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BART (version 2.9.10)

Bayesian Additive Regression Trees

Description

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 .

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Version

Install

install.packages('BART')

Monthly Downloads

4,360

Version

2.9.10

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

January 24th, 2026

Functions in BART (2.9.10)

alligator

American alligator Food Choice
ACTG175

AIDS Clinical Trials Group Study 175
class.ind

Generates Class Indicator Matrix from a Factor
BART-package

Bayesian Additive Regression Trees
bladder

Bladder Cancer Recurrences
crisk.bart

BART for competing risks
gbart

Generalized BART for continuous and binary outcomes
draw_lambda_i

Testing truncated Normal sampling
lung

NCCTG Lung Cancer Data
gewekediag

Geweke's convergence diagnostic
leukemia

Bone marrow transplantation for leukemia and multi-state models
mbart2

Multinomial BART for categorical outcomes with more categories
crisk2.bart

BART for competing risks
mc.cores.openmp

Detecting OpenMP
mbart

Multinomial BART for categorical outcomes with fewer categories
lbart

Logit BART for dichotomous outcomes with Logistic latents
mc.wbart

BART for continuous outcomes with parallel computation
predict.crisk2bart

Predicting new observations with a previously fitted BART model
mc.crisk.pwbart

Predicting new observations with a previously fitted BART model
mc.crisk2.pwbart

Predicting new observations with a previously fitted BART model
mc.wbart.gse

Global SE variable selection for BART with parallel computation
mc.lbart

Logit BART for dichotomous outcomes with Logistic latents and parallel computation
mc.pbart

Probit BART for dichotomous outcomes with Normal latents and parallel computation
pbart

Probit BART for dichotomous outcomes with Normal latents
predict.criskbart

Predicting new observations with a previously fitted BART model
mc.surv.pwbart

Predicting new observations with a previously fitted BART model
predict.recurbart

Predicting new observations with a previously fitted BART model
predict.survbart

Predicting new observations with a previously fitted BART model
predict.lbart

Predicting new observations with a previously fitted BART model
rs.pbart

BART for dichotomous outcomes with parallel computation and stratified random sampling
recur.pre.bart

Data construction for recurrent events with BART
predict.mbart

Predicting new observations with a previously fitted BART model
predict.pbart

Predicting new observations with a previously fitted BART model
predict.wbart

Predicting new observations with a previously fitted BART model
recur.bart

BART for recurrent events
pwbart

Predicting new observations with a previously fitted BART model
rtnorm

Testing truncated Normal sampling
transplant

Liver transplant waiting list
rtgamma

Testing truncated Gamma sampling
surv.pre.bart

Data construction for survival analysis with BART
xdm20.test

A data set used in example of recur.bart.
stratrs

Perform stratified random sampling to balance outcomes
surv.bart

Survival analysis with BART
wbart

BART for continuous outcomes
srstepwise

Stepwise Variable Selection Procedure for survreg
spectrum0ar

Estimate spectral density at zero
ydm20.train

A data set used in example of recur.bart.
xdm20.train

A real data example for recur.bart.
abart

AFT BART for time-to-event outcomes
arq

NHANES 2009-2010 Arthritis Questionnaire
bartModelMatrix

Create a matrix out of a vector or data.frame
crisk.pre.bart

Data construction for competing risks with BART