Abstract Effective genomic selection for ordinal traits, such as disease resistance scores, is a persistent challenge in plant breeding due to the discrete, ordered nature of these phenotypes. This study presents a novel Bayesian divergence-based framework for multitrait ordinal selection, implemented in the extended Multitrait Parental Selection R package (MPS-R). By leveraging decision-theoretic loss functions, including the Kullback–Leibler (KL) divergence, Bhattacharyya distance, and Hellinger distance, our ap…