R: Inductive Node-Splitting Cross-Validation for Community Detection

# Inductive Node-Splitting Cross-Validation for Community Detection

## Documentation for package ‘INCVCommunityDetection’ version 0.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

AUC | Negative AUC for matrix predictions
best.perm.label.match | Find the best permutation label matching
bin.dev | Binomial deviance loss
blockmodel.gen.fast | Generate a fast SBM or DCBM network (sparse)
community.sim | Simulate a Stochastic Block Model network
community.sim.sbm | Simulate an SBM with distance-decaying block probabilities
croissant.blockmodel | CROISSANT for blockmodel selection
croissant.latent | CROISSANT for latent space model dimension selection
croissant.rdpg | CROISSANT for RDPG rank selection
croissant.tune.regsp | CROISSANT for regularisation parameter tuning in spectral methods
ECV.for.blockmodel | Edge Cross-Validation for blockmodel selection
ECV.undirected.Rank | Edge Cross-Validation for RDPG rank selection
edge.index.map | Map a linear edge index to row-column indices in the upper triangle
eigen.DCBM.est | Eigenvector-based DCBM estimation
fast.DCBM.est | Fast DCBM parameter estimation
fast.SBM.est | Fast SBM block probability estimation
l2 | L2 loss between two matrices
latent.gen | Generate a latent space network
matched.lab | Apply label permutation to match reference
NCV.for.blockmodel | Node Cross-Validation for blockmodel selection
neglog | Safe negative log-likelihood term
nscv.f.fold | Inductive Node-Splitting Cross-Validation with f-fold splitting
nscv.random.split | Inductive Node-Splitting Cross-Validation with random node splits
SBM.prob | Estimate SBM connection probabilities and negative log-likelihood
SBM.spectral.clustering | Spectral clustering for a Stochastic Block Model
sparse.RDPG.gen | Generate a sparse RDPG network