LUCIDus: Latent Unknown Clustering with Integrated Data

An implementation of LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>). LUCID conducts integrated clustering using exposures, omics data (and outcome of interest). An EM algorithm is implemented to estimate MLE of LUCID model. LUCID features integrated variable selection, incorporation of missing omics data, bootstrap inference and visualization via Sankey diagram.

Version: 2.2
Depends: R (≥ 3.6.0)
Imports: boot, glasso, glmnet, jsonlite, mclust, mix, networkD3, nnet, progress
Suggests: knitr, testthat (≥ 3.0.0), rmarkdown
Published: 2022-08-07
Author: Yinqi Zhao, David V. Conti, Cheng Peng, Zhao Yang
Maintainer: Yinqi Zhao <yinqiz at usc.edu>
License: GPL-3
URL: https://github.com/USCbiostats/LUCIDus
NeedsCompilation: no
Citation: LUCIDus citation info
Materials: NEWS
CRAN checks: LUCIDus results

Documentation:

Reference manual: LUCIDus.pdf
Vignettes: LUCIDus: an R package to implement integrative clustering

Downloads:

Package source: LUCIDus_2.2.tar.gz
Windows binaries: r-devel: LUCIDus_2.2.zip, r-release: LUCIDus_2.2.zip, r-oldrel: LUCIDus_2.2.zip
macOS binaries: r-release (arm64): LUCIDus_2.1.5-2.tgz, r-oldrel (arm64): LUCIDus_2.1.5-2.tgz, r-release (x86_64): LUCIDus_2.2.tgz, r-oldrel (x86_64): LUCIDus_2.2.tgz
Old sources: LUCIDus archive

Linking:

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