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  [aut,
cre],
David Conti [ths],
Jesse Goodrich
[ctb],
Cheng Peng [ctb] |
| Maintainer: |
Yinqi Zhao <yinqiz at usc.edu> |
| BugReports: |
https://github.com/USCbiostats/LUCIDus/issues |
| 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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