SRbeta

SRbeta is a modular, open-source software framework designed for scalable regression analysis and statistical modeling. It aims to unify data preprocessing, model specification, estimation, and evaluation into a single environment, enabling researchers to reproduce analyses and scale analyses to large datasets.

The architecture comprises a core computational engine, a library of regression model types (linear, logistic, Poisson, mixed-effects), and flexible inference backends including frequentist and Bayesian options. It also includes data pipelines, feature preprocessing tools, and visualization components to assist in model diagnostics and interpretation.

SRbeta emphasizes interoperability and extensibility. It provides a Python API and a lightweight domain-specific language for model specification, along with adapters for common data formats. The project is community-driven with transparent governance and an emphasis on reproducible workflows, including versioned experiments and provenance tracking.

Use cases span academia and industry, including genomics, finance, social sciences, and engineering. The project hosts educational tutorials and example datasets to help new users explore regression techniques and interpret results. As an open-source initiative, contributions—code, documentation, or pedagogy—are welcomed, and ongoing development focuses on expanding model coverage, performance, and usability.