The Ultimate Guide to the dde Package in R
Explore valuable documentation and insights to make the most of the dde package in R. Get ready to unlock the full potential of the dde package!
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What is the dde package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
Dde is a package that provides functions for solving delay differential equations (DDEs). DDEs are a type of differential equation that includes terms representing the rate of change of the system at previous times.
- Title: Solve Delay Differential Equations
- Description: Solves ordinary and delay differential equations, where the objective function is written in either R or C. Suitable only for non-stiff equations, the solver uses a 'Dormand-Prince' method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) . Support is also included for iterating difference equations.
- Author: Rich FitzJohn [aut, cre], Wes Hinsley [aut], Imperial College of Science, Technology and Medicine [cph]
- Maintainer: Rich FitzJohn
How to install the dde package?
In this section, we’ll walk you through the process of installing and loading the dde 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 dde 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 dde 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 dde 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 dde package, visit our database of R package citations.
Technical details
- License type: MIT + file LICENSE. 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 dde package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The dde package has the following required dependencies: R (>= 3.1.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The dde package has the following suggested dependencies: deSolve, knitr, microbenchmark, rmarkdown, testthat.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The dde package uses the following external sources: ring (>= 1.0.0).
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The dde package has no imported packages.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The dde package has no enhancements.
How to get help with the dde package?
In this section, we’ll discuss a variety of available resources for getting help with the dde package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("dde") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The dde package has a dedicated website. You can visit: https://github.com/mrc-ide/dde.
- Developer support: You can email Rich FitzJohn . For contact information, visit our R community directory.
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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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