Cross-language parity matrix¶

Auto-generated — do not hand-edit. Regenerate with python scripts/build_parity_index.py. Every row traces to a committed test artifact; nothing here is asserted from memory.

StatsPAI's promise is numerical alignment with Stata / R. This page makes that promise auditable function-by-function. Query any function programmatically:

import statspai as sp
sp.parity_status("feols")     # one function
sp.parity_matrix()            # the whole matrix
sp.parity_summary()           # honest coverage counts

Taxonomy¶

grade	meaning
`bit-exact`	matches a named R/Stata reference to machine tolerance (headline relative error ≤ 1e-6)
`aligned`	matches a named reference within a documented, pre-registered looser tolerance (cross-fit / convention disagreement)
`analytical-only`	recovers a known population parameter on a deterministic DGP, or a closed-form identity (no cross-package reference)
`external-replication`	reproduces published-paper numbers on a calibrated replica
`unverified`	registered public API, no qualifying numerical-parity evidence attached yet — the honest gap

Coverage at a glance¶

status	functions
bit-exact	110
aligned	7
analytical-only	60
external-replication	4
verified (subtotal)	181
unverified	958
total registered	1139

bit-exact — 110 functions¶

Machine-tolerance agreement with a named R/Stata reference.

function	reference	versions	tolerance	rel err (R / Stata)	test
`adjust_pvalues`	base R stats::p.adjust (bonferroni/holm/BH)	R 4.5.2	exact (atol 1e-15; observed 0)	— / —	`test_mht_parity.py` (+1)
`arima`	stats::arima	R 4.5.2; stats 4.5.2	rel_est<=1e-06, rel_se<=1e-06	7.4e-07 / 9.3e-09	`39_arima.py` (+2)
`attributable_risk`	base-R closed form (attributable fraction exposed + PAF)	R 4.5.2	AFE + PAF point estimates 1e-12 abs (observed 0); CI not pinned	— / —	`test_epi_extra_parity.py` (+1)
`auc`	Mann-Whitney rank AUC (= pROC::auc / sklearn)	R 4.5.2	AUC 1e-12 abs (observed 0)	— / —	`test_auc_parity.py`
`bacon_decomposition`	bacondecomp::bacon	R 4.5.2; bacondecomp 0.1.1	rel_est<=1e-06, rel_se<=1e-06	5.6e-16 / 9.6e-09	`20_bacon.py` (+2)
`balance_panel`	base R counts == n_periods	R 4.5.2	rel_est<=1e-06, rel_se<=1e-06	0 / —	`69_balance_panel.py` (+1)
`benjamini_hochberg`	base R stats::p.adjust(method='BH')	R 4.5.2	exact (atol 1e-15; observed 0)	— / —	`test_mht_parity.py` (+1)
`betareg`	betareg::betareg(link.phi="log")	R 4.5.2; betareg 3.2.4	rel_est<=1e-06, rel_se<=0.01	2.2e-08 / 3.1e-08	`61_betareg.py` (+2)
`bonferroni`	base R stats::p.adjust(method='bonferroni')	R 4.5.2	exact (atol 1e-15; observed 0)	— / —	`test_mht_parity.py` (+1)
`callaway_santanna`	did::att_gt + aggte	R 4.5.2; did 2.3.0	rel_est<=1e-06, rel_se<=0.01	1.3e-15 / 1.3e-15	`04_csdid.py` (+2)
`clogit`	survival::clogit	R 4.5.2; survival 3.8.3	rel_est<=1e-06, rel_se<=1e-06	1.3e-08 / 1.3e-08	`46_clogit.py` (+2)
