StatOxide: High-performance statistical computing in Rust with Python bindings

StatOxide is a modern, high-performance statistical computing library written in Rust, with comprehensive Python bindings via PyO3. Designed for data scientists, statisticians, and researchers who need both performance and productivity.

🚀 Features

📊 Core Data Structures

• Series: Columnar data with metadata (name, dtype, levels)
• DataFrame: Tabular data structure with column operations
• Formula: R-style formula parsing for model specification

📈 Statistical Functions

• Descriptive Statistics: Mean, variance, skewness, kurtosis, quantiles
• Probability Distributions: 12 continuous + 6 discrete distributions
• Statistical Tests: t-test, chi-square, ANOVA, correlation tests
• Correlation Measures: Pearson, Spearman, Kendall tau

🧮 Statistical Models

• Linear Models: OLS, Ridge, Lasso, Elastic Net with proper inference
• Generalized Linear Models: Logistic, Poisson, Gamma, Negative Binomial regression
• Mixed Effects Models: Linear and GLMMs with EM algorithm estimation
• Robust Statistics: M-estimators, S-estimators, MM-estimators
• Nonparametric Methods: Kernel regression, local regression, smoothing splines

📉 Time Series Analysis

• Core Structures: TimeSeries with datetime indexing
• ARIMA Models: AR, MA, ARMA, ARIMA, SARIMA
• GARCH Models: ARCH, GARCH for volatility modeling
• Decomposition: STL, moving averages, Hodrick-Prescott filter
• Forecasting: Point forecasts, prediction intervals

🛠️ Utilities

• Linear Algebra: Matrix operations, solvers, decompositions
• Random Generation: Distributions, bootstrap, train-test split
• Data Validation: Type checking, missing value detection
• Numerical Methods: Softmax, standardization, normalization

🐍 Python API

StatOxide provides a complete Python interface through PyO3 bindings:

import statoxide
import statoxide.core as soc
import statoxide.stats as sos

# Core data structures
df = soc.DataFrame({
    "x": [1.0, 2.0, 3.0, 4.0, 5.0],
    "y": [2.0, 4.0, 5.0, 4.0, 5.0]
})

series = df.get_column("x")
print(f"Mean of x: {series.mean():.2f}")
print(f"Std of x: {series.std(1.0):.2f}")

# Statistical functions
print(f"Correlation: {sos.correlation(df.get_column('x').to_list(), 
                                      df.get_column('y').to_list()):.3f}")

summary = sos.descriptive_summary([1.0, 2.0, 3.0, 4.0, 5.0])
print(f"Summary: {summary}")

# Formula parsing
formula = soc.Formula("y ~ x + x^2")
print(f"Formula variables: {formula.variables()}")

# Models
import statoxide.models as som
result = som.linear_regression([[1, 1], [1, 2], [1, 3]], [5, 8, 11])
print(f"Regression coefficients: {result['coefficients']}")

# Mixed effects models
mixed_results = som.mixed_effects("y ~ x + (1 | group)", data)
print(f"Random effect variance: {mixed_results.random_variances}")

# Time series
import statoxide.tsa as sot
arima_result = sot.fit_arima([1.0, 2.0, 3.0, 4.0, 5.0], 1, 0, 1)
print(f"ARIMA AIC: {arima_result['aic']}")

# Utilities
import statoxide.utils as sou
train, test = sou.train_test_split([1.0, 2.0, 3.0, 4.0, 5.0], 0.2)
print(f"Train: {train}, Test: {test}")

🏗️ Architecture

StatOxide is organized as a multi-crate Rust workspace:

statoxide/
├── Cargo.toml              # Workspace configuration
├── crates/
│   ├── so-core/           # Core data structures & formula parsing
│   ├── so-linalg/         # Linear algebra abstraction
│   ├── so-stats/          # Statistical functions & distributions
│   ├── so-models/         # Statistical models (regression, GLM, mixed effects, etc.)
│   ├── so-tsa/            # Time series analysis
│   ├── so-utils/          # Utility functions
│   └── so-python/         # Python bindings (PyO3)
├── assets/logo.png        # Project logo
├── LICENSE-MIT           # MIT license
└── LICENSE-APACHE-2.0    # Apache 2.0 license

📦 Installation

Prerequisites

1. Rust Toolchain: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
2. Python Development Files:
   • Ubuntu/Debian: sudo apt-get install python3-dev python3.11-dev
   • macOS: brew install python@3.11
3. Maturin (recommended): pip install maturin

Building from Source

# Clone the repository
git clone https://github.com/EthanNOV56/StatOxide.git
cd StatOxide

# Build Python bindings with maturin
cd crates/so-python
maturin develop  # Editable install for development
# or
maturin build --release  # Build wheel for distribution

Direct Cargo Build

cd /path/to/statoxide
export PYO3_PYTHON=python3.11
cargo build --release --package so-python

The shared library will be at target/release/libso_python.so.

🧪 Testing

Rust Tests

cargo test --all

Python Tests

After installation:

python -c "import statoxide; print(statoxide.version())"
python crates/so-python/test_api.py  # API demonstration

📚 Documentation

• API Reference: Run cargo doc --all --no-deps --open for Rust documentation
• Python Docstrings: All Python functions include detailed docstrings
• Examples: See crates/so-python/test_api.py for usage examples

🎯 Design Principles

1. Performance: Leverage Rust's zero-cost abstractions and LLVM optimizations
2. Safety: Memory safety guarantees without garbage collection
3. Interoperability: Seamless Python integration with minimal overhead
4. Modularity: Independent crates for clear separation of concerns
5. API Consistency: Familiar interfaces inspired by R, pandas, and statsmodels

🔧 Development Status

Module	Status	Notes
so-core	✅ Complete	Data structures, formula parsing
so-linalg	✅ Complete	Linear algebra abstraction
so-stats	✅ Complete	Statistical functions & distributions
so-models	✅ Complete	Regression, GLM, mixed effects, robust, nonparametric
so-tsa	✅ Complete	ARIMA, GARCH, decomposition, forecasting
so-utils	✅ Complete	Random generation, validation, numerical methods
so-python	✅ Complete	Full Python bindings implemented

📄 License

StatOxide is dual-licensed under both:

• MIT License: See LICENSE-MIT for details
• Apache License 2.0: See LICENSE-APACHE-2.0 for details

You may use StatOxide under either license at your option.

🙏 Acknowledgments

• R and statsmodels for statistical API inspiration
• pandas for DataFrame design patterns
• PyO3 team for excellent Rust-Python interop
• ndarray and faer for numerical computing foundations

🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: cargo test --all
5. Submit a pull request

📞 Support

• Issues: GitHub Issues
• Repository: GitHub Repository

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High-performance statistics meets Python productivity