Package: OTclust
Title: Mean Partition, Uncertainty Assessment, Cluster Validation and
        Visualization Selection for Cluster Analysis
Version: 1.0.6
Authors@R: c(person("Lixiang", "Zhang", email = "phoelief@gmail.com", role = c("aut", "cre")),person("Beomseok", "Seo", email = "bzs32@psu.edu", role = "aut"),person("Lin", "Lin", email = "lynn.lin@duke.edu", role = "aut"),person("Jia", "Li", email = "jiali@psu.edu", role = "aut"))
Author: Lixiang Zhang [aut, cre],
  Beomseok Seo [aut],
  Lin Lin [aut],
  Jia Li [aut]
Maintainer: Lixiang Zhang <phoelief@gmail.com>
Description: Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) <doi:10.1002/sam.11418>. Lixiang Zhang, Lin Lin, and Jia Li (2020) <doi:10.1093/bioinformatics/btaa165>.
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
Suggests: knitr, rmarkdown, tsne, umap, HDclust, dbscan, flexclust,
        mclust
VignetteBuilder: knitr
LinkingTo: Rcpp
Imports: Rcpp, ggplot2, RColorBrewer, magrittr, class
NeedsCompilation: yes
Packaged: 2023-10-06 06:49:34 UTC; zhanglixiang
Repository: CRAN
Date/Publication: 2023-10-06 14:40:07 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-12-08 03:31:49 UTC; windows
Archs: x64
