: Specifically those looking to move beyond "old-school" base R graphics to more modern, publication-ready visualizations. Practical Guide To Principal Component Methods in R
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 Practical Guide To Principal Component Methods ...
: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two. : Specifically those looking to move beyond "old-school"
: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables. : Factor Analysis of Mixed Data (FAMD) and
The book categorizes methods based on the types of data you are analyzing:
: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.
: Principal Component Analysis (PCA) for quantitative variables.