# Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

## 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