SpatPCA is an R package designed for efficient regularized principal component analysis, providing the following features:

• Identify dominant spatial patterns (eigenfunctions) with both smooth and localized characteristics.
• Conduct spatial prediction (Kriging) at new locations.
• Adapt to regularly or irregularly spaced data, spanning 1D, 2D, and 3D datasets.
• Implement using the alternating direction method of multipliers (ADMM) algorithm.

You can install SpatPCA using either of the following methods:

install.packages("SpatPCA")

remotes::install_github("egpivo/SpatPCA")

To compile C++ code with the required RcppArmadillo and RcppParallel packages, follow these instructions based on your operating system:

Install Rtools

1. Install Xcode Command Line Tools
2. install the gfortran library. You can achieve this by running the following commands in the terminal:

brew update
brew install gcc

For a detailed solution, refer to this link, or download and install the library gfortran to resolve the error ld: library not found for -lgfortran.

To use SpatPCA, first load the package:

library(SpatPCA)

Then, apply the spatpca function with the following syntax:

spatpca(position, realizations)

• Input: Realizations with the corresponding positions.
• Output: Return the most dominant eigenfunctions automatically.

For more details, refer to the Demo.

• Wen-Ting Wang (GitHub)
• Hsin-Cheng Huang

Wen-Ting Wang (GitHub)

Wang, W.-T. and Huang, H.-C. (2017). Regularized principal component analysis for spatial data, "Regularized principal component analysis for spatial data"). Journal of Computational and Graphical Statistics, 26, 14-25.

GPL (>= 2)

• To cite package ‘SpatPCA’ in publications use:

  Wang W, Huang H (2023). SpatPCA: Regularized Principal Component Analysis for
  Spatial Data_. R package version 1.3.5,
  <https://CRAN.R-project.org/package=SpatPCA>.

• A BibTeX entry for LaTeX users is

  @Manual{,
    title = {SpatPCA: Regularized Principal Component Analysis for Spatial Data},
    author = {Wen-Ting Wang and Hsin-Cheng Huang},
    year = {2023},
    note = {R package version 1.3.5},
    url = {https://CRAN.R-project.org/package=SpatPCA},
  }