Package: SpatMCA 1.0.4

Wen-Ting Wang

SpatMCA: Regularized Spatial Maximum Covariance Analysis

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2017 <doi:10.1002/env.2481>).

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

SpatMCA_1.0.4.tar.gz
SpatMCA_1.0.4.tar.gz(r-4.5-noble)SpatMCA_1.0.4.tar.gz(r-4.4-noble)
SpatMCA_1.0.4.tgz(r-4.4-emscripten)SpatMCA_1.0.4.tgz(r-4.3-emscripten)
SpatMCA.pdf |SpatMCA.html✨
SpatMCA/json (API)
NEWS

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

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

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

On CRAN:

Conda:

openblascppopenmp

1.00 score 4 scripts 237 downloads 10 exports 31 dependencies

Last updated 1 years agofrom:f728f564c5. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 14 2025
R-4.5-linux-x86_64OKFeb 14 2025

Exports:checkInputDatadetrendplot_cv_fieldplot_sequentiallyplot.spatmcasetCoresspatmcaspatmcacv_rcppspatmcacvall_rcpptpm2

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr