{ "_id": "6a1eb55ab25058d4daadda67", "Type": "Package", "Package": "mildsvm", "Title": "Multiple-Instance Learning with Support Vector Machines", "Version": "0.4.1.9000", "Authors@R": "c(\nperson(\"Sean\", \"Kent\", , \"skent259@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-8697-9069\")),\nperson(\"Yifei\", \"Liou\", role = \"aut\")\n)", "Description": "Weakly supervised (WS), multiple instance (MI) data lives\nin numerous interesting applications such as drug discovery,\nobject detection, and tumor prediction on whole slide images.\nThe 'mildsvm' package provides an easy way to learn from this\ndata by training Support Vector Machine (SVM)-based\nclassifiers. It also contains helpful functions for building\nand printing multiple instance data frames. The core methods\nfrom 'mildsvm' come from the following references: Kent and Yu\n(2024) ; Xiao, Liu, and Hao (2018)\n; Muandet et al. (2012)\n;\nChu and Keerthi (2007) ; and\nAndrews et al. (2003)\n.\nMany functions use the 'Gurobi' optimization back-end to\nimprove the optimization problem speed; the 'gurobi' R package\nand associated software can be downloaded from\n after obtaining a license.", "License": "MIT + file LICENSE", "URL": "https://github.com/skent259/mildsvm", "BugReports": "https://github.com/skent259/mildsvm/issues", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://skent259.r-universe.dev", "Date/Publication": "2025-09-05 02:13:59 UTC", "RemoteUrl": "https://github.com/skent259/mildsvm", "RemoteRef": "HEAD", "RemoteSha": "9c57fe05ddfdd8d28be756a75d1f750ee123f5d8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 10:35:28 UTC", "User": "root" }, "Author": "Sean Kent [aut, cre] (ORCID: ),\nYifei Liou [aut]", "Maintainer": "Sean Kent ", "MD5sum": "84ded8f93cc87ecc5681d3d5e028222b", "_user": "skent259", 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"Build a feature map on new data", "topics": [ "build_fm", "build_fm.kfm_exact", "build_fm.kfm_nystrom" ] }, { "page": "build_instance_feature", "title": "Flatten 'mild_df' data to the instance level", "topics": [ "build_instance_feature" ] }, { "page": "classify_bags", "title": "Classify y from bags", "topics": [ "classify_bags" ] }, { "page": "cv_misvm", "title": "Fit MI-SVM model to the data using cross-validation", "topics": [ "cv_misvm", "cv_misvm.default", "cv_misvm.formula", "cv_misvm.mi_df" ] }, { "page": "formatting", "title": "Printing multiple instance data frames", "topics": [ "formatting", "print.mild_df", "print.mi_df" ] }, { "page": "generate_mild_df", "title": "Generate mild_df using multivariate t and normal distributions.", "topics": [ "generate_mild_df" ] }, { "page": "kfm_exact", "title": "Create an exact kernel feature map", "concept": [ "kernel feature map functions" ], "topics": [ "kfm_exact" ] }, { "page": "kfm_nystrom", "title": "Fit a Nyström kernel feature map approximation", "concept": [ "kernel feature map functions" ], "topics": [ "kfm_nystrom", "kfm_nystrom.default", "kfm_nystrom.mild_df" ] }, { "page": "kme", "title": "Calculate the kernel mean embedding matrix", "topics": [ "kme", "kme.default", "kme.mild_df" ] }, { "page": "mi", "title": "Create an 'mi' object", "concept": [ "multiple instance formula helper functions" ], "topics": [ "mi" ] }, { "page": "mi_df", "title": "Build a multiple instance (MI) data frame", "topics": [ "mi_df" ] }, { "page": "mild", "title": "Create a mild object", "concept": [ "multiple instance formula helper functions" ], "topics": [ "mild" ] }, { "page": "mild_df", "title": "Build a MILD data frame", "topics": [ "mild_df" ] }, { "page": "mior", "title": "Fit MIOR model to the data", "topics": [ "mior", "mior.default", "mior.formula", "mior.mi_df" ] }, { "page": "mismm", "title": "Fit MILD-SVM model to the data", "topics": [ "mismm", "mismm.default", "mismm.formula", "mismm.mild_df" ] }, { "page": "misvm", "title": "Fit MI-SVM model to the data", "topics": [ "misvm", "misvm.default", "misvm.formula", "misvm.mild_df", "misvm.mi_df" ] }, { "page": "misvm_orova", "title": "Fit MI-SVM model to ordinal outcome data using One-vs-All", "topics": [ "misvm_orova", "misvm_orova.default", "misvm_orova.formula", "misvm_orova.mi_df" ] }, { "page": "omisvm", "title": "Fit MI-SVM-OR model to ordinal outcome data", "topics": [ "omisvm", "omisvm.default", "omisvm.formula", "omisvm.mi_df" ] }, { "page": "ordmvnorm", "title": "Sample ordinal MIL data using mvnorm", "topics": [ "ordmvnorm" ] }, { "page": "predict.cv_misvm", "title": "Predict method for 'cv_misvm' object", "topics": [ "predict.cv_misvm" ] }, { "page": "predict.mior", "title": "Predict method for 'mior' object", "topics": [ "predict.mior" ] }, { "page": "predict.mismm", "title": "Predict method for 'mismm' object", "topics": [ "predict.mismm" ] }, { "page": "predict.misvm", "title": "Predict method for 'misvm' object", "topics": [ 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