GUEST: Graphical Models in Ultrahigh-Dimensional and Error-Prone Data
via Boosting Algorithm
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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
XICOR, network, GGally |
| Suggests: |
sna |
| Published: |
2024-05-21 |
| Author: |
Hui-Shan Tsao [aut, cre],
Li-Pang Chen [aut] |
| Maintainer: |
Hui-Shan Tsao <n410412 at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
GUEST results |
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