`cohen_kappa`	base-R closed form (Cohen's kappa point estimate)	R 4.5.2	kappa + agreements 1e-12 abs (observed ~1e-16); SE not pinned	— / —	`test_epi_extra_parity.py` (+1)
`cox`	survival::coxph	R 4.5.2; survival 3.8.3	rel_est<=1e-06, rel_se<=1e-06	8.4e-16 / 2.1e-10	`24_coxph.py` (+2)
`cr2_se`	clubSandwich::vcovCR(type="CR2"/"CR3")	R 4.5.2; clubSandwich 0.6.2	rel_est<=1e-06, rel_se<=1e-06	1.8e-08 / 2.2e-08	`53_cr2.py` (+2)
`das_gupta`	Das Gupta (1993) exact standardization decomposition identity	R 4.5.2	factor-effect sum + pct 1e-12 abs (observed 0)	— / —	`test_dasgupta_parity.py`
`decompose`	oaxaca::oaxaca	R 4.5.2; oaxaca 0.1.5	rel_est<=1e-06, rel_se<=0.05	6.3e-16 / 1.3e-16	`30_oaxaca.py` (+2)
`demean`	textbook mean-within (algorithmic)	R 4.5.2	rel_est<=1e-06, rel_se<=1e-06	3.5e-15 / —	`68_demean_within.py` (+1)
`dfl_decompose`	ddecompose::dfl_decompose	R 4.5.2; ddecompose 1.0.0	rel_est<=1e-06, rel_se<=1e-06	1.2e-09 / 1.8e-13	`31_dfl.py` (+2)
`did_imputation`	didimputation::did_imputation	R 4.5.2; didimputation 0.5.1	rel_est<=1e-06, rel_se<=1e-06	4.8e-08 / 3.5e-07	`16_bjs.py` (+2)
`direct_standardize`	base closed form (directly standardized rate; = Stata dstdize)	R 4.5.2	DSR 1e-12 abs (observed 0)	— / —	`test_standardize_parity.py`
`dml`	DoubleML::DoubleMLPLR	R 4.5.2; DoubleML 1.0.2	rel_est<=1e-10, rel_se<=1e-10	0 / 3.7e-15	`08_dml.py` (+2)
`drdid`	DRDID::drdid_imp_panel	R 4.5.2; DRDID 1.2.3	rel_est<=1e-06, rel_se<=1e-06	2.6e-15 / 2.2e-16	`38_drdid.py` (+2)
`etwfe`	etwfe::etwfe + emfx	R 4.5.2; etwfe 0.6.2	rel_est<=1e-06, rel_se<=0.001	1.8e-13 / 3.9e-14	`17_etwfe.py` (+2)
`etwfe_emfx`	etwfe::etwfe + emfx	R 4.5.2; etwfe 0.6.2	rel_est<=1e-06, rel_se<=0.001	1.8e-13 / 3.9e-14	`17_etwfe.py` (+2)
`evalue`	EValue::evalues.RR	R 4.2.3; EValue 4.1.4	rel_est<=1e-06, rel_se<=1e-06	5.8e-14 / 1.2e-16	`23_evalue.py` (+2)
`evalue_rr`	VanderWeele-Ding closed form (= R EValue package)	R 4.5.2	point + CI E-value 1e-12 abs (observed 0)	— / —	`test_evalue_rr_parity.py`
`feglm`	fixest::feglm (family="logit") / fixest::fepois	R 4.5.2; fixest 0.14.0	rel_est<=1e-06, rel_se<=5e-05	9.7e-09 / —	`67_panel_glm.py` (+1)
`feols`	fixest::feols	R 4.5.2; fixest 0.14.0	rel_est<=1e-06, rel_se<=1e-06	5.2e-15 / 2.9e-15	`03_hdfe.py` (+2)
`fepois`	fixest::feglm (family="logit") / fixest::fepois	R 4.5.2; fixest 0.14.0	rel_est<=1e-06, rel_se<=5e-05	9.7e-09 / —	`67_panel_glm.py` (+1)
`fracreg`	stats::glm(quasibinomial('logit')) [fractional response]	R 4.5.2	coefficients 1e-10 abs (observed ~8e-15)	— / —	`test_glm_ext_parity.py` (+1)
`frontier`	sfaR::sfacross	R 4.5.2; sfaR 1.0.1	rel_est<=1e-06, rel_se<=5e-05	4.1e-08 / 4.0e-08	`28_frontier.py` (+2)
