UPMASK: Unsupervised Photometric Membership Assignment in Stellar
Clusters
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.
| Version: |
1.2 |
| Depends: |
R (≥ 3.0) |
| Imports: |
parallel, MASS, RSQLite, DBI, dimRed, loe |
| Published: |
2019-02-01 |
| Author: |
Alberto Krone-Martins [aut, cre],
Andre Moitinho [aut],
Eduardo Bezerra [ctb],
Leonardo Lima [ctb],
Tristan Cantat-Gaudin [ctb] |
| Maintainer: |
Alberto Krone-Martins <algol at sim.ul.pt> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
ChangeLog |
| In views: |
ChemPhys |
| CRAN checks: |
UPMASK results |
Documentation:
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