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.

Functions for classical test theory analysis, following methods presented by Wu et al. (2006) <doi:10.1007/978-981-10-3302-5>.

Automatically builds 12 classification models from data. The package also returns 25 plots, 5 tables and a summary report.

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 non-linear. This
    package implements methods for understanding the division of space
    between the groups.

Fungi are ubiquitous in Earth's wonderfully diverse ecosystems. The 'ClassifyITS' package aids in the taxonomic classification of environmental internal transcribed spacer (ITS) short-read barcoding data. Unlike previous methods, it employs taxon-specific e-value and percent identity cutoffs at each taxonomic rank from kingdom to species. The package takes a conservative approach and outputs both graphics and user-friendly files to help users manually inspect fungal operational taxonomic units (OTUs) that fail classification at relevant levels (e.g., Phylum). 'ClassifyITS' is based on taxonomic cutoff criteria from "The Global Soil Mycobiome consortium dataset for boosting fungal diversity research" (Fungal Diversity, Tedersoo, 2021, <doi:10.1007/s13225-021-00493-7>) and "Best practices in metabarcoding of fungi: From experimental design to results" (Molecular Ecology, Tedersoo, 2022, <doi:10.1111/mec.16460>).