dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential Evolution
Alejandro Platas‐López, Alejandro Guerra‐Hernández, Francisco Grimaldo, Nicandro Cruz-Ramírez, Efrén Mezura‐Montes, Marcela Quiróz-Castellanos · SoftwareX · 2023
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.