Package: RaJIVE 1.0

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) <arxiv:2101.09110>.

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

RaJIVE_1.0.tar.gz
RaJIVE_1.0.zip(r-4.7)RaJIVE_1.0.zip(r-4.6)RaJIVE_1.0.zip(r-4.5)
RaJIVE_1.0.tgz(r-4.6-any)RaJIVE_1.0.tgz(r-4.5-any)
RaJIVE_1.0.tar.gz(r-4.7-any)RaJIVE_1.0.tar.gz(r-4.6-any)
RaJIVE_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RaJIVE/json (API)

# Install 'RaJIVE' in R:
install.packages('RaJIVE', repos = c('https://ericaponzi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ericaponzi/rajive/issues

On CRAN:

Conda:

2.70 score 1 scripts 199 downloads 8 exports 21 dependencies

Last updated from:7d494d1102. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK138
source / vignettesOK171
linux-release-x86_64OK130
macos-release-arm64OK162
macos-oldrel-arm64OK238
windows-develOK85
windows-releaseOK82
windows-oldrelOK85
wasm-releaseOK107

Exports:ajive.data.simdecomposition_heatmaps_robustHget_block_loadingsget_block_scoresget_individual_rankget_joint_rankRajiveshowVarExplained_robust

Dependencies:clicodetoolscpp11doParallelfarverforeachggplot2gluegtableisobanditeratorslabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr