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 (Wang and Huang, 2017, <doi:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Imports: Rcpp, RcppParallel
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Published: 2020-01-09
Author: Wen-Ting Wang, Hsin-Cheng Huang
Maintainer: Wen-Ting Wang <egpivo at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: SpatPCA results


Reference manual: SpatPCA.pdf
Package source: SpatPCA_1.2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: SpatPCA_1.2.0.1.tgz, r-oldrel: SpatPCA_1.2.0.1.tgz
Old sources: SpatPCA archive


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