mdw
2024.8-1Maximum Diversity Weighting
Overview
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) DOI:10.1002/sim.8212.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 14%
- References docs
- 13%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 2024.8-1Latest
- 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
- 2020.6-172020-06-18 · diff ↗
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 2017.12-032017-12-04 · diff ↗
- 2017.8-272017-08-28
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-08-28
- Total releases
- 4 / 9 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 47 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
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citation("mdw")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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