Package: adjclust
Maintainer: Pierre Neuvial <pierre.neuvial@math.univ-toulouse.fr>
Authors@R: c(person("Christophe", "Ambroise", role="aut"),
             person("Shubham", "Chaturvedi", role="aut"),
             person("Alia", "Dehman", role="aut"),
             person("Michel", "Koskas", role="aut"),
             person("Pierre", "Neuvial", role=c("aut", "cre"), 
                 email="pierre.neuvial@math.univ-toulouse.fr"),
             person("Guillem", "Rigaill", role="aut"),
             person("Nathalie", "Vialaneix", role="aut"))
Date: 2019-12-10
Version: 0.5.9
License: GPL-3
Title: Adjacency-Constrained Clustering of a Block-Diagonal Similarity
        Matrix
Description: 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) 
    <https://hal.archives-ouvertes.fr/tel-01288568v1>.
Depends: R(>= 2.10.0)
Imports: stats, graphics, grDevices, Matrix, matrixStats, methods,
        utils, capushe
Suggests: knitr, testthat, rmarkdown, rioja, HiTC, snpStats,
        BiocGenerics
VignetteBuilder: knitr
URL: https://github.com/pneuvial/adjclust
BugReports: https://github.com/pneuvial/adjclust/issues
RoxygenNote: 7.0.2
LazyData: true
NeedsCompilation: yes
Packaged: 2019-12-10 13:11:02 UTC; nathalie
Author: Christophe Ambroise [aut],
  Shubham Chaturvedi [aut],
  Alia Dehman [aut],
  Michel Koskas [aut],
  Pierre Neuvial [aut, cre],
  Guillem Rigaill [aut],
  Nathalie Vialaneix [aut]
Repository: CRAN
Date/Publication: 2019-12-10 13:30:02 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2020-04-22 05:19:11 UTC; windows
Archs: i386, x64
