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 returns 26 plots, 5 tables and a summary report.
    The package automatically builds six individual classification models, including error (RMSE) and predictions. That data is used to create an ensemble, which is then modeled using six methods.
    The process is repeated as many times as the user requests. The mean of the results are presented in a summary table. 
    The package returns the confusion matrices for all 12 models, tables of the correlation of the numeric data, the results of the variance inflation process, the head of the ensemble and the head of the data frame.

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!

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

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. (2025). Explainable Linear and Generalized Linear Models by the Predictions Plot. <doi:10.48550/arXiv.2412.16980> (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", and "predsplot_examples".