This concise paper introduces the inaugural Explainable and Interactive Learning to Rank (LTR) Package within the field of Information Retrieval (IR). The framework presented here is built upon the fusion of the Simulated Annealing Strategy with the (1+1)-Evolutionary Strategy, known as SAS-Rank, a ranking algorithm previously established in prior research. In this context, the paper showcases the ranking models associated with both offspring and parent chromosomes during each iterative step. Additionally, it offers three distinct options for modifying the SAS-Rank parameters, allowing for the observation of resulting evaluation outcomes. Notably, this application marks a pioneering endeavor by incorporating interactive learning within the domain of ranking problems in IR.