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

8.25 score 17 stars 3 packages 117 scripts 694 downloads 22 exports 57 dependencies

Last updated from:b4cc83d539. Checks:7 ERROR, 8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR224
linux-devel-x86_64ERROR266
source / vignettesOK455
linux-release-arm64ERROR228
linux-release-x86_64ERROR247
macos-release-arm64ERROR200
macos-release-x86_64ERROR500
macos-oldrel-arm64ERROR299
macos-oldrel-x86_64OK440
windows-devel-arm64OK283
windows-devel-x86_64OK211
windows-release-arm64OK271
windows-release-x86_64OK174
windows-oldrel-x86_64OK166
wasm-releaseOK121

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