UPMASK
1.2Unsupervised Photometric Membership Assignment in Stellar Clusters
Overview
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.
Install
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Documentation
- Examples that run
- 67%
- Documented parameters
- 100%
- Return-value docs
- 93%
- References docs
- 53%
Downloads
Dependencies
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Code & Tests
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.5.0 released · 2025-04-11
- archivedRemoved from CRAN2024-05-14requires archived package 'loe'
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 1.22019-02-01 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.12017-04-03 · diff ↗
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- 1.02014-09-15
- RR 3.1.0 released · 2014-04-10
Package metadata
- Total releases
- 3
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.0
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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