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lnmCluster
View on CRAN: Click
here
Download and install lnmCluster package within the R console
Install from CRAN:
install.packages("lnmCluster")
Install from Github:
library("remotes")
install_github("cran/lnmCluster")
Install by package version:
library("remotes")
install_version("lnmCluster", "0.3.1")
Attach the package and use:
library("lnmCluster")
Maintained by
Wangshu Tu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.lnmCluster . lnmCluster results . lnmCluster.pdf . lnmCluster . lnmCluster_0.3.1.tar.gz . lnmCluster_0.3.1.zip . lnmCluster_0.3.1.zip . lnmCluster_0.3.1.zip . lnmCluster_0.3.1.tgz . lnmCluster_0.3.1.tgz . lnmCluster_0.3.1.tgz . lnmCluster_0.3.1.tgz . lnmCluster_0.3.1.tgz . lnmCluster_0.3.1.tgz . https://CRAN.R-project.org/package=lnmCluster .
First Published: 2022-07-20
Latest Update: 2022-07-20
Description:
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) , 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) and Tu and Subedi (2022) .
How to cite:
Wangshu Tu (2022). lnmCluster: Perform Logistic Normal Multinomial Clustering for Microbiome Compositional Data. R package version 0.3.1, https://cran.r-project.org/web/packages/lnmCluster. Accessed 14 Apr. 2025.
Previous versions and publish date:
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Complete documentation for lnmCluster
Functions, R codes and Examples using
the lnmCluster R package
Some associated functions: Mico_bi_PGMM . Mico_bi_jensens . Mico_bi_lasso . initial_variational_PGMM . initial_variational_gaussian . initial_variational_lasso . lnmbiclust . lnmfa . model_selection . model_selection_PGMM . model_selection_lasso . plnmfa .
Some associated R codes: Micro_bi_PGMM.R . Micro_bi_jensens.R . Micro_bi_lasso.R . RcppExports.R . lnmbiclust.R . lnmfa.R . microb_initial_guess.R . microb_initial_guess_PGMM.R . microb_initial_guess_lasso.R . model_selection.R . model_selection_PGMM.R . model_selection_lasso.R . plnmfa.R . Full lnmCluster package functions and examples
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