The Ultimate Guide to the bayesvl Package in R

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Explore valuable documentation and insights to make the most of the bayesvl package in R. Get ready to unlock the full potential of the bayesvl package!

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What is the bayesvl package?

In this section, we’ll delve into the fundamental aspects and key features of the package.

bayesvl is an R package that provides tools for visualizing Bayesian networks. It includes functions for specifying networks, conducting inference, and creating plots. This can help users understand the structure and implications of their Bayesian networks.

Title: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'
Description: Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).
Author: Viet-Phuong La [aut, cre], Quan-Hoang Vuong [aut]
Maintainer: Viet-Phuong La

How to install the bayesvl package?

In this section, we’ll walk you through the process of installing and loading the bayesvl package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.

# Install the {Package} package if it's not already installed
if (!require("{Package}")) {
    install.packages("{Package}")
}


# Load the {Package} package
library("{Package}")

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What package information should you know?

In this section, we’ll go over the technical aspects of the bayesvl 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 bayesvl 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 bayesvl 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 bayesvl 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 bayesvl 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 bayesvl package has the following required dependencies: R (>= 3.4.0), rstan (>= 2.10.0), StanHeaders (>= 2.18.0), stats, graphics, methods.
Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The bayesvl package has the following suggested dependencies: loo (>= 2.0.0).
External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The bayesvl 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 bayesvl package has the following imported packages: coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyr.
Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The bayesvl package has no enhancements.

How to get help with the bayesvl package?

In this section, we’ll discuss a variety of available resources for getting help with the bayesvl package.

Key resources

The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("bayesvl") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
Package website: The bayesvl package has a dedicated website. You can visit: https://github.com/sshpa/bayesvl.
Developer support: You can email Viet-Phuong La . For contact information, visit our R community directory.

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