lme-python
Native Python bindings for lme-rs, a Rust implementation of linear, generalized linear, and nonlinear mixed-effects models modeled after R's lme4 workflow.
Install
pip install lme-python
Published wheels are available for CPython 3.10 on Windows, macOS, and Linux; CI also builds and tests wheels from source on Python 3.10–3.13. The package imports as lme_python and uses Polars DataFrames.
Quick start
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 bindings also expose GLMMs (glmer), nonlinear mixed models (nlmer), prediction and simulation, profile and bootstrap confidence intervals, grouped cross-validation, contrasts, Type I/II/III ANOVA, and LMM estimated marginal means with reference-grid pairwise comparisons.
Documentation
- Python guide
- Rust and Python workflow guide
- Supported workflows and limitations
- Numerical comparisons
- Release history
Development
Contributor setup, validation commands, and release policy live in the repository's CONTRIBUTING.md. The automated task consumer:smoke gate builds an isolated wheel, installs it into a clean environment, and runs the portable examples against that installed artifact.
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
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