Package: EHRmuse 0.0.2.0

Michael Kleinsasser

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>.

Authors:Ritoban Kundu [aut], Michael Kleinsasser [cre]

EHRmuse_0.0.2.0.tar.gz
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EHRmuse.pdf |EHRmuse.html✨
EHRmuse/json (API)

# Install 'EHRmuse' in R:
install.packages('EHRmuse', repos = c('https://ritoban1.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ritoban1/ehrmuse/issues

On CRAN:

Conda:

3.65 score 154 downloads 2 exports 35 dependencies

Last updated 2 months agofrom:810c028863. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-winOKMar 31 2025
R-4.5-macOKMar 31 2025
R-4.5-linuxOKMar 31 2025
R-4.4-winOKMar 31 2025
R-4.4-macOKMar 31 2025
R-4.4-linuxOKMar 31 2025
R-4.3-winOKMar 31 2025
R-4.3-macOKMar 31 2025

Exports:EHRmuseexpit

Dependencies:clidata.tableDBIdplyrfansiFormulagenericsgluejsonlitelatticelifecyclemagrittrMASSMatrixminqamitoolsnleqslvnnetnumDerivpillarpkgconfigplotrixR6RcppRcppArmadillorlangsimplexregsurveysurvivaltibbletidyselectutf8vctrswithrxgboost