| Name : R-RankPCA
| |
| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp156.2.1
| Date : 2024-07-04 11:38:49
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| Group : Development/Libraries/Other
| Source RPM : R-RankPCA-0.1.0-lp156.2.1.src.rpm
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| Size : 0.02 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Rank of Variables Based on Principal Component Analysis for Mixed Data Types
|
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>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
