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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | GUEST_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
rolling linux/noble R-4.5 | GUEST_0.2.0.tar.gz |
155.3 KiB |
0.2.0 |
rolling source/ R- | GUEST_0.2.0.tar.gz |
112.9 KiB |
0.2.0 |
latest linux/jammy R-4.5 | GUEST_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
latest linux/noble R-4.5 | GUEST_0.2.0.tar.gz |
155.3 KiB |
0.2.0 |
latest source/ R- | GUEST_0.2.0.tar.gz |
112.9 KiB |
0.2.0 |
2026-04-26 source/ R- | GUEST_0.2.0.tar.gz |
112.9 KiB |
0.2.0 |
2026-04-23 source/ R- | GUEST_0.2.0.tar.gz |
112.9 KiB |
0.2.0 |
2026-04-09 windows/windows R-4.5 | GUEST_0.2.0.zip |
158.3 KiB |
0.2.0 |
2025-04-20 source/ R- | GUEST_0.2.0.tar.gz |
112.9 KiB |