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: ) 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) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.0 by Erica Ponzi, a year ago

Browse source code at

Authors: Erica Ponzi [aut, cre] , Abhik Ghosh [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports ggplot2, doParallel, foreach

Suggests knitr, rmarkdown, testthat, cowplot, reshape2, dplyr

See at CRAN