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borrowr  

Estimate Causal Effects with Borrowing Between Data Sources
View on CRAN: Click here


Download and install borrowr package within the R console
Install from CRAN:
install.packages("borrowr")

Install from Github:
library("remotes")
install_github("cran/borrowr")

Install by package version:
library("remotes")
install_version("borrowr", "0.2.0")



Attach the package and use:
library("borrowr")
Maintained by
Jeffrey A. Boatman
[Scholar Profile | Author Map]
First Published: 2019-07-19
Latest Update: 2020-12-08
Description:
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) . For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) .
How to cite:
Jeffrey A. Boatman (2019). borrowr: Estimate Causal Effects with Borrowing Between Data Sources. R package version 0.2.0, https://cran.r-project.org/web/packages/borrowr. Accessed 01 Apr. 2025.
Previous versions and publish date:
0.1.0 (2019-07-19 12:00), 0.1.1 (2019-10-30 16:30)
Other packages that cited borrowr R package
View borrowr citation profile
Other R packages that borrowr depends, imports, suggests or enhances
Complete documentation for borrowr
Functions, R codes and Examples using the borrowr R package
Some associated functions: adapt . credint . pate . 
Some associated R codes: RcppExports.R . bart.R . bayes_lm.R . credint.R . data.R . fit_mems.R . methods.R . pate.R . posterior-functions.R . tree_funs.R . utils.R . wbart.R .  Full borrowr package functions and examples
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