Package: greed 0.6.1

Etienne Côme

greed: Clustering and Model Selection with the Integrated Classification Likelihood

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arxiv:2002:11577> for more details).

Authors:Etienne Côme [aut, cre], Nicolas Jouvin [aut]

greed_0.6.1.tar.gz
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greed.pdf |greed.html
greed/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/comeetie/greed/issues

Pkgdown:https://comeetie.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

3.31 score 41 scripts 252 downloads 48 exports 41 dependencies

Last updated 2 years agofrom:1f3b6d58ff. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-linux-x86_64NOTENov 19 2024

Exports:available_algorithmsavailable_modelsclusteringcoefCombinedModelscutDcLbmDcLbmPriorDcSbmDcSbmPriorDiagGmmDiagGmmPriorextractSubModelGeneticGmmgmmpairsGmmPriorgreedHHybridICLKLcaLcaPriorMIMoMMoMPriorMoRMoRPriorMultistartsMultSbmMultSbmPriorNMIplotpriorrdcsbmrlbmrlcarmmrmregrmultsbmrsbmSbmSbmPriorSeedshowspectralto_multinomial

Dependencies:cbaclicodetoolscolorspacedigestfansifarverfutureggplot2globalsgluegridExtragtableisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigproxyR6RColorBrewerRcppRcppArmadilloRcppEigenrlangRSpectrascalestibbleutf8vctrsviridisLitewithr

greed

Rendered fromgreed.Rmdusingknitr::rmarkdownon Nov 19 2024.

