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:
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