`g_computation`	base R stats::lm g-formula standardization (Robins 1986)	—	psi 1e-8 (observed <= 7e-16; bootstrap SE pinned loosely +/-25%)	— / —	`test_gformula_parity.py` (+1)
`gelbach`	Gelbach (2016) exact conditional decomposition identity	R 4.5.2	total_change + contribution sum 1e-12 abs (observed 0)	— / —	`test_gelbach_parity.py`
`gsynth`	gsynth::gsynth	R 4.5.2; gsynth 1.4.0	rel_est<=1e-06, rel_se<=1e-06	7.7e-14 / —	`19_gsynth.py` (+1)
`heckman`	sampleSelection::heckit	R 4.5.2; sampleSelection 1.2.14	rel_est<=1e-06, rel_se<=0.0005	1.0e-11 / 1.0e-11	`43_heckman.py` (+2)
`het_test`	lmtest::bptest (studentized Breusch-Pagan)	R 4.5.2; lmtest 0.9.40	statistic & p-value 1e-10 rel (observed ~1e-13)	— / —	`test_diagnostics_parity.py` (+1)
`holm`	base R stats::p.adjust(method='holm')	R 4.5.2	exact (atol 1e-15; observed 0)	— / —	`test_mht_parity.py` (+1)
`honest_did`	HonestDiD::createSensitivityResults_relativeMagnitudes	R 4.5.2; HonestDiD 0.2.8	abs_est<=1e-06, abs_se<=1e-06	4.4e-16 / 5.6e-17	`21_honest_relmags.py` (+2)
`hurdle`	pscl::hurdle(dist='poisson', zero.dist='binomial')	R 4.5.2; pscl 1.5.9	count + zero coefficients 1e-6 abs (observed ~2e-8)	— / —	`test_glm_ext_parity.py` (+1)
`incidence_rate_ratio`	base-R closed form (rate ratio + conditional-binomial exact CI)	R 4.5.2	estimate 1e-12; exact CI 1e-10 abs (observed ~3e-15)	— / —	`test_epi_parity.py` (+1)
`indirect_standardize`	base closed form (SMR / indirect std.; = Stata istdize)	R 4.5.2	expected + SMR 1e-12 abs (observed 0)	— / —	`test_standardize_parity.py`
`inequality_index`	base-R closed form (Gini/Theil-T/Theil-L/Atkinson; = ineq)	R 4.5.2	all indices 1e-12 abs (observed ~2e-16)	— / —	`test_inequality_parity.py` (+1)
`ipw`	base R stats::glm(binomial) + hand-rolled Hajek weighted means	—	Hajek ATE/ATT estimate 1e-9 (observed <= 2e-15; SE not pinned)	— / —	`test_ipw_parity.py` (+1)
`ivreg`	AER::ivreg	R 4.5.2; AER 1.2.16	rel_est<=1e-06, rel_se<=1e-06	1.1e-11 / 1.1e-11	`02_iv.py` (+2)
`kaplan_meier`	survival::survfit	R 4.5.2; survival 3.8.3	S(t) at every event time 1e-12 (observed ~3e-17); median exact	— / —	`test_survival_km_parity.py` (+1)
`kdensity`	Gaussian KDE closed form (= stats::density / sklearn)	R 4.5.2	density 1e-12 abs (observed ~3e-18 / 0)	— / —	`test_kdensity_parity.py`
`kitagawa_decompose`	Kitagawa (1955) two-factor rate decomposition identity	R 4.5.2	gap = rate + composition + interaction 1e-12 abs (observed 0)	— / —	`test_kitagawa_decompose_parity.py`
`liml`	ivmodel::LIML	R 4.5.2; ivmodel 1.9.1	rel_est<=1e-06, rel_se<=1e-06	1.7e-15 / 3.0e-16	`59_liml.py` (+2)
`local_projections`	lpirfs::lp_lin	R 4.5.2; lpirfs 0.2.5	rel_est<=1e-06, rel_se<=1e-06	5.0e-15 / —	`34_lp.py` (+1)
`logit`	stats::glm(family=binomial("logit"))	R 4.5.2; stats 4.5.2	rel_est<=1e-06, rel_se<=1e-06	2.7e-11 / 2.7e-11	`57_logit.py` (+2)
