bartMachine1.3.4.1 package

Bayesian Additive Regression Trees

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

bartMachine

Build a BART Model

bartMachineArr

Create an array of BART models for the same data.

bartMachineCV

Build BART-CV

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

destroy_bart_machine

Destroy BART Model (deprecated --- do not use!)

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

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

  • Maintainer: Adam Kapelner
  • License: GPL-3
  • Last published: 2023-07-06