Package: UPMASK
Type: Package
Title: Unsupervised Photometric Membership Assignment in Stellar
        Clusters
Version: 1.2
Date: 2019-01-28
Authors@R: c(
            person("Alberto", "Krone-Martins", email = "algol@sim.ul.pt", role = c("aut", "cre")),
            person("Andre", "Moitinho", email = "andre@sim.ul.pt", role = c("aut")),
            person("Eduardo", "Bezerra", email = "ebezerra@cefet-rj.br", role = c("ctb")),
            person("Leonardo", "Lima", email = "leonardo.lima@cefet-rj.br", role = c("ctb")),
            person("Tristan", "Cantat-Gaudin", email = "tcantat@fqa.ub.edu", role = c("ctb"))
            )
Maintainer: Alberto Krone-Martins <algol@sim.ul.pt>
Description: An implementation of the UPMASK method for performing membership
    assignment in stellar clusters in R. It is prepared to use photometry and
    spatial positions, but it can take into account other types of data. The
    method is able to take into account arbitrary error models, and it is
    unsupervised, data-driven, physical-model-free and relies on as few
    assumptions as possible. The approach followed for membership assessment is
    based on an iterative process, dimensionality reduction, a clustering
    algorithm and a kernel density estimation.
Depends: R (>= 3.0)
License: GPL (>= 3)
Imports: parallel, MASS, RSQLite, DBI, dimRed, loe
NeedsCompilation: no
RoxygenNote: 6.0.1
Packaged: 2019-02-01 17:18:39 UTC; brain
Author: Alberto Krone-Martins [aut, cre],
  Andre Moitinho [aut],
  Eduardo Bezerra [ctb],
  Leonardo Lima [ctb],
  Tristan Cantat-Gaudin [ctb]
Repository: CRAN
Date/Publication: 2019-02-01 17:43:33 UTC
Built: R 3.5.3; ; 2020-04-22 07:03:54 UTC; windows
