febolt

FeBOLT-

Introduction

As datasets continue to grow in size, economists, social scientists, and data analysts require more efficient tools for statistical modeling and inference. Traditional Python libraries like statsmodels provide robust inference capabilities but can be slow and memory-intensive, making them impractical for large datasets. Meanwhile, scikit-learn offers efficient machine learning tools but lacks the depth of statistical inference needed for rigorous empirical research.

Enter Febolt: a high-performance statistical modeling package built with Rust to provide fast, memory-efficient, and fully-featured inference capabilities. FeBOLT is designed to bridge the gap between performance and analytical depth, making it an ideal choice for researchers working with large-scale data.

Features

• Probit, Logit, and OLS Models: Supports fundamental regression models with additional enhancements.
• Weighted Regression: Apply observation weights to models.
• Clustered and Robust Standard Errors: More reliable inference with robust and cluster-adjusted SEs.
• Average Marginal Effects (AMEs): Compute AMEs for Logit and Probit models.
• Rust-Powered Performance: Significantly faster computations compared to Python-based alternatives.
• Optimized for 32-bit and 64-bit Floats: Choose between improved memory efficiency with 32-bit floats or higher precision with 64-bit floats.

Why FeBOLT?

Performance Meets Inference

Unlike scikit-learn, which focuses on machine learning without comprehensive inference support, FeBOLT is built specifically for statistical modeling while maintaining speed and efficiency. Unlike statsmodels, which can be bulky and slow for large datasets, FeBOLT leverages Rust’s performance optimizations to provide rapid computations without sacrificing analytical power.

Memory Efficiency for Large Datasets

Economists and social scientists often deal with panel datasets and large-scale survey data, where traditional inference models become infeasible due to memory constraints. FeBOLT allows the use of 32-bit floats to significantly reduce memory usage, while still offering 64-bit float precision for cases where accuracy is paramount.

Inference Without Compromise

While scikit-learn lacks built-in inference tools like robust and clustered standard errors, FeBOLT incorporates these essential statistical features to support rigorous empirical research. Whether you need fast OLS regression or efficient Probit/Logit estimation with AMEs, FeBOLT delivers both speed and accuracy in one package.

Installation

pip install febolt

Quick Start

import febolt

# Example usage (to be filled in)

Performance

FeBOLT outperforms statsmodels and scikit-learn by leveraging Rust’s speed and memory efficiency. This results in significantly faster execution times, especially for large datasets and models requiring robust standard errors.

Contributing

Contributions are welcome! Feel free to submit issues and pull requests on GitHub.

License

This project is licensed under the MIT License - see the LICENSE file for details.