pySHT: statistical hypothesis testing

pySHT is a Python library for statistical hypothesis testing. It provides literature-traceable implementations for mean, variance, covariance, joint-parameter, distributional, goodness-of-fit, and structured-domain questions, with a consistent result contract across methods.

The package is an extended and independently audited successor to SHT for R. Start with the installation guide, read the user guide, or go directly to the API reference.

Version 0.1.0. This initial public release is correctness-gated and fully documented. The API may still evolve before version 1.0.

Contents

• Getting started — install pySHT and run a first test.

• User guide — choose a procedure, prepare data, and interpret the result.

• API reference — inspect every public function and result type.

• Validation — review formulas, numerical checks, and independent comparisons.

• Project — see the roadmap, migration notes, and contribution guidance.

API categories

• [1] Univariate Mean — API

• [2] Multivariate Mean — API

• [3] Variance — API

• [4] Covariance — API

• [5] Mean and Variance — API

• [6] Mean and Covariance — API

• [7] Equality of Distributions — API

• [8] Normality — API

• [9] Rectangular Uniformity — API

• [10] Special Domains — API

The numbering and scientific organization follow SHT 0.1.9. The public pySHT API has 51 canonical functions covering 52 of its 54 statistical routine identities. The migration crosswalk explains the one shared implementation, the validation-blocked mean CLX and Fisher identities, and the two R-only adapters.