adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

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 Chapter 4 of Alia Dehman (2015) <>.

Version: 0.5.6
Depends: R (≥ 2.10.0)
Imports: Matrix, matrixStats, methods, utils
Suggests: knitr, testthat, rmarkdown, rioja, HiTC, snpStats
Published: 2018-02-08
Author: Christophe Ambroise [aut], Shubham Chaturvedi [aut], Alia Dehman [aut], Michel Koskas [aut], Pierre Neuvial [aut, cre], Guillem Rigaill [aut], Nathalie Villa-Vialaneix [aut]
Maintainer: Pierre Neuvial <pierre.neuvial at>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: adjclust results


Reference manual: adjclust.pdf
Vignettes: Clustering of Hi-C contact maps
Implementation notes for the adjclust package
Inferring Linkage Disequilibrium blocks from genotypes
Package source: adjclust_0.5.6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: adjclust_0.5.6.tgz
OS X Mavericks binaries: r-oldrel: adjclust_0.5.2.tgz
Old sources: adjclust archive


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