| Name | febolt JSON |
| Version |
0.1.60
JSON |
| download |
| home_page | None |
| Summary | A Rust-based Statistics and ML package, callable from Python. |
| upload_time | 2025-02-16 15:32:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
rust
python
machine learning
statistics
pyo3
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# FeBOLT-
[](https://github.com/luke-brosnan-cbc/FeBOLT/actions)
[](https://pypi.org/project/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
```bash
pip install febolt
```
## Quick Start
```python
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](https://github.com/luke-brosnan-cbc/FeBOLT).
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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