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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.
Getting started
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Package details |
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| Author | Yinqi Zhao [aut, cre] (<https://orcid.org/0000-0003-2413-732X>), David Conti [ths] (<https://orcid.org/0000-0002-2941-7833>), Jesse Goodrich [ctb] (<https://orcid.org/0000-0001-6615-0472>), Cheng Peng [ctb] |
| Maintainer | Yinqi Zhao <yinqiz@usc.edu> |
| License | GPL-3 |
| Version | 2.2.1 |
| URL | https://github.com/USCbiostats/LUCIDus |
| Package repository | View on CRAN |
| Installation |
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