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.
|Author||Alberto Krone-Martins, Andre Moitinho|
|Date of publication||2014-09-16 12:28:13|
|Maintainer||Alberto Krone-Martins <firstname.lastname@example.org>|
|License||GPL (>= 3)|
analyse_randomKde2d: Perform analysis of random 2d distributions
analyse_randomKde2d_AutoCalibrated: Perform analysis of random 2d distributions (auto calibrated)
analyse_randomKde2d_smart: Perform analysis of random 2d distributions
create_randomKde2d: Compute the density based distance quantity using a 2D Kernel...
create_smartTable: Create a look up table
getStarsAtHighestDensityRegion: Perform cut in the membership list based on the 2D space...
innerLoop: UPMASK inner loop
kde2dForSubset: Compute the density based distance quantity using a 2D Kernel...
meanThreeSigRej: Perform cuts in the data
outerLoop: UPMASK outer loop
performCuts: Perform cuts in the data
takeErrorsIntoAccount: Take Errors Into Account for UPMASK analysis
UPMASKdata: Run UPMASK in a data frame
UPMASKfile: Run UPMASK in a file
UPMASK-package: Unsupervised Photometric Membership Assignment in Stellar...