Package: SpatPCA Title: Regularized Principal Component Analysis for Spatial Data Version: 1.3.8 Authors@R: c(person( given = "Wen-Ting", family = "Wang", email = "egpivo@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3051-7302") ), person( given = "Hsin-Cheng", family = "Huang", email = "hchuang@stat.sinica.edu.tw", role = "aut", comment = c(ORCID = "0000-0002-5613-349X") ) ) Description: 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, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D. License: GPL (>= 2) ByteCompile: true BugReports: https://github.com/egpivo/SpatPCA/issues 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 SystemRequirements: GNU make VignetteBuilder: knitr, rmarkdown Encoding: UTF-8 RoxygenNote: 7.2.3 URL: https://egpivo.github.io/SpatPCA/, https://github.com/egpivo/SpatPCA Config/testthat/edition: 3 NeedsCompilation: yes Packaged: 2026-10-13 11:46:53 UTC; root Author: Wen-Ting Wang [aut, cre] (ORCID: ), Hsin-Cheng Huang [aut] (ORCID: ) Maintainer: Wen-Ting Wang Config/pak/sysreqs: make Repository: https://cran.r-universe.dev Date/Publication: 2025-09-28 10:40:18 UTC RemoteUrl: https://github.com/cran/SpatPCA RemoteRef: HEAD RemoteSha: 1aa808defb9327080ed19facd8c39b892c757568