Package: SpatPCA 1.3.8

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.

Authors:Wen-Ting Wang [aut, cre], Hsin-Cheng Huang [aut]

SpatPCA_1.3.8.tar.gz
SpatPCA_1.3.8.zip(r-4.7)SpatPCA_1.3.8.zip(r-4.6)SpatPCA_1.3.8.zip(r-4.5)
SpatPCA_1.3.8.tgz(r-4.6-x86_64)SpatPCA_1.3.8.tgz(r-4.6-arm64)SpatPCA_1.3.8.tgz(r-4.5-x86_64)SpatPCA_1.3.8.tgz(r-4.5-arm64)
SpatPCA_1.3.8.tar.gz(r-4.7-arm64)SpatPCA_1.3.8.tar.gz(r-4.7-x86_64)SpatPCA_1.3.8.tar.gz(r-4.6-arm64)SpatPCA_1.3.8.tar.gz(r-4.6-x86_64)
SpatPCA_1.3.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SpatPCA/json (API)

# Install 'SpatPCA' in R:
install.packages('SpatPCA', repos = c('https://egpivo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/egpivo/spatpca/issues

Pkgdown/docs site:https://egpivo.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

admmcovariance-estimationeigenfunctionslassomatrix-factorizationpcarcpparmadillorcppparallelregularizationspatialspatial-data-analysissplinesopenblascppopenmp

5.62 score 20 stars 21 scripts 354 downloads 20 exports 19 dependencies

Last updated from:ef5fdb3947. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK164
linux-devel-x86_64OK179
source / vignettesOK212
linux-release-arm64OK167
linux-release-x86_64OK224
macos-release-arm64OK211
macos-release-x86_64OK242
macos-oldrel-arm64OK197
macos-oldrel-x86_64OK313
windows-develOK126
windows-releaseOK130
windows-oldrelOK174
wasm-releaseOK166

Exports:checkInputDatacheckNewLocationsForSpatpcaObjectdetrendeigenFunctionfetchUpperBoundNumberEigenfunctionsplot.spatpcapredictpredictEigenfunctionscaleLocationsetCoressetGammasetL2setNumberEigenfunctionssetTau1setTau2spatialPredictionspatpcaspatpcaCVspatpcaCVWithSelectedKthinPlateSplineMatrix

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr

Capture the Dominant Spatial Pattern with One-Dimensional Locations
Objective | Basic settings | Used packages | True spatial pattern (eigenfunction) | Case I: Higher signal of the true eigenfunction | Generate realizations | Animate realizations | Apply SpatPCA::spatpca | Compare SpatPCA with PCA | Case II: Lower signal of the true eigenfunction | Generate realizations with $\sigma=3$ | Compare resultant patterns
Rendered fromdemo-one-dim-location.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2021-01-01

Capture the Dominant Spatial Pattern with Two-Dimensional Locations
Objective | Basic settings | Used packages | True spatial pattern (eigenfunction) | Experiment | Generate 2-D realizations | Animate realizations | Apply SpatPCA::spatpca | Compare SpatPCA with PCA
Rendered fromdemo-two-dim-location.Rmdusingknitr::rmarkdown

Last update: 2025-09-28
Started: 2021-01-01