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BART (version 2.5)

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 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

3,138

Version

2.5

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

June 17th, 2019

Functions in BART (2.5)

crisk.pre.bart

Data construction for competing risks with BART
ACTG175

AIDS Clinical Trials Group Study 175
bartModelMatrix

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

NHANES 2009-2010 Arthritis Questionnaire
alligator

American alligator Food Choice
abart

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

BART for competing risks
bladder

Bladder Cancer Recurrences
BART-package

Bayesian Additive Regression Trees
crisk2.bart

BART for competing risks
draw_lambda_i

Testing truncated Normal sampling
mc.cores.openmp

Detecting OpenMP
mbart2

Multinomial BART for categorical outcomes with more categories
gbart

Generalized BART for continuous and binary outcomes
leukemia

Bone marrow transplantation for leukemia and multi-state models
lbart

Logit BART for dichotomous outcomes with Logistic latents
gbmm

Generalized BART Mixed Model for continuous and binary outcomes
gewekediag

Geweke's convergence diagnostic
lung

NCCTG Lung Cancer Data
mbart

Multinomial BART for categorical outcomes with fewer categories
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.crisk2.pwbart

Predicting new observations with a previously fitted BART model
predict.crisk2bart

Predicting new observations with a previously fitted BART model
pbart

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

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

BART for continuous outcomes with parallel computation
predict.criskbart

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

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

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

Data construction for recurrent events with BART
predict.recurbart

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

Predicting new observations with a previously fitted BART model
srstepwise

Stepwise Variable Selection Procedure for survreg
spectrum0ar

Estimate spectral density at zero
recur.bart

BART for recurrent events
pwbart

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

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

Testing truncated Gamma sampling
predict.lbart

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

Predicting new observations with a previously fitted BART model
stratrs

Perform stratified random sampling to balance outcomes
surv.bart

Survival analysis with BART
surv.pre.bart

Data construction for survival analysis with BART
wbart

BART for continuous outcomes
xdm20.test

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

A real data example for recur.bart.
rtnorm

Testing truncated Normal sampling
ydm20.train

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

Liver transplant waiting list