miWQS: Multiple Imputation Using Weighted Quantile Sum Regression

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) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.

Version: 0.4.4
Depends: R (≥ 3.5.0), methods, parallel, stats, utils
Imports: coda (≥ 0.19-2), condMVNorm (≥ 2015.2-1), ggplot2 (≥ 3.1.0), glm2 (≥ 1.2.1), Hmisc (≥ 4.1-1), invgamma (≥ 1.1), MASS (≥ 7.3-49), matrixNormal (≥ 0.0.0), MCMCpack (≥ 1.4-4), mvtnorm (≥ 1.0-10), purrr (≥ 0.3.2), rlist (≥ 0.4.6.1), Rsolnp (≥ 1.16), survival (≥ 3.1-12), tidyr (≥ 1.0.0), tmvmixnorm (≥ 1.0.2), tmvtnorm (≥ 1.4-10), truncnorm (≥ 1.0-8)
Suggests: formatR, GGally (≥ 1.4.0), knitr (≥ 1.23), mice (≥ 3.3.0), norm, pander (≥ 0.6.3), rmarkdown (≥ 1.13), scales (≥ 1.0.0), sessioninfo (≥ 1.1.1), spelling (≥ 2.0), testthat (≥ 2.0.1), wqs (≥ 0.0.1)
Published: 2021-04-02
Author: Paul M. Hargarten [aut, cre], David C. Wheeler [aut, rev, ths]
Maintainer: Paul M. Hargarten <hargartenp at alumni.vcu.edu>
BugReports: https://github.com/phargarten2/miWQS/issues
License: GPL-3
NeedsCompilation: no
Language: en-US
Citation: miWQS citation info
Materials: NEWS
In views: MissingData
CRAN checks: miWQS results

Documentation:

Reference manual: miWQS.pdf
Vignettes: README

Downloads:

Package source: miWQS_0.4.4.tar.gz
Windows binaries: r-devel: miWQS_0.4.4.zip, r-release: miWQS_0.4.4.zip, r-oldrel: miWQS_0.4.4.zip
macOS binaries: r-release (arm64): miWQS_0.4.4.tgz, r-release (x86_64): miWQS_0.4.4.tgz, r-oldrel: miWQS_0.4.4.tgz
Old sources: miWQS archive

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