gamfit

Formula-based generalized additive models for Python, backed by a Rust engine.

gamfit fits Gaussian, binomial (including Bernoulli marginal-slope), Poisson, negative-binomial, Gamma, Beta, Tweedie, and multinomial GLMs with smooth terms, random effects, bounded/constrained coefficients, location-scale extensions, survival likelihoods, and flexible/learnable links. Smoothing parameters are selected by REML or LAML. Posterior sampling uses NUTS where supported, and a Gaussian Laplace approximation otherwise.

Manifold smooths handle predictor spaces that wrap or close: circles, cylinders, tori, and the sphere (intrinsic Wahba and spherical-harmonic kernels), plus periodic tensor products and boundary-conditioned B-splines. The Möbius example in the gallery is a 4π-periodic double-cover parameterization, not a twisted Möbius-strip basis.

Docs: https://gamfit.readthedocs.io/.

Install

uv add gamfit

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

Example

import gamfit

# Smooth fits need enough rows for the basis to be identified; ~20 rows
# is the minimum the default `s(x)` basis (cubic B-spline) is well-posed
# on. Use more rows when the signal is noisier.
train = [
    {"y": 1.05, "x": 0.0}, {"y": 1.32, "x": 0.5}, {"y": 1.78, "x": 1.0},
    {"y": 2.41, "x": 1.5}, {"y": 3.10, "x": 2.0}, {"y": 3.95, "x": 2.5},
    {"y": 4.80, "x": 3.0}, {"y": 5.62, "x": 3.5}, {"y": 6.25, "x": 4.0},
    {"y": 6.71, "x": 4.5}, {"y": 6.94, "x": 5.0}, {"y": 6.88, "x": 5.5},
    {"y": 6.55, "x": 6.0}, {"y": 5.99, "x": 6.5}, {"y": 5.20, "x": 7.0},
    {"y": 4.30, "x": 7.5}, {"y": 3.42, "x": 8.0}, {"y": 2.65, "x": 8.5},
    {"y": 2.10, "x": 9.0}, {"y": 1.82, "x": 9.5},
]

model = gamfit.fit(train, "y ~ s(x)")
print(model.predict([{"x": 1.5}, {"x": 5.0}], interval=0.95))
print(model.summary())
model.save("model.gam")

pandas, polars, pyarrow, numpy, dict-of-columns, and list-of-records inputs are all accepted without conversion.

Features

• Polyharmonic / Duchon smooths combine magnitude, gradient, and curvature penalty operators on the same basis. P-spline and thin-plate smooths use their standard derivative penalties. Each penalized block has its own smoothing parameter.
• Flexible link functions: flexible(base) adds a spline offset on a base link; blended(...) learns a mixture weight; sas and beta-logistic learn shape parameters.
• Surface smooths in arbitrary dimension: thin-plate, Duchon (scale-free by default, hybrid with length_scale=...), and Matérn, with automatic knot placement.
• Tensor-product and manifold smooths: te(...) / ti(...) B-spline tensors, periodic 1-D, cylinder / torus tensor products, intrinsic sphere (Wahba kernel or spherical harmonics), and boundary-conditioned B-splines.
• Dispersion GAMLSS for Gamma, Beta, negative-binomial, and Tweedie via noise_formula=.
• Per-axis anisotropy inside a single joint smooth.
• Shape-constrained smooths: s(x, shape=monotone_increasing), convex, concave.
• Difference smooths: by= factor smooths plus covariance-aware model.difference_smooth(...) contrasts with optional simultaneous bands.
• Marginal-slope models that separate baseline risk from a calibrated score's effect, for Bernoulli and survival outcomes.
• Survival in several likelihood modes (transformation, Weibull, location-scale, marginal-slope, latent-Gaussian frailty) plus competing-risks cumulative-incidence functions.
• Response geometry for spherical and compositional outcomes via Fréchet-mean tangent-space GAMs.
• Posterior sampling via NUTS where supported, Gaussian Laplace otherwise, behind one API; conformal prediction intervals via interval="conformal".

API examples

import gamfit
from gamfit.sklearn import GAMRegressor, GAMClassifier

# Validate before you fit
gamfit.validate_formula(train, "y ~ s(x) + group(site)")

# Posterior sampling and mean bands
posterior = model.sample(train, seed=42)
bands = posterior.predict(test, level=0.95)

# Survival
gamfit.fit(df,
    "Surv(entry, exit, event) ~ s(age) + bmi + timewiggle(internal_knots=6)",
    survival_likelihood="transformation",
    baseline_target="weibull",
)

# scikit-learn
est = GAMRegressor(formula="y ~ s(x)")
est.fit(X, y)

# Diagnose, plot, report
model.diagnose(train).metrics
model.plot(train, x="x", kind="prediction")
model.report("report.html")

Public API

Full reference: https://gamfit.readthedocs.io/en/latest/api-reference/.

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[cuda]"       # NVIDIA CUDA 12 wheel libraries on Linux x86_64
uv add "gamfit[all]"        # pandas + plot + sklearn extras
uv add torch                # PyTorch bridge dependency

GPU acceleration

CUDA support (cuBLAS / cuSOLVER / cuSPARSE) is built into the same wheel; there is no separate gamfit-gpu package. Install gamfit[cuda] on Linux x86_64 when you want PyPI's NVIDIA CUDA 12 runtime libraries instead of a system CUDA toolkit. Per-op dispatch thresholds are derived at probe time from measured GPU FP64 throughput, CPU FP64 throughput, and PCIe bandwidth, so small kernels stay on the CPU. Inspect the calibrated thresholds with gamfit.build_info()["cuda_diagnostics"] or gamfit.format_cuda_diagnostics().

If both a system CUDA toolkit and pip nvidia-*-cu12 wheels are present in the same environment, gamfit warns once per conflict-set and continues; glibc resolves dlopen(SONAME) to a single file, so this is usually benign. If you use gamfit with torch, install a torch build whose CUDA suffix matches your driver.

License

AGPL-3.0-or-later. See LICENSE.