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>) and a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.

Version: 3.0.4
Depends: R (≥ 3.5.0)
Imports: ggplot2, dplyr, stats, broom, rlist, MASS, reshape2, plotROC, knitr, kableExtra, nnet, future, future.apply, pscl, ggrepel, cowplot, Matrix
Suggests: markdown
Published: 2021-05-20
Author: Stefano Renzetti, Paul Curtin, Allan C Just, Ghalib Bello, Chris Gennings
Maintainer: Stefano Renzetti <stefano.renzetti88 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: gWQS results

Downloads:

Reference manual: gWQS.pdf
Vignettes: How to use gWQS package
Package source: gWQS_3.0.4.tar.gz
Windows binaries: r-devel: gWQS_3.0.4.zip, r-release: gWQS_3.0.4.zip, r-oldrel: gWQS_3.0.4.zip
macOS binaries: r-release (arm64): gWQS_3.0.4.tgz, r-release (x86_64): gWQS_3.0.4.tgz, r-oldrel: gWQS_3.0.4.tgz
Old sources: gWQS archive

Reverse dependencies:

Reverse imports: lwqs

Linking:

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