ssel
Semi-Supervised Ensemble Learning
Weighted-ensemble regression over base learners supported by 'caret' (Kuhn (2008) <doi:10.18637/jss.v028.i05>), with cross-validated hyperparameter selection, out-of-fold diagnostics, and signed residual- offset estimates. Multi-response problems use iterative input-space expansion related to Spyromitros-Xioufis et al. (2016) <doi:10.1007/s10994-016-5546-z>, with Jacobi or 'Gauss-Seidel' sweeps, package-defined companion gates and per-response iteration stitching. A package-defined pseudo-label stage promotes prediction rows by a cross-model and cross-dataset range ratio and accepts rounds with an out-of-fold squared-correlation gauge.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3.1 |
rolling linux/jammy R-4.5 | ssel_0.3.1.tar.gz |
342.9 KiB |
0.3.1 |
rolling linux/noble R-4.5 | ssel_0.3.1.tar.gz |
342.8 KiB |
0.3.1 |
rolling source/ R- | ssel_0.3.1.tar.gz |
174.0 KiB |
0.3.1 |
latest linux/jammy R-4.5 | ssel_0.3.1.tar.gz |
342.9 KiB |
0.3.1 |
latest linux/noble R-4.5 | ssel_0.3.1.tar.gz |
342.8 KiB |
0.3.1 |
latest source/ R- | ssel_0.3.1.tar.gz |
174.0 KiB |
0.3.1 |
2026-04-23 source/ R- | ssel_0.3.1.tar.gz |
0 B |