edi_kernels

Python bindings for EDI's C++ model-fitting kernels, via pybind11. The point of this package is speed: the same C++ solvers used by the EDI R package, called directly from Python with no R/Rcpp dependency, running one to three orders of magnitude faster than the pure-Python canonical fit for the same model.

Source, issue tracker, and full documentation: github.com/kapelner/EDI — this package's own subdirectory is github.com/kapelner/EDI/tree/main/python. This repo hosts two related packages: the EDI R package (not yet on CRAN) and this Python package, sharing one C++ kernel implementation.

This package has no R or Rcpp dependency. It compiles the same R/EDI/src/*.cpp model-fitting kernels the R package uses, built under an EDI_CORE_ONLY preprocessor guard that swaps out RcppEigen/Rmath for a vanilla Eigen + LBFGSpp, both fetched directly from their own upstream repositories. Nothing under python/ is a copy of a R/EDI/src/*.cpp or *.h file — the compiled extension #includes them directly from the R package's own source tree.

See the full design spec (python_bindings_package_spec.md) for kernel-by-kernel scope and baseline-benchmarking methodology.

Install

pip install edi_kernels

Speed gains

Point-estimate timings (median of 30 cold runs) for every bound kernel that has a canonical Python baseline to compare against, grouped by response type and sorted by speedup, descending, within each group. Every row here is significant at p < 0.001 (Welch's t-test, EDI vs. canonical replicate distributions). Kernels with no Python canonical baseline to compare against are omitted from this table — see Kernels with no Python canonical baseline below for those instead.

Response type	Function	Speedup	Canonical (package / function)	EDI (ms)	Canonical (ms)
continuous	`fast_robust_regression`	69.49x	statsmodels `RLM`	0.17	11.92
continuous	`wilcox_hl_point_estimate`	4.57x	numpy `median(HL pairwise diff)`	0.25	1.12
continuous	`fast_ols`	3.25x	numpy `linalg.lstsq`	0.03	0.11
incidence	`fast_identity_binomial_regression`	149.00x	statsmodels `GLM(Binomial, identity link)`	0.10	15.49
incidence	`fast_probit_regression`	21.34x	statsmodels `GLM(Binomial, probit link)`	0.44	9.38
incidence	`fast_logistic_regression`	17.04x	statsmodels `GLM(Binomial)`	0.24	4.03
incidence	`fast_log_binomial_regression`	3.15x	statsmodels `GLM(Binomial, log link)`	1.63	5.13
survival	`fast_weibull_regression`	2423.43x	lifelines `WeibullAFTFitter`	0.08	192.55
survival	`get_survival_stat_diff` (median)	1647.81x	lifelines `KaplanMeierFitter(median)`	0.02	26.52
survival	`get_survival_stat_diff` (restricted_mean)	1460.28x	lifelines `utils.restricted_mean_survival_time`	0.01	19.30
survival	`fast_stratified_coxph_regression`	583.92x	lifelines `CoxPHFitter(strata=)`	0.47	273.61
survival	`fast_logrank_stats`	212.34x	lifelines `statistics.logrank_test`	0.12	26.28
survival	`fast_coxph_regression`	102.32x	scikit-survival `CoxPHSurvivalAnalysis`	0.50	51.62
count	`fast_hurdle_negbin`	57.90x	statsmodels `HurdleCountModel(negbin)`	2.33	135.15
count	`fast_zero_augmented_poisson` (is_hurdle=True)	29.33x	statsmodels `HurdleCountModel(poisson)`	1.43	41.83
count	`fast_zinb`	25.56x	statsmodels `ZeroInflatedNegativeBinomialP`	13.96	356.91
count	`fast_poisson_regression`	22.54x	statsmodels `GLM(Poisson)`	0.18	3.94
count	`fast_neg_bin`	22.13x	statsmodels `NegativeBinomial`	0.81	17.87
count	`fast_zero_augmented_poisson` (is_hurdle=False)	4.96x	statsmodels `ZeroInflatedPoisson`	11.91	59.04
proportion	`fast_beta_regression`	11.99x	statsmodels `BetaModel`	1.40	16.81
ordinal	`fast_ordinal_regression`	129.83x	statsmodels `OrderedModel(logit)`	0.91	117.69
ordinal	`fast_ordinal_probit_regression`	106.95x	statsmodels `OrderedModel(probit)`	0.80	86.01

Three additional response-family rows in the underlying benchmark (InferenceIncidGCompRiskDiff/RiskRatio, InferencePropGCompMeanDiff, InferenceOrdinalGCompMeanDiff) time a G-computation post-processing step layered on top of fast_logistic_regression/fast_ordinal_regression — that post-processing utility isn't itself a separately exposed edi_kernels function, so those rows are left out of the table above rather than misattributing their timing to a single bindable kernel.

**→ Full results, including the Wald/full-inference table (standard errors

p-values), the utility/math-kernel table, dataset spec, and methodology notes:** benchmark_model_fits_python.html. Same table shape as the R package's own benchmark_model_fits_R.html. (GitHub does not render standalone .html files inline when linked directly, and neither htmlpreview.github.io nor jsDelivr's CDN mirror actually renders as a page either — the former needs client-side JS a plain link visit won't trigger reliably, and the latter serves GitHub files as Content-Type: text/plain regardless of extension. Both links above go through githack.com's CDN mirror of this repo instead, which serves the file with the correct text/html content type, confirmed via its response headers, so it renders as a normal web page.)

