SpatPCA: Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions 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.

Version: 1.3.5
Depends: R (≥ 3.4.0)
Imports: Rcpp (≥ 1.0.10), RcppParallel (≥ 5.1.7), ggplot2
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: knitr, rmarkdown, testthat (≥ 2.1.0), dplyr (≥ 1.0.3), gifski, tidyr, fields, scico, plot3D, pracma, RColorBrewer, maps, covr, styler, V8
Published: 2023-11-13
Author: Wen-Ting Wang ORCID iD [aut, cre], Hsin-Cheng Huang ORCID iD [aut]
Maintainer: Wen-Ting Wang <egpivo at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: SpatPCA results


Reference manual: SpatPCA.pdf
Vignettes: Capture the Dominant Spatial Pattern with One-Dimensional Locations
Capture the Dominant Spatial Pattern with Two-Dimensional Locations


Package source: SpatPCA_1.3.5.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): SpatPCA_1.3.5.tgz, r-release (arm64): SpatPCA_1.3.5.tgz, r-oldrel (arm64): SpatPCA_1.3.5.tgz, r-prerel (x86_64): SpatPCA_1.3.5.tgz, r-release (x86_64): SpatPCA_1.3.5.tgz
Old sources: SpatPCA archive


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