`logrank_test`	survival::survdiff	R 4.5.2; survival 3.8.3	chi-square 1e-10 rel (observed ~8e-16); p-value 1e-10 abs	— / —	`test_survival_km_parity.py` (+1)
`manski_bounds`	Manski (1990) no-assumption worst-case ATE bound identity	R 4.5.2	width == y_upper - y_lower 1e-12 abs (observed 0)	— / —	`test_manski_bounds_parity.py`
`mantel_haenszel`	base-R closed form (Robins-Breslow-Greenland MH; = epiR)	R 4.5.2	estimate, se_log, CI 1e-12 abs (observed 0)	— / —	`test_epi_parity.py` (+1)
`mde`	base-R closed form (RCT minimum detectable effect)	R 4.5.2	effect size 1e-6 abs (output rounded to 6 dp; observed ~2e-8)	— / —	`test_power_extra_parity.py` (+1)
`mediate`	mediation::mediate	R 4.5.2; mediation 4.5.1	rel_est<=1e-06, rel_se<=0.1	6.7e-15 / 3.6e-15	`36_mediation.py` (+2)
`mediation`	mediation::mediate	R 4.5.2; mediation 4.5.1	rel_est<=1e-06, rel_se<=0.1	6.7e-15 / 3.6e-15	`36_mediation.py` (+2)
`mediation_decompose`	natural-effects mediation (Pearl 2001; VanderWeele 2015)	R 4.5.2	total = NDE + NIE 1e-12 abs (observed 0)	— / —	`test_mediation_decompose_parity.py`
`melogit`	lme4::glmer(nAGQ=8)	R 4.5.2; lme4 2.0.1	rel_est<=1e-06, rel_se<=0.05	2.4e-07 / 8.4e-07	`27_glmm_aghq.py` (+2)
`mixed`	lme4::lmer	R 4.5.2; lme4 2.0.1	rel_est<=1e-06, rel_se<=1e-06	1.3e-10 / 4.9e-11	`25_lmm.py` (+2)
`mlogit`	nnet::multinom	R 4.5.2; nnet 7.3.20	rel_est<=1e-06, rel_se<=5e-05	2.6e-07 / 7.4e-09	`44_mlogit.py` (+2)
`multiway_cluster_vcov`	sandwich::vcovCL(cluster=~g1+g2+g3)	R 4.5.2; sandwich 3.1.1	rel_est<=1e-06, rel_se<=1e-06	2.1e-15 / 2.1e-15	`56_multiway_cluster.py` (+2)
`nbreg`	MASS::glm.nb	R 4.5.2; MASS 7.3.65	rel_est<=1e-06, rel_se<=0.005	6.0e-10 / 1.3e-10	`42_nbreg.py` (+2)
`number_needed_to_treat`	base-R closed form (NNT = 1/risk difference)	R 4.5.2	estimate 1e-12 abs (observed 0); CI not pinned	— / —	`test_epi_parity.py` (+1)
`oaxaca`	oaxaca::oaxaca	R 4.5.2; oaxaca 0.1.5	rel_est<=1e-06, rel_se<=0.05	6.3e-16 / 1.3e-16	`30_oaxaca.py` (+2)
`odds_ratio`	base-R closed form (Woolf logit; = epiR::epi.2by2)	R 4.5.2	estimate, se_log, CI 1e-12 abs (observed 0)	— / —	`test_epi_parity.py` (+1)
`ologit`	MASS::polr(method="logistic")	R 4.5.2; MASS 7.3.65	rel_est<=1e-06, rel_se<=1e-05	1.8e-07 / 3.5e-07	`45_ologit.py` (+2)
`oprobit`	MASS::polr(method="probit")	R 4.5.2; MASS 7.3.65	rel_est<=1e-06, rel_se<=1e-06	3.4e-07 / 2.8e-08	`49_oprobit.py` (+2)
`panel`	plm::plm + plm::phtest	R 4.5.2; plm 2.6.7	rel_est<=1e-06, rel_se<=0.001	4.7e-14 / 1.5e-15	`35_panel.py` (+2)
`poisson`	stats::glm(family=poisson())	R 4.5.2; stats 4.5.2	rel_est<=1e-06, rel_se<=1e-06	9.2e-15 / 8.7e-12	`58_poisson.py` (+2)
`power_case_control`	base-R closed form (case-control OR power, 2-prop z)	R 4.5.2	power 1e-12 abs (observed 0)	— / —	`test_epi_diag_parity.py`
`power_cluster_rct`	base-R closed form (design-effect-inflated z-approx power)	R 4.5.2	power 1e-12 abs (observed ~2e-16)	— / —	`test_power_extra_parity.py` (+1)
