A. Platas-López, A. Guerra-Hernández, M. Quiroz-Castellanos, N. Cruz-Ramírez. dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential Evolution. SoftwareX, 23(2023) 101442, June 2023. ISSN 2352-7110 | DOI: 10.1016/j.softx.2023.101442 | Elsevier
Abstract: The dplbnDE R package is a novel tool that implements Differential Evolution strategies for training Bayesian Network parameters using Discriminative Learning. Focusing on optimizing the Conditional Log-Likelihood rather than the log-likelihood, dplbnDE enhances the performance of Bayesian Networks models in various applications. The package offers four main functions (DErand, DEbest, jade, and lshade) that implement different DE variants, providing users with a versatile and efficient approach to Bayesian Network parameter learning. dplbnDE has the potential to impact data-driven industries by improving predictive capabilities and decision-making processes in fields such as healthcare, finance, and supply chain management. The package and its code are made freely available.