes
ergoStool-rm_anova	nlme::ergoStool (estimate)	0.459375	0.459375	yes
ergoStool-rm_anova	nlme::ergoStool (n)	36	36	yes
ergoStool-rm_anova	nlme::ergoStool (term_tests.effect_size)	0.736458	0.736458	yes
ergoStool-rm_anova	nlme::ergoStool (term_tests.effect_size_low)	0.50518	0.50518	yes
ergoStool-rm_anova	nlme::ergoStool (term_tests.effect_size_high)	0.836507	0.836507	yes
aSAH-roc_auc	pROC::aSAH (auc)	0.731369	0.731369	yes
aSAH-roc_compare	pROC::aSAH (s100b vs ndka) (auc1)	0.731369	0.731369	yes
aSAH-roc_compare	pROC::aSAH (s100b vs ndka) (auc2)	0.611958	0.611958	yes
aSAH-roc_compare	pROC::aSAH (s100b vs ndka) (auc_diff)	0.119411	0.119411	yes
aSAH-roc_compare	pROC::aSAH (s100b vs ndka) (roc_test_p)	0.164295	0.164295	yes
aSAH-roc_compare	pROC::aSAH (s100b vs ndka) (roc_test_stat)	1.39077	1.39077	yes
screening-diagnostic_accuracy	documented illustrative 2x2 screening fixture (sensitivity)	0.95	0.95	yes
screening-diagnostic_accuracy	documented illustrative 2x2 screening fixture (specificity)	0.9	0.9	yes
screening-diagnostic_accuracy	documented illustrative 2x2 screening fixture (ppv)	0.513514	0.513514	yes
screening-diagnostic_accuracy	documented illustrative 2x2 screening fixture (npv)	0.993865	0.993865	yes
screening-diagnostic_accuracy	documented illustrative 2x2 screening fixture (prevalence)	0.1	0.1	yes
davis-bland_altman	carData::Davis (measured vs self-reported weight) (bias)	0.601093	0.601093	yes
davis-bland_altman	carData::Davis (measured vs self-reported weight) (loa_lower)	-15.969	-15.969	yes
davis-bland_altman	carData::Davis (measured vs self-reported weight) (loa_upper)	17.1711	17.1711	yes
asah-case-control-roc	pROC::aSAH (outcome relabelled case/control) (auc)	0.731369	0.731369	yes
asah-case-control-roc	pROC::aSAH (outcome relabelled case/control) (sensitivity)	0.634146	0.634146	yes
asah-case-control-roc	pROC::aSAH (outcome relabelled case/control) (specificity)	0.805556	0.805556	yes
asah-level-order-roc	pROC::aSAH (category order Poor, Good; higher scores positive) (auc)	0.268631	0.268631	yes
asah-roc-conf90	pROC::aSAH (s100b, 90% interval) (auc_ci)	0.646397, 0.816341	0.646397, 0.816341	yes
asah-roc-conf99	pROC::aSAH (s100b, 99% interval) (auc_ci)	0.598303, 0.864434	0.598303, 0.864434	yes
asah-wfns-roc	pROC::aSAH (WFNS grade as an ordinal score, heavily tied) (auc)	0.823679	0.823679	yes
asah-wfns-vs-s100b-compare	pROC::aSAH (s100b vs WFNS grade) (roc_test_stat)	-2.20898	-2.20898	yes
asah-wfns-vs-s100b-compare	pROC::aSAH (s100b vs WFNS grade) (roc_test_p)	0.0271758	0.0271758	yes
asah-wfns-vs-s100b-compare	pROC::aSAH (s100b vs WFNS grade) (conf_int)	-0.1742140, -0.0104062	-0.1742140, -0.0104062	yes
asah-direction-differ-compare	pROC::aSAH (s100b vs ndka with its sign reversed, a constructed opposite-direction marker) (roc_test_p)	0.164295	0.164295	yes
tiny-perfect-roc	synthetic example (6 rows, a perfectly separating score) (auc)	1	1	yes
gssvocab-roc-large	carData::GSSvocab (vocabulary score vs native birth) (auc)	0.60761	0.60761	yes
gssvocab-roc-large	carData::GSSvocab (vocabulary score vs native birth) (n)	27470	27470	yes
gssvocab-roc_compare-large	carData::GSSvocab (vocabulary score vs years of education, against native birth) (roc_test_stat)	15.1649	15.1649	yes
gssvocab-roc_compare-large	carData::GSSvocab (vocabulary score vs years of education, against native birth) (conf_int)	0.100073, 0.129780	0.100073, 0.129780	yes
gssvocab-roc_compare-large	carData::GSSvocab (vocabulary score vs years of education, against native birth) (n)	27425	27425	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (sensitivity)	0.895349	0.895349	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (specificity)	0.627907	0.627907	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (ppv)	0.878327	0.878327	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (npv)	0.666667	0.666667	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (prevalence)	0.75	0.75	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (likelihood_ratios.lr_pos)	2.40625	2.40625	yes
illustrative-2x2-diagnostic	illustrative 2x2 (abnormal/normal labels, positive chosen) (likelihood_ratios.dor)	14.4375	14.4375	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (sensitivity)	0.895349	0.895349	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (specificity)	0.627907	0.627907	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (ppv)	0.878327	0.878327	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (npv)	0.666667	0.666667	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (prevalence)	0.75	0.75	yes
illustrative-2x2-mixed-labels	illustrative 2x2 (test Positive/Negative, reference Disease/Healthy) (likelihood_ratios.lr_pos)	2.40625	2.40625	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (sensitivity)	0.895349	0.895349	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (specificity)	0.627907	0.627907	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (ppv)	0.878327	0.878327	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (npv)	0.666667	0.666667	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (prevalence)	0.75	0.75	yes
illustrative-2x2-numeric-codes	illustrative 2x2 (coded 1 = abnormal, 2 = normal; category order puts 1 last) (likelihood_ratios.lr_pos)	2.40625	2.40625	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (sensitivity)	0.892857	0.892857	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (specificity)	0.6	0.6	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (ppv)	0.875486	0.875486	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (npv)	0.64	0.64	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (prevalence)	0.759036	0.759036	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (likelihood_ratios.lr_pos)	2.23214	2.23214	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (n)	332	332	yes
illustrative-2x2-missing	illustrative 2x2 with 12 index-test results missing (rows 1-6 and 300-305), 90% intervals (sensitivity_ci)	0.855283, 0.923290	0.855283, 0.923290	yes