Kernels with no Python canonical baseline

Fifteen bound kernels have no clean, actively-maintained Python package to benchmark against (an absent comparison is more honest than a mismatched substitute baseline). They're bound in edi_kernels regardless — EDI is still the only fast way to fit them in Python — they just have no speedup number to show:

Function	What it fits	Why there's no Python baseline
`fast_gaussian_lmm`	Gaussian LMM, random intercept, fit via MLE	statsmodels' `MixedLM` uses REML/a different estimation path by default — not a like-for-like comparison
`fast_poisson_glmm`	Poisson GLMM, random intercept, adaptive Gauss-Hermite quadrature + MLE	no pure-Python package offers ML (not variational/Bayesian) GLMM fitting
`fast_logistic_glmm`	Logistic GLMM, random intercept, adaptive Gauss-Hermite quadrature + MLE	same — no pure-Python ML GLMM fitter
`fast_hurdle_poisson_glmm`	Hurdle-Poisson GLMM, random intercept	no pure-Python package combines a hurdle count model with adaptive-quadrature GLMM fitting
`fast_cpoisson_combined`	KK combined (matched-pair + reservoir) joint-likelihood Poisson estimator	no canonical analog in either R or Python
`fast_clogit_plus_glmm`	KK combined (matched-pair + reservoir) joint-likelihood conditional-logit/GLMM estimator	no canonical analog in either R or Python
`fast_adjacent_category_logit`	Adjacent-category logit ordinal regression	no identified Python package implements this link
`fast_ordinal_cauchit_regression`	Ordinal regression, cauchit link	statsmodels' `OrderedModel(distr=)` only documents `'probit'`/`'logit'`
`fast_ordinal_cloglog_regression`	Ordinal regression, complementary log-log link	same — `distr=` doesn't officially support cloglog
`fast_continuation_ratio_regression`	Continuation-ratio ordinal regression	no identified Python package implements this link
`fast_ordinal_clmm`	Ordinal cumulative-link mixed model (logit/probit/cauchit/cloglog), random intercept	no pure-Python package offers ML ordinal-GLMM fitting with adaptive quadrature
`fast_ordinal_glmm`	Proportional-odds ordinal GLMM, random intercept	same — no pure-Python ML ordinal-GLMM fitter
`fast_stereotype_logit`	Stereotype logit ordinal regression	no Python package implements this link
`fast_zero_one_inflated_beta`	Zero-one-inflated beta regression (proportions)	no canonical package in either R or Python
`fast_weibull_frailty`	Weibull AFT with shared log-normal frailty	no clean Python package (even the R side only has a partial/PH-parameterized `frailtypack` analog)

Usage

Every bound kernel returns a dict mirroring the R package's own edi::ResultMap -> list conversion field for field. Most kernels take an estimate_only flag: True skips the variance/vcov computation for the fastest possible point estimate, False (the default on most kernels) additionally returns standard errors/vcov for Wald inference. A few families (the zero-inflated/hurdle count models, the constrained-binomial log/identity links) instead split this into two separate functions — a point-estimate-only kernel and a *_with_var sibling — rather than an estimate_only flag; those are shown as separate calls below.

Full per-argument documentation for every function is available via help(edi_kernels.<function>) — every kernel has a real docstring with a Parameters section, and the package ships a py.typed marker + type stub for IDE autocomplete.

Continuous

import numpy as np
from edi_kernels import fast_ols, fast_robust_regression, fast_gaussian_lmm

rng = np.random.default_rng(0)
n = 200
X = np.column_stack([np.ones(n), rng.binomial(1, 0.5, n), rng.normal(size=n)])
y = X @ [1.0, 0.5, -0.3] + rng.normal(size=n)

# continuous -- OLS: point estimate only
fit = fast_ols(X, y, estimate_only=True)
# continuous -- OLS: point estimate + model-based variance (SEs/p-values)
fit = fast_ols(X, y, estimate_only=False)

# continuous -- robust (M-estimator) regression: point estimate only
fit = fast_robust_regression(X, y, estimate_only=True)
# continuous -- robust (M-estimator) regression: point estimate + variance
fit = fast_robust_regression(X, y, estimate_only=False)

# group_id must partition rows into groups of size EXACTLY 1 or 2 (matched pairs / singletons) --
# a larger group silently corrupts memory for this whole GLMM/CLMM/LMM kernel family.
group_id = np.repeat(np.arange(n // 2), 2).astype(np.int32) + 1
b_re = rng.normal(scale=0.4, size=n // 2).repeat(2)
y_clustered = X @ [1.0, 0.5, -0.3] + b_re + rng.normal(scale=0.5, size=n)

# continuous -- Gaussian LMM (random intercept): point estimate only
fit = fast_gaussian_lmm(X, y_clustered, group_id, estimate_only=True)
# continuous -- Gaussian LMM: point estimate + variance
fit = fast_gaussian_lmm(X, y_clustered, group_id, estimate_only=False)

Incidence

import numpy as np
from edi_kernels import (
    fast_logistic_regression, fast_probit_regression,
    fast_log_binomial_regression, fast_log_binomial_regression_with_var,
    fast_identity_binomial_regression, fast_identity_binomial_regression_with_var,
    fast_logistic_glmm, fast_clogit_plus_glmm, gee_pairs_singletons,
)

rng = np.random.default_rng(1)
n = 200
X = np.column_stack([np.ones(n), rng.binomial(1, 0.5, n), rng.normal(size=n)])
p = 1 / (1 + np.exp(-(X @ [0.2, 0.8, -0.5])))
y_bin = rng.binomial(1, p).astype(float)

