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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>.
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Package details |
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| Author | Liangkang Wang [aut, cre] (<https://orcid.org/0000-0003-3393-243X>), Irina Gaynanova [aut] (<https://orcid.org/0000-0002-4116-0268>), Benjamin Risk [aut] (<https://orcid.org/0000-0003-1090-0777>) |
| Maintainer | Liangkang Wang <liangkang_wang@brown.edu> |
| License | MIT + file LICENSE |
| Version | 0.1.2 |
| Package repository | View on CRAN |
| Installation |
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