.PMC.CT                 A Set of Controls in Model-Based Clustering.
.pmclustEnv             Set Global Variables According to the global
                        matrix X.gbd (X.spmd)
PARAM                   A Set of Parameters in Model-Based Clustering.
assign.N.sample         Obtain a Set of Random Samples for X.spmd
e.step                  Compute One E-step and Log Likelihood Based on
                        Current Parameters
em.onestep              One EM Step for GBD
em.step                 EM-like Steps for GBD
em.update.class         Update CLASS.spmd Based on the Final Iteration
generate.MixSim         Generate MixSim Examples for Testing
generate.basic          Generate Examples for Testing
get.N.CLASS             Obtain Total Elements for Every Clusters
indep.logL              Independent Function for Log Likelihood
initial.RndEM           Initialization for EM-like Algorithms
m.step                  Compute One M-Step Based on Current Posterior
                        Probabilities
mb.print                Print Results of Model-Based Clustering
pmclust                 Parallel Model-Based Clustering and Parallel
                        K-means Algorithm
pmclust-package         Parallel Model-Based Clustering
print.pmclust           Functions for Printing or Summarizing Objects
                        According to Classes
readme                  Read Me First Function
