Changelog#

Unreleased#

Established the Python 3.12+ package, NumPy/SciPy runtime, C++17 native kernel, stable-ABI wheel, and typed-package scaffold.
Implemented and exposed 51 canonical Python functions representing 52 of the 54 public statistical routine identities in SHT 0.1.9. The catalog now covers univariate and multivariate means, variances, covariances, joint mean and variance, joint mean and covariance, equality of distributions, normality, rectangular uniformity, and simplex uniformity.
Added immutable StatisticalTestResult, HypothesisTestResult, ResamplingTestResult, DistanceTestResult, and BayesFactorTestResult contracts. Frequentist results render in an R htest-style report; Bayesian results retain component log Bayes factors without fabricating a p-value.
Added exact-enumeration and corrected Monte Carlo infrastructure, including (b + 1) / (B + 1) p-values, exceedance counts, conditional Monte Carlo standard errors, and 95% binomial tail-probability intervals.
Added shared validation, local random-number handling, stable whitening and linear algebra, log-domain tail and Bayes-factor calculations, bounded optimization, and overflow-aware scaling utilities.
Added the complete 56-export R migration crosswalk, searchable method-name glossary, category-organized API reference, interpretation-centered user guide, and method-specific validation ledgers.

Standardized every public function on lowercase snake_case author tokens and scientific category modules. Test functions are not re-exported from top-level pysht.
Renamed the equality-of-distributions entry point directly to pysht.equaldist.bg_2samp; no obsolete biswas_ghosh_2samp alias is provided.
Mapped both mvar1.1998AS and mvar1.LRT to the single algebraically identical pysht.mean_variance.as_1samp implementation.
Withheld mean2.2014CLX from the public API after its practical-size Gumbel calibration failed the release gate. Its implementation and validation ledger remain private so the unresolved scientific limitation is visible.
Withheld cov1.2012Fisher because its earlier optimized null-calibration evidence was not reproducible through the public path and practical fresh public-path regimes did not pass. The corrected implementation remains a private validation target.
Made the Lee–You–Lin procedures return maximum and component log Bayes factors. Their published default shrinkage is derived from alpha=2.01, sample size, and dimension, with an explicit gamma override.
Replaced the invalid Biswas–Ghosh asymptotic branch with exact or corrected Monte Carlo permutation calibration.
Switched release-wheel construction to cibuildwheel so Linux artifacts are repaired to portable manylinux wheels while preserving the CPython 3.12+ stable ABI.

Repaired known SHT defects in null-covariance whitening, Li–Chen scaling, two-sided Wu–Li covariance tests, Pearson–Neyman and Muirhead rejection tails, Cao–Park–He group indexing and variance branches, adjusted and robust Jarque–Bera defaults, and two-sample Dempster calibration.
Held random projection and subspace plans fixed across observed and permuted statistics, canonicalized seeded plans across row and group order, and avoided touching NumPy’s global random state.
Stabilized Zhang–Xu–Chen quadrature and roots, extreme-value tails, likelihood ratios, covariance traces, and Bayes-factor kernels for extreme float64 magnitudes.
Centered samples against null or shared anchors before scaling throughout the mean, variance, covariance, normality, and joint-parameter paths, with explicit opposite-endpoint fallbacks. This preserves representable variation near very large common locations and across heterogeneous scales.
Corrected the Yang–Modarres Q3 asymptotic law for the nonzero correlation of its signed components, retained the published robust Jarque–Bera denominator constant 64 instead of SHT’s erroneous 24, and made the Arnold–Shavelle calculation fully log-domain.
Tightened exact-resampling count consistency, Bayes-factor component/max consistency, simplex-interior validation, optimizer convergence checks, and covariance eigensolver and extreme-tail boundaries.

Added primary-paper formula ledgers, literal or trusted numerical oracles, fixed fixtures, transformation invariants, boundary tests, null-calibration audits, named-seed targeted alternative-power checks for every public frequentist procedure, Bayesian evidence-direction checks, and explicit legacy-correction records.
Added strict typing, linting and formatting, branch-coverage enforcement, warning-as-error Sphinx builds, source-distribution checks, cross-platform portable wheel builds, and isolated installed-wheel smoke tests for the exact 51-function public surface on Python 3.12–3.14.
Added a release-triggered, cross-platform GitHub Actions workflow for tokenless PyPI Trusted Publishing. The workflow assembles one source archive and four platform wheels, smoke-tests the exact artifacts, records SHA-256 hashes, and grants OIDC permission only to the final protected publishing job.
Added a minimum-supported NumPy/SciPy CI gate, replayable release-audit runners, and the BSD/MIT notices required by the native extension’s statically linked nanobind and tsl::robin_map code.