{ "_id": "6a196eb1acfb0bcc41debd55", "Package": "classmap", "Type": "Package", "Title": "Visualizing Classification Results", "Date": "2026-04-28", "Version": "1.2.7", "Authors@R": "c(\nperson(given=\"Jakob\",family=\"Raymaekers\", role = c(\"aut\",\"cre\"),\nemail=\"jakob.raymaekers@kuleuven.be\"),\nperson(given=\"Peter\",family=\"Rousseeuw\", role = c(\"aut\"))\n)", "Author": "Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut]", "Maintainer": "Jakob Raymaekers ", "Description": "Tools to visualize the results of a classification or a\nregression. The graphical displays include stacked plots,\nsilhouette plots, quasi residual plots, class maps, predictions\nplots, and predictions correlation plots. Implements the\ntechniques described and illustrated in Raymaekers J.,\nRousseeuw P.J., Hubert M. (2022). Class maps for visualizing\nclassification results. \\emph{Technometrics}, 64(2), 151–165.\n (open access), Raymaekers\nJ., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots\nfor neural nets and tree-based classifiers. \\emph{Journal of\nComputational and Graphical Statistics}, 31(4), 1332–1343.\n, and Rousseeuw, P.J.\n(2026). Explainable Linear and Generalized Linear Models by the\nPredictions Plot. The American Statistician, 80, 157-163,\n (open access), and\nMontalcini, C., Rousseeuw, P.J. (2025). The bixplot: A\nvariation on the boxplot suited for bimodal data,\n (open access). Examples can be\nfound in the vignettes:\n\"Discriminant_analysis_examples\",\"K_nearest_neighbors_examples\",\n\"Support_vector_machine_examples\", \"Rpart_examples\",\n\"Random_forest_examples\", \"Neural_net_examples\",\n\"predsplot_examples\", and \"bixplot_examples\".", "URL": ",\n,\n,\n", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr, rmarkdown", "RoxygenNote": "7.1.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-29 08:59:07 UTC", "User": "root" }, "Config/pak/sysreqs": "libicu-dev", "Repository": "https://jakobraymaekers.r-universe.dev", "Date/Publication": "2026-04-29 14:34:38 UTC", "RemoteUrl": "https://github.com/cran/classmap", "RemoteRef": "HEAD", "RemoteSha": "fff8dbab45bcd2adf4e3ce8f5ba9c5db9e768a32", "MD5sum": "ac2b0db005cdabea9a800865d941aee7", "_user": "jakobraymaekers", "_type": "src", "_file": "classmap_1.2.7.tar.gz", "_fileid": "aa42496eb5997ea5e8dd1a380f6f425d327acf48780901fbe0d9d89281f3b3ad", "_filesize": 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"data.frame" ], "fields": [ "X1", "X2", "X3", "X4", "X5", "X6", "y" ], "rows": 550, "table": true, "tojson": true }, { "name": "data_instagram", "title": "Instagram data", "object": "data_instagram", "class": [ "data.frame" ], "fields": [ "profile.pic", "nums.length.username", "fullname.words", "nums.length.fullname", "name..username", "description.length", "external.URL", "private", "X.posts", "X.followers", "X.follows", "y", "dataType" ], "rows": 696, "table": true, "tojson": true }, { "name": "data_latenc", "title": "Latenc data", "object": "data_latenc", "class": [ "matrix", "array" ], "fields": [ "round 1", "round 2", "round 3" ], "rows": 40, "table": true, "tojson": true }, { "name": "data_titanic", "title": "Titanic data", "object": "data_titanic", "class": [ "data.frame" ], "fields": [ "PassengerId", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked", "y", "dataType" ], "rows": 1309, "table": true, "tojson": true } ], "_help": [ { "page": "bixplot", "title": "Boxplot version suited for bimodal and multimodal data, combining density, box, and rug elements with automatic cluster detection", "topics": [ "bixplot" ] }, { "page": "classmap", "title": "Draw the class map to visualize classification results.", "topics": [ "classmap" ] }, { "page": "confmat.vcr", "title": "Build a confusion matrix from the output of a function 'vcr.*.*'.", "topics": [ "confmat.vcr" ] }, { "page": "data_bookReviews", "title": "Amazon book reviews data", "topics": [ "data_bookReviews" ] }, { "page": "data_floralbuds", "title": "Floral buds data", "topics": [ "data_floralbuds" ] }, { "page": "data_instagram", "title": "Instagram data", "topics": [ "data_instagram" ] }, { "page": "data_latenc", "title": "Latenc data", "topics": [ "data_latenc" ] }, { "page": "data_titanic", "title": "Titanic data", "topics": [ "data_titanic" ] }, { "page": "makeFV", "title": "Constructs feature vectors from a kernel matrix.", "topics": [ "makeFV" ] }, { "page": "makeKernel", "title": "Compute kernel matrix", "topics": [ "makeKernel" ] }, { "page": "pamc1d", "title": "Constrained k-medoids clustering for univariate data", "topics": [ "pamc1d" ] }, { "page": "predscor", "title": "Draws a predictions correlation plot, which visualizes the correlations between the prediction terms in a regression fit.", "topics": [ "predscor" ] }, { "page": "predsplot", "title": "Make a predictions plot", "topics": [ "predsplot" ] }, { "page": "qresplot", "title": "Draw a quasi residual plot of PAC versus a data feature", "topics": [ "qresplot" ] }, { "page": "silplot", "title": "Draw the silhouette plot of a classification", "topics": [ "silplot" ] }, { "page": "stackedplot", "title": "Make a vertically stacked mosaic plot of class predictions.", "topics": [ "stackedplot" ] }, { "page": "vcr.da.newdata", "title": "Carry out discriminant analysis on new data, and prepare to visualize its results.", "topics": [ "vcr.da.newdata" ] }, { "page": "vcr.da.train", 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