📍 Interactive Studio for Explanatory Model Analysis

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

Fast approximate Shapley values in R

Explainable Machine Learning in Survival Analysis

SHAP Plots in R

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Different SHAP algorithms

Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Implementation of the Anchors algorithm: Explain black-box ML models

Variable importance via oscillations

ExplaineR is an R package built for enhanced interpretation of classification and regression models based on SHAP method and interactive visualizations with unique functionalities so please feel free to check it out, See ExplaineR paper at doi:10.1093/bioadv/vbae049

R implementation of Contextual Importance and Utility for Explainable AI

Local Individual Conditional Expectation (localICE) is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI)

Network-guided greedy decision forest for feature subset selection

Robust regression algorithm that can be used for explaining black box models (R implementation)

Implementation of the mSHAP algorithm for explaining two-part models, as described by Matthews and Hartman (2021).

Permutation SHAP

Explaining black-box models through counterfactual paths and conditional permutations