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 + file LICENSE |
| NeedsCompilation: |
yes |
| Citation: |
singR citation info |
| CRAN checks: |
singR results |
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