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)
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noisySBM_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
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:

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

2.00 score 3 scripts 219 downloads 7 exports 18 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	151
source / vignettes	OK	132
linux-release-x86_64	OK	133
macos-release-arm64	OK	123
macos-oldrel-arm64	OK	89
windows-devel	OK	88
windows-release	OK	83
windows-oldrel	OK	79
wasm-release	OK	101

Exports:ARI fitNSBM getBestQ graphInference plotGraphs plotICL rnsbm

Dependencies:cli cpp11 farver ggplot2 glue gtable gtools isoband labeling lifecycle R6 RColorBrewer rlang S7 scales vctrs viridisLite withr

User guide for the noisySBM package

Rendered fromUserGuide.Rmdusingknitr::rmarkdown

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

Help page	Topics
split group q of provided tau randomly into two into	addRowToTau
Evalute the adjusted Rand index	ARI
convert a clustering into a 0-1-matrix	classInd
transform a pair of block identifiers (q,l) into an identifying integer	convertGroupPair
takes a scalar indice of a group pair (q,l) and returns the values q and l	convertGroupPairIdentifier
transform a pair of nodes (i,j) into an identifying integer	convertNodePair
corrects values of the variational parameters tau that are too close to the 0 or 1	correctTau
compute the MLE in the Gamma model using the Newton-Raphson method	emv_gamma
VEM algorithm to adjust the noisy stochastic block model to an observed dense adjacency matrix	fitNSBM
optimal number of SBM blocks	getBestQ
compute rho associated with given values of w, nu0 and nu	getRho
Evaluate tau_q*tau_l in the noisy stochastic block model	getTauql
new graph inference procedure	graphInference
computation of the Integrated Classification Likelihood criterion	ICL_Q
compute a list of initial points for the VEM algorithm	initialPoints
Construct initial values with Q groups by meging groups of a solution obtained with Q+1 groups	initialPointsByMerge
Construct initial values with Q groups by splitting groups of a solution obtained with Q-1 groups	initialPointsBySplit
compute initial values of rho	initialRho
compute intial values for tau	initialTau
evaluate the objective in the Gamma model	J.gamma
evaluation of the objective in the Gauss model	JEvalMstep
returns a list of all possible node pairs (i,j)	listNodePairs
compute conditional l-values in the noisy stochastic block model	lvaluesNSBM
main function of VEM algorithm with fixed number of SBM blocks	mainVEM_Q
main function of VEM algorithm for fixed number of latent blocks in parallel computing	mainVEM_Q_par
evaluate the density in the current model	modelDensity
M-step	Mstep
plot the data matrix, the inferred graph and/or the true binary graph	plotGraphs
plot ICL curve	plotICL
auxiliary function for the computation of q-values	q_delta_ql
compute q-values in the noisy stochastic block model	qvaluesNSBM
Output of fitNSBM() on a dataset applied in the exponential NSBM	res_exp
Output of fitNSBM() on a dataset applied in the Gamma NSBM	res_gamma
Output of fitNSBM() on a dataset applied in the Gaussian NSBM	res_gauss
simulation of a graph according the noisy stochastic block model	rnsbm
spectral clustering with absolute values	spectralClustering
Create new initial values by merging pairs of groups of provided tau	tauDown
Create new values of tau by splitting groups of provided tau	tauUp
Compute one iteration to solve the fixed point equation in the VE-step	tauUpdate
Perform one iteration of the Newton-Raphson to compute the MLE of the parameters of the Gamma distribution	update_newton_gamma
VE-step	VEstep

Package: noisySBM 0.1.4

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

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)