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
Repository: https://andrewthomasjones.r-universe.dev
Date/Publication: 2025-09-20 09:05:12 UTC
RemoteUrl: https://github.com/andrewthomasjones/ssosvm
RemoteRef: HEAD
RemoteSha: 33bf59c828e6feeb65b0ccbaaf8929ce1c6df2bc
NeedsCompilation: yes
Packaged: 2026-06-17 09:06:37 UTC; root
Author: Andrew Thomas Jones [aut, cre],
  Hien Duy Nguyen [aut],
  Geoffrey J. McLachlan [aut]
Maintainer: Andrew Thomas Jones <andrewthomasjones@gmail.com>