# incidence -- logistic regression: point estimate only
fit = fast_logistic_regression(X, y_bin, estimate_only=True)
# incidence -- logistic regression: point estimate + variance
fit = fast_logistic_regression(X, y_bin, estimate_only=False)

# incidence -- probit regression: point estimate only
fit = fast_probit_regression(X, y_bin, estimate_only=True)
# incidence -- probit regression: point estimate + variance
fit = fast_probit_regression(X, y_bin, estimate_only=False)

y_lowrisk = rng.binomial(1, 0.15 + 0.05 * X[:, 1]).astype(float)

# incidence -- log-binomial regression (risk ratio): point estimate only
fit = fast_log_binomial_regression(X, y_lowrisk, estimate_only=True)
# incidence -- log-binomial regression: point estimate + variance for coefficient j=1 (risk-ratio SE)
fit = fast_log_binomial_regression_with_var(X, y_lowrisk, j=1)

# incidence -- identity-binomial regression (risk difference): point estimate only
fit = fast_identity_binomial_regression(X, y_lowrisk, estimate_only=True)
# incidence -- identity-binomial regression: point estimate + variance for coefficient j=1 (risk-difference SE)
fit = fast_identity_binomial_regression_with_var(X, y_lowrisk, j=1)

# group_id must be groups of size EXACTLY 1 or 2 (same GLMM/CLMM/LMM-family constraint as above).
group_id = np.repeat(np.arange(n // 2), 2).astype(np.int32) + 1
b_re = rng.normal(scale=0.4, size=n // 2).repeat(2)
y_bin_clustered = rng.binomial(1, 1 / (1 + np.exp(-(X @ [0.2, 0.8, -0.5] + b_re)))).astype(float)

# incidence -- logistic GLMM (random intercept), j_T = 0-based treatment column: point estimate only
fit = fast_logistic_glmm(X, y_bin_clustered, group_id, j_T=1, estimate_only=True)
# incidence -- logistic GLMM: point estimate + variance
fit = fast_logistic_glmm(X, y_bin_clustered, group_id, j_T=1, estimate_only=False)

# KK combined estimator: discordant pairs -> conditional logit, concordant pairs -> random-intercept GLMM.
n_disc = 80
X_disc = rng.normal(size=(n_disc, 2))
y_disc = rng.binomial(1, 1 / (1 + np.exp(-(X_disc @ [0.6, -0.4])))).astype(float)
n_conc = 80
X_conc = rng.normal(size=(n_conc, 2))
group_conc = np.repeat(np.arange(n_conc // 2), 2).astype(np.int32) + 1  # concordant pairs, size EXACTLY 2
b_conc = rng.normal(scale=0.5, size=n_conc // 2).repeat(2)
y_conc = rng.binomial(1, 1 / (1 + np.exp(-(X_conc @ [0.6, -0.4] + b_conc)))).astype(float)

# incidence -- KK combined conditional-logit + random-intercept-GLMM (matched pairs): point estimate only
fit = fast_clogit_plus_glmm(X_disc, y_disc, X_conc, y_conc, group_conc, True, True, estimate_only=True)
# incidence -- KK combined conditional-logit + GLMM: point estimate + variance
fit = fast_clogit_plus_glmm(X_disc, y_disc, X_conc, y_conc, group_conc, True, True, estimate_only=False)

# incidence -- matched-pair/singleton GEE (family can be "gaussian"/"binomial"/"poisson"; always returns vcov)
fit = gee_pairs_singletons(X, y_bin_clustered, group_id, "binomial")

Survival

import numpy as np
from edi_kernels import (
    fast_coxph_regression, fast_stratified_coxph_regression,
    fast_weibull_regression, fast_weibull_frailty,
    fast_clayton_weibull_aft_optim, fast_dep_cens_transform_optim,
)

rng = np.random.default_rng(2)
n = 150
# survival kernels take X with NO intercept column -- the baseline hazard/log-scale absorbs it.
X = np.column_stack([rng.binomial(1, 0.5, n).astype(float), rng.normal(size=n)])
eta = X @ [0.5, -0.3]
event_time = rng.exponential(np.exp(-eta))
censor_time = rng.exponential(3.0, n)
dead = (event_time <= censor_time).astype(float)
y = np.minimum(event_time, censor_time)

# survival -- Cox proportional-hazards regression (unstratified): point estimate only
fit = fast_coxph_regression(X, y, dead, estimate_only=True)
# survival -- Cox PH regression: point estimate + variance
fit = fast_coxph_regression(X, y, dead, estimate_only=False)

strata = rng.integers(0, 3, n).astype(np.int32)
# survival -- stratified Cox PH regression (shared beta, per-stratum baseline hazard): point estimate only
fit = fast_stratified_coxph_regression(X, y, dead, strata, estimate_only=True)
# survival -- stratified Cox PH regression: point estimate + variance
fit = fast_stratified_coxph_regression(X, y, dead, strata, estimate_only=False)

# survival -- Weibull AFT regression: point estimate only
fit = fast_weibull_regression(X, y, dead, estimate_only=True)
# survival -- Weibull AFT regression: point estimate + variance
fit = fast_weibull_regression(X, y, dead, estimate_only=False)

# group_id: shared-frailty clusters (any size; uses adaptive Gauss-Hermite quadrature, not the fixed-2 shortcut).
group_id = np.repeat(np.arange(n // 2), 2).astype(np.int32) + 1
# survival -- Weibull AFT with shared gamma frailty (random intercept): point estimate only
fit = fast_weibull_frailty(X, y, dead, group_id, estimate_only=True)
# survival -- Weibull AFT with shared frailty: point estimate + variance
fit = fast_weibull_frailty(X, y, dead, group_id, estimate_only=False)

