Imports: magrittr, dplyr, data.table, downloader, sf, XML, httr, rgdal,
janitor, rio, jsonlite, stringr, utils, memoise, stringi,
Packaged: 2020-04-20 08:11:11 UTC; Administrateur

shiny, miniUI, rstudioapi, checkmate, glue
Packaged: 2022-04-02 09:17:58 UTC; Administrateur

Packaged: 2022-04-03 09:50:24 UTC; Administrateur

Title: Bayesian Additive Regression Trees
Author: Robert McCulloch <robert.e.mcculloch@gmail.com> and Rodney Sparapani <rsparapa@mcw.edu>
Description: Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary and time-to-event outcomes.  For more information on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.
Depends: R (>= 2.10), parallel, survival, tools

person('Rodney', 'Sparapani', role=c('aut', 'cre'), email='rsparapa@mcw.edu'),
Imports: Rcpp (>= 0.12.3), parallel, tools

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) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.

Depends: R (>= 2.10), survival, nnet, nlme

Depends: R (>= 2.10), nlme, nnet, survival
Imports: Rcpp (>= 0.12.3), MASS, parallel, tools

Description: 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>.

Title: Bayesian Model Averaging using Bayesian Adaptive Sampling
Author: Merlise Clyde <clyde@stat.duke.edu> with contributions from
Description: Package for Bayesian Model Averaging in linear models
using  stochastic or deterministic sampling without replacement
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
2008). Other  model selection criterian include AIC and BIC. Sampling
probabilities may be updated based on the sampled models.
Packaged: Sun Mar  1 19:16:28 2009; clyde