Implementation of the DISCO Metric for Internal Clustering Evaluation R package discoCVI version 0.1.1 Implementation of the DISCO (Density-based Internal Score for Clusterings with nOise) metric, a cluster validity index for evaluating density-based clustering results without ground truth labels. DISCO is the first index to explicitly assess the quality of noise point assignments in addition to cluster quality. It uses density-connectivity distance derived from a minimum spanning tree of the mutual-reachability graph, providing interpretable, bounded scores in [-1, 1]. Higher scores indicate better clustering. Based on Beer et al. (2025) <doi:10.48550/arXiv.2503.00127>. Software Depends: R (>= 3.6.0) Imports: FNN, stats Suggests: dbscan, testthat (>= 3.0.0) Amin Entezari <amin_entezari@outlook.com> Comprehensive R Archive Network (CRAN) Amin Entezari [aut, cre] (ORCID: <https://orcid.org/0009-0005-4733-6032>), Davide Chicco [ctb] (ORCID: <https://orcid.org/0000-0001-9655-7142>, Supervision and guidance), Anna Beer [aut] (Original Python implementation (arXiv:2503.00127)), Lena Krieger [aut] (Original Python implementation (arXiv:2503.00127)), Pascal Weber [aut] (Original Python implementation (arXiv:2503.00127)) MIT + file LICENSE (https://CRAN.R-project.org/package=discoCVI/LICENSE) 2026-05-05 application/tgz https://CRAN.R-project.org/package=discoCVI doi:10.32614/CRAN.package.discoCVI en-GB