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A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <doi:10.48550/arXiv.2101.09110>.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | ggplot2, doParallel, foreach |
| Suggests: | knitr, rmarkdown, testthat (≥ 2.1.0), cowplot, reshape2, dplyr |
| Published: | 2021-02-04 |
| Author: | Erica Ponzi [aut, cre], Abhik Ghosh [aut] |
| Maintainer: | Erica Ponzi <erica.ponzi at medisin.uio.no> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | RaJIVE results |
| Reference manual: | RaJIVE.pdf |
| Package source: | RaJIVE_1.0.tar.gz |
| Windows binaries: | r-devel: RaJIVE_1.0.zip, r-release: RaJIVE_1.0.zip, r-oldrel: RaJIVE_1.0.zip |
| macOS binaries: | r-release (arm64): RaJIVE_1.0.tgz, r-oldrel (arm64): RaJIVE_1.0.tgz, r-release (x86_64): RaJIVE_1.0.tgz, r-oldrel (x86_64): RaJIVE_1.0.tgz |
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These binaries (installable software) and packages are in development.
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