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. The BART model is designed to work with the following R packages: 'BART' <doi:10.18637/jss.v097.i01>, 'bartMachine' <doi:10.18637/jss.v070.i04>, and 'dbarts' <https://CRAN.R-project.org/package=dbarts>. 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> was implemented with reference to <https://github.com/jbisbee1/BARP> and is designed to work with modified functions based on that implementation. BARP 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: 1.0.8 Depends: R (≥ 3.5.0), SuperLearner Imports: bartMachine, BART, ggplot2, ggforce, data.table, ggfittext, ggpubr, foreach, gggenes, Rcpp, dplyr, tidyr, stringr, abind, utils, grid, dbarts, forcats, gridExtra, reshape2, missForest LinkingTo: Rcpp, RcppArmadillo Published: 2025-07-28 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bartXViz to link to this page.
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