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BART (version 2.9)

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 .

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Version

Install

install.packages('BART')

Monthly Downloads

5,364

Version

2.9

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

January 5th, 2021

Functions in BART (2.9)

ACTG175

AIDS Clinical Trials Group Study 175
crisk.pre.bart

Data construction for competing risks with BART
arq

NHANES 2009-2010 Arthritis Questionnaire
BART-package

Bayesian Additive Regression Trees
alligator

American alligator Food Choice
bladder

Bladder Cancer Recurrences
abart

AFT BART for time-to-event outcomes
bartModelMatrix

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

BART for competing risks
crisk2.bart

BART for competing risks
leukemia

Bone marrow transplantation for leukemia and multi-state models
lbart

Logit BART for dichotomous outcomes with Logistic latents
gewekediag

Geweke's convergence diagnostic
mc.cores.openmp

Detecting OpenMP
mc.crisk.pwbart

Predicting new observations with a previously fitted BART model
mbart2

Multinomial BART for categorical outcomes with more categories
mbart

Multinomial BART for categorical outcomes with fewer categories
draw_lambda_i

Testing truncated Normal sampling
gbart

Generalized BART for continuous and binary outcomes
lung

NCCTG Lung Cancer Data
mc.wbart.gse

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

BART for continuous outcomes with parallel computation
pbart

Probit BART for dichotomous outcomes with Normal latents
predict.crisk2bart

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

Logit BART for dichotomous outcomes with Logistic latents and parallel computation
mc.crisk2.pwbart

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

Probit BART for dichotomous outcomes with Normal latents and parallel computation
mc.surv.pwbart

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

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

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

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

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

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

Predicting new observations with a previously fitted BART model
rtgamma

Testing truncated Gamma sampling
predict.wbart

Predicting new observations with a previously fitted BART model
pwbart

Predicting new observations with a previously fitted BART model
rtnorm

Testing truncated Normal sampling
rs.pbart

BART for dichotomous outcomes with parallel computation and stratified random sampling
spectrum0ar

Estimate spectral density at zero
xdm20.test

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

A real data example for recur.bart.
surv.bart

Survival analysis with BART
surv.pre.bart

Data construction for survival analysis with BART
transplant

Liver transplant waiting list
recur.bart

BART for recurrent events
recur.pre.bart

Data construction for recurrent events with BART
wbart

BART for continuous outcomes
srstepwise

Stepwise Variable Selection Procedure for survreg
stratrs

Perform stratified random sampling to balance outcomes
ydm20.train

A data set used in example of recur.bart.