BartMixVs-package       Varibale Selection Using Bayesian Additive
                        Regression Trees
abc.vs                  Variable selection with ABC Bayesian forest
bartModelMatrix         Create a matrix out of a vector or data frame
checkerboard            Generate data for an example of Zhu, Zeng and
                        Kosorok (2015)
friedman                Generate data for an example of Friedman (1991)
mc.abc.vs               Variable selection with ABC Bayesian forest
                        (using parallel computation)
mc.backward.vs          Backward selection with two filters (using
                        parallel computation)
mc.cores.openmp         Detecting OpenMP
mc.pbart                Probit BART for binary responses with parallel
                        computation
mc.permute.vs           Permutation-based variable selection approach
                        with parallel computation
mc.pwbart               Predicting new observations based on a
                        previously fitted BART model with parallel
                        computation
mc.wbart                BART for continuous responses with parallel
                        computation
medianInclusion.vs      Variable selection with DART
mixone                  Generate data with independent and mixed-type
                        predictors
mixtwo                  Generate data with correlated and mixed-type
                        predictors
pbart                   Probit BART for binary responses with Normal
                        latents
permute.vs              Permutation-based variable selection approach
predict.pbart           Predict new observations with a fitted BART
                        model
predict.wbart           Predict new observations with a fitted BART
                        model
pwbart                  Predicting new observations with a previously
                        fitted BART model
wbart                   BART for continuous responses
