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WeightedCluster (version 2.0)

Clustering of Weighted Data

Description

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).

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Version

Install

install.packages('WeightedCluster')

Monthly Downloads

1,625

Version

2.0

License

GPL (>= 2)

Maintainer

Matthias Studer

Last Published

December 10th, 2025

Functions in WeightedCluster (2.0)

Sequence Analysis Typologies Validation Using Parametric Bootstrap

Plot sequences according to a fuzzy clustering.

wcClusterQuality

Cluster quality statistics

Automatic labeling of cluster using sequence medoids

Share of an association between an object (described by a dissimilarity matrix) and a covariate that is reproduced by a clustering solution.

Cluster Quality Indices estimation by subsampling

CLARA Clustering for Sequence Analysis

Automatic comparison of clustering methods.

wcSilhouetteObs

Compute the silhouette of each object using weighted data.

Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT)

Monothetic clustering of state sequences

plot.seqclararange

Plot of cluster quality of CLARA algorithm.

wcAggregateCases

Aggregate identical cases.

Compute wcKMedoids clustering for different number of clusters.

Build a clustrange object to compare different clustering solutions.

Convert a hierarchical clustering object to a seqtree object.

K-Medoids or PAM clustering of weighted data.

Generate nonclustered sequence data according to different null models.