SSLR: Semi-Supervised Classification, Regression and Clustering Methods
Providing a collection of techniques for semi-supervised classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled data are used to train a classifier. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface that is easy to use.
| Version: | 0.9.3.3 |
| Depends: | R (≥ 2.10) |
| Imports: | stats, parsnip, plyr, dplyr (≥ 0.8.0.1), magrittr, purrr, rlang (≥ 0.3.1), proxy, methods, generics, utils, RANN, foreach, RSSL, conclust |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | caret, tidymodels, e1071, C50, kernlab, testthat, doParallel, tidyverse, factoextra, survival, covr, kknn, randomForest, ranger, MASS, nlme, knitr, rmarkdown |
| Published: | 2021-07-22 |
| DOI: | 10.32614/CRAN.package.SSLR |
| Author: | Francisco Jesús Palomares Alabarce
 [aut, cre],
José Manuel Benítez
[ctb],
Isaac Triguero
[ctb],
Christoph Bergmeir
[ctb],
Mabel González
[ctb] |
| Maintainer: | Francisco Jesús Palomares Alabarce <fpalomares at correo.ugr.es> |
| License: | GPL-3 |
| URL: | https://dicits.ugr.es/software/SSLR/ |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | SSLR results |
Documentation:
| Reference manual: | SSLR.pdf |
| Vignettes: |
classification clustering fit introduction models regression |
Downloads:
| Package source: | SSLR_0.9.3.3.tar.gz |
| Windows binaries: | r-devel: SSLR_0.9.3.3.zip, r-release: SSLR_0.9.3.3.zip, r-oldrel: SSLR_0.9.3.3.zip |
| macOS binaries: | r-release (arm64): SSLR_0.9.3.3.tgz, r-oldrel (arm64): SSLR_0.9.3.3.tgz, r-release (x86_64): SSLR_0.9.3.3.tgz, r-oldrel (x86_64): SSLR_0.9.3.3.tgz |
| Old sources: | SSLR archive |
Linking:
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