zero-cell-diagnostic	synthetic example (a perfect 10/0/0/10 2x2) (sensitivity)	1	1	yes
zero-cell-diagnostic	synthetic example (a perfect 10/0/0/10 2x2) (specificity)	1	1	yes
zero-cell-diagnostic	synthetic example (a perfect 10/0/0/10 2x2) (sensitivity_ci)	0.691503, 1.000000	0.691503, 1.000000	yes
screening-conf90	documented illustrative 2x2 screening fixture (90% intervals) (sensitivity_ci)	0.897747, 0.980094	0.897747, 0.980094	yes
screening-conf90	documented illustrative 2x2 screening fixture (90% intervals) (likelihood_ratios.lr_pos)	9.5	9.5	yes
pefr-bland_altman	Bland & Altman (1986) Table 1 (Wright vs mini Wright peak flow, first readings) (bias)	-2.11765	-2.11765	yes
pefr-bland_altman	Bland & Altman (1986) Table 1 (Wright vs mini Wright peak flow, first readings) (sd_diff)	38.7651	38.7651	yes
pefr-bland_altman	Bland & Altman (1986) Table 1 (Wright vs mini Wright peak flow, first readings) (bias_ci)	-22.0488, 17.8135	-22.0488, 17.8135	yes
pefr-bland_altman-conf90	Bland & Altman (1986) Table 1 (Wright vs mini Wright peak flow, first readings; 90% intervals) (bias_ci)	-18.5323, 14.2970	-18.5323, 14.2970	yes
diamonds-bland_altman-large	ggplot2::diamonds (length x vs width y, mm) (bias)	-0.00336874	-0.00336874	yes
diamonds-bland_altman-large	ggplot2::diamonds (length x vs width y, mm) (bias_ci)	-0.00552430, -0.00121318	-0.00552430, -0.00121318	yes
diamonds-bland_altman-large	ggplot2::diamonds (length x vs width y, mm) (p_value)	0.0021914	0.0021914	yes
nscale-n2-wilcoxon_signed_rank	synthetic example (n=2 paired observations) (p_value)	1	1	yes
nscale-n5-pearson	synthetic example (n=5) (p_value)	5.94154e-05	5.94154e-05	yes
nscale-n5-pearson	synthetic example (n=5) (estimate)	0.998652	0.998652	yes
ToothGrowth-table_one_full	datasets::ToothGrowth (n)	60	60	yes
ToothGrowth-table_one_full	datasets::ToothGrowth (n_variables)	2	2	yes
synthetic-peto_peto	synthetic left-censored lab example (Peto-Peto test) (statistic)	7.12991	7.12991	yes
synthetic-peto_peto	synthetic left-censored lab example (Peto-Peto test) (p_value)	0.00758084	0.00758084	yes
synthetic-censored_regression	synthetic left-censored lab example (censored regression) (n)	32	32	yes
synthetic-censored_regression	synthetic left-censored lab example (censored regression) (censored_dist)	lognormal	lognormal	yes
aml-kaplan_meier	survival::aml (acute myelogenous leukemia, 23 patients) (statistic)	3.39639	3.39639	yes
aml-kaplan_meier	survival::aml (acute myelogenous leukemia, 23 patients) (p_value)	0.0653393	0.0653393	yes
aml-kaplan_meier	survival::aml (acute myelogenous leukemia, 23 patients) (group_medians)	31, 23	31, 23	yes
aml-kaplan_meier	survival::aml (acute myelogenous leukemia, 23 patients) (effect_size.value)	2.49811	2.49811	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (estimate)	1.02029	1.02029	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (conf_int)	1.00115, 1.03980	1.00115, 1.03980	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (p_value)	0.0376554	0.0376554	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (n_events)	152	152	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (model_test.statistic)	14.6691	14.6691	yes
lung-cox_ph-published	survival::lung (status as shipped: 1 = censored, 2 = dead) (ph_tests.chisq)	0.5077410, 2.5489200, 0.0144409, 3.0050500	0.5077410, 2.5489200, 0.0144409, 3.0050500	yes
ovarian-rx1-kaplan_meier	survival::ovarian, treatment arm rx = 1 (13 patients) (statistic)	4.62505	4.62505	yes
ovarian-rx1-kaplan_meier	survival::ovarian, treatment arm rx = 1 (13 patients) (p_value)	0.0315083	0.0315083	yes
ovarian-rx1-kaplan_meier	survival::ovarian, treatment arm rx = 1 (13 patients) (effect_size.value)	8.00757	8.00757	yes
colon-kaplan_meier-3groups	survival::colon, recurrence records (etype = 2; 929 patients, 3 arms) (statistic)	11.6831	11.6831	yes
colon-kaplan_meier-3groups	survival::colon, recurrence records (etype = 2; 929 patients, 3 arms) (parameter)	2	2	yes
colon-kaplan_meier-3groups	survival::colon, recurrence records (etype = 2; 929 patients, 3 arms) (p_value)	0.00290435	0.00290435	yes
nafld1-kaplan_meier	survival::nafld1 (17549 subjects; large n) (statistic)	11.2948	11.2948	yes
nafld1-kaplan_meier	survival::nafld1 (17549 subjects; large n) (p_value)	0.000777225	0.000777225	yes
nafld1-kaplan_meier	survival::nafld1 (17549 subjects; large n) (n_events)	1364	1364	yes
nafld1-cox_ph	survival::nafld1 (missing BMI excluded; large n) (estimate)	1.10584	1.10584	yes
nafld1-cox_ph	survival::nafld1 (missing BMI excluded; large n) (n)	12588	12588	yes
nafld1-cox_ph	survival::nafld1 (missing BMI excluded; large n) (n_events)	1018	1018	yes
veteran-cox_ph-published	survival::veteran (137 patients, 128 events) (estimate)	1.19745	1.19745	yes
veteran-cox_ph-published	survival::veteran (137 patients, 128 events) (p_value)	0.326016	0.326016	yes
veteran-cox_ph-published	survival::veteran (137 patients, 128 events) (n_events)	128	128	yes
lung-kaplan_meier-numeric-coded-group	survival::lung, sex recoded as 0 and 5 (a numeric code for two groups) (statistic)	10.3267	10.3267	yes
lung-kaplan_meier-numeric-coded-group	survival::lung, sex recoded as 0 and 5 (a numeric code for two groups) (effect_size.value)	0.588003	0.588003	yes
lung-kaplan_meier-event-value	survival::lung, status written as the words ‘died’ / ‘survived’ (statistic)	10.3267	10.3267	yes
lung-kaplan_meier-event-value	survival::lung, status written as the words ‘died’ / ‘survived’ (n_events)	165	165	yes
veteran-cox_ph-term-tests	survival::veteran (cell type has 4 levels) (term_tests.statistic)	17.3405, 34.5230	17.3405, 34.5230	yes
veteran-cox_ph-term-tests	survival::veteran (cell type has 4 levels) (term_tests.df)	3, 1	3, 1	yes
cats-ancova	MASS::cats (heart and body weight of 144 adult cats) (statistic)	0.0729072	0.0729072	yes
