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GUEST  

Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm
View on CRAN: Click here


Download and install GUEST package within the R console
Install from CRAN:
install.packages("GUEST")

Install from Github:
library("remotes")
install_github("cran/GUEST")

Install by package version:
library("remotes")
install_version("GUEST", "0.2.0")



Attach the package and use:
library("GUEST")
Maintained by
Hui-Shan Tsao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-05-21
Latest Update: 2024-05-21
Description:
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.
How to cite:
Hui-Shan Tsao (2024). GUEST: Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm. R package version 0.2.0, https://cran.r-project.org/web/packages/GUEST. Accessed 12 Nov. 2024.
Previous versions and publish date:
0.1.0 (2024-05-21 15:30)
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Complete documentation for GUEST
Functions, R codes and Examples using the GUEST R package
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