_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-dicer 3.0.0
Propagated dependencies: r-yardstick@1.3.1 r-tidyr@1.3.1 r-stringr@1.5.1 r-rcpp@1.0.13-1 r-rankaggreg@0.6.6 r-purrr@1.0.2 r-pheatmap@1.0.12 r-mclust@6.1.1 r-magrittr@2.0.3 r-klar@1.7-3 r-infotheo@1.2.0.1 r-ggplot2@3.5.1 r-dplyr@1.1.4 r-clvalid@0.7 r-clv@0.3-2.4 r-clustercrit@1.3.0 r-clue@0.3-66 r-class@7.3-22 r-assertthat@0.2.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/AlineTalhouk/diceR/
Licenses: Expat
Synopsis: Diverse Cluster Ensemble in R
Description:

This package performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

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