Package: GUEST 0.2.0

Hui-Shan Tsao

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.

Authors:Hui-Shan Tsao [aut, cre], Li-Pang Chen [aut]

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GUEST/json (API)

# Install 'GUEST' in R:
install.packages('GUEST', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2 exports 0.09 score 70 dependencies 292 downloads

Last updated 2 months agofrom:5e14988a1b. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 31 2024
R-4.5-linuxOKAug 31 2024

Exports:boost.graphLDA.boost

Dependencies:backportsbitbit64broombroom.helperscardsclicliprcodacolorspacecpp11crayondplyrfansifarverforcatsforeigngenericsGGallyggplot2ggstatsglueGPArotationgtablehavenhmsisobandlabelinglabelledlatticelifecyclemagrittrMASSMatrixmgcvmnormtmunsellnetworknlmepatchworkpillarpkgconfigplyrprettyunitsprogresspsychpsychToolspurrrR.methodsS3R.ooR6RColorBrewerRcppreadrrlangrtfscalesstatnet.commonstringistringrtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrXICOR