pmclust-package | Parallel Model-Based Clustering |
.PMC.CT | A Set of Controls in Model-Based Clustering. |
.pmclustEnv | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
aecm.step | EM-like Steps for GBD |
apecm.step | EM-like Steps for GBD |
apecma.step | EM-like Steps for GBD |
as.dmat | Convert between X.gbd (X.spmd) and X.dmat |
as.gbd | Convert between X.gbd (X.spmd) and X.dmat |
as.spmd | Convert between X.gbd (X.spmd) and X.dmat |
assign.N.sample | Obtain a Set of Random Samples for X.spmd |
CHECK | Read Me First Function |
CLASS.dmat | Read Me First Function |
CLASS.spmd | Read Me First Function |
COMM.RANK | Read Me First Function |
COMM.SIZE | Read Me First Function |
CONTROL | A Set of Controls in Model-Based Clustering. |
e.step | Compute One E-step and Log Likelihood Based on Current Parameters |
e.step.dmat | Compute One E-step and Log Likelihood Based on Current Parameters |
em.onestep | One EM Step for GBD |
em.onestep.dmat | One EM Step for GBD |
em.step | EM-like Steps for GBD |
em.step.dmat | EM-like Steps for GBD |
em.update.class | Update CLASS.spmd Based on the Final Iteration |
em.update.class.dmat | Update CLASS.spmd Based on the Final Iteration |
ETA | A Set of Parameters in Model-Based Clustering. |
generate.basic | Generate Examples for Testing |
generate.MixSim | Generate MixSim Examples for Testing |
get.CLASS | Obtain Total Elements for Every Clusters |
get.N.CLASS | Obtain Total Elements for Every Clusters |
get.N.CLASS.dmat | Obtain Total Elements for Every Clusters |
indep.logL | Independent Function for Log Likelihood |
indep.logL.dmat | Independent Function for Log Likelihood |
initial.center | Initialization for EM-like Algorithms |
initial.center.dmat | Initialization for EM-like Algorithms |
initial.em | Initialization for EM-like Algorithms |
initial.em.dmat | Initialization for EM-like Algorithms |
initial.RndEM | Initialization for EM-like Algorithms |
initial.RndEM.dmat | Initialization for EM-like Algorithms |
kmeans.step | EM-like Steps for GBD |
kmeans.step.dmat | EM-like Steps for GBD |
kmeans.update.class | Update CLASS.spmd Based on the Final Iteration |
kmeans.update.class.dmat | Update CLASS.spmd Based on the Final Iteration |
m.step | Compute One M-Step Based on Current Posterior Probabilities |
m.step.dmat | Compute One M-Step Based on Current Posterior Probabilities |
mb.print | Print Results of Model-Based Clustering |
MU | A Set of Parameters in Model-Based Clustering. |
p.times.logtwopi | Read Me First Function |
PARAM | A Set of Parameters in Model-Based Clustering. |
PARAM.org | A Set of Parameters in Model-Based Clustering. |
pkmeans | Parallel Model-Based Clustering and Parallel K-means Algorithm |
pmclust | Parallel Model-Based Clustering and Parallel K-means Algorithm |
print.pkmeans | Functions for Printing or Summarizing Objects According to Classes |
print.pmclust | Functions for Printing or Summarizing Objects According to Classes |
readme | Read Me First Function |
readme.dmat | Read Me First Function |
SAVE.iter | Read Me First Function |
SAVE.param | Read Me First Function |
set.global | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
set.global.dmat | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
set.global.gbd | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
SIGMA | A Set of Parameters in Model-Based Clustering. |
U.dmat | Read Me First Function |
U.spmd | Read Me First Function |
W.dmat | Read Me First Function |
W.dmat.rowSums | Read Me First Function |
W.spmd | Read Me First Function |
W.spmd.rowSums | Read Me First Function |
X.dmat | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
X.gbd | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
X.spmd | Set Global Variables According to the global matrix X.gbd (X.spmd) or X.dmat |
Z.colSums | Read Me First Function |
Z.dmat | Read Me First Function |
Z.spmd | Read Me First Function |