Implements the SoftBart Algorithm
Create a Full Set of Dummy Variables
General SoftBart Regression
Create a list of hyperparameter values
Create an Rcpp_Forest Object
MCMC options for SoftBart
Partial Dependence Function for SoftBART Probit Regression
Partial Dependence Function for SoftBART Regression
Partial dependence plots for SoftBart
BART Posterior Inclusion Probabilities
Predict for SoftBart Probit Regression
Predict for SoftBart Regression
Preprocess a dataset for use with SoftBart
Quantile normalization for predictors
Root mean squared error
SoftBart Probit Regression
SoftBart Regression
Fits the SoftBart model
SoftBart Varying Coefficient Regression
Implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package.