# KK combined estimator: matched valid pairs (Clayton-copula dependent censoring) + independent singletons.
n_pairs, n_single = 30, 20
n_tot = 2 * n_pairs + n_single
Xk = rng.normal(size=(n_tot, 1))
yk = np.exp(0.5 + Xk[:, 0] * 0.3 + rng.gumbel(size=n_tot) * 0.7)
deadk = rng.binomial(1, 0.85, n_tot).astype(float)
pair_idx = np.column_stack([np.arange(0, 2 * n_pairs, 2), np.arange(1, 2 * n_pairs, 2)]).astype(np.int32)
singleton_rows = np.arange(2 * n_pairs, n_tot).astype(np.int32)
warm_start_params = np.zeros(Xk.shape[1] + 2)  # beta, log_sigma, log_theta

# survival -- KK combined Clayton-copula Weibull AFT (matched pairs, dependent censoring): point estimate only
fit = fast_clayton_weibull_aft_optim(Xk, yk, deadk, pair_idx, singleton_rows, warm_start_params, estimate_only=True)
# survival -- KK combined Clayton-copula Weibull AFT: point estimate + variance
fit = fast_clayton_weibull_aft_optim(Xk, yk, deadk, pair_idx, singleton_rows, warm_start_params, estimate_only=False)

# survival -- dependent-censoring transformation-model AFT fit: point estimate only
fit = fast_dep_cens_transform_optim(X, y, dead, estimate_only=True)
# survival -- dependent-censoring transformation-model AFT fit: point estimate + variance
fit = fast_dep_cens_transform_optim(X, y, dead, estimate_only=False)

Count

import numpy as np
from edi_kernels import (
    fast_poisson_regression, fast_neg_bin,
    fast_zinb, fast_zinb_with_var,
    fast_zero_augmented_poisson, fast_zero_augmented_poisson_with_var,
    fast_hurdle_negbin, fast_truncated_negbin_count, fast_cpoisson_combined,
    fast_poisson_glmm, fast_hurdle_poisson_glmm,
)

rng = np.random.default_rng(3)
n = 200
X = np.column_stack([np.ones(n), rng.binomial(1, 0.5, n), rng.normal(size=n)])
mu = np.exp(0.3 + 0.4 * X[:, 1] + 0.2 * X[:, 2])
y_pois = rng.poisson(mu).astype(float)

# count -- Poisson regression: point estimate only
fit = fast_poisson_regression(X, y_pois, estimate_only=True)
# count -- Poisson regression: point estimate + variance
fit = fast_poisson_regression(X, y_pois, estimate_only=False)

r = 4.0
y_nb = rng.negative_binomial(r, r / (r + mu)).astype(float)

# count -- negative binomial regression (dispersion jointly MLE'd): point estimate only
fit = fast_neg_bin(X, y_nb, estimate_only=True)
# count -- negative binomial regression: point estimate + variance
fit = fast_neg_bin(X, y_nb, estimate_only=False)

# count -- zero-inflated negative binomial (Xc: count part, Xz: zero-inflation part): point estimate only
fit = fast_zinb(X, X, y_nb)
# count -- zero-inflated negative binomial: point estimate + full vcov
fit = fast_zinb_with_var(X, X, y_nb)

# count -- zero-inflated Poisson (is_hurdle=False) / hurdle Poisson (is_hurdle=True): point estimate only
fit = fast_zero_augmented_poisson(X, y_pois, X, is_hurdle=False)
# count -- zero-inflated Poisson: point estimate + full vcov
fit = fast_zero_augmented_poisson_with_var(X, y_pois, X, is_hurdle=False)

# count -- hurdle negative binomial (independent logistic hurdle + truncated-NegBin count part): point estimate only
fit = fast_hurdle_negbin(X, y_nb, X, estimate_only=True)
# count -- hurdle negative binomial: point estimate + variance
fit = fast_hurdle_negbin(X, y_nb, X, estimate_only=False)

y_trunc = y_nb.copy()
y_trunc[y_trunc == 0] = 1.0  # zero-truncated NegBin requires y >= 1

# count -- zero-truncated negative binomial regression: point estimate only
fit = fast_truncated_negbin_count(X, y_trunc, estimate_only=True)
# count -- zero-truncated negative binomial regression: point estimate + variance
fit = fast_truncated_negbin_count(X, y_trunc, estimate_only=False)

# KK combined estimator: conditional-Poisson matched valid pairs (binomial(n_k, p)) + Poisson reservoir singletons.
nd, nR = 40, 50
X_diff_v = rng.normal(size=(nd, 1)) * 0.5
n_k_v = rng.integers(4, 15, nd).astype(float)
yT_v = rng.binomial(n_k_v.astype(int), 1 / (1 + np.exp(-(X_diff_v @ [0.4])))).astype(float)
X_r = rng.normal(size=(nR, 1)) * 0.5
w_r = rng.binomial(1, 0.5, nR).astype(float)
y_r = rng.poisson(np.exp(0.3 + 0.4 * w_r + 0.4 * X_r[:, 0])).astype(float)

# count -- KK combined conditional-Poisson (matched pairs) + Poisson (reservoir singletons): point estimate only
fit = fast_cpoisson_combined(yT_v, n_k_v, X_diff_v, y_r, w_r, X_r, estimate_only=True)
# count -- KK combined conditional-Poisson + Poisson: point estimate + variance
fit = fast_cpoisson_combined(yT_v, n_k_v, X_diff_v, y_r, w_r, X_r, estimate_only=False)

