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. Starting with 0.2.6, the workflow builds interpreter-specific CPython 3.10–3.14 wheels; source-build CI covers those versions, including 3.14 on Windows and macOS. Older releases (including 0.2.5) only provide CPython 3.10 wheels. If pip falls back to a source build, follow the source setup.
For pandas inputs, install lme-python[pandas] to include PyArrow, which is needed
for nullable and categorical pandas columns. See the
Python integration guide
for sum coding, prediction grids, and new-animal null bootstrap fits.
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
1 maintainer
Metadata
1 maintainer
Release files for lme-python 0.2.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lme_python-0.2.6.tar.gz | 6.9 MB | Details |
Built distributions (wheels)
Total release size: 253.4 MB
Release files / lme_python-0.2.6.tar.gz
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| Tags | Source |
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