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".
| Version: | 1.2.7 |
| Depends: | R (≥ 4.1.0) |
| Imports: | stats, graphics, ggplot2, robustbase, e1071, cellWise, cluster, kernlab, gridExtra, rpart, randomForest, scales, lpSolve, diptest |
| Suggests: | knitr, reshape2, svd, rpart.plot, nnet, robCompositions, rmarkdown, fairml, robustHD, vioplot |
| Published: | 2026-04-29 |
| DOI: | 10.32614/CRAN.package.classmap |
| Author: | Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut] |
| Maintainer: | Jakob Raymaekers <jakob.raymaekers at kuleuven.be> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | <doi:10.1080/00401706.2021.1927849>, <doi:10.1080/10618600.2022.2050249>, <doi:10.1080/00031305.2025.2539235>, <doi:10.48550/arXiv.2510.09276> |
| NeedsCompilation: | no |
| CRAN checks: | classmap results |
| Reference manual: | classmap.html , classmap.pdf |
| Vignettes: |
Discriminant_analysis_examples (source, R code) K_nearest_neighbors_examples (source, R code) Neural_net_examples (source, R code) Random_forest_examples (source, R code) Rpart_examples (source, R code) Support_vector_machine_examples (source, R code) bixplot_examples (source, R code) predsplot_examples (source, R code) |
| Package source: | classmap_1.2.7.tar.gz |
| Windows binaries: | r-devel: classmap_1.2.6.zip, r-release: classmap_1.2.6.zip, r-oldrel: classmap_1.2.6.zip |
| macOS binaries: | r-release (arm64): classmap_1.2.6.tgz, r-oldrel (arm64): classmap_1.2.7.tgz, r-release (x86_64): classmap_1.2.6.tgz, r-oldrel (x86_64): classmap_1.2.6.tgz |
| Old sources: | classmap archive |
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