singR: Simultaneous Non-Gaussian Component Analysis

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: 0.1.1
Depends: R (≥ 2.10)
Imports: MASS (≥ 7.3-57), Rcpp (≥, clue (≥ 0.3-61), gam (≥ 1.20.1), ICtest (≥ 0.3-5)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, covr, testthat (≥ 3.0.0), rmarkdown
Published: 2022-09-12
Author: Liangkang Wang ORCID iD [aut, cre], Irina Gaynanova ORCID iD [aut], Benjamin Risk ORCID iD [aut]
Maintainer: Liangkang Wang <wangliangkang1130 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: singR citation info
CRAN checks: singR results


Reference manual: singR.pdf
Vignettes: singR-tutorial


Package source: singR_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): singR_0.1.1.tgz, r-oldrel (arm64): singR_0.1.1.tgz, r-release (x86_64): singR_0.1.1.tgz, r-oldrel (x86_64): singR_0.1.1.tgz
Old sources: singR archive


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