Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <https://almob.biomedcentral.com/articles/10.1186/s13015-019-0157-4>.
|Author||Christophe Ambroise [aut], Shubham Chaturvedi [aut], Alia Dehman [aut], Pierre Neuvial [aut, cre], Guillem Rigaill [aut], Nathalie Vialaneix [aut]|
|Maintainer||Pierre Neuvial <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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