SparseBiplots: 'HJ-Biplot' using Different Ways of Penalization Plotting with 'ggplot2'

'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.

Version: 4.0.1
Depends: R (≥ 3.3.0), ggplot2
Imports: ggrepel, gtable, rlang, stats, sparsepca, testthat
Published: 2021-10-24
Author: Mitzi Isabel Cubilla-Montilla, Carlos Alfredo Torres-Cubilla, Purificacion Galindo Villardon and Ana Belen Nieto-Librero
Maintainer: Mitzi Isabel Cubilla-Montilla <mitzi at usal.es>
BugReports: https://github.com/mitzicubillamontilla/SparseBiplots/issues
License: GPL (≥ 3)
URL: https://github.com/mitzicubillamontilla/SparseBiplots
NeedsCompilation: no
Citation: SparseBiplots citation info
CRAN checks: SparseBiplots results

Documentation:

Reference manual: SparseBiplots.pdf

Downloads:

Package source: SparseBiplots_4.0.1.tar.gz
Windows binaries: r-devel: SparseBiplots_4.0.1.zip, r-release: SparseBiplots_4.0.1.zip, r-oldrel: SparseBiplots_4.0.1.zip
macOS binaries: r-release (arm64): SparseBiplots_4.0.1.tgz, r-oldrel (arm64): SparseBiplots_4.0.1.tgz, r-release (x86_64): SparseBiplots_4.0.1.tgz
Old sources: SparseBiplots archive

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