`power_logrank`	base-R closed form (Schoenfeld log-rank power)	R 4.5.2	power 1e-12 abs (observed ~2e-16)	— / —	`test_power_parity.py` (+1)
`power_rct`	base-R closed form (two-sample pooled-sigma z-approx power)	R 4.5.2	power 1e-12 abs (observed ~2e-16)	— / —	`test_power_parity.py` (+1)
`power_two_proportions`	base-R closed form (unpooled Wald two-proportion z-approx)	R 4.5.2	power 1e-12 abs (observed ~2e-16)	— / —	`test_power_parity.py` (+1)
`ppmlhdfe`	fixest::fepois	R 4.5.2; fixest 0.14.0	rel_est<=1e-06, rel_se<=0.01	4.9e-13 / 2.2e-15	`37_ppmlhdfe.py` (+2)
`prevalence_ratio`	base-R closed form (Katz-log; = epiR::epi.2by2)	R 4.5.2	estimate, se_log, CI 1e-12 abs (observed ~2e-16)	— / —	`test_epi_parity.py` (+1)
`probit`	stats::glm(family=binomial("probit"))	R 4.5.2; stats 4.5.2	rel_est<=1e-06, rel_se<=0.01	3.1e-07 / 1.6e-08	`48_probit.py` (+2)
`psm`	MatchIt::matchit	R 4.5.2; MatchIt 4.7.2	rel_est<=1e-06, rel_se<=1e-06	1.2e-15 / 2.0e-16	`11_psm.py` (+2)
`qreg`	quantreg::rq	R 4.5.2; quantreg 6.1	rel_est<=1e-06, rel_se<=0.1	3.3e-15 / 4.4e-15	`40_qreg.py` (+2)
`rddensity`	rddensity::rddensity	R 4.5.2; rddensity 2.6	rel_est<=1e-06, rel_se<=1e-06	3.3e-11 / 8.9e-11	`09_rddensity.py` (+2)
`rdrobust`	rdrobust::rdrobust	R 4.5.2; rdrobust 3.0.0	rel_est<=1e-06, rel_se<=0.1	7.9e-13 / 2.4e-10	`06_rd.py` (+2)
`regress`	lm + sandwich::vcovHC	R 4.5.2; sandwich 3.1.1	rel_est<=1e-06, rel_se<=1e-06	1.1e-12 / 1.3e-12	`01_ols.py` (+2)
`relative_risk`	base-R closed form (Katz-log; = epiR::epi.2by2 / Stata epitab)	R 4.5.2	estimate, se_log, CI 1e-12 abs (observed 0)	— / —	`test_epi_parity.py` (+1)
`reset_test`	lmtest::resettest(power=2:3, type='fitted')	R 4.5.2; lmtest 0.9.40	F-statistic & p-value 1e-10 rel (observed ~1e-13)	— / —	`test_diagnostics_parity.py` (+1)
`rif_decomposition`	dineq::rif + manual OLS	R 4.5.2; dineq 0.1.0	rel_est<=1e-06, rel_se<=1e-06	2.2e-15 / 1.4e-16	`32_rif.py` (+2)
`risk_difference`	base-R closed form (Wald; = epiR::epi.2by2 / Stata epitab)	R 4.5.2	estimate, se, CI 1e-12 abs (observed 0)	— / —	`test_epi_parity.py` (+1)
`roc_curve`	Mann-Whitney rank AUC (= pROC::auc / sklearn)	R 4.5.2	AUC 1e-12 abs (observed 0)	— / —	`test_auc_parity.py`
`sar`	spatialreg::lagsarlm / spatialreg::errorsarlm / spatialreg::lagsarlm(Durbin=TRUE)	R 4.5.2; spatialreg 1.4.3	rel_est<=1e-06, rel_se<=1e-06	8.3e-08 / —	`65_spatial.py` (+1)
`sar_gmm`	spatialreg::stsls(W2X=FALSE) / spatialreg::GMerrorsar	R 4.5.2; spatialreg 1.4.3	rel_est<=1e-06, rel_se<=1e-06	4.6e-08 / —	`66_spatial_gmm.py` (+1)
`sdid`	synthdid::synthdid_estimate	R 4.5.2; synthdid 0.0.9	rel_est<=1e-06, rel_se<=1e-06	2.6e-15 / 7.2e-08	`12_sdid.py` (+2)
`sdm`	spatialreg::lagsarlm / spatialreg::errorsarlm / spatialreg::lagsarlm(Durbin=TRUE)	R 4.5.2; spatialreg 1.4.3	rel_est<=1e-06, rel_se<=1e-06	8.3e-08 / —	`65_spatial.py` (+1)