Last update: 2022-10-03
Started: 2022-03-18

Readme and manuals

Help Manual

Help pageTopics
Abstract optimization algorithm classAlg-class
Display the list of every currently available optimization algorithmavailable_algorithms
Display the list of every currently available DLVMavailable_models
Books about US politics network datasetBooks
Method to extract the clustering results from an 'IclFit-class' objectclustering clustering,IclFit-method
Extract parameters from an 'DcLbmFit-class' objectcoef,DcLbmFit-method
Extract parameters from an 'DcSbmFit-class' objectcoef,DcSbmFit-method
Extract mixture parameters from 'DiagGmmFit-class' objectcoef,DiagGmmFit-method
Extract mixture parameters from 'GmmFit-class' objectcoef,GmmFit-method
Extract parameters from an 'IclFit-class' objectcoef,IclFit-method
Extract parameters from an 'LcaFit-class' objectcoef,LcaFit-method
Extract parameters from an 'MoMFit-class' objectcoef,MoMFit-method
Extract mixture parameters from 'MoRFit-class' object using MAP estimationcoef,MoRFit-method
Extract parameters from an 'MultSbmFit-class' objectcoef,MultSbmFit-method
Extract parameters from an 'SbmFit-class' objectcoef,SbmFit-method
Combined Models classesCombinedModels CombinedModels-class
Combined Models fit results classCombinedModelsFit-class
Combined Models hierarchical fit results classCombinedModelsPath-class
Method to cut a DcLbmPath solution to a desired number of clustercut,DcLbmPath-method
Generic method to cut a path solution to a desired number of clustercut,IclPath-method
Degree Corrected Latent Block Model for bipartite graph classDcLbm DcLbm-class DcLbmPrior DcLbmPrior-class
Degree corrected Latent Block Model fit results classDcLbmFit-class
Degree corrected Latent Block Model hierarchical fit results classDcLbmPath-class
Degree Corrected Stochastic Block Model Prior classDcSbm DcSbm-class DcSbmPrior DcSbmPrior-class
Degree Corrected Stochastic Block Model fit results classDcSbmFit-class
Degree Corrected Stochastic Block Model hierarchical fit results classDcSbmPath-class
Diagonal Gaussian Mixture Model Prior description classDiagGmm DiagGmm-class DiagGmmPrior DiagGmmPrior-class
Diagonal Gaussian mixture model fit results classDiagGmmFit-class
Diagonal Gaussian mixture model hierarchical fit results classDiagGmmPath-class
Abstract class to represent a generative model for co-clusteringDlvmCoPrior-class
Abstract class to represent a generative model for clusteringDlvmPrior-class
Extract a part of a 'CombinedModelsPath-class' objectextractSubModel extractSubModel,CombinedModelsPath,character-method
Fashion mnist datasetfashion
Fifa dataFifa
American College football network datasetFootball
Genetic optimization algorithmGenetic Genetic-class
Gaussian Mixture Model Prior description classGmm Gmm-class GmmPrior GmmPrior-class
Gaussian mixture model fit results classGmmFit-class
Make a matrix of plots with a given data and gmm fitted parametersgmmpairs
Gaussian mixture model hierarchical fit results classGmmPath-class
Model based hierarchical clusteringgreed
Compute the entropy of a discrete sampleH
Hybrid optimization algorithmHybrid Hybrid-class
Generic method to extract the ICL value from an 'IclFit-class' objectICL ICL,IclFit-method
Abstract class to represent a clustering resultIclFit-class
Abstract class to represent a hierarchical clustering resultIclPath-class
Jazz musicians network datasetJazz
Generic method to get the number of clusters from an 'IclFit-class' objectK K,IclFit-method
Latent Class Analysis Model Prior classLca Lca-class LcaPrior LcaPrior-class
Latent Class Analysis fit results classLcaFit-class
Latent Class Analysis hierarchical fit results classLcaPath-class
Compute the mutual information of two discrete samplesMI
Mixture of Multinomial Model Prior description classMoM MoM-class MoMPrior MoMPrior-class
Mixture of Multinomial fit results classMoMFit-class
Mixture of Multinomial hierarchical fit results classMoMPath-class
Multivariate mixture of regression Prior model description classMoR MoR-class MoRPrior MoRPrior-class
Clustering with a multivariate mixture of regression model fit results classMoRFit-class
Multivariate mixture of regression model hierarchical fit results classMoRPath-class
Greedy algorithm with multiple start classMultistarts Multistarts-class
Multinomial Stochastic Block Model Prior classMultSbm MultSbm-class MultSbmPrior MultSbmPrior-class
Multinomial Stochastic Block Model fit results classMultSbmFit-class
Multinomial Stochastic Block Model hierarchical fit results classMultSbmPath-class
Mushroom datamushroom
Ndrangheta mafia covert network datasetNdrangheta
NewGuinea dataNewGuinea
Compute the normalized mutual information of two discrete samplesNMI
Plot a 'DcLbmFit-class'plot,DcLbmFit,missing-method
Plot a 'DcLbmPath-class'plot,DcLbmPath,missing-method
Plot a 'DcSbmFit-class' objectplot,DcSbmFit,missing-method
Plot a 'DiagGmmFit-class' objectplot,DiagGmmFit,missing-method
Plot a 'GmmFit-class' objectplot,GmmFit,missing-method
Plot an 'IclPath-class' objectplot,IclPath,missing-method
Plot a 'LcaFit-class' objectplot,LcaFit,missing-method
Plot a 'MoMFit-class' objectplot,MoMFit,missing-method
Plot a 'MultSbmFit-class' objectplot,MultSbmFit,missing-method
Plot a 'SbmFit-class' objectplot,SbmFit,missing-method
Generic method to extract the prior used to fit 'IclFit-class' objectprior prior,IclFit-method
Generates graph adjacency matrix using a degree corrected SBMrdcsbm
Generate a data matrix using a Latent Block Modelrlbm
Generate data from lca modelrlca
Generate data using a Multinomial Mixturermm
Generate data from a mixture of regression modelrmreg
Generate a graph adjacency matrix using a Stochastic Block Modelrmultsbm
Generate a graph adjacency matrix using a Stochastic Block Modelrsbm
Stochastic Block Model Prior classSbm Sbm-class SbmPrior SbmPrior-class
Stochastic Block Model fit results classSbmFit-class
Stochastic Block Model hierarchical fit results classSbmPath-class
Greedy algorithm with seeded initializationSeed Seed-class
SevenGraders dataSevenGraders
Show an IclPath objectshow,IclFit-method
Regularized spectral clusteringspectral
Convert a binary adjacency matrix with missing value to a cubeto_multinomial
Young People survey dataYoungpeoplesurvey