LearnPCA: Functions, Data Sets and Vignettes to Aid in Learning Principal Components Analysis (PCA)

Principal component analysis (PCA) is one of the most widely used data analysis techniques. This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.

Package overview Vignette 01: A Guide to Learning PCA with LearnPCA (Start Here) Vignette 02: A Conceptual Introduction to PCA Vignette 03: Step-by-Step PCA Vignette 04: Understanding Scores and Loadings Vignette 05: Visualizing PCA in 3D Vignette 06: The Math Behind PCA Vignette 07: Functions for PCA Vignette 08: Notes A Comparison of Functions for PCA A Conceptual Introduction to PCA A Guide to the LearnPCA Package Notes/FAQ/Special Cases Step-by-Step PCA The Math Behind PCA Understanding Scores and Loadings Visualizing PCA in 3D

Vignettes Man pages API and functions Files

Package details
Author	Bryan A. Hanson [aut, cre] (<https://orcid.org/0000-0003-3536-8246>), David T. Harvey [aut]
Maintainer	Bryan A. Hanson <hanson@depauw.edu>
License	GPL-3
Version	0.3.4
URL	https://bryanhanson.github.io/LearnPCA/
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("LearnPCA")`