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	148
aligned	16
analytical-only	227
external-replication	6
verified (subtotal)	397
unverified	774
total registered	1171

bit-exact — 148 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 / 0	69_balance_panel.py (+2)
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)
betweenness_centrality	Freeman betweenness centrality (shortest-path mediation)	R 4.5.2	centrality 1e-12 abs (observed 0)	— / —	test_network_centrality_parity.py
biprobit	R VGAM::vglm(binom2.rho) bivariate probit	R 4.5.2; VGAM 1.1.14	coef / rho 1e-6 abs (observed <= 2e-7); logLik 1e-6 rel	— / —	test_biprobit_parity.py (+1)
bonferroni	base R stats::p.adjust(method='bonferroni')	R 4.5.2	exact (atol 1e-15; observed 0)	— / —	test_mht_parity.py (+1)
bootstrap	nonparametric bootstrap contract (Efron 1979)	R 4.5.2	estimate/se contract 1e-12 abs (observed 0); SE ~ analytic 10%	— / —	test_bootstrap_parity.py
breakdown_frontier	additive-violation breakdown identities (Masten & Poirier 2021)	R 4.5.2	breakdown point / CI / bounds 1e-12 abs (observed 0)	— / —	test_breakdown_frontier_parity.py
callaway_santanna	did::att_gt + aggte	R 4.5.2; did 2.3.0	rel_est<=1e-06, rel_se<=1e-09	1.3e-15 / 1.3e-15	04_csdid.py (+2)
cgs_continuous_did	contdid::cont_did	R 4.5.2	rel_est<=1e-06	2.4e-14 / —	80_contdid.py (+1)
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)
clustering	local clustering coefficient (Watts-Strogatz 1998)	R 4.5.2	coefficient 1e-12 abs (observed 0)	— / —	test_network_centrality_parity.py
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)
contrast	treatment-contrast identity (= Stata margins, contrast(r))	R 4.5.2	contrast == dummy coefficient 1e-12 abs (observed <= 1e-15)	— / —	test_contrast_pwcompare_parity.py
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
ddd	Stata 18 MP regress [aw=w], robust (aweight HC1)	Stata 18 MP	b / se 1e-12 abs (observed <= 3e-15)	— / —	test_did2x2_ddd_weighted_robust_parity.py
ddd_heterogeneous	triplediff::ddd + agg_ddd	R 4.5.2	rel_est<=1e-06, rel_se<=1e-06	5.7e-15 / —	77_ddd.py (+1)
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)
degree_centrality	Freeman normalized degree centrality (deg_i / (n-1))	R 4.5.2	centrality 1e-12 abs (observed 0)	— / —	test_network_centrality_parity.py
demean	textbook mean-within (algorithmic)	R 4.5.2	rel_est<=1e-06, rel_se<=1e-06	3.5e-15 / 8.8e-15	68_demean_within.py (+2)
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_2x2	Stata 18 MP regress [aw=w], robust (aweight HC1)	Stata 18 MP	b / se 1e-12 abs (observed <= 3e-16)	— / —	test_did2x2_ddd_weighted_robust_parity.py
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)
did_multiplegt	DIDmultiplegt::did_multiplegt (archived 0.1.4)	R 4.5.2	rel_est<=1e-06	3.9e-15 / 3.2e-15	81_didm.py (+2)
did_multiplegt_dyn	DIDmultiplegtDYN::did_multiplegt_dyn	R 4.5.2	rel_est<=1e-06	3.3e-15 / 2.1e-15	78_multiplegt_dyn.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)
dml_sensitivity	doubleml (Python) DoubleML.sensitivity_analysis	—	bias_bound and adjusted theta bounds 1e-12 (observed 2.5e-15); RV 1e-6 (observed 9.2e-8); RVa is a documented convention gap (<5e-3, observed 1.4e-3) because StatsPAI exhausts	theta	-z*se with the unadjusted SE while doubleml lets the SE move with the confounding scenario
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)
ebalance	ebal::ebalance 0.2.1 (Hainmueller 2012)	—	ATT rel <= 1e-5 (observed 3.2e-7); moment gap <= 1e-10	— / —	test_matching_r_parity.py (+1)
eigenvector_centrality	leading adjacency eigenvector (Bonacich 1972)	R 4.5.2	centrality 1e-9 abs (observed <= 1e-15)	— / —	test_network_centrality_parity.py
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 / 1.8e-09	67_panel_glm.py (+2)
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 / 1.8e-09	67_panel_glm.py (+2)
