bartMachine v1.2.3
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Bayesian Additive Regression Trees
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.
Functions in bartMachine
| Name | Description | |
| bartMachineCV | Build BART-CV | |
| bart_predict_for_test_data | Predict for Test Data with Known Outcomes | |
| benchmark_datasets | benchmark_datasets | |
| dummify_data | Dummify Design Matrix | |
| calc_prediction_intervals | Calculate Prediction Intervals | |
| automobile | Data concerning automobile prices. | |
| var_selection_by_permute | Perform Variable Selection using Three Threshold-based Procedures | |
| plot_convergence_diagnostics | Plot Convergence Diagnostics | |
| calc_credible_intervals | Calculate Credible Intervals | |
| set_bart_machine_num_cores | Set the Number of Cores for BART | |
| var_selection_by_permute_cv | Perform Variable Selection Using Cross-validation Procedure | |
| predict_bartMachineArr | Make a prediction on data using a BART array object | |
| pd_plot | Partial Dependence Plot | |
| predict.bartMachine | Make a prediction on data using a BART object | |
| get_var_counts_over_chain | Get the Variable Inclusion Counts | |
| get_sigsqs | Get Posterior Error Variance Estimates | |
| summary.bartMachine | Summarizes information about a bartMachine object. | |
| bartMachineArr | Create an array of BART models for the same data. | |
| 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 | |
| bart_machine_num_cores | Get Number of Cores Used by BART | |
| plot_y_vs_yhat | Plot the fitted Versus Actual Response | |
| destroy_bart_machine | Destroy BART Model (deprecated --- do not use!) | |
| check_bart_error_assumptions | Check BART Error Assumptions | |
| print.bartMachine | Summarizes information about a bartMachine object. | |
| cov_importance_test | Importance Test for Covariate(s) of Interest | |
| rmse_by_num_trees | Assess the Out-of-sample RMSE by Number of Trees | |
| bart_machine_get_posterior | Get Full Posterior Distribution | |
| get_var_props_over_chain | Get the Variable Inclusion Proportions | |
| bartMachine | Build a BART Model | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2016-05-11 |
| License | GPL-3 |
| SystemRequirements | Java (>= 1.7.0) |
| NeedsCompilation | no |
| Packaged | 2016-05-11 18:07:40 UTC; Kapelner |
| Repository | CRAN |
| Date/Publication | 2016-05-12 14:47:18 |
| depends | bartMachineJARs (>= 1.0) , base (>= 2.14.0) , car , missForest , R (>= 2.14.0) , randomForest , rJava (>= 0.9-8) |
| imports | graphics , grDevices , stats |
| Contributors | Adam Kapelner, Justin Bleich |
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