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.
arq                     NHANES 2009-2010 Arthritis Questionnaire
bartModelMatrix         Create a matrix out of a vector or data.frame
bladder                 Bladder Cancer Recurrences
class.ind               Generates Class Indicator Matrix from a Factor
crisk.bart              BART for competing risks
crisk.pre.bart          Data construction for competing risks with BART
gewekediag              Geweke's convergence diagnostic
lbart                   BART for dichotomous outcomes with Logistic
                        latents
lung                    NCCTG Lung Cancer Data
mbart                   BART for multinomial outcomes with Logistic
                        latents
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.mbart                BART for categorical outcomes with Logistic
                        latents and parallel computation
mc.pbart                BART for dichotomous outcomes with parallel
                        computation
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                   BART for dichotomous outcomes with Normal
                        latents
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.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
rs.pbart                BART for dichotomous outcomes with parallel
                        computation and stratified random sampling
spectrum0ar             Estimate spectral density at zero
stratrs                 Perform stratified random sampling to balance
                        outcomes
surv.bart               Survival analysis with BART
surv.pre.bart           Data construction for survival analysis with
                        BART
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.train             A data set used in example of 'recur.bart'.
