data.table, stringr, gtools, BIGDAWG, gmt, DescTools, dplyr,
utils, filesstrings

Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.

Efficient algorithms for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO.

Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO. Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.

Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.

Semi-Supervised Classification and Regression Methods
c(
person("Francisco Jesús", "Palomares Alabarce", role =  c("aut", "cre"),
email = "fpalomares@correo.ugr.es",
comment = c(ORCID = "0000-0002-0499-7034")),
person("José Manuel", "Benítez", role = "ctb",
email = "j.m.benitez@decsai.ugr.es",
comment = c(ORCID = "0000-0002-2346-0793")),
person("Isaac", "Triguero", role = "ctb",
email = "isaac.triguero@nottingham.ac.uk",
comment = c(ORCID = "0000-0002-0150-0651")),
person("Christoph", "Bergmeir", role = c("ctb"),
email = "c.bergmeir@decsai.ugr.es",
comment = c(ORCID = "0000-0002-3665-9021")),
person("Mabel", "González", role = "ctb",
email = "mabelc@correo.ugr.es",
comment = c(ORCID = "0000-0003-0152-444X"))
)
Francisco Jesús Palomares Alabarce <fpalomares@correo.ugr.es>
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.
stats, parsnip, plyr, dplyr (>= 0.8.0.1), magrittr, purrr,
rlang (>= 0.3.1), proxy, methods, generics, utils, RANN,
foreach, RSSL
caret, tidymodels, e1071, C50, kernlab, testthat, doParallel,
tidyverse, survival, xgboost, covr, kknn, randomForest, ranger,
MASS, nlme, knitr, rmarkdown
Francisco Jesús Palomares Alabarce [aut, cre]
(<https://orcid.org/0000-0002-0499-7034>),
José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>),
Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>),
Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>),
Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)