Changelog

Version 0.18.0 (2025-12-01) - Automated Profiling Report Release πŸ“Šοƒ

Added: - πŸš€ NEW FUNCTION: profile_report() - Comprehensive automated EDA profiling similar to ydata-profiling

  • Generate complete dataset analysis reports in a single function call

  • Dataset overview: rows, columns, memory usage, duplicates, missing cells

  • Data types summary: numeric, categorical, boolean column counts

  • Numerical statistics: mean, std, quartiles, min/max, missing values, zeros

  • Categorical analysis: top N columns by unique count with frequency distributions

  • Visualizations: histograms for numeric columns, correlation heatmap

  • Flexible output formats: HTML file with embedded visualizations or Python dictionary

  • Customizable categorical analysis with top_n_categorical parameter

Usage: .. code-block:: python

import edaflow

# Generate HTML report (default) report = edaflow.profile_report(df) # Creates β€˜eda_report_YYYYMMDD_HHMMSS.html’

# Or get dictionary for programmatic access report_dict = edaflow.profile_report(df, output_format=”dict”) print(report_dict[β€˜overview’]) print(report_dict[β€˜numerical_summary’])

# Customize categorical analysis report = edaflow.profile_report(df, top_n_categorical=10)

Technical Details: - Implementation: New edaflow.analysis.report module with 202 statements - Test Coverage: 29 comprehensive tests with 91% coverage on report.py - Integration: Reuses existing edaflow functions (check_null_columns, visualize_histograms, visualize_heatmap) - Defensive Programming: Comprehensive input validation with clear error messages - Memory Efficiency: Automatic matplotlib figure cleanup to prevent memory warnings - HTML Output: Standalone files with embedded base64-encoded visualizations and CSS styling

Documentation: - Added comprehensive README examples showing both output formats - Updated API reference documentation - Included in quickstart guide with complete usage examples

Version 0.16.4 (2025-09-12) - Major Examples & Docs Update πŸš€οƒ

Added: - examples directory with all referenced example scripts and documentation for a seamless learning experience - All documentation and guides updated to reference new features and examples - Improved onboarding and user guidance in ReadTheDocs and README - Verified and enhanced documentation for:

  • highlight_anomalies

  • create_lag_features

  • display_facet_grid

  • scale_features

  • group_rare_categories

  • export_figure

  • Minor bug fixes and consistency improvements across docs and codebase

Documentation: - User Guide, Advanced Features, and Visualization Guide now fully document all new APIs and usage patterns - Example scripts and workflows are copy-paste ready and match documentation - External library requirements and troubleshooting tips are clearly listed

All notable changes to edaflow are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Version 0.12.33 (2025-01-11) - Major API Improvement πŸš€οƒ

Added: - πŸš€ NEW CLEAN APIs: Introduced apply_encoding() and apply_encoding_with_encoders() functions for consistent, predictable behavior

  • apply_encoding(df) - Always returns DataFrame (recommended for most users)

  • apply_encoding_with_encoders(df) - Always returns (DataFrame, encoders) tuple

  • Clear, explicit function names that indicate exactly what they return

  • Comprehensive documentation with usage examples

Fixed: - πŸ› ROOT CAUSE RESOLVED: Eliminated confusion from apply_smart_encoding() inconsistent return types

  • Previous issue: Function returned DataFrame OR tuple based on return_encoders parameter

  • Enhanced error messages with helpful guidance for wrong data types

  • Robust detection and handling of tuple inputs in visualization functions

Deprecated: - ⚠️ DEPRECATION WARNING: apply_smart_encoding() with return_encoders=True now shows deprecation warning

  • Existing code continues working with guidance toward better alternatives

  • Clear migration path to new consistent functions

Usage Examples: .. code-block:: python

# βœ… NEW RECOMMENDED - Always returns DataFrame df_encoded = edaflow.apply_encoding(df)

# βœ… NEW EXPLICIT - Always returns tuple df_encoded, encoders = edaflow.apply_encoding_with_encoders(df)

# ⚠️ DEPRECATED - Inconsistent return type (still works) df_encoded = edaflow.apply_smart_encoding(df, return_encoders=True) # tuple!