cats-ancova	MASS::cats (heart and body weight of 144 adult cats) (p_value)	0.787545	0.787545	yes
InsectSprays-one_way_anova-numeric-code	datasets::InsectSprays (spray coded 1-6) (statistic)	34.7023	34.7023	yes
InsectSprays-one_way_anova-numeric-code	datasets::InsectSprays (spray coded 1-6) (parameter)	5,66	5,66	yes
InsectSprays-one_way_anova-numeric-code	datasets::InsectSprays (spray coded 1-6) (p_value)	3.18258e-17	3.18258e-17	yes
InsectSprays-one_way_anova-numeric-code	datasets::InsectSprays (spray coded 1-6) (effect_size.value)	0.724439	0.724439	yes
InsectSprays-one_way_anova-numeric-code	datasets::InsectSprays (spray coded 1-6) (effect_size.ci)	0.601653, 0.794989	0.601653, 0.794989	yes
InsectSprays-kruskal_wallis-ties	datasets::InsectSprays (statistic)	54.6913	54.6913	yes
InsectSprays-kruskal_wallis-ties	datasets::InsectSprays (parameter)	5	5	yes
InsectSprays-kruskal_wallis-ties	datasets::InsectSprays (p_value)	1.51084e-10	1.51084e-10	yes
InsectSprays-kruskal_wallis-ties	datasets::InsectSprays (effect_size.value)	0.770301	0.770301	yes
InsectSprays-kruskal_wallis-ties	datasets::InsectSprays (posthoc.p_adjust)	bonferroni	bonferroni	yes
chickwts-welch_anova-conf99	datasets::chickwts (statistic)	19.6617	19.6617	yes
chickwts-welch_anova-conf99	datasets::chickwts (p_value)	1.17706e-08	1.17706e-08	yes
chickwts-welch_anova-conf99	datasets::chickwts (effect_size.value)	0.541685	0.541685	yes
chickwts-welch_anova-conf99	datasets::chickwts (effect_size.ci)	0.292965, 0.682367	0.292965, 0.682367	yes
chickwts-welch_anova-conf99	datasets::chickwts (effect_size.ci_level)	0.99	0.99	yes
chickwts-one_way_anova-tukey-conf90	datasets::chickwts (statistic)	15.3648	15.3648	yes
chickwts-one_way_anova-tukey-conf90	datasets::chickwts (p_value)	5.93642e-10	5.93642e-10	yes
chickwts-one_way_anova-tukey-conf90	datasets::chickwts (posthoc.comparisons.p_adjusted)	3.07020e-08, 2.10015e-04, 3.32458e-01, 8.36531e-03, 9.99890e-01, 1.41333e-01, 1.06209e-04, 4.21665e-03, 1.21989e-08, 1.27696e-01, 7.93285e-01, 8.84323e-05, 7.39136e-01, 2.20696e-01, 3.88452e-03	3.07020e-08, 2.10015e-04, 3.32458e-01, 8.36531e-03, 9.99890e-01, 1.41333e-01, 1.06209e-04, 4.21665e-03, 1.21989e-08, 1.27696e-01, 7.93285e-01, 8.84323e-05, 7.39136e-01, 2.20696e-01, 3.88452e-03	yes
chickwts-one_way_anova-tukey-conf90	datasets::chickwts (posthoc.comparisons.conf_low.1)	-225.614	-225.614	yes
chickwts-one_way_anova-tukey-conf90	datasets::chickwts (posthoc.comparisons.conf_high.1)	-101.153	-101.153	yes
airquality-kruskal_wallis-missing	datasets::airquality (Ozone by Month) (statistic)	29.2666	29.2666	yes
airquality-kruskal_wallis-missing	datasets::airquality (Ozone by Month) (parameter)	4	4	yes
airquality-kruskal_wallis-missing	datasets::airquality (Ozone by Month) (p_value)	6.90071e-06	6.90071e-06	yes
airquality-kruskal_wallis-missing	datasets::airquality (Ozone by Month) (n)	116	116	yes
airquality-kruskal_wallis-missing	datasets::airquality (Ozone by Month) (effect_size.value)	0.254492	0.254492	yes
airquality-welch_anova-missing	datasets::airquality (Ozone by Month) (statistic)	8.02668	8.02668	yes
airquality-welch_anova-missing	datasets::airquality (Ozone by Month) (p_value)	6.43908e-05	6.43908e-05	yes
airquality-welch_anova-missing	datasets::airquality (Ozone by Month) (n)	116	116	yes
airquality-welch_anova-missing	datasets::airquality (Ozone by Month) (effect_size.value)	0.235234	0.235234	yes
airquality-one_way_anova-mean-imputed	datasets::airquality (Ozone by Month) (statistic)	8.93089	8.93089	yes
airquality-one_way_anova-mean-imputed	datasets::airquality (Ozone by Month) (parameter)	4,148	4,148	yes
airquality-one_way_anova-mean-imputed	datasets::airquality (Ozone by Month) (p_value)	1.73129e-06	1.73129e-06	yes
airquality-one_way_anova-mean-imputed	datasets::airquality (Ozone by Month) (n)	153	153	yes
airquality-one_way_anova-mean-imputed	datasets::airquality (Ozone by Month) (effect_size.value)	0.194442	0.194442	yes
warpbreaks-kruskal_wallis-conf90	datasets::warpbreaks (breaks by tension) (statistic)	10.8093	10.8093	yes
warpbreaks-kruskal_wallis-conf90	datasets::warpbreaks (breaks by tension) (parameter)	2	2	yes
warpbreaks-kruskal_wallis-conf90	datasets::warpbreaks (breaks by tension) (p_value)	0.00449571	0.00449571	yes
warpbreaks-kruskal_wallis-conf90	datasets::warpbreaks (breaks by tension) (effect_size.value)	0.203948	0.203948	yes
warpbreaks-kruskal_wallis-conf90	datasets::warpbreaks (breaks by tension) (effect_size.ci_level)	0.9	0.9	yes
PlantGrowth-one_way_anova-n15	datasets::PlantGrowth (first five plants per group) (statistic)	2.42596	2.42596	yes
PlantGrowth-one_way_anova-n15	datasets::PlantGrowth (first five plants per group) (parameter)	2,12	2,12	yes
PlantGrowth-one_way_anova-n15	datasets::PlantGrowth (first five plants per group) (p_value)	0.130374	0.130374	yes
PlantGrowth-one_way_anova-n15	datasets::PlantGrowth (first five plants per group) (effect_size.value)	0.287915	0.287915	yes
PlantGrowth-one_way_anova-n15	datasets::PlantGrowth (first five plants per group) (effect_size.ci)	0.000000, 0.589724	0.000000, 0.589724	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (statistic)	4.10699	4.10699	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (parameter)	2,54	2,54	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (p_value)	0.0218603	0.0218603	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (term_tests.statistic)	15.57200, 92.00000, 4.10699	15.57200, 92.00000, 4.10699	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (term_tests.p_value)	2.31183e-04, 4.04629e-18, 2.18603e-02	2.31183e-04, 4.04629e-18, 2.18603e-02	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (term_tests.effect_size)	0.223825, 0.773109, 0.132028	0.223825, 0.773109, 0.132028	yes
