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docs: fix GitHub Pages documentation structure and rendering
- Fix badge URL from pages.yml to publish.yml workflow - Enable Mermaid diagram rendering with CDN script - Add comprehensive Key Features and Advanced Usage sections - Add proper Jekyll front matter to all documentation pages - Improve navigation with nav_order for proper page sequencing - Enhance content completeness and professional presentation
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‎docs/_config.yml‎

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- jekyll-feed
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- jekyll-sitemap
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# Mermaid configuration
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mermaid:
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src: 'https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js'
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markdown: kramdown
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highlighter: rouge
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‎docs/api-reference.md‎

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---
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title: API Reference
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layout: default
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nav_order: 5
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---
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# API Reference
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## StatClean Class

‎docs/examples.md‎

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---
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title: Examples
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layout: default
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nav_order: 3
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---
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# Examples
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## Quick Start Example

‎docs/index.md‎

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---
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title: Home
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layout: default
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nav_order: 1
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---
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# Welcome to StatClean
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Data preprocessing & outlier detection with formal statistical methods and publication-quality reporting.
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[![PyPI](https://img.shields.io/pypi/v/statclean.svg)](https://pypi.org/project/statclean/)
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[![Build](https://github.com/SubaashNair/StatClean/actions/workflows/pages.yml/badge.svg)](https://github.com/SubaashNair/StatClean/actions)
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[![Build](https://github.com/SubaashNair/StatClean/actions/workflows/publish.yml/badge.svg)](https://github.com/SubaashNair/StatClean/actions)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](../LICENSE)
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> Note: Remover methods return `self`. Access cleaned data via `cleaner.clean_df` and details via `cleaner.outlier_info`.
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## How It Flows
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```mermaid
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<div class="mermaid">
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flowchart LR
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A[DataFrame] --> B[Analyze Distribution]
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B --> C{Recommend Method}
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D --> F[Remove / Winsorize]
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E --> F
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F --> G[Report & Plots]
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```
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</div>
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<script src="https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js"></script>
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<script>
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mermaid.initialize({ startOnLoad: true });
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</script>
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## Navigation
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- [Statistical Methods](statistical-methods.md)
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- [API Reference](api-reference.md)
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## Key Features
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### 🔬 **Statistical Testing & Analysis**
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- **Formal Statistical Tests**: Grubbs' test and Dixon's Q-test with p-values
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- **Distribution Analysis**: Automatic normality testing and method recommendations
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- **Method Comparison**: Statistical agreement analysis between detection methods
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- **Publication-Quality Reporting**: P-values, confidence intervals, and effect sizes
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### 📊 **Detection Methods**
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- **Univariate**: IQR, Z-score, Modified Z-score (MAD-based)
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- **Multivariate**: Mahalanobis distance with chi-square thresholds
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- **Batch Processing**: Multi-column detection with progress tracking
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- **Automatic Selection**: Based on distribution characteristics
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### 🛠️ **Treatment Options**
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- **Removal**: Statistical validation with significance testing
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- **Winsorizing**: Cap outliers at bounds instead of removal
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- **Transformations**: Box-Cox, logarithmic, square-root with recommendations
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- **Method Chaining**: Fluent API for streamlined workflows
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## Advanced Usage
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```python
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# Formal statistical testing
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result = cleaner.grubbs_test('income', alpha=0.05)
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print(f"P-value: {result['p_value']:.6f}")
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# Multivariate outlier detection
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outliers = cleaner.detect_outliers_mahalanobis(['income', 'age'])
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# Method chaining with transformations
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cleaned = (cleaner
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.transform_boxcox('income')
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.remove_outliers_modified_zscore('income')
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.clean_df)
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```
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## Links
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- [GitHub Repository](https://github.com/SubaashNair/StatClean)

‎docs/installation.md‎

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---
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title: Installation
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layout: default
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nav_order: 2
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# Installation Guide
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## Installation

‎docs/statistical-methods.md‎

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title: Statistical Methods
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layout: default
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nav_order: 4
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# Statistical Methods
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## Overview

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