Package: lnmCluster
Type: Package
Title: Perform Logistic Normal Multinomial Clustering for Microbiome
        Compositional Data
Version: 1.0.0
Authors@R: c(
    person("Wangshu","Tu",email="wangshu.tu@carleton.ca",role=c("aut","cre")),
    person("Sanjeena","Subedi",email="sanjeena.dang@carleton.ca",role=c("aut")), 
    person("Yuan","Fang",email="yuanf@bu.edu",role=c("aut")))
Maintainer: Wangshu Tu <wangshu.tu@carleton.ca>
Description: An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <doi:10.1038/s41598-023-41318-8>, 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 (2023) <doi:10.1007/s00357-023-09452-0> and Tu and Subedi (2022) <doi:10.3329/jsr.v56i2.67469>. It also include a Biclustering algorithm that applies to multivariate normal data: Tu and Subedi (2022) <doi:10.1002/sam.11555>. 
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: mclust, foreach, doParallel, MASS, stringr, gtools, pgmm,
        utils
Suggests: knitr, rmarkdown, testthat, mvtnorm
VignetteBuilder: knitr
Depends: R (>= 3.50)
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2026-08-27 15:51:08 UTC; wangshutu
Author: Wangshu Tu [aut, cre],
  Sanjeena Subedi [aut],
  Yuan Fang [aut]
Repository: CRAN
Date/Publication: 2026-08-28 13:00:02 UTC
Built: R 4.5.3; x86_64-w64-mingw32; 2026-08-28 21:33:19 UTC; windows
Archs: x64
