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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perf(data): 880x faster loads via DuckDB C-API bulk extraction
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- root cause: duckdb-swift's element(forColumn:at:) linearly rescans every chunk on every element access — O(rows x chunks); a 10M-row CSV load took 276 s - DuckDBFastReader: pointer-based duckdb C calls via @_silgen_name (the C library is statically linked; only pointer-argument functions are declared, so no struct-ABI exposure), private in-memory instance, contiguous column_data/nullmask_data extraction - ZQDataStore.dataFrame(fromQuery:): LIMIT-0 schema probe through the supported API, SQL-side casts to DOUBLE/VARCHAR, memcpy per numeric column; dead per-element materializer removed - measured (M4-class): 10M-row parquet 0.20 s, csv 0.31 s, summarize ~0.27 s, robust reg ~0.24 s — CLAUDE.md §8 budgets met; table updated with measured numbers - 116 tests green (identical numerics through the new path) - version 1.0.1 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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docs: README with badges, screenshots, and the v1.0 story
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- hero: console session showing cluster-robust factor regression and the GPU-batched 10k bootstrap; screenshot grid (Charts by(), Metal 2M, data browser, manual) - shields badges: release, macOS 14+, Swift 6, arm64, Metal+MLX, 116 tests, R-validated 1e-10, notarized DMG - five-minute tour, feature matrix, numerical-validation section, architecture, build instructions, extensibility examples - METRIKA_AUTOPANE debug hook (headless pane selection for screenshots) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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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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fix(assets): move DMG background art to Assets/DMG (was under MetrikaKit)
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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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fix(release): stage only the .app into the DMG
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The export folder also contains DistributionSummary.plist and Packaging.log, which must not ship inside the image. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(app): user manual — in-app reference and console help
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- ZQCommandReference: one registry of every command (syntax, options, examples, notes) shared by the console and the app so they cannot drift; 35 commands across 10 categories - console: 'help' lists the reference by category, 'help <command>' (abbreviations resolve too) renders the full entry - app: Manual pane — searchable sidebar grouped by category, per-command pages (syntax/options/examples), and a 'How Metrika works' overview covering the command grammar, data handling, estimation conventions, the CPU/GPU planner, reproducibility, and user commands - release plumbing: real team ID + notary keychain-profile NAME in release.sh (both configuration, not credentials, per the security conventions), manual Developer ID signing with hardened runtime, -skipPackagePluginValidation, DMG background art - tests: help coverage test pins a manual entry for every implemented verb; UI smoke drives the Manual pane; 106 green 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(engine): predict with estimation state (e() analog)
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- PredictorDefinition (column / indicator / product / constant) records how each fitted regressor is recomputed from the dataset; threaded through buildRegressionSample so factor expansions and interactions carry their recipes - EstimationState stored after regress, logit/probit/poisson, and ivregress; cleared after xtreg (predict there needs the estimated u_i) - predict newvar [, xb | residuals | pr | n]: evaluates over ALL current observations with missing propagation; kind-aware defaults (xb for ols/iv, pr for logit/probit, n for poisson) and statistic validation - ZQFactorExpansion.expand now returns the numeric level alongside each indicator column - tests: xb + residuals reconstruct the response exactly (plain and factor models), mean fitted probability/count equals ybar (score equations), option validation; 93 green Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(plugins): harden the user-command protocol
