bossanova 0.1.0.dev9

Bridging statistical cultures with some jazz

Bossanova

Unified mixed-effects modeling in Python. JAX + NumPy backends. R/lme4 parity.

Quick Example

from bossanova import model, load_dataset

df = load_dataset("sleepstudy")

# Fit a mixed model
m = model("Reaction ~ Days + (1 + Days | Subject)", data=df).fit().infer()
m.params

# Explore marginal effects
m.explore("Days").infer()
m.effects

Features

• One unified entry point — model() handles linear, generalized, and mixed-effects models
• R-style formulas — y ~ x + (1|group), factor(), center(), scale()
• Multiple inference strategies — asymptotic, bootstrap, permutation, cross-validation
• Marginal effects — .explore() for estimated marginal means, slopes, and contrasts
• JAX acceleration — automatic GPU/TPU acceleration when JAX is available, NumPy fallback
• R parity — matches lme4/emmeans within f64 tolerance

Installation

pip install bossanova

Documentation

See the full documentation for tutorials, API reference, and examples.