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Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.
Version: | 0.1.2 |
Depends: | R (≥ 2.10) |
Imports: | MASS (≥ 7.3-57), Rcpp (≥ 1.0.8.3), clue (≥ 0.3-61), gam (≥ 1.20.1), ICtest (≥ 0.3-5) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, covr, testthat (≥ 3.0.0), rmarkdown |
Published: | 2024-02-09 |
DOI: | 10.32614/CRAN.package.singR |
Author: | Liangkang Wang |
Maintainer: | Liangkang Wang <liangkang_wang at brown.edu> |
License: | MIT + file LICENSE |
NeedsCompilation: | yes |
Citation: | singR citation info |
CRAN checks: | singR results |
Reference manual: | singR.pdf |
Vignettes: |
singR-tutorial |
Package source: | singR_0.1.2.tar.gz |
Windows binaries: | r-devel: singR_0.1.2.zip, r-release: singR_0.1.2.zip, r-oldrel: singR_0.1.2.zip |
macOS binaries: | r-release (arm64): singR_0.1.2.tgz, r-oldrel (arm64): singR_0.1.2.tgz, r-release (x86_64): singR_0.1.2.tgz, r-oldrel (x86_64): singR_0.1.2.tgz |
Old sources: | singR archive |
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