Package: SSOSVM
Type: Package
Title: Stream Suitable Online Support Vector Machines
Version: 0.2.2
Date: 2025-09-20
Authors@R: c(person(given = c("Andrew", "Thomas"),
                      family = "Jones",
                      role = c("aut", "cre"),
                      email = "andrewthomasjones@gmail.com"),
               person(given = c("Hien", "Duy"),
                      family = "Nguyen",
                      role = "aut"),
               person(given = c("Geoffrey", "J."),
                      family = "McLachlan",
                      role = "aut"))
Description: Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.
License: GPL-3
Encoding: UTF-8
Imports: Rcpp (>= 0.12.13), mvtnorm
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, ggplot2, gganimate, gifski
NeedsCompilation: yes
Packaged: 2025-09-20 08:55:34 UTC; uqajon14
Author: Andrew Thomas Jones [aut, cre],
  Hien Duy Nguyen [aut],
  Geoffrey J. McLachlan [aut]
Maintainer: Andrew Thomas Jones <andrewthomasjones@gmail.com>
Repository: CRAN
Date/Publication: 2025-09-20 09:10:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-25 12:54:24 UTC; unix
Archs: SSOSVM.so.dSYM
