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: 4.0.0
Depends: R (≥ 3.2), ggplot2
Imports: ggrepel, gtable, rlang, stats, sparsepca, testthat
Published: 2020-06-28
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
CRAN checks: SparseBiplots results

Downloads:

Reference manual: SparseBiplots.pdf
Package source: SparseBiplots_4.0.0.tar.gz
Windows binaries: r-devel: SparseBiplots_4.0.0.zip, r-release: SparseBiplots_3.5.0.zip, r-oldrel: SparseBiplots_4.0.0.zip
macOS binaries: r-release: SparseBiplots_3.5.0.tgz, r-oldrel: SparseBiplots_4.0.0.tgz
Old sources: SparseBiplots archive

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