Package: WeightedCluster 2.0

WeightedCluster: Clustering of Weighted Data

Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT).

Authors:Matthias Studer [aut, cre], Leonard Roth [ctb]

WeightedCluster_2.0.tar.gz
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WeightedCluster_2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
WeightedCluster/json (API)

# Install 'WeightedCluster' in R:
install.packages('WeightedCluster', repos = c('https://matthias-studer.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

cpp

6.00 score 1 stars 5 packages 237 scripts 1.8k downloads 2 mentions 19 exports 37 dependencies

Last updated from:336d9042a0. Checks:15 OK. Indexed: yes.

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macos-oldrel-x86_64OK542
windows-devel-arm64OK475
windows-devel-x86_64OK151
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windows-release-x86_64OK154
windows-oldrel-x86_64OK143
wasm-releaseOK119

Exports:as.clustrangeas.seqtreebootclustrangeclustassocdtcutfuzzyseqplotrarcatseqclararangeseqclustnameseqnullseqnullcqiseqpropclustwcAggregateCaseswcClusterQualitywcCmpClusterwcKMedoidswcKMedRangewcPropertyClusteringwcSilhouetteObs

Dependencies:bootclustercodetoolscolorspacedata.tabledigestdoFuturefastclusterforeachfuturefuture.applyglobalsiteratorslatticelistenvlme4marginsMASSMatrixmgcvminqanlmenloptrnnetparallellypermutepredictionprogressrrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasTraMineRveganvegclust

Le manuel de la librairie WeightedCluster: un guide pratique pour la creation de typologies de trajectoires en sciences sociales avec R
Introduction | Typologie de séquences en sciences sociales | Installation et chargement | Étapes de l'analyse en clusters | Le clustering | Combiner les algorithmes | Mesurer la qualité d'une partition | Présentation des mesures | Utiliser la silhouette pour représenter les clusters | Choix d'une partition | Nommer les clusters | Mettre en lien trajectoires-types et facteurs explicatifs | Conclusion | Agréger les séquences identiques | Notes sur les performances | Détails des mesures de qualité | Construction de la variable test
Rendered fromWeightedClusterFR.Rnwusingknitr::knitr

Last update: 2025-12-10
Started: 2014-01-16

R Tutorials: Robustness Assessment of Regressions using Cluster Analysis Typologies
Short tutorial | Creating the state sequence object | Constructing the typology | Association study | Robustness assessment | Extended Tutorial | Estimating the Number of Clusters | Plotting the AME | Pooling Effect Sizes | Diagnostics | Outliers | Bootstrap replicates of the typology | References
Rendered fromRARCAT.Rmdusingknitr::rmarkdown

Last update: 2025-12-10
Started: 2025-12-10

Short R Tutorial: Fuzzy and Property-Based Clustering for Sequence Analysis
Introduction | Data Preparation | Fuzzy Clustering | Plotting the typology | Regression models | Property-based clustering | References
Rendered fromFuzzySA.Rmdusingknitr::rmarkdown

Last update: 2025-12-10
Started: 2024-12-10

Short R Tutorial: Validating Sequence Analysis Typologies To be Used in Subsequent Regression
Introduction | Creating the State Sequence Object | Creating the typology | The clustassoc function | References
Rendered fromClusterExternalValidSA.Rmdusingknitr::rmarkdown

Last update: 2025-12-10
Started: 2024-12-10

Short R Tutorial: Sequence Analysis Typologies for Large Databases
Introduction | Data Preparation | CLARA Clustering | Cluster Quality Indices | Plotting the Typology | Fuzzy Clustering
Rendered fromBigDataSA.Rmdusingknitr::rmarkdown

Last update: 2024-12-10
Started: 2024-12-10

Short R Tutorial: Validating Sequence Analysis Typologies Using Parametric Bootstrap
Introduction | Creating the State Sequence Object | Creating the typology | Parametric Bootstrap | Combined Randomization | Randomized Sequencing | Randomized Duration | State Independence | First-order Markov Null Model | Choosing a Solution
Rendered fromClusterValidSA.Rmdusingknitr::rmarkdown

Last update: 2024-12-10
Started: 2024-12-10

WeightedCluster Preview
Installation | An illustrative example | Hierarchical clustering | Cluster quality | PAM clustering | Keeping a solution | Disaggregating data
Rendered fromWeightedClusterPreview.Rnwusingknitr::knitr

Last update: 2019-03-21
Started: 2014-01-16

WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R
Introduction | Typology of sequences in the social sciences | Installation and loading | Stages of cluster analysis | Clustering | Measuring the quality of a partition | Linking trajectory types and explanatory factors | Conclusion | Aggregating identical sequences | Notes on performances | Details of quality measures | Construction of the test variable
Rendered fromWeightedCluster.Rnwusingknitr::knitr

Last update: 2014-01-16
Started: 2014-01-16

Readme and manuals

Help Manual

Help pageTopics
Build a clustrange object to compare different clustering solutions.as.clustrange as.clustrange.dtclust as.clustrange.hclust as.clustrange.twins plot.clustrange
Convert a hierarchical clustering object to a seqtree object.as.seqtree as.seqtree.hclust as.seqtree.twins
Cluster Quality Indices estimation by subsamplingbootclustrange plot.bootclustrange print.bootclustrange
Share of an association between an object (described by a dissimilarity matrix) and a covariate that is reproduced by a clustering solution.clustassoc plot.clustassoc
Plot sequences according to a fuzzy clustering.fuzzyseqplot
Plot of cluster quality of CLARA algorithm.plot.seqclararange
Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT)plot.rarcat print.rarcat rarcat summary.rarcat
CLARA Clustering for Sequence Analysisseqclararange
Automatic labeling of cluster using sequence medoidsseqclustname
Generate nonclustered sequence data according to different null models.seqnull
Sequence Analysis Typologies Validation Using Parametric Bootstrapplot.seqnullcqi print.seqnullcqi seqnullcqi
Monothetic clustering of state sequencesdtcut seqpropclust wcPropertyClustering
Aggregate identical cases.print.wcAggregateCases wcAggregateCases wcAggregateCases.data.frame wcAggregateCases.matrix wcAggregateCases.stslist
Cluster quality statisticswcClusterQuality
Automatic comparison of clustering methods.plot.clustrangefamily print.clustrangefamily summary.clustrangefamily wcCmpCluster
K-Medoids or PAM clustering of weighted data.wcKMedoids
Compute 'wcKMedoids' clustering for different number of clusters.wcKMedRange
Compute the silhouette of each object using weighted data.wcSilhouetteObs