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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1.30 score 166 downloads 2 exports 58 dependencies

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

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-linuxOKNov 29 2024

Exports:boost.graphLDA.boost

Dependencies:clicodacolorspacecpp11crayondplyrfansifarverforcatsforeigngenericsGGallyggplot2ggstatsglueGPArotationgtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmnormtmunsellnetworknlmepatchworkpillarpkgconfigplyrprettyunitsprogresspsychpsychToolspurrrR.methodsS3R.ooR6RColorBrewerRcpprlangrtfscalesstatnet.commonstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrXICOR