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.

Author
Alberto Krone-Martins, Andre Moitinho
Date of publication
2014-09-16 12:28:13
Maintainer
Alberto Krone-Martins <algol@sim.ul.pt>
License
GPL (>= 3)
Version
1.0

View on CRAN

Man pages

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...

Files in this package

UPMASK
UPMASK/inst
UPMASK/inst/extdata
UPMASK/inst/extdata/oc_12_500_1000_1.0_p019_0880_1_25km_120nR_withcolors.dat
UPMASK/inst/extdata/oc_12_5000_4000_4.0_p019_0900_1_15km_120nR_withcolors.dat
UPMASK/NAMESPACE
UPMASK/R
UPMASK/R/kde2dForSubset.R
UPMASK/R/UPMASKfile.R
UPMASK/R/UPMASKdata.R
UPMASK/R/analyse_randomKde2d_smart.R
UPMASK/R/outerLoop.R
UPMASK/R/analyse_randomKde2d.R
UPMASK/R/performCuts.R
UPMASK/R/create_randomKde2d.R
UPMASK/R/meanThreeSigRej.R
UPMASK/R/takeErrorsIntoAccount.R
UPMASK/R/innerLoop.R
UPMASK/R/getStarsAtHighDensityRegion.R
UPMASK/R/create_smartTable.R
UPMASK/R/analyse_randomKde2d_AutoCalibrated.R
UPMASK/MD5
UPMASK/DESCRIPTION
UPMASK/man
UPMASK/man/analyse_randomKde2d_smart.Rd
UPMASK/man/figures
UPMASK/man/figures/UPMASK-SimOC-Ex-Manual.pdf
UPMASK/man/figures/UPMASK-SimOC-Ex-Manual.jpg
UPMASK/man/getStarsAtHighestDensityRegion.Rd
UPMASK/man/UPMASKdata.Rd
UPMASK/man/takeErrorsIntoAccount.Rd
UPMASK/man/kde2dForSubset.Rd
UPMASK/man/meanThreeSigRej.Rd
UPMASK/man/create_randomKde2d.Rd
UPMASK/man/outerLoop.Rd
UPMASK/man/performCuts.Rd
UPMASK/man/analyse_randomKde2d.Rd
UPMASK/man/innerLoop.Rd
UPMASK/man/UPMASK-package.Rd
UPMASK/man/analyse_randomKde2d_AutoCalibrated.Rd
UPMASK/man/create_smartTable.Rd
UPMASK/man/UPMASKfile.Rd