Package: miWQS
Type: Package
Title: Multiple Imputation Using Weighted Quantile Sum Regression
Version: 0.2.0
Date: 2019-12-12
Authors@R: c( person("Paul M.", "Hargarten", email = "hargartenp@vcu.edu", role = c("aut", "cre")),      
              person("David C.", "Wheeler", email = "david.wheeler@vcuhealth.org ", role = c("aut","rev","ths")) )
Maintainer: Paul M. Hargarten <hargartenp@vcu.edu>
Depends: R (>= 3.5.0), methods, stats, utils
Imports: coda (>= 0.19-2), ggplot2 (>= 3.1.0), glm2 (>= 1.2.1), Hmisc
        (>= 4.1-1), invgamma (>= 1.1), MASS (>= 7.3-49), matrixNormal
        (>= 0.0.0), purrr(>= 0.3.2), rlist (>= 0.4.6.1), Rsolnp (>=
        1.16), survival (>= 2.43-1), tidyr (>= 0.8.2), 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)
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. 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) <doi:10.1289/ehp.7199>) and Bayesian imputation.  
License: GPL-3
Encoding: UTF-8
LazyData: TRUE
Language: en-US
BugReports: https://github.com/phargarten2/miWQS/issues
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-12-12 17:37:25 UTC; pablo
Author: Paul M. Hargarten [aut, cre],
  David C. Wheeler [aut, rev, ths]
Repository: CRAN
Date/Publication: 2019-12-12 18:00:02 UTC
