Multiple Correspondence Analysis Toolkit (Phi-Divergence Framework)

A toolkit for performing Multiple Correspondence Analysis (MCA) based on the phi-divergence framework of Cressie and Read (1984) . Provides implementations for MCA computation mca_analysis(), visualization plot_mca(), and symbolic equation generation generate_mca_equation(), designed for interpretability, flexibility, and analytical consistency with generalized divergence measures.

MCAtools

Multiple Correspondence Analysis Toolkit (Phi-Divergence Framework)

Overview

MCAtools is an R package designed to perform Multiple Correspondence Analysis (MCA) using the φ-divergence framework introduced by Cressie & Read (1984). The package extends traditional MCA by allowing more general divergence measures, improving interpretability and robustness in categorical data analysis.

It provides three main functions:

• mca\_analysis() — performs MCA computation under the φ-divergence framework.
• plot\_mca() — visualizes individuals and variable modalities on the selected principal dimensions.
• generate\_mca\_equation() — produces a symbolic or algebraic representation of the MCA dimensions for analytical interpretation.

Installation

You can install the development version directly from GitHub:

# install.packages("devtools")
devtools::install\_github("nskamdem/MCAtools")

Usage Example

library(MCAtools)

# Sample dataset
data <- data.frame(
  Var1 = factor(sample(c("A", "B", "C"), 30, TRUE)),
  Var2 = factor(sample(c("X", "Y", "Z"), 30, TRUE))
)

# Run MCA analysis
res <- mca\_analysis(data, delta = 1)

# Visualize MCA results
plot\_mca(res, plot\_type = "both")

# Generate symbolic equation
generate\_mca\_equation(res)

Reference

Cressie, N. A., & Read, T. R. C. (1984). Goodness-of-Fit Statistics for Discrete Multivariate Data. Journal of the Royal Statistical Society, Series B (Methodological), 46(3), 440–464.

Author

Maintainer: [email protected]

License

GPL-3