The Ultimate Guide to the skpr Package in R
Explore valuable documentation and insights to make the most of the skpr package in R. Get ready to unlock the full potential of the skpr package!
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What is the skpr package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The skpr package is designed for creating, simulating, and analyzing designs for computer experiments. It includes features for Latin hypercube sampling and modeling functions that are built around Gaussian process models.
- Title: Design of Experiments Suite: Generate and Evaluate Optimal Designs
- Description: Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) .
- Author: Tyler Morgan-Wall [aut, cre], George Khoury [aut]
- Maintainer: Tyler Morgan-Wall
How to install the skpr package?
In this section, we’ll walk you through the process of installing and loading the skpr 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 skpr 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 skpr 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 skpr 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 skpr package, visit our database of R package citations.
Technical details
- License type: GPL-3. 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 skpr package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The skpr package has the following required dependencies: R (>= 3.0.2).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The skpr package has the following suggested dependencies: testthat, mbest, ggplot2, lmtest, cli, gridExtra, rintrojs, shinythemes, shiny, shinyjs, gt, shinytest2.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The skpr package uses the following external sources: Rcpp, RcppEigen.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The skpr package has the following imported packages: utils, iterators, stats, lme4, Rcpp (>= 0.11.0), foreach, doParallel, survival, future, car, viridis, magrittr, lmerTest, methods, lazyeval, progress, scales, doRNG, doFuture, progressr.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The skpr package has no enhancements.
How to get help with the skpr package?
In this section, we’ll discuss a variety of available resources for getting help with the skpr package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("skpr") 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 skpr package has a dedicated website. You can visit: https://github.com/tylermorganwall/skpr, https://tylermorganwall.github.io/skpr/.
- Developer support: You can email Tyler Morgan-Wall . 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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