Package: noisySBM 0.1.4

Tabea Rebafka

noisySBM: Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arxiv:1907.10176>.

Authors:Tabea Rebafka [aut, cre], Etienne Roquain [ctb], Fanny Villers [aut]

noisySBM_0.1.4.tar.gz
noisySBM_0.1.4.zip(r-4.7)noisySBM_0.1.4.zip(r-4.6)noisySBM_0.1.4.zip(r-4.5)
noisySBM_0.1.4.tgz(r-4.6-any)noisySBM_0.1.4.tgz(r-4.5-any)
noisySBM_0.1.4.tar.gz(r-4.7-any)noisySBM_0.1.4.tar.gz(r-4.6-any)
noisySBM_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
noisySBM/json (API)

# Install 'noisySBM' in R:
install.packages('noisySBM', repos = c('https://tabea17.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • res_exp - Output of fitNSBM() on a dataset applied in the exponential NSBM
  • res_gamma - Output of fitNSBM() on a dataset applied in the Gamma NSBM
  • res_gauss - Output of fitNSBM() on a dataset applied in the Gaussian NSBM

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 2 scripts 200 downloads 7 exports 18 dependencies

Last updated from:2ae46a0440. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK129
source / vignettesOK175
linux-release-x86_64OK113
macos-release-arm64OK183
macos-oldrel-arm64OK137
windows-develOK80
windows-releaseOK87
windows-oldrelOK87
wasm-releaseOK99

Exports:ARIfitNSBMgetBestQgraphInferenceplotGraphsplotICLrnsbm

Dependencies:clicpp11farverggplot2gluegtablegtoolsisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

User guide for the noisySBM package

Tabea Rebafka

Rendered fromUserGuide.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2020-12-16
Started: 2020-12-16

Readme and manuals

Help Manual

Help pageTopics
split group q of provided tau randomly into two intoaddRowToTau
Evalute the adjusted Rand indexARI
convert a clustering into a 0-1-matrixclassInd
transform a pair of block identifiers (q,l) into an identifying integerconvertGroupPair
takes a scalar indice of a group pair (q,l) and returns the values q and lconvertGroupPairIdentifier
transform a pair of nodes (i,j) into an identifying integerconvertNodePair
corrects values of the variational parameters tau that are too close to the 0 or 1correctTau
compute the MLE in the Gamma model using the Newton-Raphson methodemv_gamma
VEM algorithm to adjust the noisy stochastic block model to an observed dense adjacency matrixfitNSBM
optimal number of SBM blocksgetBestQ
compute rho associated with given values of w, nu0 and nugetRho
Evaluate tau_q*tau_l in the noisy stochastic block modelgetTauql
new graph inference proceduregraphInference
computation of the Integrated Classification Likelihood criterionICL_Q
compute a list of initial points for the VEM algorithminitialPoints
Construct initial values with Q groups by meging groups of a solution obtained with Q+1 groupsinitialPointsByMerge
Construct initial values with Q groups by splitting groups of a solution obtained with Q-1 groupsinitialPointsBySplit
compute initial values of rhoinitialRho
compute intial values for tauinitialTau
evaluate the objective in the Gamma modelJ.gamma
evaluation of the objective in the Gauss modelJEvalMstep
returns a list of all possible node pairs (i,j)listNodePairs
compute conditional l-values in the noisy stochastic block modellvaluesNSBM
main function of VEM algorithm with fixed number of SBM blocksmainVEM_Q
main function of VEM algorithm for fixed number of latent blocks in parallel computingmainVEM_Q_par
evaluate the density in the current modelmodelDensity
M-stepMstep
plot the data matrix, the inferred graph and/or the true binary graphplotGraphs
plot ICL curveplotICL
auxiliary function for the computation of q-valuesq_delta_ql
compute q-values in the noisy stochastic block modelqvaluesNSBM
Output of fitNSBM() on a dataset applied in the exponential NSBMres_exp
Output of fitNSBM() on a dataset applied in the Gamma NSBMres_gamma
Output of fitNSBM() on a dataset applied in the Gaussian NSBMres_gauss
simulation of a graph according the noisy stochastic block modelrnsbm
spectral clustering with absolute valuesspectralClustering
Create new initial values by merging pairs of groups of provided tautauDown
Create new values of tau by splitting groups of provided tautauUp
Compute one iteration to solve the fixed point equation in the VE-steptauUpdate
Perform one iteration of the Newton-Raphson to compute the MLE of the parameters of the Gamma distributionupdate_newton_gamma
VE-stepVEstep