PMA-package

<p>This package is called <strong>PMA</strong>, for __P__enalized __M__ultivariate
__A__nalysis.  It implements three methods: A penalized matrix
decomposition, sparse principal components analysis, and sparse
canonical correlations analysis. All are described in the reference below.
The main functions are: <code>PMD</code>, <code>CCA</code> and <code>SPC</code>.</p>

package

A modified version of PMA. The CCA() and CCA.permute() functions can also compute the
component-wise standard deviations of estimated U and V through
permutations in addition to standardize them. Furthermore, it computes
the non-parametric p-values for each components.
Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal
components analysis, and sparse canonical correlation analysis, described in
Ali Mahzarnia, Alexander Badea (2022), "Joint Estimation of Vulnerable Brain Networks and Alzheimer<e2><80><99>s Disease Risk Via Novel Extension of Sparse Canonical Correlation" at bioRxiv.

PMA-package: Penalized Multivariate Analysis

Description

Arguments

Details

References