Package: SpatPCA 1.3.7

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_1.3.7.tar.gz
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SpatPCA.pdf |SpatPCA.html✨
SpatPCA/json (API)
NEWS

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

Peer review:

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

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

On CRAN:

admmcovariance-estimationeigenfunctionslassomatrix-factorizationpcarcpparmadillorcppparallelregularizationspatialspatial-data-analysissplines

6.29 score 19 stars 17 scripts 245 downloads 20 exports 31 dependencies

Last updated 2 months agofrom:50143ac998. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64OKOct 25 2024
R-4.5-linux-x86_64OKOct 25 2024
R-4.4-win-x86_64NOTEOct 25 2024
R-4.4-mac-x86_64NOTEOct 25 2024
R-4.4-mac-aarch64NOTEOct 25 2024
R-4.3-win-x86_64NOTEOct 25 2024
R-4.3-mac-x86_64NOTEOct 25 2024
R-4.3-mac-aarch64NOTEOct 25 2024

Exports:checkInputDatacheckNewLocationsForSpatpcaObjectdetrendeigenFunctionfetchUpperBoundNumberEigenfunctionsplot.spatpcapredictpredictEigenfunctionscaleLocationsetCoressetGammasetL2setNumberEigenfunctionssetTau1setTau2spatialPredictionspatpcaspatpcaCVspatpcaCVWithSelectedKthinPlateSplineMatrix

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr

Capture the Dominant Spatial Pattern with One-Dimensional Locations

Wen-Ting Wang

Rendered fromdemo-one-dim-location.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2021-02-01
Started: 2021-01-01

Capture the Dominant Spatial Pattern with Two-Dimensional Locations

Wen-Ting Wang

Rendered fromdemo-two-dim-location.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2023-11-13
Started: 2021-01-01