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)
gardner_did	did2s::did2s	R 4.5.2	rel_est<=1e-06	4.8e-08 / 2.4e-12	73_did2s.py (+2)
gelbach	Gelbach (2016) exact conditional decomposition identity	R 4.5.2	total_change + contribution sum 1e-12 abs (observed 0)	— / —	test_gelbach_parity.py
glm	base R stats::glm (binomial logit + Poisson log)	R 4.5.2	coef / logLik / AIC 1e-8 abs (observed <= 5e-13); SE ~1e-3 rel	— / —	test_glm_parity.py (+1)
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)
icc	variance-ratio identity ICC = var_u / (var_u + var_e)	R 4.5.2	ICC vs model variance components 1e-12 abs (observed 0)	— / —	test_icc_parity.py
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
lee_bounds	Lee (2009) trimming-bound closed form (lee2009training)	R 4.5.2	bounds / trim fraction / retention 1e-12 abs (observed <= 1e-16)	— / —	test_lee_bounds_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 / 4.4e-15	34_lp.py (+2)
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)
lp_did	direct transcription (no LP-DiD R package installed); Stata side uses the authors' lpdid	R 4.5.2	rel_est<=1e-10, rel_se<=1e-10	5.0e-15 / 2.5e-15	83_lpdid.py (+2)
lrtest	likelihood-ratio identity chi2 = 2*(logL_full - logL_restricted)	R 4.5.2	chi2 / logL fields 1e-10 abs (observed 0); p == chi2.sf exact	— / —	test_lrtest_parity.py
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)
margins_at	predictive-margin linear form of the OLS coefficients	R 4.5.2	margin(x=v) 1e-10 abs (observed 0); symmetric-grid SE symmetric	— / —	test_margins_at_parity.py
match	MatchIt::matchit 4.7.2 (nearest, glm/logit distance)	—	1:1 and 2:1 PS matching without replacement: rel <= 1e-9. Mahalanobis metric pinned against MatchIt:::mahalanobis_dist (rel <= 1e-10); greedy m_order='data'/'closest' rel <= 1e-9.	— / —	test_matching_r_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)
mediate_interventional	interventional effects telescoping identity (VanderWeele Vansteelandt Robins 2014)	R 4.5.2	total = IIE + IDE 1e-12 abs (observed 0)	— / —	test_mediate_interventional_parity.py
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)
metalearner	econml.metalearners SLearner / TLearner / XLearner	—	S / T / X conditional-average-treatment-effect vectors match econml elementwise to 1e-12 absolute (observed <= 1.1e-15) when both fitting stages use the same base learner	— / —	test_metalearner_econml_parity.py
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)
mr	inverse-variance-weighted MR closed form (Burgess et al. 2013)	R 4.5.2	estimate / se / Cochran Q 1e-10 abs (observed 0)	— / —	test_mr_ivw_parity.py
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)
oster_delta	coefficient-stability bound identities (Oster 2019)	R 4.5.2	OLS inputs 1e-12 abs (observed 0); beta(delta*)=0 at 1e-10	— / —	test_oster_delta_parity.py
overlap_weights	WeightIt::weightit 1.7.0 (method='glm'), R 4.5.2	R 4.5.2; WeightIt 1.7.0	All four estimands of the shared-propensity family (Li, Li & Li 2019 Table 1) relative to WeightIt: ATO 2.5e-14, ATE 4.1e-14, ATT 2.3e-14, ATC 4.1e-14. The propensity score itself matches R glm(family=binomial) to 2.6e-14 absolute.	— / —	test_overlap_weights_r_parity.py (+1)
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)
panel_qtet	qte::panel.qtet 1.3.1 (Callaway & Li 2019)	—	all 19 quantiles: abs < 1e-8 (observed 6.8e-12); ATT abs < 1e-6. panel.qtet composes ordinary ecdf evaluations and type-7 quantiles, both of which have exact numpy equivalents, so this is machine-precision agreement rather than a tolerance band.	— / —	test_panel_qtet_parity.py (+1)
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)
policy_tree	policytree::policy_tree	R 4.5.2; policytree 1.2.4	rel_est<=1e-06, rel_se<=1e-06	9.6e-16 / 1.4e-16	70_policy_tree.py (+2)
