Bayesian Additive Regression Trees

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Documentation for package ‘BART’ version 2.9.7

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BART-package Bayesian Additive Regression Trees
abart AFT BART for time-to-event outcomes
ACTG175 AIDS Clinical Trials Group Study 175
alligator American alligator Food Choice
arq NHANES 2009-2010 Arthritis Questionnaire
BART Bayesian Additive Regression Trees
bartModelMatrix Create a matrix out of a vector or data.frame
bladder Bladder Cancer Recurrences
bladder1 Bladder Cancer Recurrences
bladder2 Bladder Cancer Recurrences
cancer NCCTG Lung Cancer Data
crisk.bart BART for competing risks
crisk.pre.bart Data construction for competing risks with BART
crisk2.bart BART for competing risks
draw_lambda_i Testing truncated Normal sampling
gbart Generalized BART for continuous and binary outcomes
gewekediag Geweke's convergence diagnostic
lbart Logit BART for dichotomous outcomes with Logistic latents
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.abart AFT BART for time-to-event outcomes
mc.cores.openmp Detecting OpenMP
mc.crisk.bart BART for competing risks
mc.crisk.pwbart Predicting new observations with a previously fitted BART model
mc.crisk2.bart BART for competing risks
mc.crisk2.pwbart Predicting new observations with a previously fitted BART model
mc.gbart Generalized BART for continuous and binary outcomes
mc.lbart Logit BART for dichotomous outcomes with Logistic latents and parallel computation
mc.mbart Multinomial BART for categorical outcomes with fewer categories
mc.mbart2 Multinomial BART for categorical outcomes with more categories
mc.pbart Probit BART for dichotomous outcomes with Normal latents and parallel computation
mc.pwbart Predicting new observations with a previously fitted BART model
mc.recur.bart BART for recurrent events
mc.recur.pwbart Predicting new observations with a previously fitted BART model
mc.surv.bart Survival analysis with BART
mc.surv.pwbart Predicting new observations with a previously fitted BART model
mc.wbart BART for continuous outcomes with parallel computation
mc.wbart.gse Global SE variable selection for BART with parallel computation
pbart Probit BART for dichotomous outcomes with Normal latents
predict.crisk2bart Predicting new observations with a previously fitted BART model
predict.criskbart Predicting new observations with a previously fitted BART model
predict.lbart Predicting new observations with a previously fitted BART model
predict.mbart Predicting new observations with a previously fitted BART model
predict.mbart2 Predicting new observations with a previously fitted BART model
predict.pbart 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.wbart Predicting new observations with a previously fitted BART model
pwbart 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
recur.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
rtnorm Testing truncated Normal sampling
spectrum0ar Estimate spectral density at zero
srstepwise Stepwise Variable Selection Procedure for survreg
stratrs Perform stratified random sampling to balance outcomes
surv.bart Survival analysis with BART
surv.pre.bart Data construction for survival analysis with BART
surv.pwbart Predicting new observations with a previously fitted BART model
transplant Liver transplant waiting list
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'.
ydm20.test A data set used in example of 'recur.bart'.
ydm20.train A data set used in example of 'recur.bart'.