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gWQS  

Generalized Weighted Quantile Sum Regression
View on CRAN: Click here


Download and install gWQS package within the R console
Install from CRAN:
install.packages("gWQS")

Install from Github:
library("remotes")
install_github("cran/gWQS")

Install by package version:
library("remotes")
install_version("gWQS", "3.0.5")



Attach the package and use:
library("gWQS")
Maintained by
Stefano Renzetti
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-10-21
Latest Update: 2023-11-16
Description:
Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) ), a random subset implementation of WQS (Curtin et al. (2019) ), a repeated holdout validation WQS (Tanner et al. (2019) ) and a WQS with 2 indices (Renzetti et al. (2023) ) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
How to cite:
Stefano Renzetti (2016). gWQS: Generalized Weighted Quantile Sum Regression. R package version 3.0.5, https://cran.r-project.org/web/packages/gWQS
Previous versions and publish date:
1.0.0 (2016-10-21 21:38), 1.1.0 (2018-05-07 18:38), 1.1.1 (2019-03-15 11:40), 2.0.0 (2019-08-27 14:40), 2.0.1 (2020-03-03 13:20), 3.0.0 (2020-06-23 15:00), 3.0.1 (2020-11-19 16:00), 3.0.2 (2021-01-28 16:10), 3.0.3 (2021-02-04 18:50), 3.0.4 (2021-05-20 11:30)
Other packages that cited gWQS R package
View gWQS citation profile
Other R packages that gWQS depends, imports, suggests or enhances
Functions, R codes and Examples using the gWQS R package
Some associated functions: main_functions . methods . secondary_functions . tiwqs_data . wqs_data . 
Some associated R codes: data.R . gwqs.R . wqs_helpers.R .  Full gWQS package functions and examples
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