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: 0.1.3 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: 2025-01-27 DOI: 10.32614/CRAN.package.singR Author: Liangkang Wang [aut, cre], Irina Gaynanova [aut], Benjamin Risk [aut] Maintainer: Liangkang Wang <liangkang_wang at brown.edu> License: MIT + file LICENSE NeedsCompilation: yes Citation: singR citation info CRAN checks: singR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=singR to link to this page.
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