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, principal component analysis, a clustering algorithm and a kernel density estimation.

Version: 1.0
Depends: R (≥ 3.0)
Imports: parallel, MASS, RSQLite, DBI
Published: 2014-09-16
Author: Alberto Krone-Martins, Andre Moitinho
Maintainer: Alberto Krone-Martins <algol at sim.ul.pt>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: UPMASK results


Reference manual: UPMASK.pdf
Package source: UPMASK_1.0.tar.gz
Windows binaries: r-devel: UPMASK_1.0.zip, r-release: UPMASK_1.0.zip, r-oldrel: UPMASK_1.0.zip
OS X Snow Leopard binaries: r-release: UPMASK_1.0.tgz, r-oldrel: UPMASK_1.0.tgz
OS X Mavericks binaries: r-release: UPMASK_1.0.tgz