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
DOI:	10.32614/CRAN.package.SpatPCA
Author:	Wen-Ting Wang [aut, cre], Hsin-Cheng Huang [aut]
Maintainer:	Wen-Ting Wang <egpivo at gmail.com>
BugReports:	https://github.com/egpivo/SpatPCA/issues
License:	GPL-3
URL:	https://github.com/egpivo/SpatPCA
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README NEWS
CRAN checks:	SpatPCA results

Documentation:

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

Downloads:

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

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