smmargins

Stata-style margins for StatsModels: adjusted predictions, marginal effects, elasticities, and difference-in-differences — with delta-method, Krinsky–Robb simulation, or bootstrap standard errors, robust covariance passthrough (HC0–HC3, cluster, HAC), and simultaneous confidence intervals (Bonferroni, Šidák, sup-t) — for any fitted model that exposes params, cov_params(), and predict(params, exog).

User guide

• Introduction
  • Why another margins module?
  • Multi-outcome models
  • Inference options
  • Difference-in-differences
  • Installation
  • Quickstart
  • Why patsy
  • Formula vs. raw exog mode
  • Where to next
• Mathematical motivation
  • The delta method
  • A single statistic schema
  • Marginal effects of a single variable
  • Elasticities
  • Outer Jacobian: analytic vs. finite differences
  • Beyond delta: simulation and bootstrap VCEs
  • Simultaneous confidence intervals
  • Why the response scale matters for DiD (Ai & Norton 2003)
  • References
• Demos
  • Williams-style logit walkthrough
  • Healthcare-style 2x2 difference-in-differences

Reference

• API reference
  • smmargins.Margins
  • smmargins.MarginsResult
  • smmargins.DiDResult
  • smmargins.utils._central_jacobian

Indices

• Index

• Module Index

• Search Page