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RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.

Version: 0.9.7
Depends: R (≥ 2.10.0)
Imports: methods, Rcpp, MASS, kernlab, quadprog, Matrix, dplyr, tidyr, ggplot2, reshape2, scales, cluster
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, rmarkdown, SparseM, numDeriv, LiblineaR, covr
Published: 2023-12-07
DOI: 10.32614/CRAN.package.RSSL
Author: Jesse Krijthe [aut, cre]
Maintainer: Jesse Krijthe <jkrijthe at gmail.com>
BugReports: https://github.com/jkrijthe/RSSL
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/jkrijthe/RSSL
NeedsCompilation: yes
Citation: RSSL citation info
Materials: README
CRAN checks: RSSL results

Documentation:

Reference manual: RSSL.pdf

Downloads:

Package source: RSSL_0.9.7.tar.gz
Windows binaries: r-devel: RSSL_0.9.7.zip, r-release: RSSL_0.9.7.zip, r-oldrel: RSSL_0.9.7.zip
macOS binaries: r-release (arm64): RSSL_0.9.7.tgz, r-oldrel (arm64): RSSL_0.9.7.tgz, r-release (x86_64): RSSL_0.9.7.tgz, r-oldrel (x86_64): RSSL_0.9.7.tgz
Old sources: RSSL archive

Reverse dependencies:

Reverse imports: SSLR

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RSSL 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.