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 |