Search

Searching for multiple words only shows matches that contain all words.

pymargins

Expert-mode marginal effects for Python.

Navigation

Getting started

Introduction

Tutorials — learning by doing

Getting started
GLM — Binomial logit
GLM — Poisson count
OLS — linear regression
Multinomial logit
Cox proportional hazards
Accelerated failure time models
Instrumental variables (2SLS / LIML / GMM)
Panel data — fixed effects
Generalised Estimating Equations (GEE)
Linear Mixed Effects
Contrasts and difference-in-differences
Inference — delta, simulation, bootstrap
Inference scales and the κ diagnostic
scikit-learn models
Survey designs — weights, strata, and clusters
Multiple imputation via reimpute
Pooling precomputed imputations (pool_imputations)

How-to guides — task-focused recipes

Robust and clustered standard errors
Bootstrap inference
Cluster and block bootstrap
Simultaneous confidence intervals and multiple-testing correction
Scenario helpers
Grid predictions
Effects by subgroup with over=
Difference-in-differences on the response scale
Elasticities and semi-elasticities
Discrete changes for binary / categorical regressors
Per-observation influence
Reading and controlling the κ fallback
Writing a custom adapter
Matching support
Exporting and persisting results
Plotting predictions and effects
Nonlinear estimands with evaluate
Linear contrasts with contrasts
Contrasts vs evaluate — choosing the right tool
Formula interface for array-fit models

Demos — end-to-end analyses

Mroz (1987) — Female labor force participation
Fair (1978) — Extramarital affairs, two ways
Spector & Mazzeo (1980) — Did PSI work?
Rossi recidivism — Cox proportional hazards end-to-end
Wage panel — union premium with entity fixed effects
Union premium heterogeneity — effects by subgroup
ANES96 — Multinomial logit on party identification
Travel mode choice — the value of travel time (WTP)
California API — stratified survey design
Bayesian marginal effects — posterior draws via from_posterior
Transform pipelines — honest CIs for data-dependent preprocessing

Reference

API reference

Replication scripts (archive)

Explanations — theory and design

Mathematical motivation
The session pre-commitment
Inference scale (phi / phi_inv)
The κ curvature diagnostic
Gradient backend: autodiff vs wrapped-FD vs FD
Delta vs simulation vs bootstrap
The scenarios model — at vs atexog
The adapter pattern
How pymargins differs from marginaleffects and margins

GitHub repository
Issue tracker
Changelog
PyPI

Related Topics

Documentation overview

©2026. | Powered by Sphinx 9.1.0 & Alabaster 1.0.0