Package: GUEST 0.2.0

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]

GUEST_0.2.0.tar.gz
GUEST_0.2.0.zip(r-4.7-any)GUEST_0.2.0.zip(r-4.6-any)GUEST_0.2.0.zip(r-4.5-any)
GUEST_0.2.0.tgz(r-4.6-any)GUEST_0.2.0.tgz(r-4.5-any)
GUEST_0.2.0.tar.gz(r-4.7-any)GUEST_0.2.0.tar.gz(r-4.6-any)
GUEST_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GUEST/json (API)

# Install 'GUEST' in R:
install.packages('GUEST', repos = c('https://n410412-beep.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

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

1.00 score 3 scripts 259 downloads 2 exports 49 dependencies

Last updated from:5e14988a1b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK133
source / vignettesOK226
linux-releaseOK134
macos-releaseOK175
macos-oldrelOK192
windows-develOK74
windows-releaseOK80
windows-oldrelOK82
wasm-releaseOK132

Exports:boost.graphLDA.boost

Dependencies:clicodacpp11crayondplyrfarverforcatsforeigngenericsGGallyggplot2ggstatsglueGPArotationgtablehmsisobandlabelinglatticelifecyclemagrittrMatrixmnormtnetworknlmepatchworkpillarpkgconfigprettyunitsprogresspsychpsychToolspurrrR6RColorBrewerrlangS7scalesstatnet.commonstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrXICOR