Package: SpatMCA 1.0.6

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_1.0.6.tar.gz
SpatMCA_1.0.6.zip(r-4.5)SpatMCA_1.0.6.zip(r-4.4)SpatMCA_1.0.6.zip(r-4.3)
SpatMCA_1.0.6.tgz(r-4.5-x86_64)SpatMCA_1.0.6.tgz(r-4.5-arm64)SpatMCA_1.0.6.tgz(r-4.4-x86_64)SpatMCA_1.0.6.tgz(r-4.4-arm64)SpatMCA_1.0.6.tgz(r-4.3-x86_64)SpatMCA_1.0.6.tgz(r-4.3-arm64)
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SpatMCA_1.0.6.tgz(r-4.4-emscripten)SpatMCA_1.0.6.tgz(r-4.3-emscripten)
SpatMCA.pdf |SpatMCA.html✨
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 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 231 downloads 10 exports 31 dependencies

Last updated 6 months agofrom:47dfeaae69. Checks:5 OK, 6 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 25 2025
R-4.5-win-x86_64	OK	Feb 25 2025
R-4.5-mac-x86_64	OK	Feb 25 2025
R-4.5-mac-aarch64	OK	Feb 25 2025
R-4.5-linux-x86_64	OK	Feb 25 2025
R-4.4-win-x86_64	NOTE	Feb 25 2025
R-4.4-mac-x86_64	NOTE	Feb 25 2025
R-4.4-mac-aarch64	NOTE	Feb 25 2025
R-4.3-win-x86_64	NOTE	Feb 25 2025
R-4.3-mac-x86_64	NOTE	Feb 25 2025
R-4.3-mac-aarch64	NOTE	Feb 25 2025