bartMachine

Data concerning automobile prices.

Build a BART Model

Create an array of BART models for the same data.

Build BART-CV

Get Full Posterior Distribution

Get Number of Cores Used by BART

Predict for Test Data with Known Outcomes

benchmark_datasets

Calculate Credible Intervals

Calculate Prediction Intervals

Check BART Error Assumptions

Importance Test for Covariate(s) of Interest

Destroy BART Model (deprecated — do not use!)

Dummify Design Matrix

Gets Raw Node data

Gets Training Sample Projection / Weights

Get Posterior Error Variance Estimates

Get the Variable Inclusion Counts

Get the Variable Inclusion Proportions

Explore Pairwise Interactions in BART Model

Explore Variable Inclusion Proportions in BART Model

Estimate Out-of-sample Error with K-fold Cross validation

Test of Linearity

Gets node predictions indices of the training data for new data.

Partial Dependence Plot

Plot Convergence Diagnostics

Plot the fitted Versus Actual Response

Make a prediction on data using a BART object

Make a prediction on data using a BART array object

Summarizes information about a bartMachine object.

Assess the Out-of-sample RMSE by Number of Trees

Set the Number of Cores for BART

Summarizes information about a bartMachine object.

Perform Variable Selection using Three Threshold-based Procedures

Perform Variable Selection Using Cross-validation Procedure