ToothGrowth-two_way_anova-numeric-dose	datasets::ToothGrowth (dose stored as the numbers 0.5, 1, 2) (effect_size.ci)	0.00146557, 0.29497200	0.00146556, 0.29497200	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (statistic)	0.686157	0.686157	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (parameter)	1,12	1,12	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (p_value)	0.423644	0.423644	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (term_tests.statistic)	3.238240, 1.508570, 0.686157	3.238240, 1.508570, 0.686157	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (term_tests.p_value)	0.0971136, 0.2429030, 0.4236440	0.0971136, 0.2429030, 0.4236440	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (term_tests.effect_size)	0.2125070, 0.1116750, 0.0540871	0.2125070, 0.1116750, 0.0540871	yes
warpbreaks-two_way_anova-n16	datasets::warpbreaks (tension L and H, first four looms per wool x tension cell) (effect_size.ci)	0.00000, 0.39443	0.00000, 0.39443	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (statistic)	6.18233	6.18233	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (parameter)	2,43	2,43	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (p_value)	0.00436585	0.00436585	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (term_tests.statistic)	3.87832, 8.25136, 6.18233	3.87832, 8.25136, 6.18233	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (term_tests.p_value)	0.055378100, 0.000926896, 0.004365850	0.055378100, 0.000926896, 0.004365850	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (term_tests.effect_size)	0.0827317, 0.2773440, 0.2233310	0.0827317, 0.2773440, 0.2233310	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (effect_size.ci)	0.0504248, 0.3806400	0.0504248, 0.3806400	yes
warpbreaks-two_way_anova-unbalanced-conf90	datasets::warpbreaks without rows 1, 2, 3, 20 and 40 (unbalanced cells) (effect_size.ci_level)	0.9	0.9	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (statistic)	2.93958	2.93958	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (parameter)	2,45	2,45	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (p_value)	0.0631148	0.0631148	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (term_tests.statistic)	4.05590, 6.48209, 2.93958	4.05590, 6.48209, 2.93958	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (term_tests.p_value)	0.05001940, 0.00335883, 0.06311480	0.05001940, 0.00335883, 0.06311480	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (term_tests.effect_size)	0.0826791, 0.2236590, 0.1155520	0.0826791, 0.2236590, 0.1155520	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (effect_size.ci)	0.000000, 0.289467	0.000000, 0.289467	yes
warpbreaks-two_way_anova-missing	datasets::warpbreaks with the outcome removed on rows 5, 23 and 41 (n)	51	51	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (statistic)	18.7027	18.7027	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (parameter)	3.32,56.46	3.32,56.46	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (p_value)	5.46309e-09	5.46309e-09	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (estimate)	0.292691	0.292691	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (n)	180	180	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (term_tests.effect_size)	0.523846	0.523846	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (term_tests.effect_size_low)	0.421465	0.421465	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (term_tests.effect_size_high)	0.586201	0.586201	yes
sleepstudy-rm_anova-conf90	lme4::sleepstudy (reaction time on each of 10 days, 18 subjects) (effect_size.ci_level)	0.9	0.9	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (statistic)	21.4375	21.4375	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (parameter)	2.05,14.35	2.05,14.35	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (p_value)	4.54722e-05	4.54722e-05	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (estimate)	0.503425	0.503425	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (n)	32	32	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (term_tests.effect_size)	0.753846	0.753846	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (term_tests.effect_size_low)	0.51131	0.51131	yes
ergoStool-rm_anova-missing	nlme::ergoStool with one effort value removed (subject 1, stool T3) (term_tests.effect_size_high)	0.851881	0.851881	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (statistic)	12.6579	12.6579	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (parameter)	2,61	2,61	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (p_value)	2.52235e-05	2.52235e-05	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (effect_size.value)	0.293293	0.293293	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (effect_size.ci)	0.0629496, 0.4974540	0.0629496, 0.4974540	yes
Baumann-ancova-two-covariates-conf99	carData::Baumann (reading comprehension, three teaching methods, two pretests) (effect_size.ci_level)	0.99	0.99	yes
anorexia-ancova-n18	MASS::anorexia (the first six patients of each treatment arm) (statistic)	3.35554	3.35554	yes
anorexia-ancova-n18	MASS::anorexia (the first six patients of each treatment arm) (parameter)	2,14	2,14	yes
anorexia-ancova-n18	MASS::anorexia (the first six patients of each treatment arm) (p_value)	0.0644876	0.0644876	yes
anorexia-ancova-n18	MASS::anorexia (the first six patients of each treatment arm) (effect_size.value)	0.324033	0.324033	yes
anorexia-ancova-n18	MASS::anorexia (the first six patients of each treatment arm) (effect_size.ci)	0.000000, 0.601292	0.000000, 0.601292	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (statistic)	7.41424	7.41424	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (parameter)	2,65	2,65	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (p_value)	0.00125763	0.00125763	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (effect_size.value)	0.185754	0.185754	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (effect_size.ci)	0.0360123, 0.3412090	0.0360123, 0.3412090	yes
anorexia-ancova-missing	MASS::anorexia with the baseline weight removed on rows 4, 30 and 60 (n)	69	69	yes

