Regularized Principal Component Analysis for Spatial Data R package SpatPCA version 1.3.8 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. Software Depends: R (>= 3.4.0) Imports: Rcpp (>= 1.0.12), ggplot2, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat (>= 2.1.0), dplyr (>= 1.0.3), tidyr, fields, scico, plot3D, pracma, RColorBrewer, maps, covr, styler, V8 Wen-Ting Wang <egpivo@gmail.com> Comprehensive R Archive Network (CRAN) Wen-Ting Wang [aut, cre] (ORCID: <https://orcid.org/0000-0003-3051-7302>), Hsin-Cheng Huang [aut] (ORCID: <https://orcid.org/0000-0002-5613-349X>) GPL (>= 2) 2025-09-28 application/tgz https://CRAN.R-project.org/package=SpatPCA doi:10.32614/CRAN.package.SpatPCA