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 |