lnmCluster: Perform Logistic Normal Multinomial Clustering for MicrobiomeCompositional Data
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <doi:10.48550/arXiv.2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <doi:10.48550/arXiv.2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.
| Version: | 0.3.1 |
| Depends: | R (≥ 3.50) |
| Imports: | mclust,foreach,MASS,stringr,gtools,pgmm, utils |
| LinkingTo: | Rcpp |
| Suggests: | knitr,rmarkdown,testthat,mvtnorm |
| Published: | 2022-07-20 |
| DOI: | 10.32614/CRAN.package.lnmCluster |
| Author: | Wangshu Tu [aut, cre], Sanjeena Dang [aut], Yuan Fang [aut] |
| Maintainer: | Wangshu Tu <wangshu.tu at carleton.ca> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| In views: | CompositionalData |
| CRAN checks: | lnmCluster results |
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