_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-adjclust 0.6.10
Propagated dependencies: r-sparsematrixstats@1.20.0 r-rlang@1.1.6 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-matrix@1.7-3 r-ggplot2@3.5.2 r-dendextend@1.19.0 r-capushe@1.1.2
Channel: guix-cran
Location: guix-cran/packages/a.scm (guix-cran packages a)
Home page: https://pneuvial.github.io/adjclust/
Licenses: GPL 3
Synopsis: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix
Description:

This package 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) <doi:10.1186/s13015-019-0157-4>.

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