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 (Wang and Huang, 2017).

AuthorWen-Ting Wang, Hsin-Cheng Huang
Date of publication2017-03-18 00:20:28 UTC
MaintainerWen-Ting Wang <egpivo@gmail.com>
LicenseGPL-2
Version1.1.1.2
https://github.com/egpivo/SpatPCA

View on CRAN

Files

SpatPCA
SpatPCA/src
SpatPCA/src/Makevars
SpatPCA/src/registerDynamicSymbol.c
SpatPCA/src/rcpp_SpatPCA.cpp
SpatPCA/src/Makevars.win
SpatPCA/src/RcppExports.cpp
SpatPCA/NAMESPACE
SpatPCA/R
SpatPCA/R/SpatPCA.R SpatPCA/R/RcppExports.R
SpatPCA/README.md
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

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