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Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi:10.1002/sim.8549>.
| Version: | 1.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, twang, graphics, survey |
| Published: | 2021-11-15 |
| Author: | Layla Parast |
| Maintainer: | Layla Parast <parast at austin.utexas.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | SBdecomp results |
| Reference manual: | SBdecomp.pdf |
| Package source: | SBdecomp_1.2.tar.gz |
| Windows binaries: | r-devel: SBdecomp_1.2.zip, r-release: SBdecomp_1.2.zip, r-oldrel: SBdecomp_1.2.zip |
| macOS binaries: | r-release (arm64): SBdecomp_1.2.tgz, r-oldrel (arm64): SBdecomp_1.2.tgz, r-release (x86_64): SBdecomp_1.2.tgz, r-oldrel (x86_64): SBdecomp_1.2.tgz |
| Old sources: | SBdecomp archive |
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These binaries (installable software) and packages are in development.
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