spb/metrika Public
Stata-class statistics, GPU-accelerated by Apple Silicon. Native Swift — no Electron, no Python runtime, no compromises.
Swift 92.4%
HTML 3.3%
R 3%
Shell 1.3%
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feat(stats): logit/probit/poisson MLE, tabulate, correlate
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- ZQGLM: Fisher-scoring IRLS through the LAPACK QR path (X'WX never formed), converged to |Δll| < 1e-13; classical, robust (HC0 score sandwich, Stata ML convention), and cluster (G/(G−1)) VCE; LR/Wald chi2, McFadden pseudo-R²; information matrix re-evaluated at the converged beta (R's vcov carries last-iteration weights and is only ~1e-6-accurate by its own stopping rule — fixtures compute expected information at the optimum explicitly) - Distributions: regularized incomplete gamma, chi-square CDF/p-value, normal quantile - ZQCorrelate: Pearson matrix on listwise-complete data - Engine: logit/probit/poisson tables (z, P>|z|, LR chi2, pseudo R²), one-way and two-way tabulate with totals, Stata-style lower-triangle correlate - Fixtures: binomial/poisson outcomes drawn after existing draws (earlier golden values bit-identical); glm at epsilon 1e-12 - 56 tests green (12 new) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>