glmm

Linear-regression family — OLS → GLM → LMM → GLMM — in pure Rust, with a two-name Python API.

License: GPL-3.0-or-later

Fits fixed-effect and mixed (random-intercept / random-slope) models for Gaussian, Binomial (logit/probit), Poisson, Gamma, and Negative-Binomial outcomes. The numerics are the glmm Rust crate — no BLAS/LAPACK system dependency, no unsafe, validated against R/lme4 and Julia/MixedModels.jl goldens.

Install

pip install glmm

Requires Python 3.10+. The only runtime dependency is NumPy.

One call

You hand fit a data table and an R-style formula. It parses the formula against the table's columns, builds the design matrix, fits, and returns a Fit. There is no model object to construct.

import glmm

data = {
    "y":     [1.02, 1.05, 1.11, 1.13, 1.21, 1.24, 1.30, 1.33, 1.42, 1.44, 1.51, 1.53],
    "x1":    [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1],
    "group": ["a", "a", "b", "b", "c", "c", "d", "d", "e", "e", "f", "f"],
}

# y ~ x1 + (1 | group) — Gaussian, random intercept.
fit = glmm.fit(data, "y ~ x1 + (1 | group)")

assert fit.converged
fit.summary()   # prints the coefficient table (and returns it as a string)

The public surface is exactly two names: glmm.fit and glmm.Fit. Families, links, and knobs are string or scalar arguments, not types.

data is documented as a dict[str, array-like], but anything column-addressable works — pandas / polars DataFrame, pyarrow Table — by duck-typing, so there is no dataframe dependency.

Model routing

Routing is inferred from the formula and family — fixed-only means OLS/GLM, adding a (… | g) term makes it LMM/GLMM.

family default link other links fixed-only with (… | g)
gaussian identity — OLS LMM
binomial logit probit GLM GLMM
poisson log — GLM GLMM
gamma log inverse GLM GLMM
negativebinomial log — GLM GLMM 
fit = glmm.fit(data, "s ~ x1 + (1 | group)", "binomial", link="probit", nagq=7)

The formula follows R conventions: * desugars to main effects + interaction, A/B to nesting, (1 + x | g) is a correlated random intercept + slope, and additional (… | g2) terms add crossed or nested grouping factors. Treatment contrasts (R's default) code the factors against the column's first level; the full syntax, rejected constructs and workarounds, and factor-coding rules are in formula.md.

Not yet implemented

Four combinations have an approved design but no kernel support, and raise a clean NotImplementedError: family="inversegaussian", link="cloglog", quasi-likelihood dispersion= on binomial/poisson, and a float init_theta= seed (only the default init_theta=None cold start is supported). See troubleshooting.md for fixes to common errors.

Documentation

Full walkthrough — every knob, the Fit object, and warm starts: TUTORIAL-PYTHON.md.

The algorithm map (dispatch graph, solver paths, tuning knobs) is traced to code in documentation/algorithms.md.

License

GPL-3.0-or-later.

Paweł Lenartowicz — Freestyler Scientist · GitHub · ORCID

Project links

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Credits

Author: Paweł Lenartowicz

GitHub Statistics

Data verified by PyPI on Jul 22, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

GPL-3.0-or-later
View SPDX License List

Requires

Python >=3.10

Provides Extra

test

Tags

statistics glmm regression mixed-models mle

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