OPTICS K-Xi Density-Based Clustering R package opticskxi version 1.2.2 Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>. Software Depends: R (>= 3.5.0) Imports: ggplot2, magrittr, Matrix, rlang Suggests: amap, dbscan, cowplot, fastICA, fpc, ggrepel, grid, grDevices, gtable, knitr, parallel, plyr, reshape2, testthat Thomas Charlon <charlon@protonmail.com> Comprehensive R Archive Network (CRAN) Thomas Charlon [aut, cre] (ORCID: <https://orcid.org/0000-0001-7497-0470>) GPL-3 2026-06-10 application/tgz https://CRAN.R-project.org/package=opticskxi doi:10.32614/CRAN.package.opticskxi