Package: classmap
Type: Package
Title: Visualizing Classification Results
Date: 2025-06-16
Version: 1.2.5
Authors@R: c(
            person(given="Jakob",family="Raymaekers", role = c("aut","cre"),
            email="jakob.raymaekers@kuleuven.be"),
            person(given="Peter",family="Rousseeuw", role = c("aut")) 
             )
Author: Jakob Raymaekers [aut, cre],
  Peter Rousseeuw [aut]
Depends: R (>= 4.1.0)
Suggests: knitr, reshape2, svd, rpart.plot, nnet, robCompositions,
        rmarkdown
Imports: stats, graphics, ggplot2, robustbase, e1071, cellWise,
        cluster, kernlab, gridExtra, rpart, randomForest
Maintainer: Jakob Raymaekers <jakob.raymaekers@kuleuven.be>
Description: Tools to visualize the results of a classification of cases.
    The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps.
    Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. \doi{10.1080/00401706.2021.1927849}
 (open access) and Raymaekers J., Rousseeuw P.J.(2021). 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}.
 Examples can be found in the vignettes:
    "Discriminant_analysis_examples","K_nearest_neighbors_examples",
    "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples",
    and "Neural_net_examples".
URL: https://doi.org/10.1080/00401706.2021.1927849,
        https://doi.org/10.1080/10618600.2022.2050249
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr, rmarkdown
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2025-06-17 10:28:32 UTC; Jakob Raymaekers
Repository: CRAN
Date/Publication: 2025-06-18 07:50:13 UTC
Built: R 4.5.0; ; 2025-06-19 11:26:16 UTC; unix