# group_id must be groups of size EXACTLY 1 or 2 (GLMM/CLMM/LMM-family constraint).
group_id = np.repeat(np.arange(n // 2), 2).astype(np.int32) + 1
b_re = rng.normal(scale=0.3, size=n // 2).repeat(2)
y_pois_clustered = rng.poisson(np.exp(X @ [0.5, 0.3, -0.2] + b_re)).astype(float)

# count -- Poisson GLMM (random intercept), j_T = 0-based treatment column: point estimate only
fit = fast_poisson_glmm(X, y_pois_clustered, group_id, j_T=1, estimate_only=True)
# count -- Poisson GLMM: point estimate + variance
fit = fast_poisson_glmm(X, y_pois_clustered, group_id, j_T=1, estimate_only=False)

# count -- hurdle-Poisson GLMM (random intercept): point estimate only
fit = fast_hurdle_poisson_glmm(X, y_pois_clustered, group_id, j_T=1, estimate_only=True)
# count -- hurdle-Poisson GLMM: point estimate + variance
fit = fast_hurdle_poisson_glmm(X, y_pois_clustered, group_id, j_T=1, estimate_only=False)

Proportion

import numpy as np
from edi_kernels import fast_beta_regression, fast_zero_one_inflated_beta

rng = np.random.default_rng(4)
n = 300
X = np.column_stack([np.ones(n), rng.binomial(1, 0.5, n).astype(float), rng.normal(size=n)])
mu = 1 / (1 + np.exp(-(0.3 + 0.4 * X[:, 1] - 0.3 * X[:, 2])))
phi = 8.0
y_beta = rng.beta(mu * phi, (1 - mu) * phi)  # in the OPEN interval (0, 1)

# proportion -- beta regression: point estimate only
fit = fast_beta_regression(X, y_beta, estimate_only=True)
# proportion -- beta regression: point estimate + variance
fit = fast_beta_regression(X, y_beta, estimate_only=False)

# zero-one-inflated beta: y may include exact 0s/1s; X_zero_one is the inflation-part design matrix.
p0 = 1 / (1 + np.exp(-(-1.0 + 0.3 * X[:, 1])))
p1 = 1 / (1 + np.exp(-(-1.2 - 0.2 * X[:, 1])))
u = rng.uniform(size=n)
y01 = y_beta.copy()
y01[u < p0] = 0.0
y01[(u >= p0) & (u < p0 + p1)] = 1.0

# proportion -- zero-one-inflated beta regression (single-mode kernel; no estimate_only/with_var split)
fit = fast_zero_one_inflated_beta(X, X, y01)

Ordinal

import numpy as np
from edi_kernels import (
    fast_adjacent_category_logit, fast_continuation_ratio_regression,
    fast_ordinal_regression, fast_ordinal_probit_regression,
    fast_ordinal_cauchit_regression, fast_ordinal_cloglog_regression,
    fast_stereotype_logit, fast_ordinal_clmm, fast_ordinal_glmm,
)

rng = np.random.default_rng(5)
n = 300
K = 4
# ordinal kernels take X with NO intercept -- the K-1 alpha thresholds serve that role --
# and y is 1-INDEXED (values in {1, ..., K}), not 0-indexed.
X = np.column_stack([rng.binomial(1, 0.5, n).astype(float), rng.normal(size=n)])
alpha_true = np.array([-1.0, 0.2, 1.3])
eta = X @ [0.6, -0.4]
y = np.zeros(n)
for i in range(n):
    cum = 1 / (1 + np.exp(-(alpha_true - eta[i])))
    y[i] = 1 + np.sum(rng.uniform() > cum)
y = y.astype(np.int32)

# ordinal -- adjacent-category logit regression (single-mode kernel; always returns full inference)
fit = fast_adjacent_category_logit(X, y)
# ordinal -- continuation-ratio regression (single-mode kernel; always returns full inference)
fit = fast_continuation_ratio_regression(X, y)

# ordinal -- proportional-odds (logit link) regression: point estimate only
fit = fast_ordinal_regression(X, y, estimate_only=True)
# ordinal -- proportional-odds regression: point estimate + variance
fit = fast_ordinal_regression(X, y, estimate_only=False)

# ordinal -- ordered-probit regression: point estimate only
fit = fast_ordinal_probit_regression(X, y, estimate_only=True)
# ordinal -- ordered-probit regression: point estimate + variance
fit = fast_ordinal_probit_regression(X, y, estimate_only=False)

# ordinal -- ordinal regression, cauchit link: point estimate only
fit = fast_ordinal_cauchit_regression(X, y, estimate_only=True)
# ordinal -- ordinal regression, cauchit link: point estimate + variance
fit = fast_ordinal_cauchit_regression(X, y, estimate_only=False)

# ordinal -- ordinal regression, complementary log-log link: point estimate only
fit = fast_ordinal_cloglog_regression(X, y, estimate_only=True)
# ordinal -- ordinal regression, cloglog link: point estimate + variance
fit = fast_ordinal_cloglog_regression(X, y, estimate_only=False)

# stereotype logit's "score" parameterization needs better-conditioned data than the generic
# proportional-odds recipe above to converge cleanly -- a separate, smaller-K synthetic dataset.
n_st, K_st = 400, 3
X_st = np.column_stack([rng.binomial(1, 0.5, n_st).astype(float), rng.normal(size=n_st)])
alpha_st, beta_st = np.array([-1.0, 1.0]), np.array([0.6, -0.4])
eta_st = X_st @ beta_st
y_st = np.zeros(n_st)
for i in range(n_st):
    cum = 1 / (1 + np.exp(-(alpha_st - eta_st[i])))
    y_st[i] = 1 + np.sum(rng.uniform() > cum)
y_st = y_st.astype(np.int32)

