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


Reference manual: UPMASK.pdf


Package source: UPMASK_1.2.tar.gz
Windows binaries: r-prerel: UPMASK_1.2.zip, r-release: UPMASK_1.2.zip, r-oldrel: UPMASK_1.2.zip
macOS binaries: r-prerel (arm64): UPMASK_1.2.tgz, r-release (arm64): UPMASK_1.2.tgz, r-oldrel (arm64): UPMASK_1.2.tgz, r-prerel (x86_64): UPMASK_1.2.tgz, r-release (x86_64): UPMASK_1.2.tgz
Old sources: UPMASK archive


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