Package: SpatMCA 1.0.7

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, 2018 <doi:10.1002/env.2481>).

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

SpatMCA_1.0.7.tar.gz
SpatMCA_1.0.7.zip(r-4.7-x86_64)SpatMCA_1.0.7.zip(r-4.7-arm64)SpatMCA_1.0.7.zip(r-4.6-x86_64)SpatMCA_1.0.7.zip(r-4.6-arm64)
SpatMCA_1.0.7.tar.gz(r-4.7-arm64)SpatMCA_1.0.7.tar.gz(r-4.7-x86_64)SpatMCA_1.0.7.tar.gz(r-4.6-arm64)SpatMCA_1.0.7.tar.gz(r-4.6-x86_64)
SpatMCA_1.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SpatMCA/json (API)

# 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

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:

openblascppopenmp

1.00 score 5 scripts 274 downloads 1 exports 21 dependencies

Last updated from:ee9d5e17e1. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK113
linux-devel-x86_64OK147
source / vignettesOK216
linux-release-arm64OK116
linux-release-x86_64OK167
windows-devel-arm64OK250
windows-devel-x86_64OK150
windows-release-arm64OK239
windows-release-x86_64OK135
wasm-releaseOK106

Exports:spatmca

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleMASSR6RColorBrewerRcppRcppArmadilloRcppParallelrlangS7scalesvctrsviridisLitewithr