Man pages for pmclust
Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model
| 00_pmclust-package | Parallel Model-Based Clustering |
| 01-pmclust_pkmeans | Parallel Model-Based Clustering and Parallel K-means... |
| 10_d.readme | Read Me First Function |
| 11_d.set.global | Set Global Variables According to the global matrix X.gbd... |
| 12_d.param | A Set of Parameters in Model-Based Clustering. |
| 13_d.control | A Set of Controls in Model-Based Clustering. |
| 20-assign.N.sample | Obtain a Set of Random Samples for X.spmd |
| 30-em_initial | Initialization for EM-like Algorithms |
| 30-em_like | EM-like Steps for GBD |
| 30-em.one | One EM Step for GBD |
| 30-em.one.e | Compute One E-step and Log Likelihood Based on Current... |
| 30-em.one.m | Compute One M-Step Based on Current Posterior Probabilities |
| 40-generate.basic | Generate Examples for Testing |
| 40-generate.MixSim | Generate MixSim Examples for Testing |
| 41-get.N.CLASS | Obtain Total Elements for Every Clusters |
| 50-indep.logL | Independent Function for Log Likelihood |
| 50-mb.print | Print Results of Model-Based Clustering |
| 50-update.class | Update CLASS.spmd Based on the Final Iteration |
| 60-print | Functions for Printing or Summarizing Objects According to... |
| zz-internal | All Internal Functions |