“matches refit (not frozen)” marks iterative-optimizer fits (glmer mixed models) whose engine value equals an independent refit on every run, but whose absolute value carries platform/optimizer jitter below the agreement tolerance. The verdict is stable and reported; the raw number is not printed because it is not byte-reproducible (which is exactly why these cases are canonical-only).

Literature-anchored cases

These cases additionally match a citable published value:

case	field	Celeus	published value	citation	match
sleep-paired_t	p_value	0.00283289	0.00283289	Student (1908), Biometrika 6(1):1-25	yes
mtcars-pearson	estimate	-0.867659	-0.867659	Henderson & Velleman (1981), Biometrics 37	yes
sleepstudy-mixed_model	estimate	10.4673	10.47	Belenky et al. (2003), J. Sleep Research 12	yes
shoes-mixed_model-balanced	statistic	11.215	11.215	Box, Hunter & Hunter (1978); the pbkrtest 0.5.5 installed vignette ‘a01-pbkrtest’ (Halekoh & Hojsgaard) prints the Kenward-Roger F, its df and p for this design	yes
shoes-mixed_model-balanced	p_value	0.00853878	0.008539	Box, Hunter & Hunter (1978); the pbkrtest 0.5.5 installed vignette ‘a01-pbkrtest’ (Halekoh & Hojsgaard) prints the Kenward-Roger F, its df and p for this design	yes
shoes-mixed_model-imbalanced	statistic	5.98932	5.9893	Box, Hunter & Hunter (1978); the pbkrtest 0.5.5 installed vignette ‘a01-pbkrtest’ deletes rows 1 and 12 and prints the Kenward-Roger F, its fractional df and p	yes
shoes-mixed_model-imbalanced	p_value	0.0441764	0.04418	Box, Hunter & Hunter (1978); the pbkrtest 0.5.5 installed vignette ‘a01-pbkrtest’ deletes rows 1 and 12 and prints the Kenward-Roger F, its fractional df and p	yes
shoes-mixed_model-imbalanced	term_tests.df_resid.1	7.0219	7.0219	Box, Hunter & Hunter (1978); the pbkrtest 0.5.5 installed vignette ‘a01-pbkrtest’ deletes rows 1 and 12 and prints the Kenward-Roger F, its fractional df and p	yes
ergoStool-mixed_model	statistic	22.3556	22.36	Pinheiro & Bates (2000), Mixed-Effects Models in S and S-PLUS, Section 1.2: F = 22.36 on 3 and 24 df for Type, as printed by the nlme package’s own run of that section’s code; p and the post-hoc labels are independent computations	yes
tea-tasting-fisher_exact-greater	p_value	0.242857	0.2429	Fisher (1935), The Design of Experiments; the one-sided p as printed in R’s ?fisher.test	yes
job-fisher_exact-rxc	p_value	0.782685	0.7827	Agresti (2002), Categorical Data Analysis, 2nd ed., p. 57, as printed in R’s ?fisher.test	yes
wine-ordinal_logistic	estimate	2.50307	2.5031	Randall (1989); Christensen, ‘Cumulative Link Models for Ordinal Regression with the R Package ordinal’ (the article shipped with the ordinal package) prints the coefficients, thresholds, likelihood-ratio tests and profile intervals of this model	yes
ToothGrowth-student_t	statistic	1.91527	1.91527	Crampton (1947), J. Nutrition 33(5)	yes
ToothGrowth-student_t	p_value	0.0603934	0.0603934	Crampton (1947), J. Nutrition 33(5)	yes
ToothGrowth-student_t	estimate	3.7	3.7	Crampton (1947), J. Nutrition 33(5)	yes
ergoStool-rm_anova	statistic	22.3556	22.36	Pinheiro & Bates (2000), Mixed-Effects Models in S and S-PLUS, Section 1.2 (A Randomized Block Design): F = 22.36 on 3 and 24 df for Type, as printed by the nlme package’s own run of that section’s code; the corrected p-value and the generalized eta-squared are independent computations	yes
screening-diagnostic_accuracy	sensitivity	0.95	0.95	Illustrative screening-test 2x2 (TP=95, FP=90, FN=5, TN=810); textbook-style table, not real patient data	yes
screening-diagnostic_accuracy	specificity	0.9	0.9	Illustrative screening-test 2x2 (TP=95, FP=90, FN=5, TN=810); textbook-style table, not real patient data	yes
screening-diagnostic_accuracy	prevalence	0.1	0.1	Illustrative screening-test 2x2 (TP=95, FP=90, FN=5, TN=810); textbook-style table, not real patient data	yes
pefr-bland_altman	bias	-2.11765	-2.1	Bland JM, Altman DG (1986), The Lancet 327(8476):307-310, Table 1	yes
pefr-bland_altman	sd_diff	38.7651	38.8	Bland JM, Altman DG (1986), The Lancet 327(8476):307-310, Table 1	yes
aml-kaplan_meier	statistic	3.39639	3.4	The survival package vignette (R package survival 3.8-6), survdiff example on aml: Chisq = 3.4 on 1 degree of freedom, p = 0.07	yes
aml-kaplan_meier	p_value	0.0653393	0.07	The survival package vignette (R package survival 3.8-6), survdiff example on aml: Chisq = 3.4 on 1 degree of freedom, p = 0.07	yes
lung-cox_ph-published	estimate	1.02029	1.02029	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
lung-cox_ph-published	conf_int	1.00115, 1.03980	1.0011, 1.0398	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
lung-cox_ph-published	p_value	0.0376554	0.0377	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
lung-cox_ph-published	n_events	152	152	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
lung-cox_ph-published	model_test.statistic	14.6691	14.67	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
lung-cox_ph-published	ph_tests.chisq	0.5077410, 2.5489200, 0.0144409, 3.0050500	0.5077, 2.5489, 0.0144, 3.0051	The survival package vignette (R package survival 3.8-6), summary(cfit1) and cox.zph(cfit1) for age + sex + wt.loss: age HR 1.0202913, 95% CI 1.0011 to 1.0398, p = 0.0377; 152 events; likelihood ratio test 14.67 on 3 df; Schoenfeld chi-squares 0.5077, 2.5489, 0.0144, global 3.0051	yes
veteran-cox_ph-published	estimate	1.19745	1.19745	The survival package vignette ‘Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model’ (survival 3.8-6): trt coefficient 0.180197, hazard ratio 1.197453, p = 0.326; 128 events	yes
veteran-cox_ph-published	p_value	0.326016	0.326	The survival package vignette ‘Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model’ (survival 3.8-6): trt coefficient 0.180197, hazard ratio 1.197453, p = 0.326; 128 events	yes
veteran-cox_ph-published	n_events	128	128	The survival package vignette ‘Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model’ (survival 3.8-6): trt coefficient 0.180197, hazard ratio 1.197453, p = 0.326; 128 events	yes

Certified reference values

Cases in this report checked against a value certified by a standards body. Celeus is printed to the number of significant digits agreement is required to; certified values are printed to at most 11 significant digits.

