EasyPCA: Principal Component Analysis with Automated Interpretation and
Visualization
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>.
| Version: |
0.1.1 |
| Imports: |
ggplot2, ggcorrplot, corrplot, psych, factoextra, stats, graphics, utils |
| Published: |
2026-08-30 |
| DOI: |
10.32614/CRAN.package.EasyPCA (may not be active yet) |
| Author: |
S. Vishnu Shankar [aut, cre],
V. Lavanya [aut],
Santosha Rathod [aut],
Mrinmoy Ray [aut],
Anil Kumar [aut],
Balaji Kannan [aut] |
| Maintainer: |
S. Vishnu Shankar <S.vishnushankar55 at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
EasyPCA results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=EasyPCA
to link to this page.