v2.3.0 - Additional Statistical Distributions & Performance Optimization

Major Feature Release - This release adds powerful new statistical distributions and comprehensive SIMD/threading optimizations, making libhmm suitable for advanced statistical modeling and high-performance applications.

🆕 New Statistical Distributions

Student's t-Distribution

• Complete implementation for robust statistical modeling with heavy tails
• Parameters: location (μ), scale (σ), and degrees of freedom (ν)
• Perfect for financial modeling, outlier-robust analysis, and extreme event modeling
• Includes MLE parameter fitting and comprehensive validation

Chi-squared Distribution

• Essential for goodness-of-fit testing and statistical hypothesis testing
• Parameter: degrees of freedom (k)
• Used in categorical data analysis and process capability studies
• Efficient implementation with numerical stability

⚡ Performance Optimizations

• 🔥 SIMD Vectorization: AVX, SSE2, and ARM NEON support with up to 3x speedup
• 🧵 Modern C++17 Thread Pool: Work-stealing algorithm for optimal multi-core performance
• 🎯 Optimized Forward-Backward Calculator: SIMD-accelerated matrix operations with cache-friendly layouts
• 🤖 Automatic Optimization: Runtime CPU feature detection and algorithm selection
• 💾 Memory Efficiency: Aligned allocators and memory pools for high-performance computing

📚 New Examples

• robust_financial_hmm_example.cpp: Heavy-tailed financial modeling using Student's t-distribution for crisis detection and robust portfolio optimization
• statistical_process_control_hmm_example.cpp: Advanced quality control using Chi-squared distribution for hypothesis testing and SPC

🔧 Enhancements & Fixes

• ✅ Zero-Warning Builds: Eliminated all compiler warnings across platforms
• 🔨 Fixed HMM Stream Parser: Resolved multiline distribution format parsing issues
• 📦 CMake Improvements: Fixed FindBoost deprecation warnings and enhanced cross-platform support
• 🛡️ Enhanced Error Handling: Improved parameter validation and exception safety
• 📖 Comprehensive Documentation: Updated README, CHANGELOG, and testing strategy

📊 Technical Specifications

Feature	Count	Status
Probability Distributions	17 total	✅ All tested
Test Suites	28 comprehensive	✅ 100% passing
Examples	9 real-world	✅ All domains covered
SIMD Support	3 architectures	✅ AVX/SSE2/NEON
Threading	C++17 standard	✅ Work-stealing pool

🎯 Use Cases

• Finance: Robust portfolio optimization, crisis detection, heavy-tailed risk modeling
• Manufacturing: Advanced statistical process control, quality monitoring, hypothesis testing
• Research: Heavy-tailed statistical modeling, goodness-of-fit testing, robust statistics
• High-Performance Computing: SIMD-accelerated matrix operations, parallel training

🔄 Backward Compatibility

✅ Fully Backward Compatible - All existing code continues to work unchanged. New features are opt-in.

🚀 Performance Gains

• Matrix Operations: Up to 3x speedup with SIMD vectorization
• Memory Access: Cache-optimized layouts reduce latency
• Parallel Processing: Multi-core training for large datasets
• Automatic Selection: Optimal algorithms chosen based on problem size and CPU features

📋 Requirements

• C++17 Compatible Compiler: GCC 7+, Clang 5+, MSVC 2017+
• CMake: 3.15 or later
• Boost Libraries: For matrix operations
• Platform: macOS, Linux, Unix-like systems

🔗 Quick Start

# Download and build
git clone https://github.com/OldCrow/libhmm.git
cd libhmm && mkdir build && cd build
cmake .. && make -j$(nproc)

# Run tests (28/28 should pass)
ctest

# Try new distributions
./examples/robust_financial_hmm_example
./examples/statistical_process_control_hmm_example

💡 Migration from v2.2.1

No breaking changes! Simply rebuild with the new version to get:
• Automatic SIMD optimizations (if supported by your CPU)
• Access to new Student's t and Chi-squared distributions
• Enhanced performance and reliability

Full CHANGELOG: v2.2.1...v2.3.0