Version 0.12.32 (2025-08-11) - Critical Input Validation Fix πŸ›οƒ

Fixed: - πŸ› CRITICAL INPUT FIX: Fixed AttributeError: β€˜tuple’ object has no attribute β€˜empty’ in visualization functions

  • Root cause: Users passing tuple result from apply_smart_encoding(..., return_encoders=True) directly to visualization functions

  • Enhanced input validation with helpful error messages for common usage mistakes

  • Better error handling in visualize_scatter_matrix and other visualization functions

  • Clear documentation showing correct vs incorrect usage patterns

  • Prevents crashes in step 14 of EDA workflows when encoding functions are misused

Technical Details: - Smart Error Detection: Automatically detects when tuple is passed instead of DataFrame - Helpful Error Messages: Guides users to correct usage pattern with code examples - Robust Input Validation: Added comprehensive type checking for all visualization functions - Workflow Stability: Eliminates common crash point in automated EDA workflows

Usage Examples: .. code-block:: python

# ❌ WRONG - This causes AttributeError: df_encoded = edaflow.apply_smart_encoding(df, return_encoders=True) # Returns tuple! edaflow.visualize_scatter_matrix(df_encoded) # Crashes

# βœ… CORRECT - Unpack the tuple: df_encoded, encoders = edaflow.apply_smart_encoding(df, return_encoders=True) edaflow.visualize_scatter_matrix(df_encoded) # Works perfectly!

Version 0.12.31 (2025-01-05) - Critical KeyError Hotfix πŸš¨οƒ

Fixed: - 🚨 CRITICAL HOTFIX: Fixed KeyError: β€˜type’ in summarize_eda_insights() function during Google Colab usage

  • Enhanced exception handling when target analysis dictionary missing expected keys

  • Implemented safe dictionary access using .get() method to prevent KeyErrors

  • All existing functionality preserved - pure stability fix

  • Verified fix across all notebook platforms (Colab, JupyterLab, VS Code)

Technical Details: - Robust Error Handling: Added comprehensive try-catch blocks for edge cases - Safe Dictionary Access: Uses .get() method instead of direct key access - Platform Compatibility: Tested and verified across all major notebook environments - Zero Regression: No functionality changes - purely stability improvements

Version 0.12.30 (2025-01-05) - Universal Display Optimization Breakthrough πŸŽ¨οƒ

Added: - 🎨 BREAKTHROUGH FEATURE: Introduced optimize_display() function for universal notebook compatibility

  • Automatic detection of Google Colab, JupyterLab, VS Code Notebooks, Classic Jupyter

  • Dynamic CSS injection for perfect dark/light mode visibility across all platforms

  • Automatic matplotlib backend optimization for each notebook environment

  • Solves visibility issues in dark mode themes universally

  • Zero configuration required - automatically detects and optimizes for your platform

  • Works flawlessly across all major notebook platforms

Usage: .. code-block:: python

from edaflow import optimize_display optimize_display() # Automatically optimizes for your platform

Technical Details: - Smart Platform Detection: Automatically identifies current notebook environment - Universal CSS Injection: Applies platform-specific styling for optimal visibility - Backend Optimization: Sets optimal matplotlib backend for each platform - Dark Mode Excellence: Perfect visibility in dark themes across all platforms - Zero Dependencies: Uses only standard library features for maximum compatibility

Version 0.12.29 (2025-08-11) - Critical Bug Fix for Unhashable Types πŸ›οƒ

Fixed: - πŸ› CRITICAL FIX: Fixed TypeError in analyze_categorical_columns when processing columns with unhashable types (lists, dicts)

  • Added proper exception handling for nunique() and unique() operations on columns containing unhashable data types

  • Function now converts unhashable types to strings before processing unique value counts

  • Added comprehensive error handling to gracefully handle any processing errors

  • Added missing return statement to provide structured data output for programmatic use

  • Returns dictionary with keys: object_columns, numeric_potential, truly_categorical, non_object_columns

Technical Details: - Enhanced Compatibility: Function now handles complex nested data structures without crashing - Robust Processing: Multiple fallback mechanisms ensure analysis completes successfully - Better API: Consistent return values enable programmatic access to analysis results

Version 0.12.28 (2025-08-11) - Comprehensive Display Formatting Excellence πŸŽ¨οƒ

Added: - ✨ NEW FUNCTION: summarize_eda_insights() - Generate comprehensive EDA insights and recommendations after completing analysis workflow

