Introduction | How to use the gWQS package | Example | Step 1 | Step 2 | Step 3 | References | Acknowledgements
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:
gWQS_3.0.5.tar.gz
gWQS_3.0.5.zip(r-4.7)gWQS_3.0.5.zip(r-4.6)gWQS_3.0.5.zip(r-4.5)
gWQS_3.0.5.tgz(r-4.6-any)gWQS_3.0.5.tgz(r-4.5-any)
gWQS_3.0.5.tar.gz(r-4.7-any)gWQS_3.0.5.tar.gz(r-4.6-any)
gWQS_3.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gWQS/json (API)
| # Install 'gWQS' in R: |
| install.packages('gWQS', repos = c('https://renzetti.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- tiwqs_data - Measurement of 38 nutrients
- wqs_data - Exposure concentrations of 34 PCB
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:c189db1b18. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 191 | ||
| source / vignettes | OK | 647 | ||
| linux-release-x86_64 | OK | 181 | ||
| macos-release-arm64 | OK | 157 | ||
| macos-oldrel-arm64 | OK | 226 | ||
| windows-devel | OK | 145 | ||
| windows-release | OK | 155 | ||
| windows-oldrel | OK | 156 | ||
| wasm-release | OK | 142 |
Exports:gwqsgwqs_barplotgwqs_boxplotgwqs_fitted_vs_residgwqs_levels_scatterplotgwqs_multinomgwqs_rankgwqs_ROCgwqs_scatterplotgwqs_summary_tabgwqs_weights_tabgwqsrhselectdatavars
Dependencies:abindbackportsbase64encbookdownbootbroombslibcachemcarcarDataclicodetoolscolorspacecommonmarkcowplotcpp11data.tableDerivdigestdoBydplyrevaluatefarverfastmapfontawesomeforecastFormulafracdifffsfuturefuture.applygenericsggplot2ggrepelglobalsgluegridSVGgtablehighrhtmltoolshttpuvisobandjquerylibjsonlitekableExtraknitrlabelinglaterlatticelifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrnlmenloptrnnetnumDerivotelparallellypbkrtestpillarpkgconfigplotROCplyrpromisespsclpurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangrlistrmarkdownrstudioapiS7sassscalesshinysourcetoolsSparseMstringistringrsurvivalsvglitesystemfontstextshapingtibbletidyrtidyselecttimeDatetinytexurcautf8vctrsviridisLitewithrxfunXMLxml2xtableyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fitting Weighted Quantile Sum regression models | gwqs gwqsrh gwqs_multinom |
| Plots and tables functions | gwqs_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 objects | coef.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 |
