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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 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

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:

Please use the canonical form https://CRAN.R-project.org/package=SSLR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.