Title: Multiple Imputation Using Weighted Quantile Sum Regression
norm, pander (>= 0.6.3), rmarkdown (>= 1.13), scales (>=
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.

Authors@R: c(person("Paul M.", "Hargarten", email = "hargartenp@vcu.edu", role = c("aut", "cre")),
Depends: R (>= 3.5.0), methods, parallel, stats, utils
tmvmixnorm (>= 1.0.2), tmvtnorm (>= 1.4-10), truncnorm (>=
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 (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.