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1.8K downloads. Trust evidence available

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Pywayne Statistics

Comprehensive statistical testing library with 37+ methods for normality tests, location tests, correlation tests, time series tests, and model diagnostics.... Skill: Pywayne Statistics Owner: wangyendt Summary: Comprehensive statistical testing library with 37+ methods for normality tests, location tests, correlation tests, time series tests, and model diagnostics.... Tags: latest:0.1.0 Version history: v0.1.0 | 2026-02-16T16:23:31.974Z | auto Initial release of pywayne-statistics. - Introduces a unified statistical testing library supporting 37+ tests across normality, lo

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Rank

Safety

Downloads

1.8k

Updated

Oct 10, 2026

Version

0.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.8K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.8K downloadsadoption · observed Oct 10, 2026
Latest release: 0.1.0release · observed Feb 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s172p3g61c0j9vy0sa1bs01b7x885y52:statistics-2

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-wangyendt-statistics-2/snapshot"

Documentation

CLAWHUB

13,147 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: pywayne-statistics
description: Comprehensive statistical testing library with 37+ methods for normality tests, location tests, correlation tests, time series tests, and model diagnostics. Use when performing hypothesis testing, A/B testing, data quality checks, time series analysis, or regression model validation. All methods return unified TestResult objects with consistent interface including p-value, statistic, confidence interval, and effect size.
---

# Pywayne Statistics

Comprehensive statistical testing library for hypothesis testing, A/B testing, and data analysis.

## Quick Start

```python
from pywayne.statistics import NormalityTests, LocationTests
import numpy as np

# Test data normality
nt = NormalityTests()
data = np.random.normal(0, 1, 100)
result = nt.shapiro_wilk(data)
print(f"p-value: {result.p_value:.4f}, is_normal: {not result.reject_null}")

# Compare two groups
lt = LocationTests()
group_a = np.random.normal(100, 15, 50)
group_b = np.random.normal(105, 15, 50)
result = lt.two_sample_ttest(group_a, group_b)
print(f"Significant difference: {result.reject_null}")
```

## Test Categories

### NormalityTests (`NormalityTests`)

Test if data follows a normal distribution or other specified distributions.

| Method | Description | Use Case |
|---------|-------------|-----------|
| `shapiro_wilk` | Shapiro-Wilk test | Small-medium samples (n ≤ 5000) |
| `ks_test_normal` | K-S normality test | Medium-large samples |
| `ks_test_two_sample` | Two-sample K-S test | Compare two sample distributions |
| `anderson_darling` | Anderson-Darling test | Tail-sensitive normality test |
| `dagostino_pearson` | D'Agostino-Pearson K² | Based on skewness and kurtosis |
| `jarque_bera` | Jarque-Bera test | Large samples, regression residuals |
| `chi_square_goodness_of_fit` | Chi-square goodness-of-fit | Categorical data |
| `lilliefors_test` | Lilliefors test | Unknown parameters K-S test |

**Example:**
```python
from pywayne.statistics import NormalityTests

nt = NormalityTests()
result = nt.shapiro_wilk(data)
if result.p_value < 0.05:
    print("Data is NOT normally distributed")
else:
    print("Data follows normal distribution")
```

### LocationTests (`LocationTests`)

Compare means or medians across groups (parametric and non-parametric).

| Method | Description | Use Case |
|---------|-------------|-----------|
| `one_sample_ttest` | One-sample t-test | Compare sample mean to a value |
| `two_sample_ttest` | Two-sample t-test | Compare two independent group means |
| `paired_ttest` | Paired t-test | Compare before/after measurements |
| `one_way_anova` | One-way ANOVA | Compare 3+ group means |
| `mann_whitney_u` | Mann-Whitney U test | Non-parametric two-sample test |
| `wilcoxon_signed_rank` | Wilcoxon signed-rank | Non-parametric paired test |
| `kruskal_wallis` | Kruskal-Wallis H test | Non-parametric multi-group test |

**Example (A/B Testing):**
```python
from pywayne.statistics import LocationTests, NormalityTests

lt = L

_meta.json

{
  "ownerId": "kn7d2b8vpaw5d7bq2ybbb9sqjh8143nf",
  "slug": "statistics-2",
  "version": "0.1.0",
  "publishedAt": 1771259011974
}

skill-card.md

## Description:

Pywayne Statistics is a usage guide for a Python statistical testing library with methods for normality, location, correlation, time series, and model diagnostics.

This skill is ready for commercial/non-commercial use.

## Publisher:

[wangyendt](https://clawhub.ai/user/wangyendt)

### License/Terms of Use:


## Use Case:

Developers, analysts, and data scientists use this skill to choose and apply pywayne.statistics tests for hypothesis testing, A/B testing, data quality checks, time series analysis, and regression diagnostics.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Statistical results can be misleading when test assumptions, sample size, or multiple comparisons are handled incorrectly.

Mitigation: Validate test assumptions, apply multiple testing correction where relevant, and report effect sizes alongside p-values.

Risk: The skill references an external Python package whose implementation was not covered by the skill security review.

Mitigation: Review and pin the external package before running it in production or sensitive environments.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/wangyendt/skills/statistics-2)
- [ClawHub publisher profile](https://clawhub.ai/user/wangyendt)

## Skill Output:

**Output Type(s):** [text, markdown, code, guidance]

**Output Format:** [Markdown with Python code examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Provides statistical method-selection guidance, result interpretation notes, and example Python snippets.]

## Skill Version(s):

0.1.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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