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(engine): margins dydx() with delta-method standard errors
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- ZQOLSResult/ZQGLMResult expose the full covariance matrix (column-major k x k aligned with coefficients); IV passes it through - EstimationState carries vce, inference df, and the estimation-sample design (GLMs only — AMEs need it) - margins, dydx(varlist): OLS/IV effects are the coefficients with their SEs; GLM average marginal effects with analytic delta gradients (logit p(1-p)(1-2p), probit -xb*phi, poisson exp) over the estimation sample; t or z inference per model kind - factor/interaction dydx rejected with a clear message (discrete-change margins later); continuous terms of factor models work - R fixtures mirror the exact formulas at the converged coefficients; logit/poisson AME and delta SE match at 1e-10 - 98 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>