rrscale
Robust Re-Scaling to Better Recover Latent Effects in Data
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | rrscale_1.0.tar.gz |
214.4 KiB |
1.0 |
rolling linux/noble R-4.5 | rrscale_1.0.tar.gz |
214.2 KiB |
1.0 |
rolling source/ R- | rrscale_1.0.tar.gz |
551.1 KiB |
1.0 |
latest linux/jammy R-4.5 | rrscale_1.0.tar.gz |
214.4 KiB |
1.0 |
latest linux/noble R-4.5 | rrscale_1.0.tar.gz |
214.2 KiB |
1.0 |
latest source/ R- | rrscale_1.0.tar.gz |
551.1 KiB |
1.0 |
2026-04-26 source/ R- | rrscale_1.0.tar.gz |
551.1 KiB |
1.0 |
2026-04-23 source/ R- | rrscale_1.0.tar.gz |
551.1 KiB |
1.0 |
2026-04-09 windows/windows R-4.5 | rrscale_1.0.zip |
217.9 KiB |
1.0 |
2025-04-20 source/ R- | rrscale_1.0.tar.gz |
551.1 KiB |