Implementation of Clarke's distribution-free test of
  non-nested models.  Currently supported model functions are: 
  lm(), glm() ('binomial', 'poisson', 'negative binomial' links), 
  polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet').  
  For more information on the test, see Clarke (2007) 
  <doi:10.1093/pan/mpm004>.

Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.

Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.

Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.

Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.

Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!

Given $p$-dimensional training data containing $d$ groups
    (the design space), a classification algorithm (classifier) predicts
    which group new data belongs to.  Generally the input to these
    algorithms is high dimensional, and the boundaries between groups will
    be high dimensional and perhaps curvilinear or multi-faceted. This
    package implements methods for understanding the division of space
    between the groups.

Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.

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, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online.
    <doi:10.1080/00401706.2021.1927849> (open access) and Raymaekers and Rousseeuw (2021),
    Silhouettes and quasi residual plots for neural nets and tree-based classifiers,
    <arXiv:2106.08814>. 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".