• (none)

Certified-value checks (last recorded run)

The NIST Statistical Reference Datasets (Standard Reference Database 140, DOI 10.18434/T43G6C, https://www.itl.nist.gov/div898/strd/) are copyright-secured reference data that we download at run time rather than redistribute, so their one-way analysis of variance cases run in a separate external check, not in this report. Its results are recorded here only when that check is deliberately recorded, so the table below shows numbers only, as of the last recorded run (2026-10-10); it is a record of that run, not a live status. Same columns as the table above.

case	quantity	certified value	Celeus	required digits	match
nist-SiRstv-one_way_anova	F statistic	1.1804623744	1.180462374	10	yes
nist-SiRstv-one_way_anova	degrees of freedom (between, within)	4,20	4,20	10	yes
nist-SiRstv-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.19099903905	0.1909990391	10	yes
nist-AtmWtAg-one_way_anova	F statistic	15.946733568	15.946734	8	yes
nist-AtmWtAg-one_way_anova	degrees of freedom (between, within)	1,46	1,46	8	yes
nist-AtmWtAg-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.25742654454	0.25742654	8	yes
nist-SmLs01-one_way_anova	F statistic	21	21	10	yes
nist-SmLs01-one_way_anova	degrees of freedom (between, within)	8,180	8,180	10	yes
nist-SmLs01-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.48275862069	0.4827586207	10	yes
nist-SmLs02-one_way_anova	F statistic	201	201	10	yes
nist-SmLs02-one_way_anova	degrees of freedom (between, within)	8,1800	8,1800	10	yes
nist-SmLs02-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47183098592	0.4718309859	10	yes
nist-SmLs03-one_way_anova	F statistic	2001	2001	10	yes
nist-SmLs03-one_way_anova	degrees of freedom (between, within)	8,18000	8,18000	10	yes
nist-SmLs03-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47071277347	0.4707127735	10	yes
nist-SmLs04-one_way_anova	F statistic	21	21	8	yes
nist-SmLs04-one_way_anova	degrees of freedom (between, within)	8,180	8,180	8	yes
nist-SmLs04-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.48275862069	0.48275862	8	yes
nist-SmLs05-one_way_anova	F statistic	201	201	8	yes
nist-SmLs05-one_way_anova	degrees of freedom (between, within)	8,1800	8,1800	8	yes
nist-SmLs05-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47183098592	0.47183099	8	yes
nist-SmLs06-one_way_anova	F statistic	2001	2001	8	yes
nist-SmLs06-one_way_anova	degrees of freedom (between, within)	8,18000	8,18000	8	yes
nist-SmLs06-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47071277347	0.47071277	8	yes
nist-SmLs07-one_way_anova	F statistic	21	21	3	yes
nist-SmLs07-one_way_anova	degrees of freedom (between, within)	8,180	8,180	3	yes
nist-SmLs07-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.48275862069	0.483	3	yes
nist-SmLs08-one_way_anova	F statistic	201	201	3	yes
nist-SmLs08-one_way_anova	degrees of freedom (between, within)	8,1800	8,1800	3	yes
nist-SmLs08-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47183098592	0.472	3	yes
nist-SmLs09-one_way_anova	F statistic	2001	2000	3	yes
nist-SmLs09-one_way_anova	degrees of freedom (between, within)	8,18000	8,18000	3	yes
nist-SmLs09-one_way_anova	R-squared (equals eta-squared in a one-way design)	0.47071277347	0.471	3	yes

Divergence detail

Per-comparison detail for every divergence counted above - case/field identify the comparison, reference names what it was checked against (independent_r, anchor, or an (engine_error)/(canonical_error) crash), and abs delta/tolerance are the exact numbers a regression’s own PR needs, not just a bare match verdict. Empty in the healthy case, by construction: the row list below is built from the SAME divergence events the summary count above totals, so it can never show a count without matching rows or vice versa.

• (none)

Test selection (does the tool steer to the right test, and show alternatives?)

Correctness of the number matters only if the tool also proposes a defendable test. This tier uses the deterministic metadata advisor + suitability grader (no AI): it checks the correct test FAMILY is proposed for the data, and that the grader recommends a sound sibling and never calls the valid test ‘Not appropriate’. The alternatives offered column is the count of sibling tests the tool surfaces for the researcher to consider - the product deliberately shows the alternatives and lets the researcher (optionally with AI assistance) explore them, rather than dictating one answer.

