SparseBiplots: 'HJ Biplot' using Different Ways of Penalization

Contains a set of functions that allow to represent multivariate on a subspace of low dimension, in such a way that most of the variability of the information is captured. This representation is carried out through the 'HJ Biplot' methodology. A first method performs the 'HJ Biplot'.Then, the package implements three new techniques and constructs in each case the 'HJ Biplot', adapting restrictions to contract and / or produce zero charges in the main components, using three methods of regularization: Ridge, LASSO and Elastic Net.

Getting started

Package details

AuthorMitzi Cubilla-Montilla <[email protected]>, Carlos Torres <[email protected]>, Ana Belen Nieto Librero <[email protected]> and Purificacion Galindo Villardon <[email protected]>
MaintainerMitzi Cubilla-Montilla <[email protected]>
LicenseGPL (>= 3)
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the SparseBiplots package in your browser

Any scripts or data that you put into this service are public.

SparseBiplots documentation built on July 2, 2019, 5:10 p.m.