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 |