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RankPCA  

Rank of Variables Based on Principal Component Analysis for Mixed Data Types
View on CRAN: Click here


Download and install RankPCA package within the R console
Install from CRAN:
install.packages("RankPCA")

Install from Github:
library("remotes")
install_github("cran/RankPCA")

Install by package version:
library("remotes")
install_version("RankPCA", "0.1.0")



Attach the package and use:
library("RankPCA")
Maintained by
Dr. Sandip Garai
[Scholar Profile | Author Map]
First Published: 2024-06-07
Latest Update: 2024-06-07
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>.
How to cite:
Dr. Sandip Garai (2024). RankPCA: Rank of Variables Based on Principal Component Analysis for Mixed Data Types. R package version 0.1.0, https://cran.r-project.org/web/packages/RankPCA. Accessed 02 May. 2025.
Previous versions and publish date:
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Complete documentation for RankPCA
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