Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

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Documentation for package ‘noisySBM’ version 0.1.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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