  • Analyzes dataset characteristics, data quality, class distribution, and feature types

  • Provides intelligent recommendations for modeling and preprocessing

  • Supports target column analysis for classification/regression tasks

  • Tracks which edaflow functions have been used in the workflow

  • Returns structured dictionary with organized insights and actionable recommendations

Fixed: - 🎨 ADDITIONAL DISPLAY FIXES: Resolved display formatting issues in multiple core functions - FIXED: convert_to_numeric - Removed unnecessary separator lines and changed to SIMPLE box style - FIXED: display_column_types - Removed separator lines and improved table border rendering - FIXED: impute_numerical_median - Cleaned up display formatting and fixed box joining issues - IMPROVED: All affected functions now use consistent SIMPLE box style for clean border joining - REMOVED: Unnecessary β€œ====” separators that cluttered the professional output

Technical Details: - Unified Styling: All core functions now share consistent, professional formatting standards - Perfect Border Joining: SIMPLE box style ensures clean table border connections - Visual Excellence: Removed all visual clutter for optimal user experience - Production Ready: Professional output suitable for client presentations and reports

Version 0.12.26 (2025-08-09) - Categorical Display Polish πŸ“Šοƒ

Fixed: - 🎨 CATEGORICAL DISPLAY FIX: Resolved display formatting issues in analyze_categorical_columns function - FIXED: Removed unnecessary blue line beneath main title - IMPROVED: CATEGORICAL COLUMNS sub heading now has clean background (removed black background) - ENHANCED: All table box styles changed to SIMPLE for proper line joining - FIXED: NON-OBJECT COLUMNS sub heading contrast improved (removed dark background) - IMPROVED: Column Type Analysis panel now uses SIMPLE box for clean borders - REMOVED: Unnecessary line under β€œAnalysis complete” message

Version 0.12.25 (2025-08-08) - Missing Data Display Enhancement πŸŽ¨οƒ

Fixed: - 🎨 DISPLAY FORMATTING FIX: Resolved unnecessary separator lines in check_null_columns function - FIXED: Removed redundant β€œ====” separators above and below MISSING DATA ANALYSIS banner - IMPROVED: Table border rendering now uses SIMPLE box style for clean line joining - ENHANCED: Professional output formatting without visual clutter

Version 0.12.24 (2025-08-08) - Texture Analysis Warning Fix πŸ”§οƒ

Fixed: - πŸ”§ TEXTURE ANALYSIS WARNING FIX: Resolved scikit-image UserWarning in analyze_image_features function - FIXED: Local Binary Pattern (LBP) analysis now properly converts images to uint8 format - RESOLVED: β€œfloating-point images may give unexpected results” warning from texture analysis - ENHANCED: Improved image preprocessing to handle both normalized [0,1] and [0,255] input images - OPTIMIZED: More robust texture feature extraction with proper data type handling

Technical Details: - Smart Data Type Detection: Automatically detects normalized vs standard image formats - Optimal Performance: LBP analysis now uses recommended integer format for better results - Professional Output: Clean execution without warnings in production environments - Backward Compatible: All existing code continues to work unchanged

Version 0.12.23 (2025-08-08) - Critical RTD Documentation Parameter Fix πŸš¨οƒ

Fixed: - 🚨 CRITICAL RTD DOCUMENTATION FIX: Corrected parameter name mismatches in analyze_image_features function - FIXED: Changed analyze_colors β†’ analyze_color in quickstart.rst documentation (3 instances) - FIXED: Changed bins β†’ bins_per_channel in RTD documentation examples - RESOLVED: TypeError when users followed RTD documentation examples exactly - ENHANCED: Documentation now matches actual function signature perfectly - TESTED: Created comprehensive test suite to prevent future parameter mismatches

Impact: - User Experience: Eliminated TypeError when following documentation examples - Documentation Quality: RTD examples now work correctly out-of-the-box - Professional Standards: Maintained edaflow’s reputation for accurate documentation

Version 0.12.22 (2025-08-08) - Google Colab Compatibility & Clean Workflow πŸŒŸοƒ

Fixed: - πŸ”§ GOOGLE COLAB COMPATIBILITY: Fixed KeyError in apply_smart_encoding documentation examples - FIXED: Removed hardcoded β€˜target’ column assumption in documentation examples - FIXED: Updated quickstart.rst and README.md with flexible column handling - RESOLVED: Documentation examples now work in Google Colab, Jupyter, and all environments - ENHANCED: More robust ML encoding workflow that adapts to user datasets

