Package: opticskxi
Title: OPTICS K-Xi Density-Based Clustering
Version: 1.2.2
Authors@R: person("Thomas", "Charlon", role = c("aut", "cre"), email = "charlon@protonmail.com", comment = c(ORCID = "0000-0001-7497-0470"))
Description: 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/>.
Imports: ggplot2, magrittr, Matrix, rlang
Depends: R (>= 3.5.0)
Suggests: amap, dbscan, cowplot, fastICA, fpc, ggrepel, grid,
        grDevices, gtable, knitr, parallel, plyr, reshape2, testthat
VignetteBuilder: knitr
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://gitlab.com/thomaschln/opticskxi
BugReports: https://gitlab.com/thomaschln/opticskxi/-/issues
NeedsCompilation: no
Packaged: 2026-06-10 12:35:20 UTC; root
Author: Thomas Charlon [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-7497-0470>)
Maintainer: Thomas Charlon <charlon@protonmail.com>
Repository: CRAN
Date/Publication: 2026-06-10 13:30:02 UTC
Built: R 4.7.0; ; 2026-06-12 02:55:57 UTC; windows
