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Quantization summarizes continuous distributions by calculating a discreteapproximation. Among the widely adopted methods for data quantization isLloyd's algorithm, which partitions the space into Vorono\"i cells, that can beseen as clusters, and constructs a discrete distribution based on theircentroids and probabilistic masses. Lloyd's algorithm estimates the optimalcentroids in a minimal expected distance sense, but this approach posessignificant challenges in scenarios where data evaluation is costly, andrelates to rare events. Then, the single cluster associated to no event takesthe majority of the probability mass. In this context, a metamodel is requiredand adapted sampling methods are necessary to increase the precision of thecomputations on the rare clusters.

FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations