Diverse Cluster Ensemble in R R package diceR version 3.1.0 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. Software 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) Derek Chiu <dchiu@bccrc.ca> Comprehensive R Archive Network (CRAN) Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com] MIT + file LICENSE (https://CRAN.R-project.org/package=diceR/LICENSE) 2025-06-19 application/tgz https://CRAN.R-project.org/package=diceR doi:10.32614/CRAN.package.diceR