Stream Suitable Online Support Vector Machines R package SSOSVM version 0.2.2 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. Software Imports: Rcpp (>= 0.12.13), mvtnorm LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, ggplot2, gganimate, gifski Andrew Thomas Jones <andrewthomasjones@gmail.com> Comprehensive R Archive Network (CRAN) Andrew Thomas Jones [aut, cre], Hien Duy Nguyen [aut], Geoffrey J. McLachlan [aut] GPL-3 2025-09-20 application/tgz https://CRAN.R-project.org/package=SSOSVM doi:10.32614/CRAN.package.SSOSVM