bgreenwell/pdp

SHAP Plots in R

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

Graphical analysis of random forests with the randomForestSRC, randomForest, varPro and ggplot2 packages.

Explaining the output of machine learning models with more accurately estimated Shapley values

R package to compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and ML models. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference

A game theoretic approach to explain the output of any machine learning model.

Advanced Machine Learning and Visualization

Shapley Interactions and Shapley Values for Machine Learning

Estimated Marginal Means and Marginal Effects from Regression Models for ggplot2