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: 2.0.0
Depends: R (≥ 3.6.0)
Imports: mclust, nnet, networkD3, parallel, boot, lbfgs, glasso, glmnet
Suggests: knitr, rmarkdown
Published: 2020-05-18
Author: Yinqi Zhao, Cheng Peng, Zhao Yang, David V. Conti
Maintainer: Yinqi Zhao <yinqiz at usc.edu>
License: GPL-3
URL: https://github.com/Yinqi93/LUCIDus
NeedsCompilation: no
Citation: LUCIDus citation info
Materials: README NEWS
CRAN checks: LUCIDus results

Downloads:

Reference manual: LUCIDus.pdf
Vignettes: LUCIDus
Package source: LUCIDus_2.0.0.tar.gz
Windows binaries: r-devel: LUCIDus_2.0.0.zip, r-release: LUCIDus_2.0.0.zip, r-oldrel: LUCIDus_2.0.0.zip
macOS binaries: r-release: LUCIDus_2.0.0.tgz, r-oldrel: LUCIDus_2.0.0.tgz
Old sources: LUCIDus archive

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