# ordinal -- stereotype logit regression: point estimate only
fit = fast_stereotype_logit(X_st, y_st, estimate_only=True)
# ordinal -- stereotype logit regression: point estimate + variance
fit = fast_stereotype_logit(X_st, y_st, estimate_only=False)

# group_id must be groups of size EXACTLY 1 or 2 (GLMM/CLMM/LMM-family constraint).
group_id = np.repeat(np.arange(n // 2), 2).astype(np.int32) + 1
b_re = np.repeat(rng.normal(scale=0.4, size=n // 2), 2)
eta_clustered = X @ [0.6, -0.4] + b_re
y_clustered = np.zeros(n)
for i in range(n):
    cum = 1 / (1 + np.exp(-(np.array([-1.0, 0.5]) - eta_clustered[i])))
    y_clustered[i] = 1 + np.sum(rng.uniform() > cum)
y_clustered = y_clustered.astype(np.int32)

# ordinal -- cumulative-link mixed model (link: "logit"/"probit"/"cauchit"/"cloglog"), K=3 categories: point estimate only
fit = fast_ordinal_clmm(X, y_clustered, group_id, K=3, j_T=0, link="logit", estimate_only=True)
# ordinal -- cumulative-link mixed model: point estimate + variance
fit = fast_ordinal_clmm(X, y_clustered, group_id, K=3, j_T=0, link="logit", estimate_only=False)

# ordinal -- proportional-odds ordinal GLMM (logit link only), random intercept: point estimate only
fit = fast_ordinal_glmm(X, y_clustered, group_id, K=3, j_T=0, estimate_only=True)
# ordinal -- proportional-odds ordinal GLMM: point estimate + variance
fit = fast_ordinal_glmm(X, y_clustered, group_id, K=3, j_T=0, estimate_only=False)

Utilities

The functions below don't fit a parametric model -- they're nonparametric test statistics, closed-form CIs/p-values on count data, a post-fit robust-SE adjustment, and one pure math function. Grouped here rather than under a response type since none of them takes an estimate_only flag or has a "with variance" counterpart -- each is already a single, complete call.

import numpy as np
from edi_kernels import (
    ols_hc2_post_fit, wilcox_hl_point_estimate,
    mn_ci, mn_pvalue, newcombe_independent_ci,
    fast_gehan_wilcox_stats, fast_logrank_stats, get_survival_stat_diff,
    fast_ridit_analysis, fast_pchisq_upper,
)

rng = np.random.default_rng(6)
n = 150
X = np.column_stack([np.ones(n), rng.binomial(1, 0.5, n), rng.normal(size=n)])
y = X @ [1.0, 0.5, -0.3] + rng.normal(size=n)

# continuous -- heteroskedasticity-robust (HC2) SE, computed post-fit (on an OLS coefficient
# vector fit however you like -- see fast_ols above) for coefficient j_treat=1
b_hat, *_ = np.linalg.lstsq(X, y, rcond=None)
robust = ols_hc2_post_fit(X, y, b_hat, j_treat=1)

# continuous -- Wilcoxon/Hodges-Lehmann pairwise-difference point estimate (nonparametric two-group contrast)
hl = wilcox_hl_point_estimate(y, X[:, 1].astype(np.int32))

# incidence -- Miettinen-Nurminen score CI for a two-arm risk difference (30/100 treated, 18/95 control)
lower, upper = mn_ci(30.0, 100.0, 18.0, 95.0, 30.0 / 100.0, 18.0 / 95.0)
# incidence -- Miettinen-Nurminen score p-value for the same two-arm comparison, testing delta=0
pval = mn_pvalue(30.0, 100.0, 18.0, 95.0, 0.0, 30.0 / 100.0, 18.0 / 95.0)
# incidence -- Newcombe (Wilson-score-based) CI for the difference of two independent proportions
lower, upper = newcombe_independent_ci(30.0, 100.0, 18.0, 95.0)

# survival kernels take X with no intercept; dead/w are 0/1 event/treatment indicators.
Xs = np.column_stack([rng.binomial(1, 0.5, n).astype(float), rng.normal(size=n)])
event_time = rng.exponential(np.exp(-(Xs @ [0.5, -0.3])))
censor_time = rng.exponential(3.0, n)
dead = (event_time <= censor_time).astype(np.int32)
ys = np.minimum(event_time, censor_time)
w = rng.binomial(1, 0.5, n).astype(np.int32)

# survival -- Gehan-Wilcoxon two-sample test statistic (nonparametric)
stats = fast_gehan_wilcox_stats(ys, dead, w)
# survival -- (rho=0) log-rank two-sample test statistic (nonparametric)
stats = fast_logrank_stats(ys, dead, w)
# survival -- treatment-minus-control Kaplan-Meier median survival-time difference (nonparametric)
diff = get_survival_stat_diff(ys, dead, w, "median")

# ordinal -- Ridit analysis (nonparametric Mann-Whitney-style group contrast); y is 1-indexed {1,...,K}
w_ridit = rng.binomial(1, 0.5, n).astype(np.int32)
y_ridit = rng.integers(1, 5, n).astype(np.int32)
ridit = fast_ridit_analysis(w_ridit, y_ridit, "control")

# not tied to any response type -- upper-tail (survival function) p-value for a chi-squared statistic
pval = fast_pchisq_upper(3.84, df=1)

License

GPL-3.0-only, matching the EDI R package this compiles against (see LICENSE).

