ssr
Semi-Supervised Regression Methods
An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | ssr_0.1.1.tar.gz |
193.6 KiB |
0.1.1 |
rolling linux/noble R-4.5 | ssr_0.1.1.tar.gz |
193.6 KiB |
0.1.1 |
rolling source/ R- | ssr_0.1.1.tar.gz |
157.1 KiB |
0.1.1 |
latest linux/jammy R-4.5 | ssr_0.1.1.tar.gz |
193.6 KiB |
0.1.1 |
latest linux/noble R-4.5 | ssr_0.1.1.tar.gz |
193.6 KiB |
0.1.1 |
latest source/ R- | ssr_0.1.1.tar.gz |
157.1 KiB |
0.1.1 |
2026-04-26 source/ R- | ssr_0.1.1.tar.gz |
157.1 KiB |
0.1.1 |
2026-04-23 source/ R- | ssr_0.1.1.tar.gz |
157.1 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | ssr_0.1.1.zip |
195.9 KiB |
0.1.1 |
2025-04-20 source/ R- | ssr_0.1.1.tar.gz |
157.1 KiB |