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  "Description": "Principal Component Analysis (PCA) is a statistical\ntechnique used to reduce the dimensionality of a dataset while\npreserving as much variability as possible. By transforming the\noriginal variables into a new set of uncorrelated variables\ncalled principal components, PCA helps in identifying patterns\nand simplifying the complexity of high-dimensional data. The\n'RankPCA' package provides a streamlined workflow for\nperforming PCA on datasets containing both categorical and\ncontinuous variables. It facilitates data preprocessing,\nencoding of categorical variables, and computes PCA to\ndetermine the optimal number of principal components based on a\nspecified variance threshold. The package also computes\ncomposite indices for ranking observations, which can be useful\nfor various analytical purposes. Garai, S., & Paul, R. K.\n(2023) <doi:10.1016/j.iswa.2023.200202>.",
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