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.

Refer to SELPCCA_1.0.pdf for how to use the package.

Sandra E. Safo, Jeongyoun Ahn, Yongho Jeon, and Sungkyu Jung (2018) , Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data. Biometrics