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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling linux/jammy R-4.5 | singR_0.1.3.tar.gz |
2.5 MiB |
0.1.3 |
rolling linux/noble R-4.5 | singR_0.1.3.tar.gz |
2.5 MiB |
0.1.3 |
rolling source/ R- | singR_0.1.3.tar.gz |
2.4 MiB |
0.1.3 |
latest linux/jammy R-4.5 | singR_0.1.3.tar.gz |
2.5 MiB |
0.1.3 |
latest linux/noble R-4.5 | singR_0.1.3.tar.gz |
2.5 MiB |
0.1.3 |
latest source/ R- | singR_0.1.3.tar.gz |
2.4 MiB |
0.1.3 |
2026-04-26 source/ R- | singR_0.1.3.tar.gz |
2.4 MiB |
0.1.3 |
2026-04-23 source/ R- | singR_0.1.3.tar.gz |
2.4 MiB |
0.1.3 |
2026-04-09 windows/windows R-4.5 | singR_0.1.3.zip |
2.9 MiB |
0.1.3 |
2025-04-20 source/ R- | singR_0.1.3.tar.gz |
2.4 MiB |