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 usc.edu> |
| License: | GPL-3 |
| URL: | https://github.com/Yinqi93/LUCIDus |
| 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: LUCIDus_2.1.0.zip, r-release: LUCIDus_2.1.0.zip, r-oldrel: LUCIDus_2.1.0.zip |
| macOS binaries: | r-release: LUCIDus_2.1.0.tgz, r-oldrel: LUCIDus_2.1.0.tgz |
| Old sources: | LUCIDus archive |
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