BART2.9.9 package

Bayesian Additive Regression Trees

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...

Probit BART for dichotomous outcomes with Normal latents and parallel ...

Predicting new observations with a previously fitted BART model

Global SE variable selection for BART with parallel computation

BART for continuous outcomes with parallel computation

Probit BART for dichotomous outcomes with Normal latents

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

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

Predicting new observations with a previously fitted BART model

BART for recurrent events

Data construction for recurrent events with BART

BART for dichotomous outcomes with parallel computation and stratified...

Testing truncated Gamma sampling

Testing truncated Normal sampling

AFT BART for time-to-event outcomes

Bayesian Additive Regression Trees

Create a matrix out of a vector or data.frame

Generates Class Indicator Matrix from a Factor

BART for competing risks

Data construction for competing risks with BART

BART for competing risks

Testing truncated Normal sampling

Generalized BART for continuous and binary outcomes

Geweke's convergence diagnostic

Logit BART for dichotomous outcomes with Logistic latents

Multinomial BART for categorical outcomes with fewer categories

Multinomial BART for categorical outcomes with more categories

Detecting OpenMP

Estimate spectral density at zero

Stepwise Variable Selection Procedure for survreg

Perform stratified random sampling to balance outcomes

Survival analysis with BART

Data construction for survival analysis with BART

BART for continuous outcomes

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Maintainer: Rodney Sparapani License: GPL (>= 2) Last published: 2024-06-21