RankPCA: Rank of Variables Based on Principal Component Analysis for Mixed Data Types

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.

Version:	0.1.0
Imports:	stats, caret
Published:	2024-06-07
DOI:	10.32614/CRAN.package.RankPCA
Author:	Dr. Sandip Garai [aut, cre, cph]
Maintainer:	Dr. Sandip Garai <sandipnicksandy at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	RankPCA results

Documentation:

Reference manual:

RankPCA.pdf

Downloads:

Package source:	RankPCA_0.1.0.tar.gz
Windows binaries:	r-devel: RankPCA_0.1.0.zip, r-release: RankPCA_0.1.0.zip, r-oldrel: RankPCA_0.1.0.zip
macOS binaries:	r-release (arm64): RankPCA_0.1.0.tgz, r-oldrel (arm64): RankPCA_0.1.0.tgz, r-release (x86_64): RankPCA_0.1.0.tgz, r-oldrel (x86_64): RankPCA_0.1.0.tgz

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