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miWQS  

Multiple Imputation Using Weighted Quantile Sum Regression
View on CRAN: Click here


Download and install miWQS package within the R console
Install from CRAN:
install.packages("miWQS")

Install from Github:
library("remotes")
install_github("cran/miWQS")

Install by package version:
library("remotes")
install_version("miWQS", "0.4.4")



Attach the package and use:
library("miWQS")
Maintained by
Paul M. Hargarten
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-12-23
Latest Update: 2021-04-02
Description:
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) ). The imputation models are: bootstrapping imputation (Lubin et.al (2004) ), univariate Bayesian imputation (Hargarten & Wheeler (2020) ), and multivariate Bayesian regression imputation.
How to cite:
Paul M. Hargarten (2018). miWQS: Multiple Imputation Using Weighted Quantile Sum Regression. R package version 0.4.4, https://cran.r-project.org/web/packages/miWQS. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.0.9 (2018-12-23 16:50), 0.1.0 (2019-07-31 07:00), 0.2.0 (2019-12-12 19:00), 0.4.2 (2021-01-21 11:40), 0.4.4 (2021-04-02 23:50)
Other packages that cited miWQS R package
View miWQS citation profile
Other R packages that miWQS depends, imports, suggests or enhances
Complete documentation for miWQS
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