`sem`	spatialreg::lagsarlm / spatialreg::errorsarlm / spatialreg::lagsarlm(Durbin=TRUE)	R 4.5.2; spatialreg 1.4.3	rel_est<=1e-06, rel_se<=1e-06	8.3e-08 / —	`65_spatial.py` (+1)
`sem_gmm`	spatialreg::stsls(W2X=FALSE) / spatialreg::GMerrorsar	R 4.5.2; spatialreg 1.4.3	rel_est<=1e-06, rel_se<=1e-06	4.6e-08 / —	`66_spatial_gmm.py` (+1)
`sensemakr`	sensemakr::sensemakr	R 4.5.2; sensemakr 0.1.6	rel_est<=1e-06, rel_se<=1e-06	5.0e-08 / 5.0e-08	`22_sensemakr.py` (+2)
`sensitivity_specificity`	base closed form (2x2 diagnostic accuracy; = epiR)	R 4.5.2	sens/spec/PPV/NPV/LR 1e-12 abs (observed 0)	— / —	`test_epi_diag_parity.py`
`source_decompose`	Lerman-Yitzhaki (1985) Gini source decomposition identity	R 4.5.2	sum(contribution) == total_gini 1e-12 abs (observed ~1e-16)	— / —	`test_source_decompose_parity.py`
`subgroup_decompose`	Theil within+between exact additive identity (Shorrocks 1980)	R 4.5.2	total == within + between 1e-12 abs (observed 0)	— / —	`test_subgroup_decompose_parity.py`
`sun_abraham`	fixest::sunab	R 4.5.2; fixest 0.14.0	rel_est<=1e-06, rel_se<=0.25	2.8e-11 / 2.7e-11	`05_sunab.py` (+2)
`sureg`	systemfit::systemfit(method="SUR", noDfCor)	R 4.5.2; systemfit 1.1.30	rel_est<=1e-06, rel_se<=1e-06	1.5e-14 / 1.5e-15	`60_sureg.py` (+2)
`svyglm`	survey::svyglm (design-based GLM + linearization SE)	R 4.5.2	coefficients + SE 1e-10 abs (observed ~2e-15 / 6e-15)	— / —	`test_survey_parity.py` (+1)
`svymean`	survey::svymean (Horvitz-Thompson/Hajek + Taylor SE)	R 4.5.2	estimate + SE 1e-10 abs (observed ~5e-15 / 8e-17)	— / —	`test_survey_parity.py` (+1)
`svytotal`	survey::svytotal (Horvitz-Thompson + Taylor SE)	R 4.5.2	estimate 1e-12 rel; SE 1e-10 rel (observed ~2e-12 / 1e-14)	— / —	`test_survey_parity.py` (+1)
`synth`	Synth::synth	R 4.5.2; Synth 1.1.10	rel_est<=1e-06, rel_se<=1e-06	7.8e-08 / 7.7e-08	`52_scm_unique.py` (+2)
`tmle`	base R stats::glm TMLE (van der Laan & Rubin 2006)	—	psi 1e-9 (observed 5.6e-12), EIF SE 1e-9, epsilon 1e-8	— / —	`test_tmle_parity.py` (+1)
`tobit`	censReg::censReg	R 4.5.2; censReg 0.5.38	rel_est<=1e-06, rel_se<=1e-05	2.8e-08 / 2.8e-08	`41_tobit.py` (+2)
`truncreg`	truncreg::truncreg(method="NR")	R 4.5.2; truncreg 0.2.5	rel_est<=1e-06, rel_se<=0.0001	3.3e-08 / 9.5e-08	`62_truncreg.py` (+2)
`twoway_cluster`	sandwich::vcovCL(cluster=~g1+g2)	R 4.5.2; sandwich 3.1.1	rel_est<=1e-06, rel_se<=1e-06	7.8e-16 / 7.8e-16	`54_twoway_cluster.py` (+2)
`var`	vars::VAR	R 4.5.2; vars 1.6.1	rel_est<=1e-06, rel_se<=0.001	3.1e-15 / 6.6e-15	`33_var.py` (+2)
`xtabond`	plm::pgmm	R 4.5.2; plm 2.6.7	rel_est<=1e-06, rel_se<=1e-06	9.0e-16 / 1.4e-15	`50_xtabond.py` (+2)
`zip_model`	pscl::zeroinfl(dist="poisson")	R 4.5.2; pscl 1.5.9	rel_est<=1e-06, rel_se<=0.0001	7.7e-08 / 1.1e-07	`63_zip.py` (+2)

