Package: SpatMCA 1.0.7

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.zip(r-4.5-x86_64)
SpatMCA_1.0.7.tgz(r-4.6-x86_64)SpatMCA_1.0.7.tgz(r-4.6-arm64)SpatMCA_1.0.7.tgz(r-4.5-x86_64)SpatMCA_1.0.7.tgz(r-4.5-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://egpivo.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:

admm cca cross-covariance lasso matrix-factorization rcpparmadillo rcppparallel splines openblas cpp openmp

3.40 score 5 stars 4 scripts 357 downloads 10 exports 21 dependencies

Last updated from:4c3776f6fe. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	143
linux-devel-x86_64	OK	164
source / vignettes	OK	203
linux-release-arm64	OK	139
linux-release-x86_64	OK	156
macos-release-arm64	OK	140
macos-release-x86_64	OK	289
macos-oldrel-arm64	OK	98
macos-oldrel-x86_64	OK	177
windows-devel-arm64	OK	230
windows-devel-x86_64	OK	130
windows-release-arm64	OK	214
windows-release-x86_64	OK	133
windows-oldrel-x86_64	OK	125
wasm-release	OK	109