efaPCA is a dimensional analysis application that provides users with the capability to perform Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA). PCA involves transforming data into a set of uncorrelated variables to simplify its complexity. For instance, PCA can identify the primary dimensions that contribute the most to the overall variation, such as size, weight, and color. EFA is a statistical method used to identify factors that explain observed correlations among variables. efaPCA enables users perform analysis to estimate the number of components, generate correlation plots, and obtain result tables for factors and loadings. efaPCA facilitates the creation of 2D and 3D plots illustrating the distribution of factors. efaPCA serves as a comprehensive tool for conducting advanced dimensional analyses, providing valuable insights into the underlying structure of the analyzed data. original publication: BMC Bioinformatics. 2020 May 11;21(1):183.

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