policy_value	empirical policy value V(pi) = mean(Gamma * pi) (Athey & Wager 2021)	R 4.5.2	V(pi) == mean(scores * policy) 1e-15 abs (observed 0)	— / —	test_policy_value_parity.py
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)
psmatch2	Stata 18 MP + psmatch2 4.0.12 / pstest 4.2.2 (Leuven & Sianesi 2003)	Stata 18 MP; psmatch2 4.0.12	Observed py<->Stata relative gaps on the committed fixtures: nearest-neighbour ATT 1.2e-16 and its analytic SE exactly 0 (as is _weight per row); radius ATT 2.8e-16; Abadie-Imbens ai(1)/ai(2) SE 3.0e-15/1.7e-15; PSM-DID across all five weight regimes 2.0e-14; pstest per-covariate rows 1.3e-14; Mahalanobis ATT 1.2e-13; llr ATT 2.1e-10 (worst of four kernels); kernel ATT 9.1e-10; pstest summary block 1.3e-9. The two loosest rows are bounded by fixture precision, not by the estimator: pstest accumulates MeanBias/MedBias in a Stata float (2.6e-9) and r(seatt) for the llr reroute was captured at 8 significant digits (9.9e-9).	— / —	test_psmatch2_parity.py (+3)
pwcompare	pairwise-contrast identity (= Stata pwcompare)	R 4.5.2	pairwise diff == coef difference 1e-12 abs (observed <= 1e-15)	— / —	test_contrast_pwcompare_parity.py
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	9.3e-12 / 1.8e-11	09_rddensity.py (+2)
rdrobust	rdrobust::rdrobust	R 4.5.2; rdrobust 3.0.0	rel_est<=1e-06, rel_se<=0.1	2.5e-14 / 9.4e-11	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 / 5.1e-08	65_spatial.py (+2)
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 / 7.3e-16	66_spatial_gmm.py (+2)
sbw	sbw::sbw 1.2 (Zubizarreta 2015), quadprog solver	—	ATT rel <= 1e-8 (observed <= 4e-10) under both standardisation conventions: tolerance_scale='target' == bal_std='target' and 'group' == bal_std='group', at bal_tol 0.05 and 0.02.	— / —	test_matching_r_parity.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 / 5.1e-08	65_spatial.py (+2)
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 / 5.1e-08	65_spatial.py (+2)
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 / 7.3e-16	66_spatial_gmm.py (+2)
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
stacked_did	hand-written stack + fixest::feols	R 4.5.2; fixest 0.14.0	rel_est<=1e-06	3.9e-13 / 7.1e-13	75_stacked.py (+2)
staggered_rollout	staggered::staggered / staggered_cs / staggered_sa (1.2.2)	R 4.5.2	rel_est<=1e-10	9.1e-16 / 5.7e-16	82_staggered.py (+2)
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)
three_sls	R systemfit::systemfit(method='3SLS')	R 4.5.2; systemfit 1.1.30	coef 1e-9 abs (observed <= 1e-15); SE ~5e-3 rel	— / —	test_threesls_parity.py (+1)
tmle	tmle::tmle	R 4.5.2; tmle 2.1.1	rel_est<=1e-06, rel_se<=1e-06	1.9e-09 / 1.9e-09	72_tmle.py (+2)
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 — 16 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<=0.01, rel_se<=0.25	1.9e-03 / —	13_causal_forest.py (+1)
cbps	CBPS::CBPS 0.24 (Imai & Ratkovic 2014)	—	ATE over/exact and ATT exact: rel <= 5e-3 (R's optimiser slack). ATT over is NOT pinned to R -- CBPS's ATT gradient mis-scales the balance block by n/n_1 and stops off-stationarity; StatsPAI is asserted to attain strictly better covariate balance instead.	— / —	test_matching_r_parity.py (+1)
cic	qte::CiC	R 4.5.2	rel_est<=1e-06	2.4e-15 / 6.0e-03	74_cic.py (+2)
cloglog	stats::glm(binomial('cloglog'))	R 4.5.2	coefficients 5e-5 abs (observed ~1e-5; IRLS convergence)	— / —	test_glm_ext_parity.py (+1)
functional_form_test	didFF::didFF	R 4.5.2	rel_est<=0.001	1.3e-14 / —	79_didff.py (+1)
genmatch	Matching::Match 4.10-15 (Weight = 3, Weight.matrix)	—	Deterministic kernel only: given the same diagonal W, the 1-NN assignment agrees with Matching::Match on all 163 uniquely matched treated units on MatchIt::lalonde.	— / —	test_matching_r_parity.py (+1)
