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
DOI:	10.32614/CRAN.package.borrowr
Author:	Jeffrey A. Boatman [aut, cre], David M. Vock [aut], Joseph S. Koopmeiners [aut]
Maintainer:	Jeffrey A. Boatman <jeffrey.boatman at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	README
In views:	CausalInference
CRAN checks:	borrowr results

Documentation:

Reference manual:	borrowr.pdf
Vignettes:	Estimating Population Average Treatment Effects with the borrowr Package

Downloads:

Package source:	borrowr_0.2.0.tar.gz
Windows binaries:	r-devel: borrowr_0.2.0.zip, r-release: borrowr_0.2.0.zip, r-oldrel: borrowr_0.2.0.zip
macOS binaries:	r-release (arm64): borrowr_0.2.0.tgz, r-oldrel (arm64): borrowr_0.2.0.tgz, r-release (x86_64): borrowr_0.2.0.tgz, r-oldrel (x86_64): borrowr_0.2.0.tgz
Old sources:	borrowr archive

Linking:

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