Package: bartMachine 1.3.4.1

Adam Kapelner

bartMachine: Bayesian Additive Regression Trees

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

Authors:Adam Kapelner and Justin Bleich

bartMachine_1.3.4.1.tar.gz
bartMachine_1.3.4.1.tar.gz(r-4.5-noble)bartMachine_1.3.4.1.tar.gz(r-4.4-noble)
bartMachine_1.3.4.1.tgz(r-4.4-emscripten)bartMachine_1.3.4.1.tgz(r-4.3-emscripten)
bartMachine.pdf |bartMachine.html✨
bartMachine/json (API)

# Install 'bartMachine' in R:
install.packages('bartMachine', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openjdk

6.07 score 6 packages 300 scripts 2.2k downloads 12 mentions 31 exports 11 dependencies

Last updated 1 years agofrom:10b6438c70. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linuxOKDec 02 2024

Exports:bart_machine_get_posteriorbart_machine_num_coresbart_predict_for_test_databartMachinebartMachineArrbartMachineCVbuild_bart_machinebuild_bart_machine_cvcalc_credible_intervalscalc_prediction_intervalscheck_bart_error_assumptionscov_importance_testdummify_dataextract_raw_node_dataget_projection_weightsget_sigsqsget_var_counts_over_chainget_var_props_over_chaininteraction_investigatorinvestigate_var_importancek_fold_cvlinearity_testnode_prediction_training_data_indicespd_plotplot_convergence_diagnosticsplot_y_vs_yhatpredict_bartMachineArrrmse_by_num_treesset_bart_machine_num_coresvar_selection_by_permutevar_selection_by_permute_cv

Dependencies:bartMachineJARscodetoolsdigestdoRNGforeachiteratorsitertoolsmissForestrandomForestrJavarngtools

bartMachine

Rendered frombartMachine.Rnwusingutils::Sweaveon Dec 02 2024.

Last update: 2016-03-25
Started: 2014-12-02

Readme and manuals

Help Manual

Help pageTopics
Data concerning automobile prices.automobile
Get Full Posterior Distributionbart_machine_get_posterior
Get Number of Cores Used by BARTbart_machine_num_cores
Predict for Test Data with Known Outcomesbart_predict_for_test_data
Build a BART ModelbartMachine build_bart_machine
Create an array of BART models for the same data.bartMachineArr
Build BART-CVbartMachineCV build_bart_machine_cv
benchmark_datasetsankara baseball boston compactiv ozone pole triazine wine.red wine.white
Calculate Credible Intervalscalc_credible_intervals
Calculate Prediction Intervalscalc_prediction_intervals
Check BART Error Assumptionscheck_bart_error_assumptions
Importance Test for Covariate(s) of Interestcov_importance_test
Destroy BART Model (deprecated - do not use!)destroy_bart_machine
Dummify Design Matrixdummify_data
Gets Raw Node dataextract_raw_node_data
Gets Training Sample Projection / Weightsget_projection_weights
Get Posterior Error Variance Estimatesget_sigsqs
Get the Variable Inclusion Countsget_var_counts_over_chain
Get the Variable Inclusion Proportionsget_var_props_over_chain
Explore Pairwise Interactions in BART Modelinteraction_investigator
Explore Variable Inclusion Proportions in BART Modelinvestigate_var_importance
Estimate Out-of-sample Error with K-fold Cross validationk_fold_cv
Test of Linearitylinearity_test
Gets node predictions indices of the training data for new data.node_prediction_training_data_indices
Partial Dependence Plotpd_plot
Plot Convergence Diagnosticsplot_convergence_diagnostics
Plot the fitted Versus Actual Responseplot_y_vs_yhat
Make a prediction on data using a BART array objectpredict_bartMachineArr
Make a prediction on data using a BART objectpredict.bartMachine
Summarizes information about a 'bartMachine' object.print.bartMachine
Assess the Out-of-sample RMSE by Number of Treesrmse_by_num_trees
Set the Number of Cores for BARTset_bart_machine_num_cores
Summarizes information about a 'bartMachine' object.summary.bartMachine
Perform Variable Selection using Three Threshold-based Proceduresvar_selection_by_permute
Perform Variable Selection Using Cross-validation Procedurevar_selection_by_permute_cv