aligned — 7 functions¶

Agreement within a documented, pre-registered looser tolerance.

function	reference	versions	tolerance	rel err (R / Stata)	test
`aft`	survival::survreg (Weibull AFT)	R 4.5.2; survival 3.8.3	coefficients & log-scale 5e-5 abs (observed ~1e-5)	— / —	`test_aft_parity.py` (+1)
`augsynth`	augsynth::augsynth	R 4.5.2; augsynth 0.2.0	rel_est<=2e-05, rel_se<=1e-06	7.9e-06 / —	`18_augsynth.py` (+1)
`causal_forest`	grf::causal_forest	R 4.5.2; grf 2.6.1	rel_est<=0.005, rel_se<=0.5	1.9e-03 / —	`13_causal_forest.py` (+1)
`cloglog`	stats::glm(binomial('cloglog'))	R 4.5.2	coefficients 5e-5 abs (observed ~1e-5; IRLS convergence)	— / —	`test_glm_ext_parity.py` (+1)
`survreg`	survival::survreg (Weibull AFT)	R 4.5.2; survival 3.8.3	coefficients & log-scale 5e-5 abs (observed ~1e-5)	— / —	`test_aft_parity.py` (+1)
`xtfrontier`	frontier::sfa	R 4.5.2; frontier 1.1.8	rel_est<=0.001, rel_se<=0.001	2.8e-06 / 8.6e-04	`29_panel_sfa.py` (+2)
`zinb`	pscl::zeroinfl(dist="negbin")	R 4.5.2; pscl 1.5.9	rel_est<=1e-05, rel_se<=0.001	1.1e-06 / 2.1e-07	`64_zinb.py` (+2)

external-replication — 4 functions¶

Reproduces published-paper numbers; sources in tests/external_parity/PUBLISHED_REFERENCE_VALUES.md.

