Introduction | The adapt Data | Using the pate function to estimate the PATE | References
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:
borrowr_0.2.0.tar.gz
borrowr_0.2.0.tar.gz(r-4.7-arm64)borrowr_0.2.0.tar.gz(r-4.7-x86_64)borrowr_0.2.0.tar.gz(r-4.6-arm64)borrowr_0.2.0.tar.gz(r-4.6-x86_64)
borrowr_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
borrowr/json (API)
| # Install 'borrowr' in R: |
| install.packages('borrowr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
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.
Last updated from:810befe2e6. Checks:6 OK. Indexed: yes.
