Package: s2net
Type: Package
Title: The Generalized Semi-Supervised Elastic-Net
Version: 1.0.1
Date: 2020-01-08
Authors@R: c(person("Juan C.",
           "Laria",,
           role = c("aut", "cre"),
           email = "juank.laria@gmail.com",
           comment = c(ORCID = "0000-0001-7734-9647")),
    person("Line H.",
           "Clemmensen",,
           role = c("aut"),
           email = "lkhc@dtu.dk"))
Description: Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
License: GPL (>= 2)
Imports: Rcpp, methods, MASS
Depends: stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, glmnet, Metrics, testthat
VignetteBuilder: knitr
URL: https://github.com/jlaria/s2net
BugReports: https://github.com/jlaria/s2net/issues
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-01-15 08:16:47 UTC; juank
Author: Juan C. Laria [aut, cre] (<https://orcid.org/0000-0001-7734-9647>),
  Line H. Clemmensen [aut]
Maintainer: Juan C. Laria <juank.laria@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-16 13:20:06 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 06:49:09 UTC; windows
Archs: i386, x64
