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BART (version 1.6)

Bayesian Additive Regression Trees

Description

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary and time-to-event outcomes. For more information on BART, see Chipman, George and McCulloch (2010) and Sparapani, Logan, McCulloch and Laud (2016) .

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Version

Install

install.packages('BART')

Monthly Downloads

2,855

Version

1.6

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

March 20th, 2018

Functions in BART (1.6)

class.ind

Generates Class Indicator Matrix from a Factor
ACTG175

AIDS Clinical Trials Group Study 175
BART-package

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.
lbart

BART for dichotomous outcomes with Logistic latents
gewekediag

Geweke's convergence diagnostic
arq

NHANES 2009-2010 Arthritis Questionnaire
crisk.bart

BART for competing risks
crisk.pre.bart

Data construction for competing risks with BART
bartModelMatrix

Create a matrix out of a vector or data.frame
bladder

Bladder Cancer Recurrences
mc.cores.openmp

Detecting OpenMP
mc.crisk.pwbart

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

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

BART for continuous outcomes with parallel computation
lung

NCCTG Lung Cancer Data
mc.wbart.gse

Global SE variable selection for BART with parallel computation
mbart

BART for multinomial outcomes with Logistic latents
mc.pbart

BART for dichotomous outcomes with parallel computation
mc.surv.pwbart

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

BART for categorical outcomes with Logistic latents and parallel computation
predict.mbart

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.criskbart

Predicting new observations with a previously fitted BART model
pwbart

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
pbart

BART for dichotomous outcomes with Normal latents
predict.lbart

Predicting new observations with a previously fitted BART model
xdm20.test

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

Survival analysis with BART
wbart

BART for continuous outcomes
predict.pbart

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

Data construction for survival analysis with BART
rs.pbart

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

Data construction for recurrent events with BART
transplant

Liver transplant waiting list
ydm20.train

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

A real data example for recur.bart.
spectrum0ar

Estimate spectral density at zero
stratrs

Perform stratified random sampling to balance outcomes