Package: borrowr 0.2.0

Jeffrey A. Boatman

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>.

Authors:Jeffrey A. Boatman [aut, cre], David M. Vock [aut], Joseph S. Koopmeiners [aut]

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borrowr.pdf |borrowr.html✨
borrowr/json (API)

# Install 'borrowr' in R:
install.packages('borrowr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • adapt - Data set used in the package vignette

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2 exports 0.00 score 7 dependencies 2 scripts 309 downloads

Last updated 4 years agofrom:810befe2e6. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-linux-x86_64OKSep 13 2024

Exports:credintpate

Dependencies:BARTlatticeMatrixmvtnormnlmeRcppsurvival

Estimating Population Average Treatment Effects with the borrowr Package

Rendered fromborrowr-package.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2020-12-08
Started: 2019-07-19