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The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).
| Version: | 2.1.1 |
| Depends: | mvtnorm, leaps, boot |
| Published: | 2023-10-10 |
| DOI: | 10.32614/CRAN.package.BACprior |
| Author: | Denis Talbot, Genevieve Lefebvre, Juli Atherton |
| Maintainer: | Denis Talbot <denis.talbot at fmed.ulaval.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | BACprior results |
| Reference manual: | BACprior.pdf |
| Package source: | BACprior_2.1.1.tar.gz |
| Windows binaries: | r-devel: BACprior_2.1.1.zip, r-release: BACprior_2.1.1.zip, r-oldrel: BACprior_2.1.1.zip |
| macOS binaries: | r-release (arm64): BACprior_2.1.1.tgz, r-oldrel (arm64): BACprior_2.1.1.tgz, r-release (x86_64): BACprior_2.1.1.tgz, r-oldrel (x86_64): BACprior_2.1.1.tgz |
| Old sources: | BACprior archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.