crabbymetrics 0.3.1

Rust-backed econometrics models with a scikit-adjacent Python API.

crabbymetrics

Rust-backed econometrics 🦀🔢 models with a scikit-adjacent Python API. Focus: extremely low dependency (just numpy), simple, fast estimators with robust standard errors and bootstrap support.

Features

• OLS, FixedEffectsOLS, SyntheticControl, ElasticNet, Logit, Multinomial Logit, Poisson, TwoSLS, FTRL
• fit, predict, summary, bootstrap
• HC1 standard errors where applicable

Install

This package is built with pyo3/maturin and ships as native wheels.

uv pip install crabbymetrics

Example

import numpy as np
from crabbymetrics import OLS

x = np.random.randn(200, 3)
beta = np.array([1.0, -2.0, 0.5])
y = 0.3 + x @ beta + np.random.randn(200) * 0.1

model = OLS()
model.fit(x, y)
print(model.summary())

Development

Create and populate the project virtual environment, then build the extension into that venv.

uv sync
uv run maturin develop

uv run maturin develop is sufficient for rebuilding and reinstalling the package in .venv once the environment exists. If you change Python dependencies or the pyproject.toml metadata, run uv sync again first.

Package versioning is sourced from Cargo.toml. The Python package metadata is dynamic, and commit_tag_release.sh reads the crate version directly before creating the vX.Y.Z tag.

Rendered examples and API docs live under docs/. Rebuild the site with quarto render docs.

Wheels

Wheels are platform-specific and included in GitHub releases. See the releases tab.