SpatPCA: Regularized Principal Component Analysis for Spatial Data

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.

AuthorWen-Ting Wang and Hsin-Cheng Huang
Date of publication2016-05-27 10:45:08
MaintainerWen-Ting Wang <egpivo@gmail.com>
LicenseGPL-2
Version1.1.1.1

View on CRAN

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.