Enhanced: - πŸ“š CLEAN WORKFLOW: Removed redundant print statements from documentation examples - IMPROVED: Professional rich-styled output eliminates need for manual formatting - MODERNIZED: Documentation examples now showcase rich styling capabilities - CREATED: Google Colab compatibility test suite for validation

Impact: - Universal Compatibility: Documentation works across all Python environments - Modern Presentation: Clean, professional output using rich styling - Educational Value: Enhanced learning experience for users across platforms

Version 0.12.3 (2025-08-06) - Complete Positional Argument Compatibility Fix πŸ”§οƒ

CRITICAL BUG FIX:

Fixed: - CRITICAL: Resolved TypeError when calling visualize_image_classes(image_paths, ...) with positional arguments - Positional Arguments: Function now properly handles legacy positional argument usage from Jupyter notebooks - Backward Compatibility: Complete support for all three usage patterns:

  1. visualize_image_classes(path, ...) - Positional (deprecated, shows warning)

  2. visualize_image_classes(image_paths=path, ...) - Keyword deprecated (shows warning)

  3. visualize_image_classes(data_source=path, ...) - Recommended (no warning)

Improved: - User Experience: Clear deprecation warnings guide users toward recommended data_source= syntax - Function Architecture: Refactored to wrapper function pattern for robust argument handling - Error Messages: Enhanced error messages provide clear guidance for parameter usage - Documentation: Updated all examples to show modern data_source= syntax

Technical Details: - Implementation: Split function into public wrapper and internal implementation - Argument Handling: Proper detection and mapping of positional arguments to correct parameters - Warning System: Contextual warnings for different deprecated usage patterns - Testing: Comprehensive test suite validates all backward compatibility scenarios

Notes: - Zero Breaking Changes: All existing code continues to work unchanged - Jupyter Notebook Fix: Resolves the specific TypeError reported in Jupyter notebook usage - Migration Path: Users can migrate at their own pace with clear guidance

Version 0.12.2 (2025-08-06) - Documentation Refresh Release πŸ“šοƒ

Documentation: - PyPI Description: Refreshed PyPI package description with latest feature updates - Changelog Display: Fixed PyPI changelog display showing current version information - Version Alignment: Ensured all documentation reflects current v0.12.2 capabilities

Version 0.12.1 (2025-08-05) - Enhanced Computer Vision EDA πŸ–ΌοΈοƒ

Enhanced Functions: - visualize_image_classes(): Now supports both directory paths and pandas DataFrames as data sources

  • DataFrame Support: Pass image metadata as DataFrame with β€˜image_path’ and β€˜class’ columns

  • Directory Support: Continues to support organized folder structure (folder = class)

  • Flexible Input: Unified interface for different dataset organizations

  • Error Handling: Clear error messages guide proper usage for both input types

Improvements: - Data Source Flexibility: Switch between directory-based and metadata-based workflows - DataFrame Integration: Perfect for datasets with existing metadata and annotations - Backward Compatibility: Maintains all existing directory-based functionality - User Experience: Enhanced error messages and parameter validation

Version 0.10.0 (2025-08-05) - Image Quality Assessment Release πŸ”οƒ

Major New Feature: Comprehensive Image Quality Assessment

NEW Functions: - assess_image_quality(): Complete automated quality assessment for image datasets

Key Capabilities: - Corruption Detection: Automatically identify unreadable or damaged image files - Brightness Analysis: Flag overly dark or bright images with statistical thresholds - Contrast Assessment: Detect low-contrast images that might impact training - Blur Detection: Use Laplacian variance to identify potentially blurry images - Color Analysis: Distinguish between grayscale and color images, detect mixed modes - Dimension Consistency: Find unusual aspect ratios and size outliers using statistical methods - Artifact Detection: Identify compression artifacts and unusual patterns - Quality Scoring: Statistical quality scoring system (0-100) for overall dataset health - Automated Recommendations: Actionable suggestions for dataset improvement - Production Integration: Quality gates with customizable thresholds for ML pipelines - Scalable Analysis: Sampling support for efficient processing of large datasets

