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The Ultimate Guide to the stepp Package in R
Explore valuable documentation and insights to make the most of the stepp package in R. Get ready to unlock the full potential of the stepp package!
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What is the stepp package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The stepp package in R stands for ‘Subpopulation Treatment Effect Pattern Plot (STEPP)’. It allows for the exploration of treatment effect patterns in subpopulations, useful in fields such as clinical trials and personalized medicine.
- Title: Subpopulation Treatment Effect Pattern Plot (STEPP)
- Description: A method to explore the treatment-covariate interactions in survival or generalized linear model (GLM) for continuous, binomial and count data arising from two or more treatment arms of a clinical trial. A permutation distribution approach to inference is implemented, based on permuting the covariate values within each treatment group.
- Author: Wai-ki Yip [aut, cre], Ann Lazar [ctb], David Zahrieh [ctb], Chip Cole [ctb], Ann Lazar [ctb], Marco Bonetti [ctb], Victoria Wang [ctb], William Barcella [ctb], Sergio Venturini [aut] Richard Gelber [ctb]
- Maintainer: Wai-ki Yip
How to install the stepp package?
In this section, we’ll walk you through the process of installing and loading the stepp 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 stepp 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 stepp 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 stepp 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 stepp 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 stepp package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The stepp package has the following required dependencies: methods, car, survival, splines.
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The stepp package has no suggested dependencies.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The stepp 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 stepp package has the following imported packages: rstudioapi, scales.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The stepp package has no enhancements.
How to get help with the stepp package?
In this section, we’ll discuss a variety of available resources for getting help with the stepp package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("stepp") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The stepp package has a dedicated website. You can visit: https://www.r-project.org.
- Developer support: You can email Wai-ki Yip . 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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