Package: miWQS 0.5.0

Paul M. Hargarten

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.

Authors:Paul M. Hargarten [aut, cre], David C. Wheeler [aut, rev, ths]

miWQS_0.5.0.tar.gz
miWQS_0.5.0.zip(r-4.5)miWQS_0.5.0.zip(r-4.4)miWQS_0.5.0.zip(r-4.3)
miWQS_0.5.0.tgz(r-4.5-any)miWQS_0.5.0.tgz(r-4.4-any)miWQS_0.5.0.tgz(r-4.3-any)
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miWQS_0.5.0.tgz(r-4.4-emscripten)miWQS_0.5.0.tgz(r-4.3-emscripten)
miWQS.pdf |miWQS.html
miWQS/json (API)
NEWS

# Install 'miWQS' in R:
install.packages('miWQS', repos = c('https://phargarten2.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/phargarten2/miwqs/issues

Datasets:

On CRAN:

Conda:

4.78 score 2 stars 1 packages 20 scripts 284 downloads 12 exports 92 dependencies

Last updated 1 years agofrom:501aba6728. Checks:1 OK, 3 ERROR, 4 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 03 2025
R-4.5-winERRORMar 03 2025
R-4.5-macERRORMar 03 2025
R-4.5-linuxERRORMar 03 2025
R-4.4-winNOTEMar 03 2025
R-4.4-macNOTEMar 03 2025
R-4.3-winNOTEMar 03 2025
R-4.3-macNOTEMar 03 2025

Exports:analyze.individuallycombine.AICdo.many.wqsestimate.wqsestimate.wqs.formulaimpute.bootimpute.Lubinimpute.multivariate.bayesianimpute.subimpute.univariate.bayesian.mimake.quantile.matrixpool.mi

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodacolorspacecondMVNormcpp11data.tabledigestdplyrevaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2glm2gluegmmgridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsinvgammaisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmatrixNormalmcmcMCMCpackmemoisemgcvmimemunsellmvtnormnlmennetpillarpkgconfigpurrrquantregR6rappdirsRColorBrewerrlangrlistrmarkdownrpartRsolnprstudioapisandwichsassscalesSparseMstringistringrsurvivaltibbletidyrtidyselecttinytextmvmixnormtmvtnormtruncnormutf8vctrsviridisviridisLitewithrxfunXMLyamlzoo

README: miWQS

Rendered fromREADME.Rmdusingknitr::rmarkdownon Mar 03 2025.

Last update: 2023-11-06
Started: 2023-11-06