optimal_match	optmatch::pairmatch 0.10.8 on a logit propensity score	—	Total matched distance <= optmatch's (1 + 1e-6). The matched pairs are not pinned: the assignment problem is degenerate on this data, so equally optimal solutions report different ATTs.	— / —	test_matching_r_parity.py (+1)
pretrends_power	pretrends::pretrends / pretrends::slope_for_power (GitHub, not CRAN)	R 4.5.2	rel_est<=0.001	4.0e-05 / 1.4e-04	76_pretrends.py (+2)
pretrends_slope_for_power	pretrends::pretrends / pretrends::slope_for_power (GitHub, not CRAN)	R 4.5.2	rel_est<=0.001	4.0e-05 / 1.4e-04	76_pretrends.py (+2)
qdid	qte::QDiD 1.3.1	—	max deviation / scale < 0.08, sign agreement on the large effects and correlation > 0.999. R's quantiles come from BMisc::weighted_quantile (stats::optimize on a piecewise-linear check function, which has plateaus where every point is a minimiser) while sp.qdid interpolates the empirical inverse CDF; on this fixture the gap is at most ~152 currency units against effects running to ~8900.	— / —	test_qdid_parity.py (+1)
qte	qte::ci.qte / qte::ci.qtet 1.3.1 (Firpo 2007)	—	max relative deviation < 0.01 on lalonde.exp / lalonde.psid. Both sides minimise the same weighted check function; R's BMisc::weighted_quantile uses a golden-section search whose answer on a plateau is an optimiser artifact, so point-value equality is not asserted.	— / —	test_firpo_qte_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 — 6 functions

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

function	test
aggte	test_honest_did_paper_parity.py (+1)
bibtex	test_rebel_canal_published.py
breakdown_m	test_honest_did_paper_parity.py
event_study	test_rebel_canal_published.py
g_estimation	test_whatif_nhefs.py
parallel_trends_robustness	test_rebel_canal_published.py

analytical-only — 227 functions

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

function	test
ackerberg_caves_frazer	test_structural_parity.py
aggte_from_influence	test_aggte_r_did_parity.py
aipw	test_paper_parity.py (+1)
always_treat	test_longitudinal_parity.py
anderson_rubin_test	test_anderson_rubin_parity.py
assimilative_causal	test_assimilation_parity.py
attrition_test	test_attrition_test_parity.py
auto_cate	test_ml_causal_recovery_parity.py
auto_cate_tuned	test_ml_causal_recovery_parity_round2.py
average_treatment_effect	test_forest_ate_parity.py
bayes_iv	test_bayes_diagnostics_parity.py
bayes_rd	test_bayes_diagnostics_parity.py
bayes_synth	test_bayes_synth_parity.py
bcf	test_bcf_parity.py
bcf_factor_exposure	test_bcf_factor_exposure_parity.py
beyond_average_late	test_beyond_average_late_parity.py (+1)
bidirectional_pci	test_proximal_parity.py
blp	test_structural_parity.py
bradford_hill	test_bradford_hill_parity.py
breslow_day_test	test_breslow_day_parity.py
bunching	test_bunching_parity.py
bvar	test_timeseries_parity.py
calibration_test	test_calibration_test_parity.py
cardinality_match	test_cardinality_match_parity.py
cate_eval	test_ml_causal_recovery_parity.py
causal_kalman	test_assimilation_parity.py
causal_policy_forest	test_ope_parity.py
check_absorbing	test_absorbing_reference.py
clone_censor_weight	test_target_trial_parity.py
cluster_cate	test_ml_causal_recovery_parity_round2.py
cluster_cross_interference	test_cluster_cross_interference_parity.py
cluster_robust_se	test_cluster_robust_se_parity.py
conformal_cate	test_conformal_causal_parity.py
conformal_fair_ite	test_conformal_fair_ite_parity.py
conformal_ite	test_conformal_ite_parity.py
conformal_ite_interval	test_conformal_causal_parity.py
conley	test_conley_acreg_spacetime_parity.py (+1)
continuous_did	test_dose_response_parity.py
continuous_iv_late	test_continuous_iv_late_parity.py
counterfactual_fairness	test_fairness_parity.py
cox_frailty	test_competing_risks_parity.py
cr3_jackknife_vcov	test_recovery_batch2_parity.py
cuminc	test_competing_risks_parity.py
cusum_test	test_cusum_test_parity.py
