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
DOI: 10.32614/CRAN.package.SparseBiplots
Author: Mitzi Isabel Cubilla-Montilla, Carlos Alfredo Torres-Cubilla, Purificacion Galindo Villardon and Ana Belen Nieto-Librero
Maintainer: Mitzi Isabel Cubilla-Montilla <mitzi at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: SparseBiplots citation info
CRAN checks: SparseBiplots results


Reference manual: SparseBiplots.pdf


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


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