EHRmuse: Multi-Cohort Selection Bias Correction using IPW and AIPW Methods

Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. It utilizes Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce selection bias effectively in multiple non-probability cohorts by integrating data from either individual-level or summary-level external sources. The package also provides a variety of variance estimation techniques. Please refer to Kundu et al. <doi:10.48550/arXiv.2412.00228>.

Version:	0.0.2.1
Depends:	R (≥ 4.0.0)
Imports:	dplyr (≥ 1.0.0), magrittr, MASS, nleqslv (≥ 3.3.2), xgboost (≥ 1.4.1), survey (≥ 4.1.0), stats, nnet (≥ 7.3-17), simplexreg (≥ 0.1.6)
Published:	2025-01-28
DOI:	10.32614/CRAN.package.EHRmuse
Author:	Ritoban Kundu [aut], Michael Kleinsasser [cre]
Maintainer:	Michael Kleinsasser <biostat-cran-manager at umich.edu>
BugReports:	https://github.com/Ritoban1/EHRmuse/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/Ritoban1/EHRmuse
NeedsCompilation:	no
CRAN checks:	EHRmuse results

Documentation:

Reference manual:

EHRmuse.pdf

Downloads:

Package source:	EHRmuse_0.0.2.1.tar.gz
Windows binaries:	r-devel: EHRmuse_0.0.2.1.zip, r-release: EHRmuse_0.0.2.1.zip, r-oldrel: EHRmuse_0.0.2.1.zip
macOS binaries:	r-release (arm64): EHRmuse_0.0.2.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): EHRmuse_0.0.2.1.tgz, r-oldrel (x86_64): not available
Old sources:	EHRmuse archive

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