gamfit 0.1.15

Formula-first generalized additive models with a high-performance Rust core

gamfit

A formula-first generalized additive model library for Python, backed by a high-performance Rust engine.

gamfit fits Gaussian, binomial, Poisson, and Gamma GLMs with smooth terms, random effects, location-scale extensions, survival likelihoods, and flexible/learnable links. Smoothing parameters are selected by REML or LAML. Posterior sampling uses NUTS.

Install

uv add gamfit

Or with a managed Python (one-off, no project required):

uv pip install gamfit

Wheels are published for Linux (x86_64, aarch64), macOS (x86_64, Apple silicon), and Windows. No Rust toolchain required.

Quick start

import gamfit

train = [
    {"y": 1.2, "x": 0.0},
    {"y": 1.9, "x": 1.0},
    {"y": 3.1, "x": 2.0},
    {"y": 4.5, "x": 3.0},
]

model = gamfit.fit(train, "y ~ s(x)")
predictions = model.predict([{"x": 1.5}, {"x": 2.5}], interval=0.95)
print(model.summary())
model.save("model.gam")

Pandas, pyarrow, dict-of-columns, and list-of-records inputs all work without conversion.

What's different

• Three-part penalty structure. Each smooth gets separate penalties for magnitude, gradient, and curvature. Most GAM libraries use one (curvature only) or two; the three-part decomposition gives the smoother more degrees of freedom to distinguish flat-but-offset functions from wiggly ones.
• Flexible link functions. A spline offset from a base link (e.g. probit) lets the data correct for link misspecification while encoding the belief that the base link is approximately right.
• Surface smooths. Thin-plate splines, Duchon radial bases with triple-operator regularization, and Matérn covariance smooths in arbitrary dimension, with automatic knot placement.
• Adaptive anisotropy. Per-axis spatial anisotropy lets the model shrink or stretch each feature axis independently within a single joint smooth.
• Composable basis/kernel. Mix and match the kernel of one spline family with the length-scale behavior of another (e.g. Duchon kernel with Matérn-style global κ scaling).

scikit-learn integration

from gamfit.sklearn import GAMRegressor

est = GAMRegressor(formula="y ~ s(x)")
est.fit(train)
preds = est.predict([{"x": 1.5}, {"x": 2.5}])

Public API

Symbol	Purpose
gamfit.fit(data, formula, **kwargs)	Fit a model from a dataset and a Wilkinson-style formula.
gamfit.load(path) / gamfit.loads(bytes)	Reload a saved model.
gamfit.validate_formula(data, formula, ...)	Type-check a formula against a dataset without fitting.
gamfit.build_info()	Native-extension build metadata.
gamfit.explain_error(exc)	Convert a gamfit exception into a human-readable hint.
gamfit.Model	Fitted-model handle: predict, summary, check, diagnose, plot, report, save.
gamfit.sklearn.GAMRegressor / GAMClassifier	scikit-learn-compatible estimators.
gamfit.pgs	Polygenic-score helpers.

See the project documentation for the full guide, the formula DSL reference, and the CLI.

Optional extras

uv add "gamfit[pandas]"     # pandas + pyarrow input/output
uv add "gamfit[plot]"       # matplotlib-based plotting
uv add "gamfit[sklearn]"    # scikit-learn integration
uv add "gamfit[all]"        # everything

License

AGPL-3.0-or-later. See LICENSE. A commercial license is available for closed-source or SaaS use — see COMMERCIAL.md.