Title: Causal Inference using Bayesian Additive Regression Trees
person("Vincent", "Dorie", role = c("aut", "cre"), email = "vdorie@gmail.com",      comment = c(ORCID = "0000-0002-9576-3064")),
person("Jennifer", "Hill", role = "aut",           email = "jennifer.hill@nyu.edu", comment = c(ORCID = "0000-0003-4983-2206")))
Imports: dbarts (>= 0.9-16), methods, stats, graphics, parallel, utils,
Suggests: testthat (>= 0.9-0), lme4, rpart, tmle
Description: Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).
Author: Vincent Dorie [aut, cre] (<https://orcid.org/0000-0002-9576-3064>),

Title: bartMachine: Bayesian Additive Regression Trees
Description: An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization
Depends: R (>= 2.14.0), rJava (>= 0.8-4), car, randomForest, missForest

License: GPL-2 | GPL-3 | MIT + file LICENSE

Author: Adam Kapelner and Justin Bleich (R package), see COPYRIGHTS file for the authors of the java libraries

Description: An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

Author: Adam Kapelner and Justin Bleich (R package)

Description: These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.

Title: Bayesian Additive Regression Trees for Confounder Selection
person("Yeonghoon", "Yoo", , "yooyh.stat@gmail.com", role = c("aut", "cre"))
Description: Fit Bayesian Regression Additive Trees (BART) models to
select relevant confounders among a large set of potential confounders
and to estimate average treatment effect. For more information, see
Imports: ggcharts, ggplot2, invgamma, MCMCpack, Rcpp, rlang, rootSolve,

Title: Bayesian Variable Selection with Shrinking and Diffusing Priors
Author: Qingyan Xiang <qxiang@illinois.edu>, Naveen Narisetty <naveen@illinois.edu>
Description: Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.