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: 2026-10-06 11:13:17 UTC; root
Config/pak/sysreqs: libicu-dev
Repository: https://sandipgarai.r-universe.dev
Date/Publication: 2024-06-08 02:40:52 UTC
RemoteUrl: https://github.com/cran/RankPCA
RemoteRef: HEAD
RemoteSha: 2d028578da39fe1a945112e57a8ee26e09a75d01