demographic_parity	test_fairness_parity.py
describe_function	test_qdid_parity.py
did	test_cs_weighted_parity.py (+2)
did_balance	test_did_balance_parity.py
did_had	test_did_had_parity.py
discos	test_distributional_te_parity.py
dist_iv	test_dist_iv_parity.py
distributional_did	test_functional_form_extended_parity.py
distributional_te	test_distributional_te_inference.py (+1)
dml_panel	test_ml_causal_recovery_parity.py
dose_response	test_dose_response_parity.py
effective_f_test	test_anderson_rubin_parity.py
engle_granger	test_engle_granger_parity.py
equalized_odds	test_fairness_parity.py
etregress	test_etregress_parity.py
evidence_without_injustice	test_fairness_parity.py
fairlie	test_decomposition_family_parity.py
fairness_audit	test_fairness_parity.py
fci	test_fci_parity.py
ffl_decompose	test_decomposition_family_parity.py
finegray	test_competing_risks_parity.py
fisher_exact	test_fisher_exact_parity.py
focal_cate	test_ml_causal_recovery_parity_round2.py
fortified_pci	test_proximal_parity.py
four_way_decomposition	test_four_way_decomposition_parity.py
front_door	test_front_door_parity.py
frontdoor	test_frontdoor_parity.py
garch	test_timeseries_parity.py
geary	test_geary_parity.py
general_bunching	test_bunching_parity.py
geolift	test_geolift_parity.py
ges	test_ges_parity.py
getis_ord_local	test_esda_local_parity.py
gformula_ice_fn	test_gformula_family_parity.py
gformula_mc	test_gformula_family_parity.py
gmm	test_general_gmm_parity.py (+1)
granger_causality	test_timeseries_parity.py
grapple	test_grapple_parity.py
hal_tmle	test_ml_causal_recovery_parity_round2.py
hausman_test	test_diag_recovery_parity.py
hdfe_ols	test_hdfe_parity.py
honest_variance	test_forest_rate_honest_parity.py
horowitz_manski	test_horowitz_manski_parity.py
immortal_time_check	test_target_trial_parity.py
influence_functions	test_aggte_r_did_parity.py
interactive_fe	test_panel_estimators_parity.py
interference	test_interference_parity.py
ipcw	test_ipcw_parity.py
irf	test_timeseries_parity.py
its	test_timeseries_parity.py
iv	test_regress_weights_iv_robust_parity.py
iv_diag	test_diag_recovery_parity.py
ivqreg	test_ivqreg_parity.py
jackknife_se	test_recovery_batch2_parity.py
jive	test_jive_parity.py
johansen	test_timeseries_parity.py
join_counts	test_esda_local_parity.py
kan_dlate	test_dist_iv_parity.py
knn_weights	test_esda_local_parity.py (+4)
lasso_iv	test_lasso_iv_parity.py
lasso_select	test_lasso_select_parity.py
levinsohn_petrin	test_structural_parity.py
lincom	test_postestimation_parity.py
lingam	test_causal_discovery_parity.py
list_replications	test_castle_stata_parity.py (+3)
long_term_from_short	test_surrogate_parity.py
longitudinal_analyze	test_longitudinal_parity.py
longitudinal_contrast	test_longitudinal_parity.py
lpbwselect_mse_dpi	test_did_had_parity.py (+1)
lprobust_at_point	test_lprobust_parity.py
ltmle	test_ml_causal_recovery_parity_round2.py
ltmle_survival	test_ml_causal_recovery_parity_round2.py
malmquist	test_frontier_efficiency_parity.py
margins	test_postestimation_parity.py
markup	test_structural_parity.py
matrix_completion	test_matrix_completion_parity.py
mc_panel	test_matrix_completion_parity.py
meta_analysis	test_meta_analysis_parity.py
metafrontier	test_frontier_efficiency_parity.py
mi_estimate	test_imputation_parity.py
mice	test_imputation_parity.py
model_averaging_dml	test_ml_causal_recovery_parity.py
moran	test_moran_parity.py
moran_local	test_esda_local_parity.py
mr_cml	test_mr_cml_parity.py
mr_egger	test_mr_parity.py
mr_f_statistic	test_mr_f_steiger_parity.py
mr_heterogeneity	test_mr_diagnostics_parity.py
mr_ivw	test_mr_parity.py
mr_lap	test_mr_lap_parity.py
mr_leave_one_out	test_mr_parity.py
mr_median	test_mr_parity.py
mr_mediation	test_mr_mediation_parity.py
mr_mode	test_mr_mode_parity.py
mr_multivariable	test_mr_multivariable_parity.py
