Package: diceR 3.2.0.9000

Derek Chiu

diceR: Diverse Cluster Ensemble in R

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.

Authors:Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]

diceR_3.2.0.9000.tar.gz
diceR_3.2.0.9000.zip(r-4.7-x86_64)diceR_3.2.0.9000.zip(r-4.7-arm64)diceR_3.2.0.9000.zip(r-4.6-x86_64)diceR_3.2.0.9000.zip(r-4.6-arm64)diceR_3.2.0.9000.zip(r-4.5-x86_64)
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diceR_3.2.0.9000.tar.gz(r-4.7-arm64)diceR_3.2.0.9000.tar.gz(r-4.7-x86_64)diceR_3.2.0.9000.tar.gz(r-4.6-arm64)diceR_3.2.0.9000.tar.gz(r-4.6-x86_64)
diceR_3.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
diceR/json (API)

# Install 'diceR' in R:

install.packages('diceR', repos = c('https://alinetalhouk.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alinetalhouk/dicer/issues

Pkgdown/docs site:https://alinetalhouk.github.io

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

hgsc - Gene expression data for High Grade Serous Carcinoma from TCGA

On CRAN:

cpp

8.12 score 39 stars 2 packages 94 scripts 799 downloads 3 mentions 30 exports 98 dependencies

Last updated from:ebdd903156. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	213
linux-devel-x86_64	OK	246
source / vignettes	OK	294
linux-release-arm64	OK	207
linux-release-x86_64	OK	199
macos-release-arm64	OK	225
macos-release-x86_64	OK	255
macos-oldrel-arm64	OK	258
macos-oldrel-x86_64	OK	538
windows-devel-arm64	OK	449
windows-devel-x86_64	OK	273
windows-release-arm64	OK	489
windows-release-x86_64	OK	189
windows-oldrel-x86_64	OK	156
wasm-release	OK	176

Exports:asrs compactness consensus_cluster consensus_combine consensus_evaluate consensus_matrix CSPA cts dice ev_confmat ev_nmi graph_all graph_cdf graph_delta_area graph_heatmap graph_tracking impute_knn impute_missing k_modes LCA LCE majority_voting min_fnorm PAC pcn_select pcn_simulate prepare_data relabel_class sigclust srs

Dependencies:abind assertthat base64enc bit bit64 bslib cachem class classInt cli clipr clue cluster clusterCrit clValid combinat commonmark cpp11 crayon digest dplyr e1071 farver fastmap fontawesome forcats fs generics ggplot2 glue gtable gtools hardhat haven highr hms htmltools httpuv infotheo isoband jquerylib jsonlite KernSmooth klaR labeling labelled later lifecycle magrittr MASS mclust memoise mime miniUI otel pheatmap pillar pkgconfig prettyunits progress promises proxy purrr questionr R.cache R.methodsS3 R.oo R.utils R6 RankAggreg rappdirs RColorBrewer Rcpp readr rlang rprojroot rstudioapi S7 sass scales shiny sourcetools sparsevctrs stringi stringr styler tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom withr xfun xtable yardstick

Cluster Analysis using diceR

Rendered fromoverview.Rmdusingknitr::rmarkdown

Last update: 2025-02-01
Started: 2016-11-08

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compactness Measure	compactness
Consensus clustering	consensus_cluster
Combine algorithms	consensus_combine
Evaluate, trim, and reweigh algorithms	consensus_evaluate
Consensus matrix	consensus_matrix
Cluster-based Similarity Partitioning Algorithm (CSPA)	CSPA
Diverse Clustering Ensemble	dice
External validity indices	ev_confmat ev_nmi external_validity
Graphical Displays	graphs graph_all graph_cdf graph_delta_area graph_heatmap graph_tracking
Gene expression data for High Grade Serous Carcinoma from TCGA	hgsc
K-Nearest Neighbours imputation	impute_knn
Impute missing values	impute_missing
K-modes	k_modes
Latent Class Analysis	LCA
Linkage Clustering Ensemble	LCE
Majority voting	majority_voting
Minimize Frobenius norm for between two matrices	min_fnorm
Proportion of Ambiguous Clustering	PAC
Simulate and select null distributions on empirical gene-gene correlations	pcn pcn_select pcn_simulate
Prepare data for consensus clustering	prepare_data
Relabel classes to a standard	relabel_class
Significant Testing of Clustering Results	sigclust
Similarity Matrices	asrs cts similarity srs