Package index • bartMachine

All functions

automobile: Data concerning automobile prices.
bartMachine() build_bart_machine(): Build a BART Model
bartMachineArr(): Create an array of BART models for the same data.
bartMachineCV() build_bart_machine_cv(): Build BART-CV
bart_machine_get_posterior(): Get Full Posterior Distribution
bart_machine_num_cores(): Get Number of Cores Used by BART
bart_predict_for_test_data(): Predict for Test Data with Known Outcomes
benchmark_datasets: benchmark_datasets
calc_credible_intervals(): Calculate Credible Intervals
calc_prediction_intervals(): Calculate Prediction Intervals
check_bart_error_assumptions(): Check BART Error Assumptions
cov_importance_test(): Importance Test for Covariate(s) of Interest
dummify_data(): Dummify Design Matrix
extract_raw_node_data(): Gets Raw Node data
get_projection_weights(): Gets Training Sample Projection / Weights
get_sigsqs(): Get Posterior Error Variance Estimates
get_var_counts_over_chain(): Get the Variable Inclusion Counts
get_var_props_over_chain(): Get the Variable Inclusion Proportions
interaction_investigator(): Explore Pairwise Interactions in BART Model
investigate_var_importance(): Explore Variable Inclusion Proportions in BART Model
k_fold_cv(): Estimate Out-of-sample Error with K-fold Cross validation
linearity_test(): Test of Linearity
node_prediction_training_data_indices(): Gets node predictions indices of the training data for new data.
pd_plot(): Partial Dependence Plot
plot_convergence_diagnostics(): Plot Convergence Diagnostics
plot_y_vs_yhat(): Plot the fitted Versus Actual Response
predict(<bartMachine>): Make a prediction on data using a BART object
predict_bartMachineArr(): Make a prediction on data using a BART array object
print(<bartMachine>): Summarizes information about a bartMachine object.
rmse_by_num_trees(): Assess the Out-of-sample RMSE by Number of Trees
set_bart_machine_num_cores(): Set the Number of Cores for BART
summary(<bartMachine>): Summarizes information about a bartMachine object.
var_selection_by_permute(): Perform Variable Selection using Three Threshold-based Procedures
var_selection_by_permute_cv(): Perform Variable Selection Using Cross-validation Procedure