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| BART-package | Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. |
| ACTG175 | AIDS Clinical Trials Group Study 175 |
| arq | NHANES 2009-2010 Arthritis Questionnaire |
| BART | Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. |
| 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 |
| 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.bart | BART for competing risks |
| 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.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 | 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 |
| recur.pwbart | Predicting new observations with a previously fitted BART model |
| 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 |
| 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'. |