Explainable Machine Learning in Survival Analysis

SHAP Plots in R

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

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Different SHAP algorithms

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

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

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

This is an online repository gathering all codes employed for the analyses presented in the paper (Torres-Martos Á et al. (2024)). The purpose of this repository is to allow researchers to reproduce the Machine Learning pipeline presented in the paper, as well as to adapt provided codes for the analyses of their own datasets.

Random Forest Algorithms to predict climate impact-drivers (CID), a.k.a., climate extreme indices for impact studies, in crop yields of soybean maize using Random Forest and XGBoost in a SHAP (SHapley Additive exPlanations) framework

Wrapper for shapjs node package for easy force plots in R without Python dependencies

Optimizing the hyperparameters of the XGBoost model for regression using a Genetic Algorithm to analyze data relationships and interpret the results with SHAP.

🤖 Credit risk prediction with AutoML in R + fairness analysis by gender. Includes SHAP explainability, parity metrics, and a clear risk-duration visualization.

Cross-sectional study that analyzed retrospective data from pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021.

R pipeline for TCGA transcriptomics biomarker discovery with ML & SHAP

Code for integrating transcriptomics, methylation, and proteomics data using DIABLO