Variable Selection Using Bayesian Additive Regression Trees


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Documentation for package ‘BartMixVs’ version 1.0.0

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BartMixVs-package Varibale Selection Using Bayesian Additive Regression Trees
abc.vs Variable selection with ABC Bayesian forest
BartMixVs Varibale Selection Using Bayesian Additive Regression Trees
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