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)SpatMCA_1.0.7.zip(r-4.6)SpatMCA_1.0.7.zip(r-4.5)
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-4.6-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-x86_64)
SpatMCA_1.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SpatMCA/json (API)
NEWS

# 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 143 downloads 10 exports 21 dependencies

Last updated from:4c3776f6fe. Checks:12 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	155
linux-devel-x86_64	OK	160
source / vignettes	OK	209
linux-release-x86_64	OK	154
macos-release-arm64	OK	147
macos-release-x86_64	OK	268
macos-oldrel-arm64	OK	124
macos-oldrel-x86_64	OK	234
windows-devel	OK	192
windows-release	OK	157
windows-oldrel	OK	239
wasm-release	OK	123