lasandrall/SELPCCA: Sparse Canonical Correlation Analysis for Associating Mutiple High Dimensional Data

Sparse canonical correlation analysis method to associate two high dimensional data types. The algorithm obtains linear combinations of subsets of variables for each data type that contribute to overall dependency structure between the data types.

Getting started

Package details

AuthorHaoyu Chen and Sandra E Safo
MaintainerSandra E. Safo <>
LicenseGPL (>=2.0)
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
lasandrall/SELPCCA documentation built on June 8, 2020, 12:38 a.m.