SSLR: Semi-Supervised Classification and Regression Methods

Providing a collection of techniques for semi-supervised classification and regression. 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: 0.9.1
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
LinkingTo: Rcpp, RcppArmadillo
Suggests: caret, tidymodels, e1071, C50, kernlab, testthat, doParallel, tidyverse, survival, xgboost, covr, kknn, randomForest, ranger, MASS, nlme, knitr, rmarkdown
Published: 2020-04-13
Author: Francisco Jesús Palomares Alabarce ORCID iD [aut, cre], José Manuel Benítez ORCID iD [ctb], Isaac Triguero ORCID iD [ctb], Christoph Bergmeir ORCID iD [ctb], Mabel González ORCID iD [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

Downloads:

Reference manual: SSLR.pdf
Vignettes: classification
fit
introduction
models
regression
Package source: SSLR_0.9.1.tar.gz
Windows binaries: r-devel: SSLR_0.9.1.zip, r-release: SSLR_0.9.1.zip, r-oldrel: SSLR_0.9.1.zip
macOS binaries: r-release: SSLR_0.9.1.tgz, r-oldrel: SSLR_0.9.1.tgz
Old sources: SSLR archive

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