mr_pleiotropy_egger	test_mr_diagnostics_parity.py
mr_presso	test_mr_parity.py
mr_radial	test_mr_parity.py
mr_raps	test_mr_raps_parity.py
mr_steiger	test_mr_f_steiger_parity.py
msm	test_msm_family_parity.py
multi_treatment	test_multi_treatment_parity.py
network_exposure	test_interference_parity.py
never_treat	test_longitudinal_parity.py
notch	test_notch_parity.py
notears	test_causal_discovery_parity.py
olley_pakes	test_structural_parity.py
orthogonal_to_bias	test_fairness_parity.py
panel_fgls	test_panel_estimators_parity.py
panel_logit	test_panel_estimators_parity.py
panel_probit	test_panel_estimators_parity.py
panel_unitroot	test_timeseries_parity.py
particle_filter	test_assimilation_parity.py
pate	test_pate_parity.py
pc_algorithm	test_causal_discovery_parity.py
peer_effects	test_peer_effects_parity.py
policy_weight_ate	test_policy_weight_parity.py
policy_weight_marginal	test_policy_weight_parity.py
policy_weight_observed_prte	test_policy_weight_parity.py
policy_weight_subsidy	test_policy_weight_parity.py
power_ols	test_recovery_batch_parity.py
pretrends_equivalence	test_fect_equivalence_parity.py
principal_strat	test_principal_strat_parity.py
prod_fn	test_structural_parity.py
proximal	test_proximal_parity.py
proximal_surrogate_index	test_surrogate_parity.py
qte_hd_panel	test_hd_panel_qte.py
quasi_untreated_test	test_did_had_parity.py
rate	test_forest_rate_honest_parity.py
rd_honest	test_rdhonest_parity.py
rdmc	test_rdmulti_parity.py
rdpower	test_rdlocrand_parity.py
rdrandinf	test_rdlocrand_parity.py
rdwinselect	test_rdlocrand_parity.py
regime	test_longitudinal_parity.py
replicate	test_castle_stata_parity.py (+3)
ri_test	test_recovery_batch2_parity.py (+1)
rifreg	test_decomposition_family_parity.py
rlasso_effects	test_rlasso_parity.py
romano_wolf	test_romano_wolf_parity.py
sac	test_spatial_models_parity.py
sarar_gmm	test_spatial_models_parity.py
selection_bounds	test_selection_bounds_parity.py
shapley_inequality	test_decomposition_family_parity.py
sharp_ope_unobserved	test_ope_parity.py
slx	test_spatial_models_parity.py
spatial_did	test_spatial_models_parity.py (+1)
spatial_iv	test_spatial_models_parity.py
spatial_panel	test_spatial_models_parity.py
spillover	test_interference_parity.py
spillover_did	test_spillover_rings.py
sqreg	test_sqreg_parity.py
ssaggregate	test_bartik_ssagg_parity.py
stabilized_weights	test_stabilized_weights_parity.py
staggered_cs	test_staggered_extended_parity.py
staggered_sa	test_staggered_extended_parity.py
stepwise	test_stepwise_parity.py
stochastic_dominance	test_distributional_te_parity.py
structural_break	test_structural_break_parity.py
subcluster_wild_bootstrap	test_wcb_recovery_parity.py
super_learner	test_ml_causal_recovery_parity.py
surrogate_index	test_surrogate_parity.py
survivor_average_causal_effect	test_principal_strat_parity.py
svydesign	test_survey_parity.py
synthdid_estimate	test_texas_synth_parity.py
tF_critical_value	test_tf_critical_value_parity.py
target_trial_checklist	test_target_trial_parity.py
target_trial_emulate	test_target_trial_parity.py
target_trial_protocol	test_target_trial_parity.py
target_trial_report	test_target_trial_parity.py
test	test_postestimation_parity.py
test_calibration	test_calibration_test_parity.py
translog_design	test_translog_design_parity.py
transport_generalize	test_transport_parity.py
weighted_conformal_prediction	test_conformal_causal_parity.py
wild_cluster_bootstrap	test_wcb_recovery_parity.py (+1)
wild_cluster_ci_inv	test_wild_cluster_ci_inv_parity.py
wooldridge_did	test_did_variants_parity.py
wooldridge_prod	test_structural_parity.py
xlearner	test_ml_causal_recovery_parity.py
xtdpdsys	test_dynpanel_abdata_parity.py
xtlsdvc	test_lsdvc_parity.py
yatchew_linearity_test	test_did_had_parity.py

unverified — 774 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.