Learn R Programming

BART (version 2.9.9)

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 .

Copy Link

Version

Install

install.packages('BART')

Version

2.9.9

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

June 21st, 2024

Functions in BART (2.9.9)

abart

AFT BART for time-to-event outcomes
alligator

American alligator Food Choice
ACTG175

AIDS Clinical Trials Group Study 175
arq

NHANES 2009-2010 Arthritis Questionnaire
bladder

Bladder Cancer Recurrences
BART-package

Bayesian Additive Regression Trees
bartModelMatrix

Create a matrix out of a vector or data.frame
class.ind

Generates Class Indicator Matrix from a Factor
crisk.bart

BART for competing risks
gbart

Generalized BART for continuous and binary outcomes
lbart

Logit BART for dichotomous outcomes with Logistic latents
gewekediag

Geweke's convergence diagnostic
leukemia

Bone marrow transplantation for leukemia and multi-state models
lung

NCCTG Lung Cancer Data
mbart

Multinomial BART for categorical outcomes with fewer categories
mbart2

Multinomial BART for categorical outcomes with more categories
mc.cores.openmp

Detecting OpenMP
predict.crisk2bart

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

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

Global SE variable selection for BART with parallel computation
mc.crisk.pwbart

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

Logit BART for dichotomous outcomes with Logistic latents and parallel computation
predict.survbart

Predicting new observations with a previously fitted BART model
crisk.pre.bart

Data construction for competing risks with BART
crisk2.bart

BART for competing risks
predict.wbart

Predicting new observations with a previously fitted BART model
pwbart

Predicting new observations with a previously fitted BART model
pbart

Probit BART for dichotomous outcomes with Normal latents
predict.pbart

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

Probit BART for dichotomous outcomes with Normal latents and parallel computation
predict.recurbart

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

BART for recurrent events
spectrum0ar

Estimate spectral density at zero
stratrs

Perform stratified random sampling to balance outcomes
surv.bart

Survival analysis with BART
rtgamma

Testing truncated Gamma sampling
srstepwise

Stepwise Variable Selection Procedure for survreg
rtnorm

Testing truncated Normal sampling
mc.crisk2.pwbart

Predicting new observations with a previously fitted BART model
draw_lambda_i

Testing truncated Normal sampling
mc.surv.pwbart

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

BART for continuous outcomes with parallel computation
predict.lbart

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

Data construction for recurrent events with BART
rs.pbart

BART for dichotomous outcomes with parallel computation and stratified random sampling
predict.mbart

Predicting new observations with a previously fitted BART model
surv.pre.bart

Data construction for survival analysis with BART
wbart

BART for continuous outcomes
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.train

A data set used in example of recur.bart.