: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.
: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables. Practical Guide To Principal Component Methods ...
The by Alboukadel Kassambara is widely considered an excellent resource for those who want to apply multivariate analysis without getting bogged down in heavy mathematical proofs. Why It Is Highly Rated : Simple Correspondence Analysis (CA) for two variables
: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It which creates elegant
: Principal Component Analysis (PCA) for quantitative variables.
: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results.