bayesvl: R Package for Visual Learning of Bayesian Networks and MCMC

The 'bayesvl' R package provides functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo computations. It was authored by Viet-Phuong La from Université Libre de Bruxelles, with methods described in a 2019 reference. The package enables users to create, examine, and generate Stan code for Bayesian network structures.

Use Cases

• Visually learning the graphical structure of Bayesian networks based on the package's plotting functions.
• Generating Stan code for Bayesian network structures based on the described code generation feature.
• Performing Markov chain Monte Carlo computations and posterior checks based on the package's MCMC functionality.
• Creating random Bayesian networks with customized constraints based on the described dataset utility.

Strengths

• Designed for pedagogical purposes, suggesting a focus on clarity and learning.
• Provides multiple integrated functions for creating, visualizing, and computing with Bayesian networks.
• Includes a reference to a 2019 paper describing the underlying methods and algorithms.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Likely contains code and example data associated with the 'bayesvl' R package.

Time Range

Method reference is from 2019.

Freshness

Last updated date is unknown.

Geography

Author affiliation is Université Libre de Bruxelles.