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EHRmuse
View on CRAN: Click
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Download and install EHRmuse package within the R console
Install from CRAN:
install.packages("EHRmuse")
Install from Github:
library("remotes")
install_github("cran/EHRmuse")
Install by package version:
library("remotes")
install_version("EHRmuse", "0.0.2.1")
Attach the package and use:
library("EHRmuse")
Maintained by
Michael Kleinsasser
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.EHRmuse . https://github.com/Ritoban1/EHRmuse/issues . https://github.com/Ritoban1/EHRmuse . EHRmuse results . EHRmuse.pdf . EHRmuse_0.0.2.1.tar.gz . EHRmuse_0.0.2.1.zip . EHRmuse_0.0.2.1.zip . EHRmuse_0.0.2.1.zip . EHRmuse_0.0.2.1.tgz . EHRmuse_0.0.2.1.tgz . EHRmuse_0.0.2.1.tgz . EHRmuse_0.0.2.1.tgz . EHRmuse_0.0.2.1.tgz . EHRmuse_0.0.2.1.tgz . EHRmuse archive . https://CRAN.R-project.org/package=EHRmuse .
First Published: 2025-01-20
Latest Update: 2025-01-20
Description:
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>.
How to cite:
Michael Kleinsasser (2025). EHRmuse: Multi-Cohort Selection Bias Correction using IPW and AIPW Methods. R package version 0.0.2.1, https://cran.r-project.org/web/packages/EHRmuse. Accessed 17 Feb. 2025.
Previous versions and publish date:
0.0.2.0 (2025-01-20 17:20)
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