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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 |
| Reference manual: | UPMASK.pdf |
| Package source: | UPMASK_1.2.tar.gz |
| Windows binaries: | r-devel: UPMASK_1.2.zip, r-release: UPMASK_1.2.zip, r-oldrel: UPMASK_1.2.zip |
| macOS binaries: | r-release (arm64): UPMASK_1.2.tgz, r-oldrel (arm64): UPMASK_1.2.tgz, r-release (x86_64): UPMASK_1.2.tgz, r-oldrel (x86_64): UPMASK_1.2.tgz |
| Old sources: | UPMASK archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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