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:
GUEST_0.2.0.tar.gz
GUEST_0.2.0.tar.gz(r-4.5-noble)GUEST_0.2.0.tar.gz(r-4.4-noble)
GUEST_0.2.0.tgz(r-4.4-emscripten)GUEST_0.2.0.tgz(r-4.3-emscripten)
GUEST.pdf |GUEST.html✨
GUEST/json (API)
| # Install 'GUEST' in R: |
| install.packages('GUEST', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- MedulloblastomaData - The medulloblastoma dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 months agofrom:5e14988a1b. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-linux | OK | Oct 30 2024 |
Exports:boost.graphLDA.boost
Dependencies:clicodacolorspacecpp11crayondplyrfansifarverforcatsforeigngenericsGGallyggplot2ggstatsglueGPArotationgtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmnormtmunsellnetworknlmepatchworkpillarpkgconfigplyrprettyunitsprogresspsychpsychToolspurrrR.methodsS3R.ooR6RColorBrewerRcpprlangrtfscalesstatnet.commonstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrXICOR
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimation of precision matrix and detection of graphical structure | boost.graph |
| Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm | GUEST_package |
| Implementation of the linear discriminant function for multi-label classification. | LDA.boost |
| The medulloblastoma dataset | MedulloblastomaData |