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

edi_kernels-1.0.0.post1.tar.gz (394.7 kB view details)

Uploaded Aug 11, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

edi_kernels-1.0.0.post1-cp313-cp313-win_amd64.whl (8.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.13Windows x86-64

edi_kernels-1.0.0.post1-cp313-cp313-manylinux_2_28_x86_64.whl (13.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.13manylinux: glibc 2.28+ x86-64

edi_kernels-1.0.0.post1-cp313-cp313-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.13macOS 11.0+ ARM64

edi_kernels-1.0.0.post1-cp313-cp313-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.13macOS 10.13+ x86-64

edi_kernels-1.0.0.post1-cp312-cp312-win_amd64.whl (8.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.12Windows x86-64

edi_kernels-1.0.0.post1-cp312-cp312-manylinux_2_28_x86_64.whl (13.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.12manylinux: glibc 2.28+ x86-64

edi_kernels-1.0.0.post1-cp312-cp312-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.12macOS 11.0+ ARM64

edi_kernels-1.0.0.post1-cp312-cp312-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.12macOS 10.13+ x86-64

edi_kernels-1.0.0.post1-cp311-cp311-win_amd64.whl (8.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.11Windows x86-64

edi_kernels-1.0.0.post1-cp311-cp311-manylinux_2_28_x86_64.whl (13.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.11manylinux: glibc 2.28+ x86-64

edi_kernels-1.0.0.post1-cp311-cp311-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.11macOS 11.0+ ARM64

edi_kernels-1.0.0.post1-cp311-cp311-macosx_10_9_x86_64.whl (1.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.11macOS 10.9+ x86-64

edi_kernels-1.0.0.post1-cp310-cp310-win_amd64.whl (8.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.10Windows x86-64

edi_kernels-1.0.0.post1-cp310-cp310-manylinux_2_28_x86_64.whl (13.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.10manylinux: glibc 2.28+ x86-64

edi_kernels-1.0.0.post1-cp310-cp310-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.10macOS 11.0+ ARM64

edi_kernels-1.0.0.post1-cp310-cp310-macosx_10_9_x86_64.whl (1.2 MB view details)

Uploaded Aug 11, 2026 CPython 3.10macOS 10.9+ x86-64

edi_kernels-1.0.0.post1-cp39-cp39-win_amd64.whl (9.9 MB view details)

Uploaded Aug 11, 2026 CPython 3.9Windows x86-64

edi_kernels-1.0.0.post1-cp39-cp39-manylinux_2_28_x86_64.whl (15.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.9manylinux: glibc 2.28+ x86-64

edi_kernels-1.0.0.post1-cp39-cp39-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Aug 11, 2026 CPython 3.9macOS 11.0+ ARM64

edi_kernels-1.0.0.post1-cp39-cp39-macosx_10_9_x86_64.whl (3.1 MB view details)

Uploaded Aug 11, 2026 CPython 3.9macOS 10.9+ x86-64

File details

Details for the file edi_kernels-1.0.0.post1.tar.gz.

File metadata

Download URL: edi_kernels-1.0.0.post1.tar.gz
Upload date: Aug 11, 2026
Size: 394.7 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for edi_kernels-1.0.0.post1.tar.gz
Algorithm	Hash digest
SHA256	`c9946cc7d573366289fc381d26d54f6180ead2053978be7f46c2d0dad13bd630`
MD5	`d2743c2a9b17cab2c19b5718de9a1d3d`
BLAKE2b-256	`f1f636d2ff2ac77ea29f8f9ece5f32e3f5b3624ce80aa54e7e548a254e752a66`

Algorithm	Hash digest
SHA256	`418af3bb6751e26f005234a28e818a1f37c69a6fd31de7570f845cfea79ec874`
MD5	`e7052c8886e1a92120603eb3bbe7a7ec`
BLAKE2b-256	`06178f2368933d0d103f529e285cf2bb510818666b9d893fcd6fb37580f941db`

Algorithm	Hash digest
SHA256	`a87a7564d5fd4a0403a6b93f99dc0168f13fd54c4f245250c1c018544353b906`
MD5	`04a67ed863d0be2c5a534fc2cd44ec9c`
BLAKE2b-256	`0aa80383d0abb7e867d15ea387643edd6ebede7ca8be52e0e192383af79568e5`

Algorithm	Hash digest
SHA256	`64c1ad380fb338b0a35bff35872f20c67c3821fd455fa99509acf5ac42a046df`
MD5	`12b76565feed8813e844dc3991262e22`
BLAKE2b-256	`ade10711a82b4ec9ddd72b586cd42c6a1e4d98f115a67fa27ece58728b8899bb`

Algorithm	Hash digest
SHA256	`19e78e34fe3e201de5c6142af8dcbb6e36e28d290ce38468fa3b2a9785f7d6d5`
MD5	`e61168c29dae35ad24f76a482a4f1b8c`
BLAKE2b-256	`33374cb123205de48c4d14d3db340e0ac6b4a8648ebe9a076519bf1e5333b632`

Algorithm	Hash digest
SHA256	`5078d32c40ab95e31b61979220ea2419f423b7e7e819de6c6ad2fa2a66cd1183`
MD5	`4b8f0368b0e504c02a9607d5dbeb67e2`
BLAKE2b-256	`7695974066ab4991513b05e91623dcfea7bd909e182a72e3e75b1d06f5224e0a`

Algorithm	Hash digest
SHA256	`29d34fc84c30d8c6335faeee178bc398ef0009755dede84e4f3981d440c1fbff`
MD5	`717a9780fac7e12e55eafae7a7e5b511`
BLAKE2b-256	`bd73447f35e143d4c043d8a2a7d8a245f3bc8d6e589027176669647c4ca75910`

