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BART (version 2.7)

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

2,088

Version

2.7

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

December 5th, 2019

Functions in BART (2.7)

Generalized BART Mixed Model for continuous and binary outcomes

NCCTG Lung Cancer Data

Multinomial BART for categorical outcomes with fewer categories

BART for competing risks

Generalized BART for continuous and binary outcomes

Testing truncated Normal sampling

Multinomial BART for categorical outcomes with more categories

Bone marrow transplantation for leukemia and multi-state models

Logit BART for dichotomous outcomes with Logistic latents

mc.cores.openmp

Detecting OpenMP

Geweke's convergence diagnostic

Global SE variable selection for BART with parallel computation

Predicting new observations with a previously fitted BART model

BART for continuous outcomes with parallel computation

Logit BART for dichotomous outcomes with Logistic latents and parallel computation

predict.crisk2bart

Predicting new observations with a previously fitted BART model

Probit BART for dichotomous outcomes with Normal latents

Probit BART for dichotomous outcomes with Normal latents and parallel computation

predict.criskbart

Predicting new observations with a previously fitted BART model

mc.crisk.pwbart

Predicting new observations with a previously fitted BART model

mc.crisk2.pwbart

Predicting new observations with a previously fitted BART model

Predicting new observations with a previously fitted BART model

Predicting new observations with a previously fitted BART model

BART for continuous outcomes

BART for recurrent events

Predicting new observations with a previously fitted BART model

predict.survbart

Predicting new observations with a previously fitted BART model

Data construction for survival analysis with BART

predict.recurbart

Predicting new observations with a previously fitted BART model

A real data example for recur.bart.

A data set used in example of recur.bart.

Data construction for recurrent events with BART

Estimate spectral density at zero

Stepwise Variable Selection Procedure for survreg

Predicting new observations with a previously fitted BART model

BART for dichotomous outcomes with parallel computation and stratified random sampling

Testing truncated Gamma sampling

Predicting new observations with a previously fitted BART model

Testing truncated Normal sampling

Liver transplant waiting list

A data set used in example of recur.bart.

Survival analysis with BART

Perform stratified random sampling to balance outcomes

Data construction for competing risks with BART

Bladder Cancer Recurrences

AFT BART for time-to-event outcomes

Bayesian Additive Regression Trees

bartModelMatrix

Create a matrix out of a vector or data.frame

NHANES 2009-2010 Arthritis Questionnaire

American alligator Food Choice

AIDS Clinical Trials Group Study 175

BART for competing risks