crabbymetrics

crabbymetrics is a Rust-backed econometrics library with a compact Python API. The docs are organized around the public estimator surface, a single binding crash course, focused numerical ablations, and a small set of supporting internals pages.

Start Here

• API reference: verified public surface, summary schemas, optimizer catalog, and runtime smoke checks.
• Binding Crash Course: OLS: the shortest end-to-end walkthrough of the Rust-to-Python wrapper pattern in this codebase.

Estimator Examples

• OLS: baseline linear regression with switchable vanilla and HC1 covariance estimators.
• Fixed Effects OLS: partial out one-way or multi-way categorical fixed effects with within, then estimate slopes without an intercept.
• ElasticNet: regularized linear regression.
• Synthetic Control: simplex-constrained donor weighting for treated-versus-donor panel matching under latent-factor drift.
• Logit: binary logistic regression.
• Multinomial Logit: multiclass classification.
• Poisson: count regression, with model-based or QMLE sandwich inference available from summary(vcov=...).
• TwoSLS: instrumental variables regression, including multiple endogenous regressors and multiple excluded instruments.
• GMM: fit just-identified score equations, overidentified two-step IV moments, and stacked nuisance-parameter moments with the first-class GMM estimator.
• FTRL: online-style binary classification.
• MEstimator Poisson: callback-driven estimation matched against the built-in Poisson estimator.

Ablations

• Variance Estimators: cached Monte Carlo coverage experiments for OLS, Poisson, and GMM variance estimators under heteroskedasticity or overdispersion.

Optimization

• Optimizers: direct optimizer usage for smooth likelihoods, rougher objective surfaces, and solver behavior comparisons.
• GMM With Optimizers: the lower-level notebook that motivated the first-class GMM estimator and still shows the residual-collection view directly.

Supporting Pages

• Binding Internals: Poisson: a deeper built-in-estimator walkthrough after the OLS crash course.
• Binding Internals: MEstimator: the callback-heavy bridge where Rust owns optimization and Python supplies the objective and scores.
• PCA And Kernel Basis: transformer examples for richer design matrices.
• Richer Regression With Transformer Pipelines: a longer transformer-based modeling example built on KernelBasis and PCA.

Runtime Snapshot

Repo-level runtime comparison for OLS, Logit, Poisson, and MultinomialLogit across crabbymetrics, scikit-learn, and statsmodels. Lower is faster.

The benchmark figure is generated from the repo-level benchmark assets in benchmarks/ and gives a quick scale check for the estimators that overlap cleanly with mainstream Python baselines.

Notes

• api.qmd remains the main documentation page and renders to api.html.
• The site is a Quarto website, so shared navigation and search are generated under docs/.
• All pages are rendered with embedded resources so the checked-in HTML files remain self-contained.