All functions
bayesImageS	Package bayesImageS
exactPotts	Calculate the distribution of the Potts model using a brute force algorithm.
getBlocks	Get Blocks of a Graph
getEdges	Get Edges of a Graph
getNeighbors	Get Neighbours of All Vertices of a Graph
gibbsGMM	Fit a mixture of Gaussians to the observed data.
gibbsNorm	Fit a univariate normal (Gaussian) distribution to the observed data.
initSedki	Initialize the ABC algorithm using the method of Sedki et al. (2013)
mcmcPotts	Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.
mcmcPottsNoData	Simulate pixel labels using chequerboard Gibbs sampling.
smcPotts	Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).
sufficientStat	Calculate the sufficient statistic of the Potts model for the given labels.
swNoData	Simulate pixel labels using the Swendsen-Wang algorithm.
testResample	Test the residual resampling algorithm.