Package: dbscan
Version: 1.0-0
Date: 2017-02-02
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 DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms and the LOF (local outlier
    factor) algorithm. The implementations uses 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.
Imports: Rcpp, graphics, stats, methods
LinkingTo: Rcpp
Suggests: fpc, microbenchmark, testthat, dendextend
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: 2017-02-02 23:03:42 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: 2017-02-03 00:19:26
Built: R 3.2.5; x86_64-w64-mingw32; 2017-03-17 20:29:48 UTC; windows
Archs: i386, x64
