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%
-
feat(stats): Bayesian linear regression via Gibbs sampling (bayes prefix)
…
- 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>