statlingo

Explain statistical model output with LLMs, in R and Python

statlingo translates the dense output of statistical models—coefficients, p-values, model fit indices, and more—into clear, context-aware, natural language explanations using Large Language Models (LLMs).

It ships as two packages, sharing a common design and a single canonical set of LLM prompts:

Package	Language	LLM interface	Get started
statlingo	R	ellmer	R quickstart
statlingo	Python	chatlas	Python quickstart

Why statlingo?

Statistical models are powerful, but their outputs can be intimidating. statlingo empowers you to:

• Democratize understanding — make complex analyses accessible to people with varying levels of statistical expertise.
• Accelerate learning — students can connect statistical theory to practical model output, with explanations tailored to their level.
• Streamline reporting — quickly draft plain-language interpretations for reports and presentations.
• Work in either language — the same audience/verbosity/style options and prompt engineering are shared across the R and Python packages.

Supported models

Both packages support a growing set of common statistical models — see the R function reference and Python API reference for the full, current list per language.

Source code

statlingo is developed in the open on GitHub: github.com/bgreenwell/statlingo.