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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.
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Package details |
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| Author | Hui-Shan Tsao [aut, cre], Li-Pang Chen [aut] |
| Maintainer | Hui-Shan Tsao <n410412@gmail.com> |
| License | GPL-2 |
| Version | 0.2.0 |
| Package repository | View on CRAN |
| Installation |
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