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
DOI: 10.32614/CRAN.package.GUEST
Author: Hui-Shan Tsao [aut, cre], Li-Pang Chen [aut]
Maintainer: Hui-Shan Tsao <n410412 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: GUEST results


Reference manual: GUEST.pdf


Package source: GUEST_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GUEST_0.1.0.tgz, r-oldrel (arm64): GUEST_0.1.0.tgz, r-release (x86_64): GUEST_0.1.0.tgz, r-oldrel (x86_64): GUEST_0.1.0.tgz


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