function	test
`aggte`	`test_honest_did_paper_parity.py` (+1)
`breakdown_m`	`test_honest_did_paper_parity.py`
`g_estimation`	`test_whatif_nhefs.py`
`metalearner`	`test_causalml_book.py`

analytical-only — 60 functions¶

Recovers a known DGP truth / closed-form identity within tolerance; no cross-package reference. See tests/reference_parity/REFERENCES.md.

function	test
`aipw`	`test_paper_parity.py` (+1)
`assimilative_causal`	`test_assimilation_parity.py`
`average_treatment_effect`	`test_forest_ate_parity.py`
`bcf`	`test_bcf_parity.py`
`bidirectional_pci`	`test_proximal_parity.py`
`bunching`	`test_bunching_parity.py`
`causal_kalman`	`test_assimilation_parity.py`
`cbps`	`test_matching_parity.py` (+1)
`continuous_did`	`test_dose_response_parity.py`
`cr3_jackknife_vcov`	`test_recovery_batch2_parity.py`
`did_multiplegt`	`test_did_multiplegt_parity.py`
`discos`	`test_distributional_te_parity.py`
`distributional_te`	`test_distributional_te_parity.py`
`dose_response`	`test_dose_response_parity.py`
`ebalance`	`test_matching_parity.py` (+1)
`fortified_pci`	`test_proximal_parity.py`
`gardner_did`	`test_did_variants_parity.py`
`general_bunching`	`test_bunching_parity.py`
`genmatch`	`test_matching_recovery_parity.py`
`gmm`	`test_gmm_dynamic_panel_parity.py`
`hausman_test`	`test_diag_recovery_parity.py`
`hdfe_ols`	`test_hdfe_parity.py`
`interference`	`test_interference_parity.py`
`iv`	`test_regress_weights_iv_robust_parity.py`
`iv_diag`	`test_diag_recovery_parity.py`
`jackknife_se`	`test_recovery_batch2_parity.py`
`lincom`	`test_postestimation_parity.py`
`lingam`	`test_causal_discovery_parity.py`
`margins`	`test_postestimation_parity.py`
`match`	`test_matching_parity.py` (+1)
`matrix_completion`	`test_matrix_completion_parity.py`
`mc_panel`	`test_matrix_completion_parity.py`
`mr_egger`	`test_mr_parity.py`
`mr_ivw`	`test_mr_parity.py`
`mr_leave_one_out`	`test_mr_parity.py`
`mr_median`	`test_mr_parity.py`
`mr_presso`	`test_mr_parity.py`
`mr_radial`	`test_mr_parity.py`
`network_exposure`	`test_interference_parity.py`
`notears`	`test_causal_discovery_parity.py`
`overlap_weights`	`test_matching_parity.py` (+1)
`particle_filter`	`test_assimilation_parity.py`
`pc_algorithm`	`test_causal_discovery_parity.py`
`power_ols`	`test_recovery_batch_parity.py`
`principal_strat`	`test_principal_strat_parity.py`
`proximal`	`test_proximal_parity.py`
`psmatch2`	`test_psmatch2_parity.py`
`qdid`	`test_qte_parity.py`
`qte`	`test_qte_parity.py`
`ri_test`	`test_recovery_batch2_parity.py`
`rlasso_effects`	`test_rlasso_parity.py`
`sbw`	`test_matching_recovery_parity.py`
`spillover`	`test_interference_parity.py`
`stochastic_dominance`	`test_distributional_te_parity.py`
`subcluster_wild_bootstrap`	`test_wcb_recovery_parity.py`
`survivor_average_causal_effect`	`test_principal_strat_parity.py`
`svydesign`	`test_survey_parity.py`
`test`	`test_postestimation_parity.py`
`wild_cluster_bootstrap`	`test_wcb_recovery_parity.py`
`wooldridge_did`	`test_did_variants_parity.py`

unverified — 958 functions¶

These are registered public functions with no cross-language or published-reference parity evidence attached yet. This is the honest coverage gap, not a claim of incorrectness — many are frontier methods with no Stata/R sibling to align against. Query any of them with sp.parity_status(name); the closing roadmap lives in docs/dev/parity_status_roadmap.md.