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SparseBiplots  

'HJ-Biplot' using Different Ways of Penalization Plotting with 'ggplot2'
View on CRAN: Click here


Download and install SparseBiplots package within the R console
Install from CRAN:
install.packages("SparseBiplots")

Install from Github:
library("remotes")
install_github("cran/SparseBiplots")

Install by package version:
library("remotes")
install_version("SparseBiplots", "4.0.1")



Attach the package and use:
library("SparseBiplots")
Maintained by
Mitzi Isabel Cubilla-Montilla
[Scholar Profile | Author Map]
First Published: 2019-07-02
Latest Update: 2021-10-24
Description:
'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.
How to cite:
Mitzi Isabel Cubilla-Montilla (2019). SparseBiplots: 'HJ-Biplot' using Different Ways of Penalization Plotting with 'ggplot2'. R package version 4.0.1, https://cran.r-project.org/web/packages/SparseBiplots. Accessed 29 Mar. 2025.
Previous versions and publish date:
3.5.0 (2019-07-02 18:30), 4.0.0 (2020-06-28 02:00)
Other packages that cited SparseBiplots R package
View SparseBiplots citation profile
Other R packages that SparseBiplots depends, imports, suggests or enhances
Complete documentation for SparseBiplots
Functions, R codes and Examples using the SparseBiplots R package
Some associated functions: ElasticNet_HJBiplot . HJBiplot . LASSO_HJBiplot . Plot_Biplot . Ridge_HJBiplot . 
Some associated R codes: ElasticNet_HJBiplot.R . HJBiplot.R . LASSO_HJBiplot.R . Plot_Biplot.R . Ridge_HJBiplot.R .  Full SparseBiplots package functions and examples
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