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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SpatPCA to link to this page.
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