Package: classmap 1.2.7

classmap: Visualizing Classification Results

Tools to visualize the results of a classification or a regression. The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. <doi:10.1080/00401706.2021.1927849> (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emph{Journal of Computational and Graphical Statistics}, 31(4), 1332–1343. <doi:10.1080/10618600.2022.2050249>, and Rousseeuw, P.J. (2026). Explainable Linear and Generalized Linear Models by the Predictions Plot. The American Statistician, 80, 157-163, <doi:10.1080/00031305.2025.2539235> (open access), and Montalcini, C., Rousseeuw, P.J. (2025). The bixplot: A variation on the boxplot suited for bimodal data, <doi:10.48550/arXiv.2510.09276> (open access). Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", "Neural_net_examples", "predsplot_examples", and "bixplot_examples".

Authors:Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut]

classmap_1.2.7.tar.gz
classmap_1.2.7.zip(r-4.7)classmap_1.2.7.zip(r-4.6)classmap_1.2.7.zip(r-4.5)
classmap_1.2.7.tgz(r-4.6-any)classmap_1.2.7.tgz(r-4.5-any)
classmap_1.2.7.tar.gz(r-4.7-any)classmap_1.2.7.tar.gz(r-4.6-any)
classmap_1.2.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
classmap/json (API)

# Install 'classmap' in R:
install.packages('classmap', repos = c('https://jakobraymaekers.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.68 score 20 scripts 539 downloads 23 exports 45 dependencies

Last updated from:fff8dbab45. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK229
source / vignettesOK427
linux-release-x86_64OK209
macos-release-arm64OK175
macos-oldrel-arm64OK225
windows-develOK145
windows-releaseOK171
windows-oldrelOK213
wasm-releaseOK153

Exports:bixplotclassmapconfmat.vcrmakeFVmakeKernelpamc1dpredscorpredsplotqresplotsilplotstackedplotvcr.da.newdatavcr.da.trainvcr.forest.newdatavcr.forest.trainvcr.knn.newdatavcr.knn.trainvcr.neural.newdatavcr.neural.trainvcr.rpart.newdatavcr.rpart.trainvcr.svm.newdatavcr.svm.train

Dependencies:cellWiseclasscliclustercpp11DEoptimRdipteste1071farverggplot2gluegridExtragtableisobandkernlablabelinglatticelifecyclelpSolvemagrittrMASSmatrixStatsmvtnormpcaPPplyrproxyR6randomForestRColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserpartrrcovS7scalesshapestringistringrsvdvctrsviridisLitewithr

bixplot_examples
Introduction | Unimodal, bimodal and multimodal data | Latency data and penguin bill length | Iris data | Body sizing options for multimodal variables | Bill length by island and sex | Rug colored by an external numeric variable | Rug colored by a factor variable | Top Gear car data | Tooth growth and iris data with side = "both" | Iris data with rug coloring | Titanic data

Last update: 2026-04-29
Started: 2026-04-29

predsplot_examples
TopGear data | Numerical example in the introduction | Figure 2 and TopGear figures in Supplementary Material: | Three figures in the Supplementary Material: | Test combination of expressions and types | Titanic data | Figure 3: | Three figures in Supplementary Material: | German credit data | Figure in Supplementary Material: | Figure 4: | Figure 5: | Figure 6: | Two figures in Supplementary Material: | Artificial example to illustrate high correlation:

Last update: 2025-07-14
Started: 2025-07-14

Support_vector_machine_examples
Introduction | Small toy example | Amazon book review data | Sweets data

Last update: 2025-07-14
Started: 2021-05-10

Discriminant_analysis_examples
Introduction | Iris data | Floral buds data (shown in paper) | MNIST data

Last update: 2025-06-18
Started: 2021-05-10

K_nearest_neighbors_examples
Introduction | Iris data | Spam data

Last update: 2025-06-18
Started: 2021-05-10

Neural_net_examples
Introduction | Iris data | Training data | New data | floral buds data:

Last update: 2025-06-18
Started: 2021-06-27

Random_forest_examples
Introduction | Instagram training data | Instagram test data

Last update: 2025-06-18
Started: 2021-06-27

Rpart_examples
Introduction | Titanic training data | Titanic test data

Last update: 2025-06-18
Started: 2021-06-27

Readme and manuals

Help Manual

Help pageTopics
Boxplot version suited for bimodal and multimodal data, combining density, box, and rug elements with automatic cluster detectionbixplot
Draw the class map to visualize classification results.classmap
Build a confusion matrix from the output of a function 'vcr.*.*'.confmat.vcr
Amazon book reviews datadata_bookReviews
Floral buds datadata_floralbuds
Instagram datadata_instagram
Latenc datadata_latenc
Titanic datadata_titanic
Constructs feature vectors from a kernel matrix.makeFV
Compute kernel matrixmakeKernel
Constrained k-medoids clustering for univariate datapamc1d
Draws a predictions correlation plot, which visualizes the correlations between the prediction terms in a regression fit.predscor
Make a predictions plotpredsplot
Draw a quasi residual plot of PAC versus a data featureqresplot
Draw the silhouette plot of a classificationsilplot
Make a vertically stacked mosaic plot of class predictions.stackedplot
Carry out discriminant analysis on new data, and prepare to visualize its results.vcr.da.newdata
Carry out discriminant analysis on training data, and prepare to visualize its results.vcr.da.train
Prepare for visualization of a random forest classification on new data.vcr.forest.newdata
Prepare for visualization of a random forest classification on training datavcr.forest.train
Carry out a k-nearest neighbor classification on new data, and prepare to visualize its results.vcr.knn.newdata
Carry out a k-nearest neighbor classification on training data, and prepare to visualize its results.vcr.knn.train
Prepare for visualization of a neural network classification on new data.vcr.neural.newdata
Prepare for visualization of a neural network classification on training data.vcr.neural.train
Prepare for visualization of an rpart classification on new data.vcr.rpart.newdata
Prepare for visualization of an rpart classification on training data.vcr.rpart.train
Prepare for visualization of a support vector machine classification on new data.vcr.svm.newdata
Prepare for visualization of a support vector machine classification on training data.vcr.svm.train