Package: qcluster 3.0.0

qcluster: Clustering via Quadratic Scoring

Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.

Authors:Luca Coraggio [cre, aut], Pietro Coretto [aut]

qcluster_3.0.0.tar.gz
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qcluster_3.0.0.tgz(r-4.6-x86_64)qcluster_3.0.0.tgz(r-4.6-arm64)qcluster_3.0.0.tgz(r-4.5-x86_64)qcluster_3.0.0.tgz(r-4.5-arm64)
qcluster_3.0.0.tar.gz(r-4.7-arm64)qcluster_3.0.0.tar.gz(r-4.7-x86_64)qcluster_3.0.0.tar.gz(r-4.6-arm64)qcluster_3.0.0.tar.gz(r-4.6-x86_64)
qcluster_3.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
qcluster/json (API)

# Install 'qcluster' in R:
install.packages('qcluster', repos = c('https://lcorag.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas

1.48 score 1 scripts 280 downloads 16 exports 5 dependencies

Last updated from:8898898f5c. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK97
linux-devel-x86_64OK121
source / vignettesOK170
linux-release-arm64OK100
linux-release-x86_64OK128
macos-release-arm64OK141
macos-release-x86_64OK307
macos-oldrel-arm64OK101
macos-oldrel-x86_64OK295
windows-devel-arm64OK371
windows-devel-x86_64OK106
windows-release-arm64OK367
windows-release-x86_64OK110
windows-oldrel-x86_64OK93
wasm-releaseOK85

Exports:apply_methodclust2paramsgmixmbindmset_gmixmset_kmeansmset_pammset_screenmset_tmixmset_userplot_clusteringqclusterqcluster_rankqcluster_selectqscoretmix

Dependencies:clustercodetoolsdoParallelforeachiterators

Readme and manuals

Help Manual

Help pageTopics
Fit a Single Method From a Method Setapply_method
Swiss Banknotes Databanknote
Converts Hard Assignment Into Cluster Parametersclust2params
Gaussian Mixture Modellinggmix
Combines Methods Settingsmbind
Generates Methods Settings for Gaussian Mixture Model-Based Clusteringmset_gmix
Generates Methods Settings for K-Means Clusteringmset_kmeans
Generates Methods Settings for Partitioning Around Medoids (Pam) Clusteringmset_pam
Two-Step Method-Set Preselection Filtermset_screen
Generates Methods Settings for Student-t Mixture Model-Based Clusteringmset_tmix
Generates Clustering Methods Settings for a Prototype Methodology Provided by the Usermset_user
Plot Data With Clustering Informationplot_clustering
Plot Fitted Mixture Modelsplot.gmix plot.tmix
Plot Held-Out Quadratic Score Resultsplot.qcluster
Predict Cluster Assignments From a Fitted Mixture Modelpredict.gmix predict.tmix
Print a Fitted Method (apply_method Result)print.apply_method
Display a Summary of a Method-Set Screening Resultprint.mset_screen
Display Information for Mixture Model Objectsprint.qcfit
Display Information on Held-Out Quadratic Score Objectsprint.qcluster
Display a Compact Summary of a Method Setprint.qcmethod
Held-Out Validation of Clustering Solutions by Quadratic Scoringqcluster
Ranking Clustering Solutions Scored by Held-Out Validationqcluster_rank
Select Ranked Clustering Solutions by Held-Out Quadratic Scoreqcluster_select
Clustering Quadratic Scoreqscore
Inspect a Method Set or a Single Methodsummary.qcmethod
Student-t Mixture Modellingtmix