Multi-Cohort Selection Bias Correction using IPW and AIPW Methods R package EHRmuse version 0.0.2.2 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>. Software Depends: R (>= 4.0.0) Imports: Formula, plotrix, dplyr (>= 1.0.0), magrittr, MASS, nleqslv (>= 3.3.2), xgboost (>= 1.4.1), survey (>= 4.1.0), stats, graphics, nnet (>= 7.3-17) Michael Kleinsasser <biostat-cran-manager@umich.edu> Comprehensive R Archive Network (CRAN) Ritoban Kundu [aut], Michael Kleinsasser [cre] GPL (>= 2) 2025-07-08 application/tgz https://CRAN.R-project.org/package=EHRmuse doi:10.32614/CRAN.package.EHRmuse