Enhanced Capabilities: - Expanded from 15 to 16 comprehensive EDA functions - Extended computer vision capabilities with production-ready quality assessment - Added scipy optimization for advanced blur detection algorithms - Comprehensive statistical analysis with detailed reporting - Educational focus on image quality standards and best practices

Perfect For: - Medical and scientific imaging with strict quality requirements - Production ML pipelines with automated data validation - Research and development with dataset quality monitoring - Educational purposes for learning image quality assessment

Version 0.9.0 (2025-08-05) - Computer Vision EDA Release πŸ–ΌοΈοƒ

Added

  • NEW: visualize_image_classes() function for comprehensive image classification dataset analysis

  • NEW: Computer Vision EDA workflow support with class-wise sample visualization

  • NEW: Directory-based and DataFrame-based image dataset analysis capabilities

  • NEW: Automatic class distribution analysis with imbalance detection

  • NEW: Image quality assessment with corrupted image detection

  • NEW: Statistical insights for image datasets (balance ratios, sample counts, warnings)

  • NEW: Professional grid layouts for image sample visualization with smart sizing

  • NEW: Technical image information display (dimensions, file sizes, aspect ratios)

  • NEW: Comprehensive documentation for computer vision EDA workflows

Enhanced

  • Complete EDA suite now includes 15 functions (expanded from 14)

  • Added Pillow dependency for robust image processing and visualization

  • Extended edaflow’s educational philosophy to computer vision domains

  • Professional documentation with computer vision examples and workflows

  • Updated package metadata and dependencies for image processing capabilities

Technical Features

  • Flexible Input Support: Both directory structures and DataFrame-based workflows

  • Quality Assessment: Automatic detection of corrupted images and data quality issues

  • Statistical Analysis: Comprehensive class balance analysis with actionable warnings

  • Professional Visualization: Smart grid layouts with customizable sampling strategies

  • Educational Integration: Maintains edaflow’s core principle of teaching through analysis

  • Production Ready: Robust error handling and validation for real-world datasets

Version 0.8.6 (2025-08-05) - PyPI Changelog Display Fix

Fixed

  • CRITICAL: Fixed PyPI changelog not displaying latest releases (v0.8.4, v0.8.5)

  • DOCUMENTATION: Updated README.md changelog section that PyPI displays instead of CHANGELOG.md

  • PYPI: Synchronized README.md changelog with comprehensive CHANGELOG.md content

  • ENHANCED: Ensured PyPI users see complete version history and latest features

Version 0.8.5 (2025-08-05) - Code Organization and Structure Improvement

Changed

  • REFACTORED: Renamed missing_data.py to core.py to better reflect comprehensive EDA functionality

  • ENHANCED: Updated module docstring to describe complete suite of analysis functions

  • IMPROVED: Better project structure with appropriately named core module containing all 14 EDA functions

  • FIXED: Updated all imports and tests to reference the new core module structure

  • MAINTAINED: Full backward compatibility - all functions work exactly the same

Version 0.8.4 (2025-08-05) - Comprehensive Scatter Matrix Visualization Release

Added

  • NEW: visualize_scatter_matrix() function with advanced pairwise relationship analysis

  • NEW: Flexible diagonal plots: histograms, KDE curves, and box plots

  • NEW: Customizable upper/lower triangles: scatter plots, correlation coefficients, or blank

  • NEW: Color coding by categorical variables for group-specific pattern analysis

  • NEW: Multiple regression line types: linear, polynomial (2nd/3rd degree), and LOWESS smoothing

  • NEW: Comprehensive statistical insights: correlation analysis, pattern identification

  • NEW: Professional scatter matrix layouts with adaptive figure sizing

  • NEW: Full integration with existing edaflow workflow and styling consistency

Enhanced

  • Complete EDA visualization suite now includes 14 functions (from 13)

  • Added scikit-learn and statsmodels dependencies for advanced analytics

  • Updated package metadata and documentation for scatter matrix capabilities

Technical Features

  • Matrix Customization: Independent control of diagonal, upper, and lower triangle content

  • Statistical Analysis: Automatic correlation strength categorization and reporting

  • Regression Analysis: Advanced trend line fitting with multiple algorithm options

  • Color Intelligence: Automatic categorical/numerical variable handling for color coding

  • Performance Optimization: Efficient handling of large datasets with smart sampling suggestions

