Package: discoCVI Type: Package Title: Implementation of the DISCO Metric for Internal Clustering Evaluation Version: 0.2.1 Authors@R: c( person("Amin", "Entezari", email = "amin_entezari@outlook.com", role = c("aut", "cre"), comment = c(ORCID = "0009-0005-4733-6032")), person("Davide", "Chicco", email = "davidechicco@davidechicco.it", role = "ctb", comment = c(ORCID = "0000-0001-9655-7142", "Supervision and guidance")), person("Anna", "Beer", role = "aut", comment = "Original Python implementation (arXiv:2503.00127)"), person("Lena", "Krieger", role = "aut", comment = "Original Python implementation (arXiv:2503.00127)"), person("Pascal", "Weber", role = "aut", comment = "Original Python implementation (arXiv:2503.00127)")) Description: 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) . License: MIT + file LICENSE Encoding: UTF-8 Language: en-GB Depends: R (>= 3.6.0) Imports: FNN, Rcpp, stats LinkingTo: Rcpp Suggests: dbscan, knitr, rmarkdown, testthat (>= 3.0.0) VignetteBuilder: knitr RoxygenNote: 7.3.3 URL: https://github.com/aminentezari/discoCVI BugReports: https://github.com/aminentezari/discoCVI/issues Repository: https://aminentezari.r-universe.dev Date/Publication: 2026-10-28 09:57:39 UTC RemoteUrl: https://github.com/aminentezari/discocvi RemoteRef: HEAD RemoteSha: a259e29362c4810459dd63093a1edf7927dd6b8f NeedsCompilation: yes Packaged: 2026-10-28 13:19:19 UTC; root Author: Amin Entezari [aut, cre] (ORCID: ), Davide Chicco [ctb] (ORCID: , 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)) Maintainer: Amin Entezari