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 gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README |
| In views: |
CausalInference |
| CRAN checks: |
borrowr results |
Documentation:
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