Package: BART 2.9.9

BART: Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Authors:Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [ctb], Matthew Pratola [ctb], Charles Spanbauer [ctb], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]

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BART.pdf |BART.html✨
BART/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • ACTG175 - AIDS Clinical Trials Group Study 175
  • alligator - American alligator Food Choice
  • arq - NHANES 2009-2010 Arthritis Questionnaire
  • bladder - Bladder Cancer Recurrences
  • bladder1 - Bladder Cancer Recurrences
  • bladder2 - Bladder Cancer Recurrences
  • leukemia - Bone marrow transplantation for leukemia and multi-state models
  • lung - NCCTG Lung Cancer Data
  • transplant - Liver transplant waiting list
  • xdm20.test - A data set used in example of 'recur.bart'.
  • xdm20.train - A real data example for 'recur.bart'.
  • ydm20.test - A data set used in example of 'recur.bart'.
  • ydm20.train - A data set used in example of 'recur.bart'.

On CRAN:

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

8.05 score 14 stars 8 packages 422 scripts 2.6k downloads 63 mentions 45 exports 5 dependencies

Last updated 5 months agofrom:4e177d08a1. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-win-x86_64OKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024
R-4.4-win-x86_64OKOct 31 2024
R-4.4-mac-x86_64OKOct 31 2024
R-4.4-mac-aarch64OKOct 31 2024
R-4.3-win-x86_64OKOct 31 2024
R-4.3-mac-x86_64OKOct 31 2024
R-4.3-mac-aarch64OKOct 31 2024

Exports:abartbartModelMatrixclass.indcrisk.bartcrisk.pre.bartcrisk2.bartdraw_lambda_igbartgewekediaglbartmbartmbart2mc.abartmc.cores.openmpmc.crisk.bartmc.crisk.pwbartmc.crisk2.bartmc.crisk2.pwbartmc.gbartmc.lbartmc.mbartmc.mbart2mc.pbartmc.pwbartmc.recur.bartmc.recur.pwbartmc.surv.bartmc.surv.pwbartmc.wbartmc.wbart.gsepbartpwbartrecur.bartrecur.pre.bartrecur.pwbartrs.pbartrtgammartnormspectrum0arsrstepwisestratrssurv.bartsurv.pre.bartsurv.pwbartwbart

Dependencies:latticeMatrixnlmeRcppsurvival

The BART R package

Rendered fromthe-BART-R-package.Rnwusingknitr::knitron Oct 31 2024.

Last update: 2023-03-25
Started: 2019-03-28

Readme and manuals

Help Manual

Help pageTopics
Bayesian Additive Regression TreesBART-package BART
AFT BART for time-to-event outcomesabart mc.abart
AIDS Clinical Trials Group Study 175ACTG175
American alligator Food Choicealligator
NHANES 2009-2010 Arthritis Questionnairearq
Create a matrix out of a vector or data.framebartModelMatrix
Bladder Cancer Recurrencesbladder bladder1 bladder2
Generates Class Indicator Matrix from a Factorclass.ind
BART for competing riskscrisk.bart mc.crisk.bart
Data construction for competing risks with BARTcrisk.pre.bart
BART for competing riskscrisk2.bart mc.crisk2.bart
Testing truncated Normal samplingdraw_lambda_i
Generalized BART for continuous and binary outcomesgbart mc.gbart
Geweke's convergence diagnosticgewekediag
Logit BART for dichotomous outcomes with Logistic latentslbart
Bone marrow transplantation for leukemia and multi-state modelsleukemia
NCCTG Lung Cancer Datacancer lung
Multinomial BART for categorical outcomes with fewer categoriesmbart mc.mbart
Multinomial BART for categorical outcomes with more categoriesmbart2 mc.mbart2
Detecting OpenMPmc.cores.openmp
Predicting new observations with a previously fitted BART modelmc.crisk.pwbart
Predicting new observations with a previously fitted BART modelmc.crisk2.pwbart
Logit BART for dichotomous outcomes with Logistic latents and parallel computationmc.lbart
Probit BART for dichotomous outcomes with Normal latents and parallel computationmc.pbart
Predicting new observations with a previously fitted BART modelmc.recur.pwbart mc.surv.pwbart recur.pwbart surv.pwbart
BART for continuous outcomes with parallel computationmc.wbart
Global SE variable selection for BART with parallel computationmc.wbart.gse
Probit BART for dichotomous outcomes with Normal latentspbart
Predicting new observations with a previously fitted BART modelpredict.crisk2bart
Predicting new observations with a previously fitted BART modelpredict.criskbart
Predicting new observations with a previously fitted BART modelpredict.lbart
Predicting new observations with a previously fitted BART modelpredict.mbart predict.mbart2
Predicting new observations with a previously fitted BART modelpredict.pbart
Predicting new observations with a previously fitted BART modelpredict.recurbart
Predicting new observations with a previously fitted BART modelpredict.survbart
Predicting new observations with a previously fitted BART modelpredict.wbart
Predicting new observations with a previously fitted BART modelmc.pwbart pwbart
BART for recurrent eventsmc.recur.bart recur.bart
Data construction for recurrent events with BARTrecur.pre.bart
BART for dichotomous outcomes with parallel computation and stratified random samplingrs.pbart
Testing truncated Gamma samplingrtgamma
Testing truncated Normal samplingrtnorm
Estimate spectral density at zerospectrum0ar
Stepwise Variable Selection Procedure for survregsrstepwise
Perform stratified random sampling to balance outcomesstratrs
Survival analysis with BARTmc.surv.bart surv.bart
Data construction for survival analysis with BARTsurv.pre.bart
Liver transplant waiting listtransplant
BART for continuous outcomeswbart
A data set used in example of 'recur.bart'.xdm20.test
A real data example for 'recur.bart'.xdm20.train
A data set used in example of 'recur.bart'.ydm20.test ydm20.train