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: | 0.1.3 |
| 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 + fileLICENSE |
| NeedsCompilation: | yes |
| Citation: | singR citation info |
| CRAN checks: | singR results |
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