qcluster: qcluster: Clustering via Quadratic Scoring

In qcluster: Clustering via Quadratic Scoring

qcluster: Clustering via Quadratic Scoring

Description

qcluster provides tools for tuning clustering models, methods, and algorithms by quadratic scoring with resampling.

Details

The package combines three main components:

• method generators such as mset_user, mset_gmix, mset_kmeans, mset_pam, and mbind;

• bootstrap quadratic-score estimation, ranking, and selection through bqs, bqs_rank, and bqs_select;

• direct Gaussian model-based clustering and scoring via gmix and qscore.

A typical workflow is:

1. define a collection of candidate clustering methods with mset_*() and optionally combine them with mbind;

2. estimate bootstrap quadratic scores with bqs;

3. rank candidate methods with bqs_rank and extract selected full-data refits with bqs_select.

The package also provides plotting and printing methods for fitted mbcfit and bqs objects, together with the sample data set banknote.

Author(s)

Maintainer: Luca Coraggio [e-mail] (ORCID)

Authors:

• Pietro Coretto [e-mail] (ORCID)

References

Coraggio, Luca and Pietro Coretto (2023). Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score. Journal of Multivariate Analysis, Vol. 196(105181), 1-20. doi: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmva.2023.105181")}

See Also

Useful links:

• https://luca-coraggio.com

• https://pietro-coretto.github.io

qcluster documentation built on June 5, 2026, 5:07 p.m.