The Ultimate Guide to the cuRe Package in R
Explore valuable documentation and insights to make the most of the cuRe package in R. Get ready to unlock the full potential of the cuRe package!
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What is the cuRe package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
CuRe is a package that provides functions for survival analysis with cure models. Cure models are used when a proportion of the population is considered ‘cured’ or not at risk of experiencing the event of interest.
- Title: Parametric Cure Model Estimation
- Description: Contains functions for estimating generalized parametric mixture and non-mixture cure models , loss of lifetime, mean residual lifetime, and crude event probabilities.
- Author: Lasse Hjort Jakobsen [aut, cre], Mark Clements [cph], Rasmus Kuhr Jensen [ctb], Lars Klingen Gjærde [ctb]
- Maintainer: Lasse Hjort Jakobsen
How to install the cuRe package?
In this section, we’ll walk you through the process of installing and loading the cuRe 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 cuRe 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 cuRe 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 cuRe 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 cuRe 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 cuRe 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 cuRe package has the following required dependencies: R (>= 3.5.0), survival, rstpm2, date.
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The cuRe package has no suggested dependencies.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The cuRe 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 cuRe package has the following imported packages: numDeriv, statmod, relsurv, reshape2.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The cuRe package has no enhancements.
How to get help with the cuRe package?
In this section, we’ll discuss a variety of available resources for getting help with the cuRe package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("cuRe") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The cuRe package has a dedicated website. You can visit: https://github.com/LasseHjort/cuRe.
- Developer support: You can email Lasse Hjort Jakobsen . For contact information, visit our R community directory.
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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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