Author: Stephan Artmann, Klaus Jung, Tim Beissbarth
Depends: globaltest, GlobalAncova, limma, RepeatedHighDim, corpcor,

Imports: globaltest, GlobalAncova, limma, RepeatedHighDim

Description: Package for combined miRNA- and mRNA-testing.

Title: Multiple Imputation using Weighted Quantile Sum Analysis
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")) )
Imports: graphics, stats, utils, grid, coda (>= 0.19-2), glm2 (>=
Suggests: spelling, knitr, mice (>= 3.3.0), pander (>= 0.6.3),
Description: Consider a set/mixture of continuous, correlated, and censored components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, that may be different among 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.  In summary, MI consists of three stages: (1) imputation, (2) analysis, and (3) pooling. First,  the missing values are imputed by bootstrapping (Lubin et.al (2004) <doi:10.1289/ehp.7199>), Bayesian imputation, or placing the below the detection limits in the first quantile (BDLQ1) (Ward et.al. (2014) <doi:10.1289/ehp.1307602>). Second, the estimate.wqs() function implements WQS regression if the components are complete, imputed, or missing (Carrico et.al. (2014) <doi:10.1007/s13253-014-0180-3>) . If the data is missing, BDLQ1 is automatically implemented.  Lastly, the pool.mi() function calculates the pooled statistics according to Rubin's rules (Rubin 1987).

Title: Multiple Imputation using Weighted Quantile Sum Regression
Imports: stats, utils, grid, coda (>= 0.19-2), ggplot2 (>= 3.1.0), glm2
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 two imputation models coded in `miWQS` package are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>) and Bayesian imputation.