Package: DirichletMultinomial 1.49.0

Martin Morgan

DirichletMultinomial: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data

Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial.

Authors:Martin Morgan [aut, cre]

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DirichletMultinomial.pdf |DirichletMultinomial.html
DirichletMultinomial/json (API)

# Install 'DirichletMultinomial' in R:
install.packages('DirichletMultinomial', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mtmorgan/dirichletmultinomial/issues

Pkgdown site:https://mtmorgan.github.io

Uses libs:
  • gsl– GNU Scientific Library (GSL)
Datasets:
  • bestgrp - Data objects used for examples and the vignette
  • fit - Data objects used for examples and the vignette
  • xval - Data objects used for examples and the vignette

On BioConductor:DirichletMultinomial-1.49.0(bioc 3.21)DirichletMultinomial-1.48.0(bioc 3.20)

immunooncologymicrobiomesequencingclusteringclassificationmetagenomicsgsl

10.97 score 11 stars 26 packages 125 scripts 9.2k downloads 9 mentions 16 exports 4 dependencies

Last updated 4 months agofrom:776fd59c4b. Checks:1 OK, 7 NOTE, 3 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 17 2025
R-4.5-win-x86_64WARNINGFeb 17 2025
R-4.5-mac-x86_64NOTEFeb 17 2025
R-4.5-mac-aarch64NOTEFeb 17 2025
R-4.5-linux-x86_64NOTEFeb 17 2025
R-4.4-win-x86_64WARNINGFeb 17 2025
R-4.4-mac-x86_64NOTEFeb 17 2025
R-4.4-mac-aarch64NOTEFeb 17 2025
R-4.3-win-x86_64WARNINGFeb 17 2025
R-4.3-mac-x86_64NOTEFeb 17 2025
R-4.3-mac-aarch64NOTEFeb 17 2025

Exports:AICBICcsubsetcvdmngroupdmndmngroupfittedgoodnessOfFitheatmapdmnlaplacemixturemixturewtpredictrocshowsummary

Dependencies:BiocGenericsgenericsIRangesS4Vectors

DirichletMultinomial for Clustering and Classification of Microbiome Data

Rendered fromDirichletMultinomial.Rmdusingknitr::rmarkdownon Feb 17 2025.

Last update: 2024-10-19
Started: 2024-10-19

Readme and manuals

Help Manual

Help pageTopics
Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome DataDirichletMultinomial-package
Cross-validation on Dirichlet-Multinomial classifiers.cvdmngroup
Data objects used for examples and the vignettebestgrp fit xval
Fit Dirichlet-Multinomial models to count data.dmn
Class '"DMN"'DMN-class
Dirichlet-Multinomial generative classifiers.dmngroup
Class '"DMNGroup"'DMNGroup-class
Heatmap representation of samples assigned to Dirichlet components.heatmapdmn
Access model components.AIC,DMN-method BIC,DMN-method fitted,DMN-method fitted,DMNGroup-method goodnessOfFit laplace mixture mixturewt predict,DMN-method predict,DMNGroup-method show,DMN-method show,DMNGroup-method summary,DMNGroup-method
Summarize receiver-operator characteristicsroc
Helpful utility functionscsubset