bartXViz: Visualization of BART and BARP using SHAP
Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> is also considered.The BARP model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480> extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods.
| Version: |
1.0.5 |
| Depends: |
R (≥ 3.5.0), SuperLearner |
| Imports: |
bartMachine, BART, ggplot2, ggforce, data.table, ggfittext, ggpubr, foreach, gggenes, Rcpp, dplyr, tidyr, stringr, abind, data.table, utils, grid, dbarts, forcats, gridExtra, reshape2, missForest |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2025-06-24 |
| DOI: |
10.32614/CRAN.package.bartXViz |
| Author: |
Dong-eun Lee [aut, cre],
Eun-Kyung Lee [aut] |
| Maintainer: |
Dong-eun Lee <ldongeun.leel at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
bartXViz results |
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