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.

Development documentation. This website follows the master branch and includes unreleased numerical corrections beyond the 0.5.0rc1 candidate. Version 0.1.0 remains the latest stable release; 0.5.0rc1 is still a draft candidate. The development API integrates the validated 0.2–0.5 expansion. Methods that did not pass their scientific release gate remain private or absent. See the Unreleased changelog for the current corrections.

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

• [11] Independence — API

• [12] Circular Data — API

Categories [0]–[10] preserve SHT 0.1.9’s numbering and scientific organization. The development API has 72 canonical functions: 51 SHT compatibility functions covering 52 of 54 statistical routine identities and 21 independently cataloged pySHT-native methods. The migration crosswalk explains the one shared implementation, the validation-blocked mean CLX and Fisher identities, and the two R-only adapters; the native catalog records the additional research methods and blocked candidates.