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.
- Version0.2.0
- R version≥ 3.5.0
- LicenseGPL-2
- Needs compilation?No
- Last release07/30/2024
Documentation
Team
Hui-Shan Tsao
Li-Pang Chen
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 149 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,005 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was May 22, 2024 with 42 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports3 packages
- Suggests1 package