Package: RankPCA
Type: Package
Title: Rank of Variables Based on Principal Component Analysis for
        Mixed Data Types
Version: 0.1.0
Authors@R: c(person("Dr. Sandip", "Garai", role = c("aut", "cre","cph"), email = "sandipnicksandy@gmail.com"))
Author: Dr. Sandip Garai [aut, cre, cph]
Maintainer: Dr. Sandip Garai <sandipnicksandy@gmail.com>
Description: 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>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.1
Imports: stats, caret
NeedsCompilation: no
Packaged: 2024-06-07 06:03:50 UTC; Administrator
Repository: CRAN
Date/Publication: 2024-06-07 14:20:06 UTC
Built: R 4.6.0; ; 2026-04-25 13:50:03 UTC; unix
