SBdecomp: Estimation of the Proportion of SB Explained by Confounders

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

Documentation:

Reference manual:

SBdecomp.pdf

Downloads:

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

Linking:

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