Simultaneous Non-Gaussian Component Analysis R package singR version 0.1.3 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>. Software 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 Liangkang Wang <liangkang_wang@brown.edu> Comprehensive R Archive Network (CRAN) Liangkang Wang [aut, cre] (ORCID: <https://orcid.org/0000-0003-3393-243X>), Irina Gaynanova [aut] (ORCID: <https://orcid.org/0000-0002-4116-0268>), Benjamin Risk [aut] (ORCID: <https://orcid.org/0000-0003-1090-0777>) MIT + file LICENSE (https://CRAN.R-project.org/package=singR/LICENSE) 2025-01-27 application/tgz https://CRAN.R-project.org/package=singR doi:10.32614/CRAN.package.singR