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  "Title": "Perform Logistic Normal Multinomial Clustering for Microbiome\nCompositional Data",
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  "Authors@R": "c(\nperson(\"Wangshu\",\"Tu\",email=\"wangshu.tu@carleton.ca\",role=c(\"aut\",\"cre\")),\nperson(\"Sanjeena\",\"Subedi\",email=\"sanjeena.dang@carleton.ca\",role=c(\"aut\")),\nperson(\"Yuan\",\"Fang\",email=\"yuanf@bu.edu\",role=c(\"aut\")))",
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  "Description": "An implementation of logistic normal multinomial (LNM)\nclustering. It is an extension of LNM mixture model proposed by\nFang and Subedi (2020) <doi:10.1038/s41598-023-41318-8>, and is\ndesigned for clustering compositional data. The package\nincludes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM\nBicluster Mixture Model (LNM-BMM) and Penalized LNM Factor\nAnalyzer (LNM-FA). There are several advantages of LNM models:\n1. LNM provides more flexible covariance structure; 2. Factor\nanalyzer can reduce the number of parameters to estimate; 3.\nBicluster can simultaneously cluster subjects and taxa, and\nprovides significant biological insights; 4. Penalty term\nallows sparse estimation in the covariance matrix. Details for\nmodel assumptions and interpretation can be found in papers: Tu\nand Subedi (2023) <doi:10.1007/s00357-023-09452-0> and Tu and\nSubedi (2022) <doi:10.3329/jsr.v56i2.67469>. It also include a\nBiclustering algorithm that applies to multivariate normal\ndata: Tu and Subedi (2022) <doi:10.1002/sam.11555>.",
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