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- fix: script-command verbs were routed AFTER parsing, which rejects unknown verbs — .zyq commands could never run; they now route before the parser with raw macro arguments - .zyq scripts: ado-style 'args name…' declaration with backtick macro substitution (`0', `k', `name'); scratch-variable scripts verified end-to-end against the built-in path - ZQPluginRegistry: verb-keyed native plugin registry; verbs shadowing built-ins rejected at session start; parsed commands validated against the declared ZQSyntaxSpec (varlist/if/in/weights/options) - mutation gating: plugins return a replacement dataset via ZQResult.replacementFrame, installed only under 'mutates: true' — a non-mutating plugin returning one is an error and the dataset is untouched (ZQContext is now a plain value, no writable back-channel) - ZScorePlugin ships as the reference native plugin, registered by the app ('zscore varname' -> standardized z_varname) - CLAUDE.md: document the mlx-swift build quirks (xcodebuild for GPU tests, -skipPackagePluginValidation everywhere) - 87 tests green (swift test and xcodebuild with GPU suites); app builds Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> -
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(gpu): MLX-batched pairs bootstrap with bit-identical Philox streams
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- mlx-swift dependency in ZQGPU (the only module allowed to import MLX) - Philox4x32-10 as vectorized MLX uint64 ops with the reference key schedule: GPU resample indices are bit-identical to the CPU generator (asserted per replicate) — the §5 reproducibility contract - batched gather + float32 cross-products on GPU, float64 Cholesky solves on CPU (§5 hybrid); chunked to the memory budget - conditioning: predictors standardized by fixed full-sample mean/scale (exact reparametrization) before forming X'X — raw data with an intercept lost 2-3 digits in float32 - MLX workaround: batched GEMM mis-accumulates small k x k outputs at batch >= 2 (~6e-4 rel, stride/fusion-independent); X'X is built one column at a time through the exact (count,k,n)@(count,n,1) path, with regression tests pinning the behavior - planner: gpuAvailable detected from actual metallib presence (SwiftPM CLI cannot compile Metal shaders - swift test skips GPU suites, the xcodebuild-built app and tests get the GPU); reps >= 500 dispatches bootstrap to .gpu - engine: backend-aware bootstrap, drops singular resamples, output titled 'GPU batched' with gpu scalar - 68 tests green under xcodebuild (66 + GPU suites under swift test skip) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(ui): data browser and do-file editor panes
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- DataBrowserPane (§7 pane 3): NSTableView bridge (natively virtualized), sortable columns with missing-last ordering, observation-number gutter, filter bar compiled to an if-expression through the real parser (ZQCommandParser.parseExpression + ZQSession.conditionMask are the new public entry points) - DoFileEditorPane (§7 pane 2): NSTextView bridge with ZQL highlighting (verbs resolved via ZQVerbTable, comments, strings), run selection or file with Cmd-R through the shared session path, open/save .zyq - ContentView: segmented Console / Data / Do-file switcher in the toolbar - UI smoke extended: pane switching, filter narrowing (2 of 5 obs), do-file run surfacing in the console and sidebar - 61 kit tests + 2 UI tests green Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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feat(data): native Stata .dta reader (117-119) and writer (118)
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- DTAReader: formats 117/118/119, both byte orders, all numeric storage types widened to Float64 with Stata missing codes (., .a-.z) mapped to the validity mask; str# and strL (GSO) load as strings; value labels read past but not yet applied; pre-117 files rejected with a clear message - DTAWriter: format 118 (UTF-8, LSF), doubles + str# up to 2045 bytes, real map offsets so Stata/haven/pandas can seek - ZQDataStore routes .dta through the native path for use and save - ZQColumnData equality now ignores value slots at missing positions (undefined by contract, often NaN — synthesized == failed on NaN != NaN) - Fixtures: haven-written 117 + 118 files; tests assert bit-identical numeric loads vs the CSV, lossless write/read roundtrip; haven cross-reads Metrika-written files (verified) - 61 tests green Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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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>
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test(ui): add XCUITest console smoke per CLAUDE.md §9
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- MetrikaUITests target (xcodegen scheme test action) - launch-environment hook (DEBUG only): sandboxed app materializes the test CSV in its own container, since the runner cannot stage files across the sandbox boundary reliably - queries predicate on AX 'value' (SwiftUI Text exposes value, not label) - smoke: launch, auto-load dataset, type gen + reg robust, assert regression table and sidebar variable 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>