Package: dbscan
Version: 1.1-6
Date: 2021-02-24
Title: Density Based Clustering of Applications with Noise (DBSCAN) and
        Related Algorithms
Authors@R: c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
                email = "mhahsler@lyle.smu.edu"),
	    person("Matthew", "Piekenbrock", role = c("aut", "cph")),
	    person("Sunil", "Arya", role = c("ctb", "cph")),
	    person("David", "Mount", role = c("ctb", "cph")))
Description: A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the clustering algorithms 
    DBSCAN (density-based spatial clustering of applications with noise)
    and HDBSCAN (hierarchical DBSCAN), the ordering algorithm
    OPTICS (ordering points to identify the clustering structure), 
    and the outlier detection algorithm LOF (local outlier factor). 
    The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. 
    An R interface to fast kNN and fixed-radius NN search is also provided. 
    Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
SystemRequirements: C++11
Imports: Rcpp (>= 1.0.0), graphics, stats
LinkingTo: Rcpp
Suggests: fpc, microbenchmark, testthat, dendextend, igraph, knitr,
        rmarkdown, DMwR
VignetteBuilder: knitr
URL: https://github.com/mhahsler/dbscan
BugReports: https://github.com/mhahsler/dbscan/issues
License: GPL (>= 2)
Copyright: ANN library is copyright by University of Maryland, Sunil
        Arya and David Mount. All other code is copyright by Michael
        Hahsler and Matthew Piekenbrock.
NeedsCompilation: yes
Packaged: 2021-02-25 23:55:21 UTC; hahsler
Author: Michael Hahsler [aut, cre, cph],
  Matthew Piekenbrock [aut, cph],
  Sunil Arya [ctb, cph],
  David Mount [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
Repository: CRAN
Date/Publication: 2021-02-26 06:00:20 UTC
Built: R 4.0.3; x86_64-w64-mingw32; 2021-02-26 08:16:29 UTC; windows
Archs: x64
