spb/metrika Public

- 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>

- 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>

- 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>

- 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>