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.

Package for Bayesian Variable Selection and  Model Averaging 
    in linear models and generalized linear models using stochastic or
    deterministic sampling without replacement from posterior
    distributions.  Prior distributions on coefficients are
    from Zellner's g-prior or mixtures of g-priors
    corresponding to the Zellner-Siow Cauchy Priors or the
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors from  Li and Clyde
    (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
    Other model selection criteria include AIC, BIC and Empirical Bayes 
    estimates of g. Sampling probabilities may be updated based on the sampled
    models using sampling w/out replacement or an efficient MCMC algorithm which
    samples models using a tree structure of the model space 
    as an efficient hash table.  See  Clyde, Ghosh and Littman (2010) 
    <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
    Uniform priors over all models or beta-binomial prior distributions on
    model size are allowed, and for large p truncated priors on the model
    space may be used to enforce sampling models that are full rank.  
    The user may force variables to always be included in addition to imposing
    constraints that higher order interactions are included only if their 
    parents are included in the model.
    This material is based upon work supported by the National Science
    Foundation under Division of Mathematical Sciences grant 1106891.
    Any opinions, findings, and
    conclusions or recommendations expressed in this material are those of
    the author(s) and do not necessarily reflect the views of the
    National Science Foundation.