Algorithm	Hash digest
SHA256	`5760f1033bfe439a77257f75f005b73fe896f5a14f94c758bb9f8cf21922779a`
MD5	`e91d2ad813f75fffdaf0788967e3af34`
BLAKE2b-256	`f1d66db1f4a59c64de0bff714fb1e781bb0003479565c0b907d1ad79cabd0ecb`

Algorithm	Hash digest
SHA256	`4e41bf5ec2e756e8fe7e3b043f3eabf7b68af378c5b9ebf4c972ac3aa8dd6947`
MD5	`2d223848e36c7ce24ac5931759719869`
BLAKE2b-256	`60aee210b2548078e6604a08d746745465c86936eb252a488358ce3787743827`

Algorithm	Hash digest
SHA256	`67ca4d8c745bf2ee8908daaec9feac6155bcf88797f442afdea4a0d10303aeeb`
MD5	`73610cf536f0e2d05e9daa9ad67448f0`
BLAKE2b-256	`8e3cef81ad658ad2075e044b433a03865ef00047c639d9406592a7adef27a675`

Algorithm	Hash digest
SHA256	`2b1cd7a97f2e534ace164e7c1c051a2a3474e5686f1715a55336df4e75b5b8a4`
MD5	`126f5461378162ea1bc67464beca6c9e`
BLAKE2b-256	`3e0cfef16b649f4f35a42251beb5d06c2b13ba960a5d125af62251b82d5fe77e`

Algorithm	Hash digest
SHA256	`d2b000edbd6c9533f87ce1b68ca4da3590f056ee6781a4452a1786e07610a62d`
MD5	`1c79f221f05a55db7ecbbd0710c72f83`
BLAKE2b-256	`1e9273b969c15da41d71908794c806017cea3c660ea641787d402af530ad25ad`

Algorithm	Hash digest
SHA256	`5229945955d8d87e016a18efe9c2f376d6fe3a9219e7ca0bfba5320ff34eef1b`
MD5	`e5dcb5fa32dfc087c609d88000b65a0f`
BLAKE2b-256	`6d462ffd4b3172714f86a7d5dc8c15e4074f468ce4a241cdaa85c51a68ada5df`

Algorithm	Hash digest
SHA256	`60eb827c18d2f44f1954872261142986cb9b5819fd4d8c8a38a180e8d66f99b9`
MD5	`889afa4ff232076c13ea18bbd6735348`
BLAKE2b-256	`05a5dbc08c84ad0c02223a7450f90c7cf308df986fa717654ec0ec3706b191e7`

Algorithm	Hash digest
SHA256	`b765cbc7dd6ad9d51aa27a63fb3b19a63ff6dcb7cdeb367d5dd78fc4b25ecf64`
MD5	`d2cc758a54d4bd32edefa7531e711a5b`
BLAKE2b-256	`f9af012d1bb83a8be4ebaf92bf2ad8f1a33445838b802a57e6acee4c9024a090`

Algorithm	Hash digest
SHA256	`e8e8687f1981f749843972f4b4a54d7fc5673af2d5d25f910b629c4d248d250a`
MD5	`baf15dd6095704fa5219a6fc52523c8b`
BLAKE2b-256	`11d0d439fc3cb357c09f714adc85f9a3cf81cb433e7bd6f70e4715c95f9f81d4`

Algorithm	Hash digest
SHA256	`b263ee0739092b624d0da24ed2421fa92d748d2f9a4bd4373f721af1514a8341`
MD5	`52dda2fc92b6dc57973e27a7c8891b59`
BLAKE2b-256	`7278575ecffc23a282cb0a0a6707331201e98db6e0e44e32190f32adcc37c085`

Algorithm	Hash digest
SHA256	`a6d4ed9e2d2aec32140ba56dcf01c95a8b21e5db25f720477efde371bcf7b18e`
MD5	`bd9f614a3a7a3051d68be130b3c839ec`
BLAKE2b-256	`fad5115c72fdcc586e3e4fd813d6c761e6cb5918ea5be0ddbc6ceb83c542cab1`

Algorithm	Hash digest
SHA256	`efd900e2d88509a4cb1fd75b54a9e44de9b89041566a931af070c658729f62e5`
MD5	`01f9ffc4052c9275efcabbb42d0b8ca6`
BLAKE2b-256	`5ca838c60c555a38bc0941acaec80c442786cf49e60ad1bb4dfa31be2e7b94d5`

Algorithm	Hash digest
SHA256	`932b078d0335b665d69e46c14f590918de0b8f8aab59d27fb41b78101be7de26`
MD5	`28a74ed7607ec67aa2f4d5a184fef8c2`
BLAKE2b-256	`7c8b639fd5325c02a8a589c7c618fb2aa20375747a066b1d94b9f6650831a4f8`

Algorithm	Hash digest
SHA256	`0d382c0c6dc58dfe4f87429f2755bf49bb6f976ebb00f6810b07d6da532949b0`
MD5	`ebd7e28e921463aac09238f630af8cb1`
BLAKE2b-256	`eb946b939851408e8b879ba946b20bf1287aff079b6658a3b272550dc18adc89`

edi-kernels 1.0.0.post1

edi_kernels

Install

Speed gains

Kernels with no Python canonical baseline

Usage

Continuous

Incidence

Survival

Count

Proportion

Ordinal

Utilities

License

Key dates

1 maintainer

License expression

Requires

Provides Extra

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata