glmm

Linear-regression family — OLS → GLM → LMM → GLMM — in pure Rust, with an eight-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 eight names: glmm.fit, glmm.Fit, and the six warning categories the diagnostics channel raises — glmm.DiagnosticWarning (the base) plus glmm.IllConditionedWarning, glmm.PirlsExhaustedWarning, glmm.UnusedGroupingLevelsWarning, glmm.ReDesignScaleWarning and glmm.HessianSeFallbackWarning. 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

Data verified by PyPI on Aug 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for pawellenartowicz from gravatar.com pawellenartowicz

Credits

Author: Paweł Lenartowicz

GitHub Statistics

Data verified by PyPI on Aug 24, 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

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

glmm-0.3.0.tar.gz (928.9 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

glmm-0.3.0-cp310-abi3-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

glmm-0.3.0-cp310-abi3-manylinux_2_28_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.28+ x86-64

glmm-0.3.0-cp310-abi3-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

File details

Details for the file glmm-0.3.0.tar.gz.

File metadata

  • Download URL: glmm-0.3.0.tar.gz
  • Upload date:
  • Size: 928.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for glmm-0.3.0.tar.gz
Algorithm Hash digest
SHA256 5817eb4944a3d0aa578ce5db3775c2fe61bbf251e53e0fed9ac16ce69cfbab5f
MD5 f0b27b265da25af2fa870a7445dcfa4c
BLAKE2b-256 e09b631e1179052d9083e3e3faf7e8e48a21e6a845182e18660afb5ade9fee72

See more details on using hashes here.

Provenance

The following attestation bundles were made for glmm-0.3.0.tar.gz:

Publisher: release.yml on pawlenartowicz/glmm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file glmm-0.3.0-cp310-abi3-win_amd64.whl.

File metadata

  • Download URL: glmm-0.3.0-cp310-abi3-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.10+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for glmm-0.3.0-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 087950b4ce7266d46e9f33e9977e3971dba709e39707f1016719855a311164b7
MD5 2890ec2387a6fb71ad343149f6ea8c7e
BLAKE2b-256 832d965b6901095ccfd087d1d680b1c51d0787290bc93a4b6780ee28041577d2

See more details on using hashes here.

Provenance

The following attestation bundles were made for glmm-0.3.0-cp310-abi3-win_amd64.whl:

Publisher: release.yml on pawlenartowicz/glmm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file glmm-0.3.0-cp310-abi3-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for glmm-0.3.0-cp310-abi3-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9261addea97e5ca65e86b816fe6e6224e490a45bfc32afb3cbecd02bdd0c9825
MD5 f36ac4f5178ca1ba394a6f18e1ae6328
BLAKE2b-256 bb4c516cfeafc3df3303e496ff52b83808f7d0a85b6fa424926e673c89bc6383

See more details on using hashes here.

Provenance

The following attestation bundles were made for glmm-0.3.0-cp310-abi3-manylinux_2_28_x86_64.whl:

Publisher: release.yml on pawlenartowicz/glmm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file glmm-0.3.0-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: glmm-0.3.0-cp310-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.10+, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for glmm-0.3.0-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cf072ec5205783c8ae8aa59ac1189ff9bee249ee5a7aee2f6b8b7bbf772976f0
MD5 f4fc47339b8c84db679e258e0259b785
BLAKE2b-256 bbec7b9609e23d49dc617c30a18cb9f8ea35d78695a76b9fb00adefd73d9270c

See more details on using hashes here.

Provenance

The following attestation bundles were made for glmm-0.3.0-cp310-abi3-macosx_11_0_arm64.whl:

Publisher: release.yml on pawlenartowicz/glmm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.3.1

4 files

This release

0.3.0 This release

4 files

0.2.0

4 files

0.1.3

4 files

0.1.2

4 files

0.1.1

4 files

0.1.0

4 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page