.EMControl()
.show.option()

EMC.1 <- .EMC
EMC.1$substitution.model <- "HKY85"
EMC.1$identifier <- "EV"
EMC.2 <- .EMControl(substitution.model = "HKY85",
                    identifier = "EV")

EMC.3 <- .EMC
EMC.3$substitution.model <- "SNP_JC69"
EMC.3$edist.model <- "HAMMING"
EMC.3$code.type <- "SNP"
EMC.4 <- .EMControl(substitution.model = "SNP_JC69",
                    edist.model = "HAMMING",
                    code.type = "SNP")

EMC.5 <- .EMControl(init.procedure = "RndEM",
                    init.method = "randomNJ",
                    substitution.model = "K80",
                    identifier = "EV",
                    em.method = "ACEM")

EMC.6 <- .EMControl(EM.eps = 1e-8,
                    nm.abstol.Mu.given.QA = 1e-16,
                    nm.reltol.Mu.given.QA = 1e-8,
                    nm.maxit.Mu.given.QA = 500,
                    nm.abstol.QA.given.Mu = 1e-16,
                    nm.reltol.QA.given.Mu = 1e-8,
                    nm.maxit.QA.given.Mu = 500)

<- This gives a return object as .EMC.
<- This prints all options. 

<- These 3 lines provide EMC.1 which is
exactly the same as EMC.2 using "HKY85"
for the Q matrix, and time t is varied for
clusters. EMC.1 and EMC.2 can be used
in phyclust() or find.best().

<- These 4 lines provide EMC.3 which is
exactly the same as EMC.4 using "SNP_JC69"
for the Q matrix, HAMMING distance for
initialization, and the data should be
SNP sequences.



<- This line changes more options
including initialization procedure,
initialization method, model for
the Q matrix, identifier, and the EM
algorithm.

<- This line uses a convergent condition
looser than the default, and originally
conditions in optim() for the Nelder-Mead
method.