Principal Component Analysis with Automated Interpretation and Visualization R package EasyPCA version 0.1.1 Provides an automated workflow for Principal Component Analysis (PCA) that simplifies multivariate data analysis by performing essential preprocessing, statistical tests, component extraction, and visualization in a single function call. The package automatically standardizes variables, computes correlation matrices, performs Kaiser-Meyer-Olkin (KMO) and Bartlett's tests, determines the optimal number of principal components using multiple selection criteria, generates component loadings and scores, and produces publication-ready tables and graphical outputs for researchers and students. Methodological background is described in Shankar et al. (2024) <doi:10.1007/s12665-024-11985-5>. Software Imports: ggplot2, ggcorrplot, corrplot, psych, factoextra, stats, graphics, utils S. Vishnu Shankar <S.vishnushankar55@gmail.com> Comprehensive R Archive Network (CRAN) S. Vishnu Shankar [aut, cre], V. Lavanya [aut], Santosha Rathod [aut], Mrinmoy Ray [aut], Anil Kumar [aut], Balaji Kannan [aut] GPL-3 2026-08-30 application/tgz https://CRAN.R-project.org/package=EasyPCA doi:10.32614/CRAN.package.EasyPCA