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)sbm_0.4.7.zip(r-4.6)sbm_0.4.7.zip(r-4.5)
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

8.08 score 17 stars 2 packages 117 scripts 704 downloads 22 exports 57 dependencies

Last updated from:a21af071cc. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK294
linux-devel-x86_64OK312
source / vignettesOK291
linux-release-arm64OK299
linux-release-x86_64OK311
macos-release-arm64OK223
macos-release-x86_64OK522
macos-oldrel-arm64OK221
macos-oldrel-x86_64OK578
windows-develOK187
windows-releaseOK189
windows-oldrelOK192
wasm-releaseOK204

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