LUCIDus: Latent Unknown Clustering with Integrated Data

An implementation for the 'LUCID' model (Peng (2019) <doi:10.1093/bioinformatics/btz667>) to jointly estimate latent unknown clusters/subgroups with integrated data. An EM algorithm is used to obtain the latent cluster assignment and model parameter estimates. Feature selection is achieved by applying the L1 regularization method.

Version: 2.1.0
Depends: R (≥ 3.6.0)
Imports: mclust, nnet, networkD3, parallel, boot, lbfgs, glasso, glmnet
Suggests: knitr, rmarkdown
Published: 2020-07-22
Author: Yinqi Zhao, David V. Conti, Cheng Peng, Zhao Yang
Maintainer: Yinqi Zhao <yinqiz at>
License: GPL-3
NeedsCompilation: no
Citation: LUCIDus citation info
Materials: README NEWS
CRAN checks: LUCIDus results


Reference manual: LUCIDus.pdf
Vignettes: LUCIDus
Package source: LUCIDus_2.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: LUCIDus_2.1.0.tgz, r-oldrel: LUCIDus_2.1.0.tgz
Old sources: LUCIDus archive


Please use the canonical form to link to this page.