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.

Version:	0.2.0
Imports:	markdown, shiny, stats, graphics
Suggests:	ChemoSpec, chemometrics, knitr, tinytest, roxut, rmarkdown, plot3D, ade4, plotrix, latex2exp, plotly, xtable, bookdown
Published:	2022-05-02
Author:	Bryan A. Hanson [aut, cre], David T. Harvey [aut]
Maintainer:	Bryan A. Hanson <hanson at depauw.edu>
BugReports:	https://github.com/bryanhanson/LearnPCA/issues
License:	GPL-3
URL:	https://bryanhanson.github.io/LearnPCA/
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	LearnPCA results

Documentation:

Reference manual:	LearnPCA.pdf
Vignettes:	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

Downloads:

Package source:	LearnPCA_0.2.0.tar.gz
Windows binaries:	r-devel: LearnPCA_0.2.0.zip, r-release: LearnPCA_0.2.0.zip, r-oldrel: LearnPCA_0.2.0.zip
macOS binaries:	r-release (arm64): LearnPCA_0.2.0.tgz, r-oldrel (arm64): LearnPCA_0.2.0.tgz, r-release (x86_64): LearnPCA_0.2.0.tgz, r-oldrel (x86_64): LearnPCA_0.2.0.tgz
Old sources:	LearnPCA archive

Linking:

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