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bartMachine (version 1.2.4.2)

Bayesian Additive Regression Trees

Description

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

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Version

Install

install.packages('bartMachine')

Monthly Downloads

4,324

Version

1.2.4.2

License

GPL-3

Maintainer

Adam Kapelner

Last Published

May 4th, 2018

Functions in bartMachine (1.2.4.2)

automobile

Data concerning automobile prices.

get_var_props_over_chain

Get the Variable Inclusion Proportions

interaction_investigator

Explore Pairwise Interactions in BART Model

plot_convergence_diagnostics

Plot Convergence Diagnostics

bart_machine_get_posterior

Get Full Posterior Distribution

get_var_counts_over_chain

Get the Variable Inclusion Counts

bart_machine_num_cores

Get Number of Cores Used by BART

plot_y_vs_yhat

Plot the fitted Versus Actual Response

bart_predict_for_test_data

Predict for Test Data with Known Outcomes

get_sigsqs

Get Posterior Error Variance Estimates

linearity_test

Test of Linearity

pd_plot

Partial Dependence Plot

benchmark_datasets

destroy_bart_machine

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

bartMachineArr

Create an array of BART models for the same data.

bartMachineCV

Build BART-CV

investigate_var_importance

Explore Variable Inclusion Proportions in BART Model

set_bart_machine_num_cores

Set the Number of Cores for BART

dummify_data

Dummify Design Matrix

k_fold_cv

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

calc_credible_intervals

Calculate Credible Intervals

summary.bartMachine

Summarizes information about a bartMachine object.

print.bartMachine

Summarizes information about a bartMachine object.

rmse_by_num_trees

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

var_selection_by_permute

Perform Variable Selection using Three Threshold-based Procedures

calc_prediction_intervals

Calculate Prediction Intervals

var_selection_by_permute_cv

Perform Variable Selection Using Cross-validation Procedure

check_bart_error_assumptions

Check BART Error Assumptions

cov_importance_test

Importance Test for Covariate(s) of Interest

predict.bartMachine

Make a prediction on data using a BART object

predict_bartMachineArr

Make a prediction on data using a BART array object

bartMachine

Build a BART Model