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:	0.9.2
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:	2020-07-20
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

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