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.
- Version1.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- rrscale citation info
- Last release05/26/2020
Documentation
Team
Gregory Hunt
Johann Gagnon-Bartsch
Show author detailsRolesAuthor
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN