borrowr: Estimate Causal Effects with Borrowing Between Data Sources

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>.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: mvtnorm (≥ 1.0.8), BART (≥ 2.1), Rcpp (≥ 1.0.0)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, ggplot2
Published: 2020-12-08
Author: Jeffrey A. Boatman [aut, cre], David M. Vock [aut], Joseph S. Koopmeiners [aut]
Maintainer: Jeffrey A. Boatman <jeffrey.boatman at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README
CRAN checks: borrowr results


Reference manual: borrowr.pdf
Vignettes: Estimating Population Average Treatment Effects with the borrowr Package
Package source: borrowr_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: borrowr_0.2.0.tgz, r-oldrel: borrowr_0.2.0.tgz
Old sources: borrowr archive


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