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 ).

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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


To get the current released version from CRAN:


To get the current development version from GitHub:



Wen-Ting Wang and Hsin-Cheng Huang


Wen-Ting Wang




Reference manual

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install.packages("SpatMCA") by Wen-Ting Wang, a year ago

Report a bug at

Browse source code at

Authors: Wen-Ting Wang [aut, cre] , Hsin-Cheng Huang [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, RcppParallel, fields, MASS

Linking to Rcpp, RcppArmadillo, RcppParallel

System requirements: GNU make

See at CRAN