Package: DirichletMultinomial 1.55.0

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 This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial.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]

DirichletMultinomial_1.55.0.tar.gz
DirichletMultinomial_1.55.0.zip(r-4.7)DirichletMultinomial_1.55.0.zip(r-4.6)DirichletMultinomial_1.55.0.zip(r-4.5)
DirichletMultinomial_1.55.0.tgz(r-4.6-x86_64)DirichletMultinomial_1.55.0.tgz(r-4.6-arm64)DirichletMultinomial_1.55.0.tgz(r-4.5-x86_64)DirichletMultinomial_1.55.0.tgz(r-4.5-arm64)
DirichletMultinomial_1.55.0.tar.gz(r-4.7-arm64)DirichletMultinomial_1.55.0.tar.gz(r-4.7-x86_64)DirichletMultinomial_1.55.0.tar.gz(r-4.6-arm64)DirichletMultinomial_1.55.0.tar.gz(r-4.6-x86_64)
DirichletMultinomial_1.55.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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/docs 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-1.55.0(bioc 3.24)DirichletMultinomial-1.54.0(bioc 3.23)

immunooncologymicrobiomesequencingclusteringclassificationmetagenomicsgsl

10.94 score 12 stars 27 packages 154 scripts 9.3k downloads 9 mentions 16 exports 4 dependencies

Last updated from:15e0e5b4fb. Checks:4 WARNING, 8 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING147
linux-devel-arm64NOTE149
linux-devel-x86_64NOTE174
source / vignettesOK183
linux-release-arm64NOTE173
linux-release-x86_64NOTE166
macos-release-arm64NOTE118
macos-release-x86_64NOTE249
macos-oldrel-arm64NOTE118
macos-oldrel-x86_64NOTE287
windows-develWARNING116
windows-releaseWARNING99
windows-oldrelWARNING100
wasm-releaseOK122

Exports:AICBICcsubsetcvdmngroupdmndmngroupfittedgoodnessOfFitheatmapdmnlaplacemixturemixturewtpredictrocshowsummary

Dependencies:BiocGenericsgenericsIRangesS4Vectors

DirichletMultinomial for Clustering and Classification of Microbiome Data

Martin Morgan

Rendered fromDirichletMultinomial.Rmdusingknitr::rmarkdownon May 30 2026.

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