Learn R Programming
⚠️There's a newer version (2.9.9) of this package.Take me there.

BART (version 2.9.6)

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')

Monthly Downloads

2,274

Version

2.9.6

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

January 9th, 2024

Functions in BART (2.9.6)

crisk.pre.bart

Data construction for competing risks with BART
arq

NHANES 2009-2010 Arthritis Questionnaire
bartModelMatrix

Create a matrix out of a vector or data.frame
BART-package

Bayesian Additive Regression Trees
alligator

American alligator Food Choice
ACTG175

AIDS Clinical Trials Group Study 175
abart

AFT BART for time-to-event outcomes
crisk.bart

BART for competing risks
crisk2.bart

BART for competing risks
bladder

Bladder Cancer Recurrences
leukemia

Bone marrow transplantation for leukemia and multi-state models
draw_lambda_i

Testing truncated Normal sampling
lung

NCCTG Lung Cancer Data
mc.crisk.pwbart

Predicting new observations with a previously fitted BART model
mbart2

Multinomial BART for categorical outcomes with more categories
gewekediag

Geweke's convergence diagnostic
gbart

Generalized BART for continuous and binary outcomes
mbart

Multinomial BART for categorical outcomes with fewer categories
lbart

Logit BART for dichotomous outcomes with Logistic latents
mc.cores.openmp

Detecting OpenMP
pbart

Probit BART for dichotomous outcomes with Normal latents
mc.wbart.gse

Global SE variable selection for BART with parallel computation
predict.criskbart

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

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

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

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

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

BART for continuous outcomes with parallel computation
predict.crisk2bart

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

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

BART for dichotomous outcomes with parallel computation and stratified random sampling
rtgamma

Testing truncated Gamma sampling
predict.wbart

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

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

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

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

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

BART for recurrent events
recur.pre.bart

Data construction for recurrent events with BART
pwbart

Predicting new observations with a previously fitted BART model
srstepwise

Stepwise Variable Selection Procedure for survreg
xdm20.test

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

Survival analysis with BART
rtnorm

Testing truncated Normal sampling
transplant

Liver transplant waiting list
wbart

BART for continuous outcomes
surv.pre.bart

Data construction for survival analysis with BART
stratrs

Perform stratified random sampling to balance outcomes
spectrum0ar

Estimate spectral density at zero
xdm20.train

A real data example for recur.bart.
ydm20.train

A data set used in example of recur.bart.