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: | 0.0.2.2 |
| 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) |
| Published: | 2025-07-08 |
| 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: | yes |
| SystemRequirements: | GNU Scientific Library version >= 1.8 |
| Citation: | EHRmuse citation info |
| CRAN checks: | EHRmuse results |