OHPL
Ordered Homogeneity Pursuit Lasso for Group Variable Selection
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.4.2 |
rolling linux/jammy R-4.5 | OHPL_1.4.2.tar.gz |
1002.3 KiB |
1.4.2 |
rolling linux/noble R-4.5 | OHPL_1.4.2.tar.gz |
1002.1 KiB |
1.4.2 |
rolling source/ R- | OHPL_1.4.2.tar.gz |
643.7 KiB |
1.4.2 |
latest linux/jammy R-4.5 | OHPL_1.4.2.tar.gz |
1002.3 KiB |
1.4.2 |
latest linux/noble R-4.5 | OHPL_1.4.2.tar.gz |
1002.1 KiB |
1.4.2 |
latest source/ R- | OHPL_1.4.2.tar.gz |
643.7 KiB |
1.4.2 |
2026-04-26 source/ R- | OHPL_1.4.2.tar.gz |
643.7 KiB |
1.4.2 |
2026-04-23 source/ R- | OHPL_1.4.2.tar.gz |
643.7 KiB |
1.4.1 |
2026-04-09 windows/windows R-4.5 | OHPL_1.4.1.zip |
1004.2 KiB |
1.4.1 |
2025-04-20 source/ R- | OHPL_1.4.1.tar.gz |
643.7 KiB |