case	method	correct family proposed	grader recommends	alternatives offered
sleep-paired_t	paired_t	yes	wilcoxon_signed_rank	2
sleep-wilcoxon	wilcoxon_signed_rank	yes	wilcoxon_signed_rank	2
ToothGrowth-welch_t	welch_t	yes	mann_whitney	3
ToothGrowth-kruskal_wallis	kruskal_wallis	yes	welch_anova	3
iris-welch_anova	welch_anova	yes	welch_anova	3
mtcars-pearson	pearson	yes	pearson	2
mtcars-spearman	spearman	yes	pearson	2
mtcars-linear_regression	linear_regression	yes	-	1
mtcars-logistic_regression	logistic_regression	yes	-	1
sex2-firth_logistic	firth_logistic	n/a - by design (offered after a fit separates, never before one exists - see the separation flag)	-	1
sex2-logistic_regression	logistic_regression	yes	-	1
mtcars-fisher_exact	fisher_exact	yes	chi_square	2
tea-tasting-fisher_exact	fisher_exact	yes	fisher_exact	2
titanic-chi_square	chi_square	yes	chi_square	2
birthwt-logistic_regression	logistic_regression	yes	-	1
sleepstudy-mixed_model	mixed_model	yes	-	1
sleepstudy-intercept-mixed_model	mixed_model	yes	-	1
shoes-mixed_model-balanced	mixed_model	no (documented gap)	-	1
shoes-mixed_model-imbalanced	mixed_model	no (documented gap)	-	1
ergoStool-mixed_model	mixed_model	yes	-	1
orthodont-mixed_model-conf90	mixed_model	yes	-	1
chickweight-mixed_model	mixed_model	no (documented gap)	-	1
sleepstudy-missing-mixed_model	mixed_model	yes	-	1
airquality-mann_whitney-filtered	mann_whitney	yes	mann_whitney	3
lung-kaplan_meier	kaplan_meier	yes	-	1
lung-cox_ph	cox_ph	yes	-	2
lung-aft_weibull	aft_weibull	yes	-	2
mcnemar-disease-example	mcnemar	yes	-	1
respiratory-mcnemar	mcnemar	yes	-	1
haireye-chi_square	chi_square	yes	chi_square	2
haireye-chi_square-conf90	chi_square	yes	chi_square	2
mtcars-chi_square-numeric-yates	chi_square	yes	chi_square	2
mtcars-fisher_exact-cyl-am	fisher_exact	no (documented gap)	fisher_exact	2
survey-fisher_exact-smoke-exer-missing	fisher_exact	yes	fisher_exact	2
survey-fisher_exact-sex-hand-missing	fisher_exact	yes	chi_square	2
survey-fisher_exact-sex-hand-conf99	fisher_exact	yes	chi_square	2
tea-tasting-fisher_exact-greater	fisher_exact	yes	fisher_exact	2
convictions-fisher_exact-less	fisher_exact	yes	chi_square	2
convictions-fisher_exact-conf99	fisher_exact	yes	chi_square	2
job-fisher_exact-rxc	fisher_exact	yes	fisher_exact	2
performance-mcnemar	mcnemar	yes	-	1
performance-mcnemar-conf90	mcnemar	yes	-	1
anorexia-ancova	ancova	yes	-	1
warpbreaks-poisson_regression	poisson_regression	yes	negbin_regression	2
quine-negbin_regression	negbin_regression	yes	negbin_regression	2
housing-ordinal_logistic	ordinal_logistic	yes	-	1
warpbreaks-two_way_anova	two_way_anova	yes	-	1
iris-multinomial_logistic	multinomial_logistic	yes	-	1
warpbreaks-poisson_regression-terms-conf90	poisson_regression	yes	negbin_regression	2
warpbreaks-L-poisson_regression-small	poisson_regression	yes	negbin_regression	2
warpbreaks-L-negbin_regression-small	negbin_regression	yes	negbin_regression	2
insectsprays-poisson_regression	poisson_regression	yes	poisson_regression	2
quine-negbin_regression-terms-conf90	negbin_regression	yes	negbin_regression	2
insurance-poisson_regression	poisson_regression	yes	negbin_regression	2
txhousing-negbin_regression-missing	negbin_regression	yes	negbin_regression	2
wine-ordinal_logistic	ordinal_logistic	yes	-	1
wine-ordinal_logistic-integer-outcome	ordinal_logistic	no (documented gap)	-	1
wine-ordinal_logistic-text-level-orders	ordinal_logistic	no (documented gap)	-	1
wine-ordinal_logistic-small	ordinal_logistic	yes	-	1
wine-ordinal_logistic-missing	ordinal_logistic	yes	-	1
housing-ordinal_logistic-terms-conf90	ordinal_logistic	yes	-	1
wvs-ordinal_logistic-po-violated	ordinal_logistic	yes	-	1
iris-multinomial_logistic-terms-conf90	multinomial_logistic	yes	-	1
wvs-multinomial_logistic	multinomial_logistic	yes	-	1
mtcars-factors_continuous	factors_continuous	yes	-	1
birthwt-factors_binary	factors_binary	no (documented gap)	-	1
ovarian-factors_survival	factors_survival	no (documented gap)	-	1
PlantGrowth-one_way_anova	one_way_anova	yes	welch_anova	3
ToothGrowth-student_t	student_t	yes	mann_whitney	3
ToothGrowth-welch_t-greater	welch_t	yes	mann_whitney	3
ToothGrowth-welch_t-conf90	welch_t	yes	mann_whitney	3
ToothGrowth-student_t-cohens_d	student_t	yes	mann_whitney	3
ToothGrowth-student_t-less	student_t	yes	mann_whitney	3
sleep-paired_t-less	paired_t	yes	wilcoxon_signed_rank	2
sleep-paired_t-conf99	paired_t	yes	wilcoxon_signed_rank	2
sleep-paired_t-mean-imputed	paired_t	yes	paired_t	2
sleep-paired_t-missing	paired_t	yes	paired_t	2
sleep-wilcoxon-conf90	wilcoxon_signed_rank	yes	wilcoxon_signed_rank	2
depression-wilcoxon_signed_rank-greater	wilcoxon_signed_rank	yes	paired_t	2
permeability-mann_whitney-greater	mann_whitney	yes	welch_t	3
mtcars-welch_t-numeric-group	welch_t	yes	welch_t	3
mtcars-mann_whitney-ties	mann_whitney	yes	welch_t	3
chickwts-mann_whitney-hl	mann_whitney	yes	welch_t	3
chickwts-mann_whitney-hl-conf99	mann_whitney	yes	welch_t	3
anorexia-FT-paired_t	paired_t	yes	paired_t	2
anorexia-FT-wilcoxon_signed_rank	wilcoxon_signed_rank	yes	paired_t	2
diamonds-welch_t-large	welch_t	yes	welch_t	3
VerbAgg-mixed_logistic	mixed_logistic	yes	-	1
cbpp-mixed_logistic-conf99	mixed_logistic	no (documented gap)	-	1
bacteria-slope-mixed_logistic	mixed_logistic	yes	-	1
epil-slope-mixed_poisson-conf90	mixed_poisson	yes	-	1
epil-mixed_poisson	mixed_poisson	yes	-	1
ergoStool-rm_anova	rm_anova	yes	-	1
aSAH-roc_auc	roc_auc	yes	-	1
aSAH-roc_compare	roc_compare	yes	-	1
screening-diagnostic_accuracy	diagnostic_accuracy	yes	-	1
davis-bland_altman	bland_altman	via post-analysis follow-up	-	1
asah-case-control-roc	roc_auc	no (documented gap)	-	1
asah-level-order-roc	roc_auc	yes	-	1
asah-roc-conf90	roc_auc	yes	-	1
asah-roc-conf99	roc_auc	yes	-	1
asah-wfns-roc	roc_auc	yes	-	1
asah-wfns-vs-s100b-compare	roc_compare	yes	-	1
asah-direction-differ-compare	roc_compare	yes	-	1
tiny-perfect-roc	roc_auc	yes	-	1
gssvocab-roc-large	roc_auc	yes	-	1
gssvocab-roc_compare-large	roc_compare	yes	-	1
illustrative-2x2-diagnostic	diagnostic_accuracy	yes	-	1
illustrative-2x2-mixed-labels	diagnostic_accuracy	yes	-	1
illustrative-2x2-numeric-codes	diagnostic_accuracy	yes	-	1
illustrative-2x2-missing	diagnostic_accuracy	yes	-	1
zero-cell-diagnostic	diagnostic_accuracy	yes	-	1
screening-conf90	diagnostic_accuracy	yes	-	1
pefr-bland_altman	bland_altman	via post-analysis follow-up	-	1
pefr-bland_altman-conf90	bland_altman	via post-analysis follow-up	-	1
diamonds-bland_altman-large	bland_altman	via post-analysis follow-up	-	1
nscale-n2-wilcoxon_signed_rank	wilcoxon_signed_rank	no (documented gap)	paired_t	2
nscale-n5-pearson	pearson	yes	pearson	2
ToothGrowth-table_one_full	table_one_full	yes	table_one_full	1
synthetic-peto_peto	peto_peto	no (documented gap)	peto_peto	1
synthetic-censored_regression	censored_regression	no (documented gap)	-	1
aml-kaplan_meier	kaplan_meier	yes	-	1
lung-cox_ph-published	cox_ph	yes	-	2
ovarian-rx1-kaplan_meier	kaplan_meier	no (documented gap)	-	1
colon-kaplan_meier-3groups	kaplan_meier	yes	-	1
nafld1-kaplan_meier	kaplan_meier	yes	-	1
nafld1-cox_ph	cox_ph	yes	-	2
veteran-cox_ph-published	cox_ph	yes	-	2
lung-kaplan_meier-numeric-coded-group	kaplan_meier	yes	-	1
lung-kaplan_meier-event-value	kaplan_meier	no (documented gap)	-	1
veteran-cox_ph-term-tests	cox_ph	yes	-	2
cats-ancova	ancova	n/a - by design (the tool asks which variables to adjust for instead of guessing a covariate)	-	1
InsectSprays-one_way_anova-numeric-code	one_way_anova	no (documented gap)	kruskal_wallis	3
InsectSprays-kruskal_wallis-ties	kruskal_wallis	yes	kruskal_wallis	3
chickwts-welch_anova-conf99	welch_anova	yes	welch_anova	3
chickwts-one_way_anova-tukey-conf90	one_way_anova	yes	welch_anova	3
airquality-kruskal_wallis-missing	kruskal_wallis	no (documented gap)	kruskal_wallis	3
airquality-welch_anova-missing	welch_anova	no (documented gap)	kruskal_wallis	3
airquality-one_way_anova-mean-imputed	one_way_anova	no (documented gap)	kruskal_wallis	3
warpbreaks-kruskal_wallis-conf90	kruskal_wallis	yes	welch_anova	3
PlantGrowth-one_way_anova-n15	one_way_anova	yes	welch_anova	3
ToothGrowth-two_way_anova-numeric-dose	two_way_anova	no (documented gap)	-	1
warpbreaks-two_way_anova-n16	two_way_anova	yes	-	1
warpbreaks-two_way_anova-unbalanced-conf90	two_way_anova	yes	-	1
warpbreaks-two_way_anova-missing	two_way_anova	yes	-	1
sleepstudy-rm_anova-conf90	rm_anova	no (documented gap)	-	1
ergoStool-rm_anova-missing	rm_anova	yes	-	1
Baumann-ancova-two-covariates-conf99	ancova	yes	-	1
anorexia-ancova-n18	ancova	yes	-	1
anorexia-ancova-missing	ancova	yes	-	1

