The Ultimate Guide to the plspm Package in R
Explore valuable documentation and insights to make the most of the plspm package in R. Get ready to unlock the full potential of the plspm package!
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What is the plspm package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The plspm package is an extensive suite of tools for performing Partial Least Squares Path Modeling (PLS-PM), a form of structural equation modeling. This package is widely used in social and behavioral sciences research.
- Title: Partial Least Squares Path Modeling (PLS-PM)
- Description: Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) , analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) .
- Author: Frederic Bertrand [cre] (), Gaston Sanchez [aut], Laura Trinchera [aut], Giorgio Russolillo [aut]
- Maintainer: Frederic Bertrand
How to install the plspm package?
In this section, we’ll walk you through the process of installing and loading the plspm 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 plspm 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 plspm 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 plspm 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 plspm 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 plspm 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 plspm package has the following required dependencies: R (>= 3.0.1).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The plspm package has the following suggested dependencies: FactoMineR, ggplot2, reshape, testthat, knitr.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The plspm 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 plspm package has the following imported packages: tester, turner, diagram, shape, amap, methods.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The plspm package has no enhancements.
How to get help with the plspm package?
In this section, we’ll discuss a variety of available resources for getting help with the plspm package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("plspm") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The plspm package has a dedicated website. You can visit: https://github.com/gastonstat/plspm.
- Developer support: You can email Frederic Bertrand . 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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