Estimate Causal Effects with Borrowing Between Data Sources R package borrowr version 0.2.0 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) <doi:10.1093/biostatistics/kxx031>. Software Depends: R (>= 3.5.0) Imports: mvtnorm(>= 1.0.8), BART(>= 2.1), Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, ggplot2 Jeffrey A. Boatman <jeffrey.boatman@gmail.com> Comprehensive R Archive Network (CRAN) Jeffrey A. Boatman [aut, cre], David M. Vock [aut], Joseph S. Koopmeiners [aut] GPL (>= 3) 2020-12-08 application/tgz https://CRAN.R-project.org/package=borrowr doi:10.32614/CRAN.package.borrowr