R package citation, R package reverse dependencies, R package scholars, install an r package from GitHub hy is package acceptance pending why is package undeliverable amazon why is package on hold dhl tour packages why in r package r and r package full form why is r free why r is bad which r package to install which r package has which r package which r package version which r package readxl which r package ggplot which r package fread which r package license where is package.json where is package-lock.json where is package.swift where is package explorer in eclipse where is package where is package manager unity where is package installer android where is package manager console in visual studio who r package which r package to install which r package version who is package who is package deal who is package design r and r package full form r and r package meaning what r package has what package r what is package in java what is package what is package-lock.json what is package in python what is package.json what is package installer do r package can't install r packages r can't find package r can't load package can't load xlsx package r can't install psych package r can't install sf package r Write if else in NONMEM pk pd
EHRmuse
View on CRAN: Click
here
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", "0.0.2.0")
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
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 0.0.2.0, https://cran.r-project.org/web/packages/EHRmuse. Accessed 03 Feb. 2025.
Previous versions and publish date:
0.0.2.0 (2025-01-20 17:20)
Other packages that cited EHRmuse R package
View EHRmuse citation profile
Other R packages that EHRmuse depends,
imports, suggests or enhances
Complete documentation for EHRmuse
Functions, R codes and Examples using
the EHRmuse R package
Full EHRmuse package
functions and examples
Downloads during the last 30 days
Get rewarded with contribution points by
helping add
Reviews / comments / questions /suggestions ↴↴↴
Today's Hot Picks in Authors and Packages
predictoR
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Oldemar Rodriguez (view profile)
SMR
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers. ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Daniel Furtado Ferreira (view profile)
metaboData
Data sets from a variety of biological sample matrices,
analysed using a number of mass spectrometr ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Jasen Finch (view profile)
Bolstad2
A set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." T ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: James Curran (view profile)
sgof
Seven different methods for multiple testing problems. The SGoF-type methods (see for example, Carva ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Irene Castro Conde (view profile)
nextGenShinyApps
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Obinna Obianom (view profile)