miWQS (0.2.0)

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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. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. The imputation models are: bootstrapping imputation (Lubin et.al (2004) ) and Bayesian imputation.

Maintainer: Paul Hargarten
Author(s): Paul M. Hargarten [aut, cre], David C. Wheeler [aut, rev, ths]

License: GPL-3

Uses: coda, ggplot2, glm2, Hmisc, invgamma, MASS, matrixNormal, rlist, Rsolnp, survival, tidyr, truncnorm, mice, norm, testthat, GGally, formatR, scales, knitr, pander, rmarkdown, wqs, sessioninfo, spelling

Released 2 months ago.

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