c(person('Robert', 'McCulloch', role='aut'),
person('Rodney', 'Sparapani', role=c('aut', 'cre'),
email='rsparapa@mcw.edu'),
person('Robert', 'Gramacy', role='aut'),
person('Charles', 'Spanbauer', role='aut'),
person('Matthew', 'Pratola', role='aut'))
Robert McCulloch [aut],
Rodney Sparapani [aut, cre],
Robert Gramacy [aut],
Charles Spanbauer [aut],
Matthew Pratola [aut]

c(person('Robert', 'McCulloch', role='aut'),
person('Rodney', 'Sparapani', role=c('aut', 'cre'),
email='rsparapa@mcw.edu'),
person('Robert', 'Gramacy', role='aut'),
person('Charles', 'Spanbauer', role='aut'),
person('Matthew', 'Pratola', role='aut'),
person('Martyn', 'Plummer', role='ctb'),
person('Nicky', 'Best', role='ctb'),
person('Kate', 'Cowles', role='ctb'),
person('Karen', 'Vines', role='ctb'))
Robert McCulloch [aut],
Rodney Sparapani [aut, cre],
Robert Gramacy [aut],
Charles Spanbauer [aut],
Matthew Pratola [aut],
Martyn Plummer [ctb],
Nicky Best [ctb],
Kate Cowles [ctb],
Karen Vines [ctb]

c(person('Robert', 'McCulloch', role='aut'),
person('Rodney', 'Sparapani', role=c('aut', 'cre'),
email='rsparapa@mcw.edu'),
person('Charles', 'Spanbauer', role='aut'),
person('Robert', 'Gramacy', role='aut'),
person('Matthew', 'Pratola', role='aut'),
person('Martyn', 'Plummer', role='ctb'),
person('Nicky', 'Best', role='ctb'),
person('Kate', 'Cowles', role='ctb'),
person('Karen', 'Vines', role='ctb'))
Robert McCulloch [aut],
Rodney Sparapani [aut, cre],
Charles Spanbauer [aut],
Robert Gramacy [aut],
Matthew Pratola [aut],
Martyn Plummer [ctb],
Nicky Best [ctb],
Kate Cowles [ctb],
Karen Vines [ctb]

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>.

Bayesian Model Averaging using Bayesian Adaptive Sampling
Merlise Clyde <clyde@stat.duke.edu> with contributions from
Michael Littman
Package for Bayesian Model Averaging in linear models
using  stochastic or deterministic sampling without replacement
from posterior distributions.
Prior distributions on coefficients are from Zellner's g-prior
or mixtures of g-priors  corresponding to the
Zellner-Siow Cauchy Priors or the Liang et al hyper-g priors (to
appear JASA
2008). Other  model selection criterian include AIC and BIC. Sampling
probabilities may be updated based on the sampled models.