SpatPCA: Regularized Principal Component Analysis for Spatial Data

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Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.

Author
Wen-Ting Wang and Hsin-Cheng Huang
Date of publication
2016-05-27 10:45:08
Maintainer
Wen-Ting Wang <egpivo@gmail.com>
License
GPL-2
Version
1.1.1.1

View on CRAN

Man pages

spatpca
Regularized PCA for spatial data
SpatPCAinternal1
internal_function
SpatPCAinternal2
internal_function
SpatPCAinternal3
internal_function
SpatPCA-package
Regularized Principal Component Analysis for Spatial Data

Files in this package

SpatPCA
SpatPCA/src
SpatPCA/src/Makevars
SpatPCA/src/rcpp_SpatPCA.cpp
SpatPCA/src/RcppExports.cpp
SpatPCA/NAMESPACE
SpatPCA/R
SpatPCA/R/SpatPCA.R
SpatPCA/R/RcppExports.R
SpatPCA/MD5
SpatPCA/DESCRIPTION
SpatPCA/man
SpatPCA/man/SpatPCAinternal2.Rd
SpatPCA/man/spatpca.Rd
SpatPCA/man/SpatPCA-package.Rd
SpatPCA/man/SpatPCAinternal3.Rd
SpatPCA/man/SpatPCAinternal1.Rd