C5.0 decision trees and rule-based models for pattern
    recognition that extend the work of Quinlan (1993,
    ISBN:1-55860-238-0).

Computation and visualization of simple, multiple and joint correspondence analysis.

Provides four variants of three-way correspondence analysis (ca):
 three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca
 and three-way ordered non-symmetrical ca.

Identify the most relative data points by dividing a numeric data set into three classes A, B, and C, where class A items are the "import few", class C items are the "trivial many" with class B items being something in between, resembling the idea of the Pareto principle.
 This ABC classification is done using an ABC curve, which plots cumulative "Yield" against "Effort", similar to a Lorenz curve. Class borders are then precisely mathematically defined on that curve, aiding in interpretation. Based on: Ultsch A, Lotsch J (2015) "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data". PLoS ONE 10(6): e0129767. <doi:10.1371/journal.pone.0129767>.

Creates project specific directory and file templates that are 
 written to a .Rprofile file. Upon starting a new R session, these templates 
 can be used to streamline the creation of new directories that are 
 standardized to the user's preferences and can include the initiation of a 
 git repository, an RStudio R project, and project-local dependency management 
 with the 'renv' package.

Performs simple correspondence analysis on a two-way contingency table, 
    or multiple correspondence analysis (homogeneity analysis) 
    on data with p categorical variables,
    and produces bootstrap-based elliptical confidence regions around the 
    projected coordinates for the category points. 
    Includes routines to plot the results in a variety of styles. 
    Also reports the standard numerical output for correspondence analysis.

A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.