Type: Package Package: mildsvm Title: Multiple-Instance Learning with Support Vector Machines Version: 0.4.2.9000 Authors@R: c( person("Sean", "Kent", , "skent259@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8697-9069")), person("Yifei", "Liou", role = "aut") ) Description: Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2024) ; Xiao, Liu, and Hao (2018) ; Muandet et al. (2012) ; Chu and Keerthi (2007) ; and Andrews et al. (2003) . Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from after obtaining a license. License: MIT + file LICENSE URL: https://github.com/skent259/mildsvm BugReports: https://github.com/skent259/mildsvm/issues Depends: R (>= 3.5.0) Imports: dplyr, e1071, kernlab, magrittr, mvtnorm, pillar, pROC, purrr, rlang, stats, tibble, tidyr, utils Suggests: covr, gurobi, Matrix, testthat Config/testthat/edition: 3 Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) Config/roxygen2/version: 8.0.0 Config/pak/sysreqs: libicu-dev Repository: https://skent259.r-universe.dev Date/Publication: 2026-10-02 00:11:39 UTC RemoteUrl: https://github.com/skent259/mildsvm RemoteRef: HEAD RemoteSha: 3ae8a1845dc2154e5991846b486cf2c0c6ae1a7f NeedsCompilation: no Packaged: 2026-10-02 00:31:14 UTC; root Author: Sean Kent [aut, cre] (ORCID: ), Yifei Liou [aut] Maintainer: Sean Kent