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: |
2.2.0 |
Depends: |
R (≥ 3.5) |
Imports: |
abind, assertthat, class, clue, clusterSim, clv, clValid, dplyr (≥ 0.7.5), ggplot2, infotheo, klaR, magrittr, mclust, methods, NMF, purrr (≥ 0.2.3), RankAggreg, Rcpp, stringr, tidyr, yardstick |
LinkingTo: |
Rcpp |
Suggests: |
apcluster, blockcluster, cluster, covr, dbscan, e1071, kernlab, knitr, kohonen, pander, poLCA, progress, RColorBrewer, rlang, rmarkdown, Rtsne, sigclust, testthat |
Published: |
2024-01-22 |
DOI: |
10.32614/CRAN.package.diceR |
Author: |
Derek Chiu [aut, cre],
Aline Talhouk [aut],
Johnson Liu [ctb, com] |
Maintainer: |
Derek Chiu <dchiu at bccrc.ca> |
BugReports: |
https://github.com/AlineTalhouk/diceR/issues |
License: |
MIT + file LICENSE |
URL: |
https://github.com/AlineTalhouk/diceR/,
https://alinetalhouk.github.io/diceR/ |
NeedsCompilation: |
yes |
Materials: |
README NEWS |
CRAN checks: |
diceR results |