  • Error Handling: Comprehensive validation with informative error messages

  • Professional Output: Publication-ready visualizations with consistent edaflow styling

Version 0.8.3 (2025-08-04) - Critical Documentation Fix Release

Fixed

  • CRITICAL: Updated README.md changelog section that PyPI was displaying instead of CHANGELOG.md

  • PYPI: Fixed PyPI changelog display by synchronizing README.md changelog with main CHANGELOG.md

  • DOCUMENTATION: Ensured consistent changelog information across all package files

Version 0.8.2 (2025-08-04) - Metadata Enhancement Release

Fixed

  • METADATA: Enhanced PyPI metadata to ensure proper changelog display

  • PYPI: Forced PyPI cache refresh by updating package metadata

  • LINKS: Added additional project URLs for better discoverability

Version 0.8.1 (2025-08-04) - Changelog Formatting Release

Fixed

  • Updated changelog dates to current date format

  • Removed duplicate changelog header that was causing PyPI display issues

  • Improved changelog formatting for better PyPI presentation

Version 0.8.0 (2025-08-04) - Statistical Histogram Analysis Release

Added

  • NEW: visualize_histograms() function with advanced statistical analysis and skewness detection

  • Comprehensive distribution analysis with normality testing (Shapiro-Wilk, Jarque-Bera, Anderson-Darling)

  • Advanced skewness interpretation: Normal (|skew| < 0.5), Moderate (0.5-1), High (β‰₯1)

  • Kurtosis analysis: Normal, Heavy-tailed (leptokurtic), Light-tailed (platykurtic)

  • KDE curve overlays and normal distribution comparisons

  • Statistical text boxes with comprehensive distribution metrics

  • Transformation recommendations based on skewness analysis

  • Multi-column histogram visualization with automatic subplot layout

Enhanced

  • Updated Complete EDA Workflow to include 12 functions (from 9)

  • Added histogram analysis as Step 10 in the comprehensive workflow

  • Enhanced README documentation with detailed histogram function examples

  • Comprehensive test suite with 7 test scenarios covering various distribution types

Fixed

  • Fixed Anderson-Darling test attribute error and improved statistical test error handling

Version 0.7.0 (2025-08-03) - Comprehensive Heatmap Visualization Release

Added

  • NEW: visualize_heatmap() function with comprehensive heatmap visualizations

  • Four distinct heatmap types: correlation, missing data patterns, values, and cross-tabulation

  • Multiple correlation methods: Pearson, Spearman, and Kendall

  • Missing data pattern visualization with threshold highlighting

  • Data values heatmap for detailed small dataset inspection

  • Cross-tabulation heatmaps for categorical relationship analysis

Enhanced

  • Complete EDA workflow now includes 11 steps with comprehensive heatmap analysis

  • Updated package features to highlight new heatmap visualization capabilities

Version 0.6.0 (2025-08-02) - Interactive Boxplot Visualization Release

Added

  • NEW: visualize_interactive_boxplots() function with full Plotly Express integration

  • Interactive boxplot visualization with hover tooltips, zoom, and pan functionality

  • Statistical summaries with emoji-formatted output for better readability

  • Customizable styling options (colors, dimensions, margins)

  • Smart column selection for numerical data

Enhanced

  • Complete EDA workflow now includes 10 steps with interactive final visualization

  • Added plotly>=5.0.0 dependency for interactive visualizations

Version 0.5.1 (2024-01-14) - Documentation Enhancement

Fixed

  • Updated PyPI documentation to properly showcase handle_outliers_median() function

  • Ensured PyPI page displays the complete 9-step EDA workflow including outlier handling

  • Synchronized local documentation improvements with PyPI display

Version 0.5.0 (2025-08-04) - Outlier Handling Release

Added

  • handle_outliers_median() function for automated outlier detection and replacement

  • Multiple outlier detection methods: IQR, Z-score, and Modified Z-score

  • Complete outlier analysis workflow integration with boxplot visualization

  • Median-based outlier replacement for robust statistical handling

  • Flexible column selection with automatic numerical column detection

Fixed

  • Dtype compatibility improvements to eliminate pandas FutureWarnings

  • Enhanced error handling and validation for numerical column processing

Earlier Versions

For complete version history, see the GitHub Releases page.

Note

This changelog covers the major releases. For detailed commit history and minor updates, visit the GitHub repository.