Regularized Spatial Maximum Covariance Analysis

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2017 ).

Description

SpatMCA provides regularized maximum covariance analysis with

• identifying smooth and localized couple patterns to understand how one spatial process is affected by another
• suitable for either regularly or irregularly spaced data
• by the alternating direction method of multipliers (ADMM) algorithm

Installation

To get the current released version from CRAN:

install.packages("SpatMCA")

To get the current development version from GitHub:

devtools::install_github("egpivo/SpatMCA")

Author

Wen-Ting Wang and Hsin-Cheng Huang

Maintainer

Wen-Ting Wang

License

GPL-2