RaJIVE: Robust Angle Based Joint and Individual Variation Explained

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
DOI:	10.32614/CRAN.package.RaJIVE
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

Documentation:

Reference manual:

RaJIVE.pdf

Downloads:

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-devel (arm64): RaJIVE_1.0.tgz, r-release (arm64): RaJIVE_1.0.tgz, r-oldrel (arm64): RaJIVE_1.0.tgz, r-devel (x86_64): RaJIVE_1.0.tgz, r-release (x86_64): RaJIVE_1.0.tgz, r-oldrel (x86_64): RaJIVE_1.0.tgz

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