Variable Selection Using Bayesian Additive Regression Trees

Documentation for package ‘BartMixVs’ version 1.0.0

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