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pywayne-statistics

Run statistical hypothesis tests across arrays with a unified TestResult interface.

8|Updated Feb 2, 2026
Search Tags:#statistics#regression#time-series#data-quality#hypothesis-testing#a/b-testing#normality
One-click install
npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-statistics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pywayne-statistics
Source: https://github.com/wangyendt/wayne-skills/tree/main/pywayne/statistics
Command: npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-statistics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Comprehensive statistical testing library providing a unified interface for hypothesis testing, normality checks, time-series analysis, and model diagnostics to accelerate data-driven decisions.

Core Features & Use Cases

  • NormalityTests: assess distributional assumptions with Shapiro-Wilk, KS, and more.
  • LocationTests: compare means/medians across groups (t-tests, ANOVA, non-parametric tests).
  • CorrelationTests: measure relationships and independence (Pearson, Spearman, Kendall, chi-square, Fisher).
  • TimeSeriesTests: test stationarity, autocorrelation, and cointegration in time-series data.
  • ModelDiagnostics: diagnose heteroscedasticity, autocorrelation, multicollinearity, and residual properties.
  • Use cases: data-quality checks, A/B testing workflows, and regression diagnostics across datasets.

Quick Start

Install the library and run NormalityTests on a sample array to obtain a p-value and a verdict.

Frequently Asked Questions about pywayne-statistics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run normality tests and hypothesis testing on data arrays?▼

Run normality tests and hypothesis testing by passing arrays or lists into a unified suite that returns a consistent TestResult interface, delivering p-values and verdicts for data analysis.

What statistical tests are available for comparing means across groups?▼

For comparing means across groups, LocationTests provides t-tests, ANOVA, and non-parametric tests to evaluate statistical differences between sample arrays.

Can I use this for A/B testing workflows and regression diagnostics?▼

Yes, you can use this for A/B testing workflows and regression diagnostics, as it includes ModelDiagnostics for heteroscedasticity, multicollinearity, and residual property checks.

Does this library support time-series analysis for stationarity and autocorrelation?▼

Yes, TimeSeriesTests support time-series analysis by testing for stationarity, autocorrelation, and cointegration directly on your sequential data.

Do I need external dependencies to perform correlation tests?▼

No external dependencies are required to perform correlation tests; the library offers dependency-light usage for Pearson, Spearman, Kendall, chi-square, and Fisher measures.

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