What this does NOT validate (read this)

• 141 of 168 cases are canonical-only - they match an independent computation but no single citable published number. Presenting them as literature-validated would be dishonest; they are not.
• For several cases (paired t, Welch t, the regressions, Kaplan-Meier, Cox, the mixed models) the selection check is a ‘not wrong’ bar - it asserts the grader does not reject the correct test - rather than ‘graded strictly best’. Textbook-unambiguous cases assert the stronger ‘recommended’.
• One case’s test family is proposed only AFTER a related analysis actually runs, not before: a strongly-correlated, similar-scale pair run as a correlation gets a deterministic follow-up note recommending an agreement analysis instead - the advisor deliberately never guesses this pairing from column names alone (a false guess would steer a researcher to the wrong test), but once real data confirms the pattern, a conditional, honest suggestion is safe to make.
• The three glmer mixed-model cases are canonical-only ON PURPOSE: a frozen published coefficient would risk cross-platform optimizer jitter, so same-run engine-vs-refit equality is the guard.
• This is a self-generated report. It is real evidence, but weaker than an independent statistician’s review, which is the explicit next credibility step and is not yet done.
• Celeus is research statistics, not clinical decision support: nothing here validates or implies patient-specific diagnosis or treatment.

Reproducibility guarantee

This report carries no timestamp: regenerating it against the same pinned environment (same code, same package versions, same corpus) produces a byte-identical file, character for character - so a clean regeneration finding zero diff is itself the determinism demonstration this artifact claims, not an assertion taken on faith.

Every individual analysis run on the platform emits its own seed, exact package versions, and a standalone reproduce script alongside its results - the same determinism guarantee this report demonstrates, applied per-run. This report’s own determinism rests on the pinned lockfile (md5 above) plus the fixed catalog version, not on a captured full session/locale snapshot (which would break byte-identity across otherwise-identical environments).

Next credibility step

Independent review by a qualified biostatistician of the method-selection logic and this corpus. A self-generated report cannot substitute for it; it is named here as the next step, deliberately out of scope for this artifact.

Archived copy: doi:10.5281/zenodo.23308990 (Zenodo).

To cite this report, see how to cite Celeus.

Last updated October 22, 2026.