The Ultimate Guide to the gWQS Package in R
Explore valuable documentation and insights to make the most of the gWQS package in R. Get ready to unlock the full potential of the gWQS package!
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What is the gWQS package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The gWQS package provides functions for generalized weighted quantile sum regression, a method for assessing the combined effects of multiple exposures. This can be useful when dealing with environmental health data and needing to assess the combined effects of multiple pollutants.
- Title: Generalized Weighted Quantile Sum Regression
- 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.
- Author: Stefano Renzetti [aut, cre], Paul Curtin [aut], Allan C Just [ctb], Ghalib Bello [ctb], Chris Gennings [aut]
- Maintainer: Stefano Renzetti
How to install the gWQS package?
In this section, we’ll walk you through the process of installing and loading the gWQS package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the gWQS package.
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the gWQS package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the gWQS package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the gWQS package, visit our database of R package citations.
Technical details
- License type: GPL (>= 2). For license details, visit the Open Source Initiative website.
- Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The gWQS package does not have compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The gWQS package has the following required dependencies: R (>= 3.5.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The gWQS package has the following suggested dependencies: markdown.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The gWQS package does not use any external sources.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The gWQS package has the following imported packages: ggplot2, stats, broom, rlist, MASS, reshape2, plotROC, knitr, kableExtra, nnet, future, future.apply, pscl, ggrepel, cowplot, Matrix, car, utils, bookdown.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The gWQS package has no enhancements.
How to get help with the gWQS package?
In this section, we’ll discuss a variety of available resources for getting help with the gWQS package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("gWQS") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
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Package website: The gWQS package does not have a dedicated website.
- Developer support: You can email Stefano Renzetti . For contact information, visit our R community directory.
Additional resources
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- Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.
Other package guides
Explore our comprehensive guides for other R packages. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.
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