Package: EHRmuse 0.0.2.2

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_0.0.2.2.tar.gz
EHRmuse_0.0.2.2.tar.gz(r-4.7-arm64)EHRmuse_0.0.2.2.tar.gz(r-4.7-x86_64)EHRmuse_0.0.2.2.tar.gz(r-4.6-arm64)EHRmuse_0.0.2.2.tar.gz(r-4.6-x86_64)
EHRmuse_0.0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EHRmuse/json (API)

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

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

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

gslcpp

1.00 score 143 downloads 2 exports 33 dependencies

Last updated from:b70f2f80e3. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK127
linux-devel-x86_64OK149
source / vignettesOK178
linux-release-arm64OK147
linux-release-x86_64OK149
wasm-releaseOK115

Exports:EHRmuseexpit

Dependencies:clidata.tableDBIdplyrFormulagenericsgluejsonlitelatticelifecyclemagrittrMASSMatrixminqamitoolsnleqslvnnetnumDerivpillarpkgconfigplotrixR6RcppRcppArmadillorlangsurveysurvivaltibbletidyselectutf8vctrswithrxgboost