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.


Reference manual

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1.2 by Alberto Krone-Martins, 2 years ago

Browse source code at https://github.com/cran/UPMASK

Authors: Alberto Krone-Martins [aut, cre] , Andre Moitinho [aut] , Eduardo Bezerra [ctb] , Leonardo Lima [ctb] , Tristan Cantat-Gaudin [ctb]

Documentation:   PDF Manual  

Task views: Chemometrics and Computational Physics

GPL (>= 3) license

Imports parallel, MASS, RSQLite, DBI, dimRed, loe

See at CRAN