Stata-class statistics, GPU-accelerated by Apple Silicon. Native Swift — no Electron, no Python runtime, no compromises.
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Wed, Aug 5, 2026 8
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feat: v1.0 features — sample datasets, docs site, Metal 2M-point renderer
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- sysuse command with bundled samples (sales: 200-firm x 5-year panel; mtcars) shipped as ZQEngine resources, generated by scripts/make_samples.R - metrika-cli docs: static documentation site rendered from the shared command registry (docs/index.html, 38 commands, light/dark); CLI restructured with a default 'run' subcommand - Metal point-sprite renderer (CLAUDE.md §7 pane 4): packed 12-byte vertices, unified-memory buffer written once, scroll-pan / pinch-zoom / double-click-reset touching only a 24-byte uniform; takes over scatter plots past 100k points (METRIKA_METAL_THRESHOLD override for tests); verified rendering 2,000,000 points in-app - METRIKA_AUTORUN debug hook for headless app driving - MARKETING_VERSION 1.0.0 - 116 kit tests + 4 UI tests green Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(stats): gradient-boosted regression trees (boost command)
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- ZQGradientBoosting: exact-greedy trees cloning xgboost's algorithm — gain 1/2[GL2/(HL+l) + GR2/(HR+l) - G2/(H+l)] - gamma, leaf -G/(H+l), midpoint splits between consecutive distinct values, missing rows default left, pre-sorted feature indices; squared loss, deterministic (no subsampling) - engine: 'boost y x…, rounds(#) [eta() maxdepth() lambda()]' reporting training R2/RMSE with an in-sample caveat; model stored in the estimation state (Kind.boost) so predict routes through the trees (missing features follow the default direction); margins and GLM statistics refused after boost - validation: per-observation prediction parity with R xgboost 3.2 (exact method, base_score = mean) at 1e-4 (xgboost is float32 internally); a stump finds the exact midpoint split with lambda 0; training loss decreases monotonically in rounds - manual entry + coverage test - 115 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(stats): Bayesian linear regression via Gibbs sampling (bayes prefix)
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- PhiloxStream in ZQGPU: sequential variate stream over a dedicated counter block (bit 62 + per-stream 2^48 words, disjoint from bootstrap and permutation streams); Box-Muller normals, Marsaglia-Tsang gammas (moments verified against theory at 200k draws) - ZQBayesianRegression: semi-conjugate Gibbs with Stata bayes default priors (coefficients N(0, 10000), variance InvGamma(.01, .01)); exact full conditionals via dense Cholesky; posterior mean/sd and equal-tailed 95% credible intervals; ZQStats now depends on ZQGPU for the shared RNG - engine: 'bayes [, mcmcsize() burnin() seed() normalprior()]: reg …' with reproducible chains; manual entry included - validation: with diffuse priors the posterior reproduces OLS (mean within 5% of a posterior SD, sd ratio in [0.9, 1.15], CrI brackets the estimate, sigma recovers the DGP); tight priors shrink toward zero; chains bit-reproducible per seed - 111 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(stats): elastic net and lasso via coordinate descent
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- ZQElasticNet: cyclic coordinate descent with residual updates, glmnet-convention objective ((1/2n)RSS + lambda(alpha*l1 + (1-alpha)/2*l2)), internal predictor standardization (1/n variance), unpenalized intercept, coefficients reported on the original scale; lambdaMax helper - matched glmnet's gaussian y-standardization quirk deliberately: the L1 penalty is invariant to it but the effective ridge penalty scales by 1/sd(y) — without this, alpha<1 fits diverge from glmnet by ~10% - engine: 'elasticnet y x…, lambda(#) [alpha(#)]' and 'lasso' (alpha fixed at 1); missing lambda() errors with the data's lambda_max as a hint; predict works afterwards, margins refuses (no VCE) - fixtures: glmnet 5.0 at thresh 1e-15 over deliberately correlated regressors; coefficients match at 1e-6 (documented tolerance for penalized iterative solvers) and the selection pattern (which coefficients are exactly zero) matches exactly - ZQCoefficient gains a public initializer - 104 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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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>
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feat(stats): permutation tests and single-variable graphics
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- ZQResampling.permutationIndices: permutation as argsort of 64-bit Philox keys — order-free and counter-addressable so a future GPU argsort path reproduces it exactly; dedicated stream offset (bit 63, applied after word expansion) keeps permutations disjoint from bootstrap draws under the same seed - engine: 'permute, reps(#) [seed(#)]: reg …' — permutes the response within the estimation sample, parallel chunked refits, empirical two-sided p per coefficient (c, reps, p, SE(p)); degenerate _cons row omitted - planner: GPU heuristic restricted to bootstrap until the permute argsort path lands - graphics: standalone histogram/scatter/kdensity verbs; single-variable histogram (Sturges default, bins() option) and Epanechnikov kdensity with Silverman bandwidth (density integrates to 1 in tests) - 81 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(stats): xtreg fixed effects and ivregress 2sls
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- parser: (endog = instruments) varlist groups -> ZQIVSpec; digit-led sub-commands (2sls) reassembled from number+identifier tokens via column adjacency - ZQFixedEffects: within estimator with Stata conventions (add-back means, reported _cons, df = N-K-G), within R-squared, panel-clustered VCE with G/(G-1) and t on G-1 df; v0.2 restriction: cluster variable must equal the panel variable - ZQIV: 2SLS via thin-Q projection of the instrument matrix (Z'Z never formed), residuals from original regressors, Stata 'small' inference, classical/HC1/cluster VCE built on projected regressors - engine: xtreg (requires xtset + fe), ivregress 2sls with dedicated listwise deletion across depvar/exog/endog/instruments; shared coefficient-table renderer extracted - fixtures: z1/z2 instrument columns (drawn after existing draws, earlier golden values bit-identical), manual within/2SLS algebra in R - 75 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat: bootstrap Metrika v0.1 skeleton with working ZQL vertical slice
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- MetrikaKit SPM package: ZQParser, ZQPlanner, ZQEngine, ZQData, ZQStats, ZQGPU, ZQGraphics, ZQPlugins (Swift 6, strict concurrency) - ZQL parser: lexer, Pratt expressions, factor variables, prefix commands, column-cited errors with Levenshtein verb suggestions - ZQData: DuckDB-backed load/save (parquet, csv, json, arrow) - ZQStats: OLS via LAPACK QR, HC0-HC3 and cluster-robust SE, summarize, t/F distributions accurate in the far tails - ZQGPU: Philox4x32-10 reference RNG, counter-addressable bootstrap - ZQEngine: session actor with use/save/gen/replace/drop/keep/summarize/ regress/count/list/graph/bootstrap/set seed/xtset/log - SwiftUI app (xcodegen): console with history, variables sidebar, Swift Charts plots; sandboxed + hardened runtime entitlements - Tests: 44 green (parser golden, R fixtures at 1e-10, Philox KAT, end-to-end engine); Tests/Fixtures/generate.R; Tests/Bench harness - scripts: check_headers.sh + pre-commit hook, make_icns.sh, release.sh Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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