Package: sbm 0.4.7

Julien Chiquet

sbm: Stochastic Blockmodels

A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.

Authors:Julien Chiquet [aut, cre], Sophie Donnet [aut], großBM team [ctb], Pierre Barbillon [aut]

sbm_0.4.7.tar.gz
sbm_0.4.7.zip(r-4.7-x86_64)sbm_0.4.7.zip(r-4.7-arm64)sbm_0.4.7.zip(r-4.6-x86_64)sbm_0.4.7.zip(r-4.6-arm64)sbm_0.4.7.zip(r-4.5-x86_64)
sbm_0.4.7.tgz(r-4.6-x86_64)sbm_0.4.7.tgz(r-4.6-arm64)sbm_0.4.7.tgz(r-4.5-x86_64)sbm_0.4.7.tgz(r-4.5-arm64)
sbm_0.4.7.tar.gz(r-4.7-arm64)sbm_0.4.7.tar.gz(r-4.7-x86_64)sbm_0.4.7.tar.gz(r-4.6-arm64)sbm_0.4.7.tar.gz(r-4.6-x86_64)
sbm_0.4.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sbm/json (API)

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

Bug tracker:https://github.com/grosssbm/sbm/issues

Pkgdown/docs site:https://grosssbm.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

network-analysissbmstochastic-block-modelcpp

7.29 score 17 stars 3 packages 1.3k downloads 22 exports 57 dependencies

Last updated from:b4cc83d539. Checks:15 OK. Indexed: yes.

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linux-devel-x86_64OK275
source / vignettesOK324
linux-release-arm64OK286
linux-release-x86_64OK272
macos-release-arm64OK237
macos-release-x86_64OK651
macos-oldrel-arm64OK260
macos-oldrel-x86_64OK680
windows-devel-arm64OK402
windows-devel-x86_64OK216
windows-release-arm64OK399
windows-release-x86_64OK180
windows-oldrel-x86_64OK181
wasm-releaseOK143

Exports:%>%BipartiteSBMBipartiteSBM_fitdefineSBMestimateBipartiteSBMestimateMultipartiteSBMestimateMultiplexSBMestimateSimpleSBMis_SBMMultipartiteSBMMultipartiteSBM_fitMultiplexSBM_fitplotAlluvialplotMyMatrixplotMyMultipartiteMatrixplotMyMultiplexMatrixsampleBipartiteSBMsampleMultipartiteSBMsampleMultiplexSBMsampleSimpleSBMSimpleSBMSimpleSBM_fit

Dependencies:alluvialaricodeblockmodelsclicodetoolscpp11data.tablediagramdigestdplyrfarverfuturefuture.applygenericsggplot2globalsglueGREMLINSgtableigraphisobandKernSmoothlabelinglatticelavalifecyclelistenvmagrittrMatrixnumDerivparallellypbmcapplypillarpkgconfigplyrprodlimprogressrpurrrR6RColorBrewerRcppRcppArmadilloreshape2rlangS7scalesshapeSQUAREMstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

Stochastic Block Models for Multiplex networks
Preliminaries | Requirements | Data set | Data manipulation | Fitting a multiplex SBM model where the two layers are assumed to be independent | Fitting a multiplex SBM model where the two layers are assumed to be dependent | References
Rendered fromMultiplex_allianceNwar_case_study.Rmdusingknitr::rmarkdown

Last update: 2023-01-07
Started: 2021-05-19

Multipartite Stochastic Block Models
Preliminaries | Requirements | Dataset | Formatting the data | Mathematical Background | Inference | Plots | References
Rendered fromMultipartite_EcologicalNetwork.Rmdusingknitr::rmarkdown

Last update: 2022-09-12
Started: 2020-12-18

Simple and Bipartite Stochastic Block Models
Preliminaries | Requirements | Data set: antagonistic tree/fungus interaction network | Mathematical Background | Analysis of the tree/tree data | Tree-tree binary interaction networks | About model selection and choice of the number of blocks | Analysis of the weighted interaction network | Introduction of covariates | Analysis of the tree/fungi data | References
Rendered fromSBM_fungus_tree_network.Rmdusingknitr::rmarkdown

Last update: 2022-09-12
Started: 2020-06-25

Stochastic Block Models for Multiplex networks
Preliminaries | Requirements | Multiplex network data | Stochastic Block models for multiplex networks | General formulation of the model | Dependent and independent layers conditionally to $Z$ | Bipartite multiplex networks | Inference | Implementation | Data simulation | References
Rendered fromMultiplexNetwork_principle.Rmdusingknitr::rmarkdown

Last update: 2022-09-12
Started: 2021-05-05