lme-python

Python bindings for lme-rs: linear, generalized linear, and nonlinear mixed-effects models with lme4-style formulas. Fitting runs in the same Rust engine used by the crate.

Python guide · Examples · Workflow scope

Install

python -m pip install lme-python

Install lme-python, import lme_python. Polars is a package dependency. A compatible binary wheel does not require a Rust toolchain or an R installation.

Check the selected version's PyPI files for your Python interpreter, OS, and architecture. The release workflow builds CPython 3.10 wheels; source-build CI tests Python 3.10–3.13. Those tests do not imply that a wheel is published for each tested interpreter. If pip falls back to a source build, follow the source setup.

Quick start

This example needs no repository files:

import lme_python
import polars as pl

data = pl.DataFrame(
    {
        "y": [10.0, 12.0, 13.0, 15.0, 9.0, 11.0, 14.0, 17.0, 8.0, 10.0, 12.0, 14.0],
        "x": [0.0, 1.0, 2.0, 3.0] * 3,
        "group": ["a"] * 4 + ["b"] * 4 + ["c"] * 4,
    }
)
fit = lme_python.lmer("y ~ x + (1 | group)", data=data, reml=True)
print(fit.summary())
print(fit.predict(data))

The formula estimates a shared intercept and slope plus a random intercept for each group. The small synthetic dataset is an API demonstration. predict returns fixed-effects predictions; use predict_conditional when you want the fitted group effects.

Continue your analysis

The bindings expose lm, lmer, glmer, and nlmer, along with prediction, intervals, simulation, grouped cross-validation, bootstrap refits, ANOVA, contrasts, and LMM estimated marginal means.

  • Python guide: data requirements, fitting, inference, and structured results.
  • Type reference: function signatures and result fields.
  • Supported workflows: model-specific scope and limitations.
  • Troubleshooting: installation, data, convergence, and prediction problems.
  • Changelog: release history. Use the matching repository tag for version-specific docs.

Development

See Contributing for the locked development environment and extension build. After building, use uv run --no-sync for examples and tests.

task consumer:smoke builds a wheel, verifies it in isolated environments, and runs portable examples against the installed artifact.

Repeated responses and fit diagnostics

Prepare once when fitting several response vectors against the same design:

prepared = lme_python.prepare_lmer("Reaction ~ Days + (Days | Subject)", data)
control = lme_python.FitControl(max_iterations=2000, require_convergence=True)
fit = prepared.fit(y=data["Reaction"].to_list(), reml=True, control=control)
print(fit.diagnostics)

prepare_lmer(..., weights=...) retains observation precision weights. Responses must contain finite values and match the original row count. Offsets are applied exactly once. prepare_glmer also supports prepared.fit(y=..., control=...). The existing fit_prepared(..., y=..., control=...) and fit_prepared_glmer functions remain available.

FitControl accepts max_iterations, tolerance, max_inner_iterations, starting covariance parameters start, and require_convergence. The iteration limit is per outer search stage. Nonlinear fits use max_inner and max_outer_iters instead. Inspect converged and diagnostics before interpreting a fit; diagnostics report termination, iteration counts, objective value, and requested/effective quadrature.

Conversion serializes columns needed by an explicit formula. Dot formulas retain all columns for expansion. Native fitting releases the interpreter lock; custom Python nonlinear mean callbacks reacquire it while evaluating Python code. Bootstrap generates each response as its worker needs it, retaining only replicate summaries. Thread counts no longer change BLAS/OpenMP process settings. License-File: THIRD_PARTY_NOTICES.md License-File: RELINKING.md License-File: Apache-2.0.txt License-File: BSD-3-Clause-OpenBLAS.txt License-File: GPL-2.0-or-later.txt License-File: LGPL-2.1-only.txt License-File: Intel-Simplified-Software-License.txt

Project links

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