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.">

diceR: Diverse Cluster Ensemble in R (original) (raw)

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.

Version: 3.1.0
Depends: R (≥ 4.1)
Imports: abind, assertthat, class, clue, clusterCrit, clValid, dplyr (≥ 0.7.5), ggplot2, grDevices, infotheo, klaR, magrittr, mclust, methods, pheatmap, purrr (≥ 0.2.3), RankAggreg, Rcpp, stringr, tidyr, yardstick
LinkingTo: Rcpp
Suggests: apcluster, blockcluster, cluster, covr, dbscan, e1071, kernlab, knitr, kohonen, NMF, pander, poLCA, progress, RColorBrewer, rlang, rmarkdown, Rtsne, sigclust, testthat (≥ 3.0.0)
Published: 2025-06-19
DOI: 10.32614/CRAN.package.diceR
Author: Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]
Maintainer: Derek Chiu
BugReports: https://github.com/AlineTalhouk/diceR/issues
License: MIT + file
URL: https://github.com/AlineTalhouk/diceR/,https://alinetalhouk.github.io/diceR/
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: diceR results

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