FunQuant: A R package to perform quantization in the context of rare
  events and time-consuming simulations
Charlie Sire Yann Richet Rodolphe Le Riche Didier Rullière Jérémy Rohmer Lucie Pheulpin
Quantization summarizes continuous distributions by calculating a discrete
approximation. Among the widely adopted methods for data quantization is
Lloyd's algorithm, which partitions the space into Vorono\"i cells, that can be
seen as clusters, and constructs a discrete distribution based on their
centroids and probabilistic masses. Lloyd's algorithm estimates the optimal
centroids in a minimal expected distance sense, but this approach poses
significant challenges in scenarios where data evaluation is costly, and
relates to rare events. Then, the single cluster associated to no event takes
the majority of the probability mass. In this context, a metamodel is required
and adapted sampling methods are necessary to increase the precision of the
computations on the rare clusters.