Estimated Marginal Means and Marginal Effects from Regression Models for ggplot2
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Updated
Aug 19, 2026 - R
Estimated Marginal Means and Marginal Effects from Regression Models for ggplot2
An R Port of Stata's 'margins' Command
📈 Estimate effects, contrasts and means based on statistical models
Calculating Marginal Effects and Levels with Errors Using the Delta Method
lightweight R package around covariate adjustment for binary outcomes focusing on marginal estimands
Main repository for the 2nd paper of my PhD.
Replication syntax for Öney Ardag 2021
Extended discrete choice modeling notebook with Fair's affair data & STAR98 education analysis. Covers Logit, Probit, GLM, diagnostics, model comparison, marginal effects, and advanced topics like censored regression and count models. Complete with visualizations and validation techniques.
Prédiction du trafic élevé de recettes de cuisine à partir de leurs caractéristiques nutritionnelles, catégorie et portions. Modèles de classification comparés : LPM, Logit (avec effets marginaux), arbre CART et Random Forest (CV 10-fold). Évaluation par matrice de confusion, courbe ROC et AUC.
R Package: Tame your models' output with INTerval-bAsed Marginal Effects!
MMMs-Develop and compare parametric and machine learning models to explain and predict prescription drug sales volume. Estimate marketing response (elasticity, marginal effects) for Detailing and DTCA spending, and produce actionable managerial recommendations.
Margins-based comparison and interaction plotting utilities for Stata
Flexible effect plots for statistical models in R: every effect curve paired with a rug of the raw data. GAMs via gratia, everything else via marginaleffects.
Python replication of an ordered logit study on sustainability-driven e-commerce purchasing behaviour in Bangalore (n=60). Covers model estimation, link test, marginal effects, and predicted probability curves. Original analysis in Stata, co-authored with Anwesha Sinha.
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