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.
| Version: | 0.1.1 | 
| Depends: | R (≥ 3.6.0) | 
| Imports: | caret, e1071 | 
| Suggests: | knitr, rmarkdown, tgp | 
| Published: | 2019-09-02 | 
| DOI: | 10.32614/CRAN.package.ssr | 
| Author: | Enrique Garcia-Ceja | 
| Maintainer: | Enrique Garcia-Ceja <e.g.mx at ieee.org> | 
| BugReports: | https://github.com/enriquegit/ssr/issues | 
| License: | GPL-3 | 
| URL: | https://github.com/enriquegit/ssr | 
| NeedsCompilation: | no | 
| Materials: | README, NEWS | 
| CRAN checks: | ssr results | 
| Reference manual: | ssr.html , ssr.pdf | 
| Vignettes: | Introduction to the ssr package (source, R code) | 
| Package source: | ssr_0.1.1.tar.gz | 
| Windows binaries: | r-devel: ssr_0.1.1.zip, r-release: ssr_0.1.1.zip, r-oldrel: ssr_0.1.1.zip | 
| macOS binaries: | r-release (arm64): ssr_0.1.1.tgz, r-oldrel (arm64): ssr_0.1.1.tgz, r-release (x86_64): ssr_0.1.1.tgz, r-oldrel (x86_64): ssr_0.1.1.tgz | 
| Old sources: | ssr archive | 
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