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 |
Linking:
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