LUCIDus: Latent Unknown Clustering Integrating Multi-View Data

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

Version: 2.2.1
Depends: R (≥ 3.6.0)
Imports: boot, glasso, glmnet, jsonlite, mclust, mix, networkD3, nnet, progress
Suggests: knitr, testthat (≥ 3.0.0), rmarkdown
Published: 2022-11-08
Author: Yinqi Zhao ORCID iD [aut, cre], David Conti ORCID iD [ths], Jesse Goodrich ORCID iD [ctb], Cheng Peng [ctb]
Maintainer: Yinqi Zhao <yinqiz at>
License: GPL-3
NeedsCompilation: no
Citation: LUCIDus citation info
Materials: NEWS
In views: Omics
CRAN checks: LUCIDus results


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


Package source: LUCIDus_2.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): LUCIDus_2.2.1.tgz, r-oldrel (arm64): LUCIDus_2.2.1.tgz, r-release (x86_64): LUCIDus_2.2.1.tgz, r-oldrel (x86_64): LUCIDus_2.2.1.tgz
Old sources: LUCIDus archive


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