GUEST
0.2.0Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm
Overview
We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.
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
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- References docs
- 75%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
2 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
- 0.2.0Latest
- 0.1.02024-05-21
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-05-21
- Total releases
- 2 / 2 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 105 KB / 1 file
- Download size
- 112 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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