Description: 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>.

Title: Semi-Supervised Classification and Regression Methods
person("Francisco Jesús", "Palomares Alabarce", role =  c("aut", "cre"),
person("José Manuel", "Benítez", role = "ctb",
person("Isaac", "Triguero", role = "ctb",
person("Christoph", "Bergmeir", role = c("ctb"),
person("Mabel", "González", role = "ctb",
Maintainer: Francisco Jesús Palomares Alabarce <fpalomares@correo.ugr.es>
Description: 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
Imports: stats, parsnip, plyr, dplyr (>= 0.8.0.1), magrittr, purrr,
rlang (>= 0.3.1), proxy, methods, generics, utils, RANN,
Suggests: caret, tidymodels, e1071, C50, kernlab, testthat, doParallel,
tidyverse, survival, xgboost, covr, kknn, randomForest, ranger,
Author: Francisco Jesús Palomares Alabarce [aut, cre]
José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>),

Title: Semi-Supervised Classification, Regression and Clustering
classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
tidyverse, factoextra, survival, covr, kknn, randomForest,

tidyverse, factoextra, survival, xgboost, covr, kknn,
randomForest, ranger, MASS, nlme, knitr, rmarkdown

Title: Fit and Analyze Smooth Supersaturated Models
Authors@R: person("Peter","Curtis", email = "peterrobertcurtis@gmail.com",
Description: Creates an S4 class "SSM" and defines functions for fitting smooth
supersaturated models, a polynomial model with spline-like behaviour.
Functions are defined for the computation of Sobol indices for sensitivity
analysis and plotting the main effects using FANOVA methods. It also
implements the estimation of the SSM metamodel error using a GP model with
a variety of defined correlation functions.