Barnard's unconditional test for 2x2 contingency tables.

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.  For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).

Fit Bayesian Regression Additive Trees (BART) models to
    select true confounders from a large set of potential confounders and
    to estimate average treatment effect. For more information, see Kim et
    al. (2023) <doi:10.1111/biom.13833>.

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits.  We construct conventional plots to analyze a model’s performance and stability  as well as create new tree-based plots to analyze variable importance, interaction, and tree structure.  We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance  and interactions jointly using colour scale to represent posterior uncertainty.  Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>,  'dbarts'  (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>,  and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

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.