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BART (version 2.8)

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

3,043

Version

2.8

License

GPL (>= 2)

Maintainer

Rodney Sparapani

Last Published

December 4th, 2020

Functions in BART (2.8)

bartModelMatrix

Create a matrix out of a vector or data.frame

Bayesian Additive Regression Trees

Bladder Cancer Recurrences

BART for competing risks

NHANES 2009-2010 Arthritis Questionnaire

AIDS Clinical Trials Group Study 175

Data construction for competing risks with BART

American alligator Food Choice

BART for competing risks

AFT BART for time-to-event outcomes

NCCTG Lung Cancer Data

Testing truncated Normal sampling

Generalized BART Mixed Model for continuous and binary outcomes

Generalized BART for continuous and binary outcomes

Bone marrow transplantation for leukemia and multi-state models

Logit BART for dichotomous outcomes with Logistic latents

Multinomial BART for categorical outcomes with fewer categories

Geweke's convergence diagnostic

mc.cores.openmp

Detecting OpenMP

Multinomial BART for categorical outcomes with more categories

predict.crisk2bart

Predicting new observations with a previously fitted BART model

Predicting new observations with a previously fitted BART model

Logit BART for dichotomous outcomes with Logistic latents and parallel computation

mc.crisk.pwbart

Predicting new observations with a previously fitted BART model

Global SE variable selection for BART with parallel computation

mc.crisk2.pwbart

Predicting new observations with a previously fitted BART model

Probit BART for dichotomous outcomes with Normal latents

predict.criskbart

Predicting new observations with a previously fitted BART model

BART for continuous outcomes with parallel computation

Probit BART for dichotomous outcomes with Normal latents and parallel computation

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

Predicting new observations with a previously fitted BART model

predict.survbart

Predicting new observations with a previously fitted BART model

predict.recurbart

Predicting new observations with a previously fitted BART model

Data construction for recurrent events with BART

BART for dichotomous outcomes with parallel computation and stratified random sampling

Predicting new observations with a previously fitted BART model

BART for recurrent events

Testing truncated Normal sampling

Liver transplant waiting list

Testing truncated Gamma sampling

BART for continuous outcomes

Stepwise Variable Selection Procedure for survreg

Data construction for survival analysis with BART

Perform stratified random sampling to balance outcomes

Estimate spectral density at zero

A data set used in example of recur.bart.

A real data example for recur.bart.

Survival analysis with BART

A data set used in example of recur.bart.