Package: gWQS 3.0.5

gWQS: Generalized Weighted Quantile Sum Regression

Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.

Authors:Stefano Renzetti [aut, cre], Paul Curtin [aut], Allan C Just [ctb], Ghalib Bello [ctb], Chris Gennings [aut]

gWQS_3.0.5.tar.gz
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gWQS_3.0.5.tgz(r-4.4-any)gWQS_3.0.5.tgz(r-4.3-any)
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gWQS.pdf |gWQS.html✨
gWQS/json (API)

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

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.81 score 7 stars 2 packages 41 scripts 1.1k downloads 7 mentions 13 exports 110 dependencies

Last updated 1 years agofrom:c189db1b18. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-winOKNov 11 2024
R-4.5-linuxOKNov 11 2024
R-4.4-winOKNov 11 2024
R-4.4-macOKNov 11 2024
R-4.3-winOKNov 11 2024
R-4.3-macOKNov 11 2024

Exports:gwqsgwqs_barplotgwqs_boxplotgwqs_fitted_vs_residgwqs_levels_scatterplotgwqs_multinomgwqs_rankgwqs_ROCgwqs_scatterplotgwqs_summary_tabgwqs_weights_tabgwqsrhselectdatavars

Dependencies:abindbackportsbase64encbookdownbootbroombslibcachemcarcarDataclicodetoolscolorspacecommonmarkcowplotcpp11crayondata.tableDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomeFormulafsfuturefuture.applygenericsggplot2ggrepelglobalsgluegridSVGgtablehighrhtmltoolshttpuvisobandjquerylibjsonlitekableExtraknitrlabelinglaterlatticelifecyclelistenvlme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigplotROCplyrpromisespsclpurrrquantregR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrlistrmarkdownrstudioapisassscalesshinysourcetoolsSparseMstringistringrsurvivalsvglitesystemfontstibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunXMLxml2xtableyaml

How to use gWQS package

Stefano Renzetti, Paul Curtin, Allan C Just, Ghalib Bello, Chris Gennings

Rendered fromgwqs-vignette.Rmdusingknitr::rmarkdownon Nov 11 2024.

Last update: 2023-11-17
Started: 2016-10-21

Readme and manuals

Help Manual

Help pageTopics
Fitting Weighted Quantile Sum regression modelsgwqs gwqsrh gwqs_multinom
Plots and tables functionsgwqs_barplot gwqs_boxplot gwqs_fitted_vs_resid gwqs_levels_scatterplot gwqs_rank gwqs_ROC gwqs_scatterplot gwqs_summary_tab gwqs_weights_tab selectdatavars
Methods for gwqs objectscoef.gwqs fitted.gwqs predict.gwqs print.gwqs print.summary.gwqs residuals.gwqs summary.gwqs vcov.gwqs
Measurement of 38 nutrients (NHANES dataset)tiwqs_data
Exposure concentrations of 34 PCB (simulated dataset)wqs_data