```
# Construct a 2 hidden states DCMM for the pewee data
# with hidden order set to 2 and visible order set to 1.
# The estimation procedure uses both the evolutionary algorithm (population size 2,
# one generation) and the Bauw-Welch algorithm (one iteration).
march.dcmm.construct(y=pewee,orderHC=2,orderVC=1,M=2,popSize=2,gen=1,iterBw=1,stopBw=0.0001)
# Same as above, but the DCMM is replaced by a HMM (the visible order OrderVC is set to zero).
HMM<-march.dcmm.construct(y=pewee,orderHC=2,orderVC=0,M=2,popSize=2,gen=1,iterBw=1,stopBw=0.0001)
# A first model is computed using both EA and Baum-Welch algorithms.
# The previous model is improved through two rounds of Baum-Welch optimization.
models <- list()
models[[length(models)+1]] <- HMM
models[[length(models)+1]] <- march.dcmm.construct(y=pewee,seedModel=models[[1]],
orderVC=0,iterBw=10,stopBw=0.001)
models[[length(models)+1]] <- march.dcmm.construct(y=pewee,seedModel=models[[2]],
orderVC=0,iterBw=10,stopBw=0.0001)
# Show performance indicators (ll, number of independent parameters,
# BIC and AIC) for all computed models.
r <- do.call(rbind,lapply(models,march.summary))
print(r)
# Construct a three hidden states, first-order HMM (hence OrderVC=0) for the sleep data.
# By setting gen=1 and popSize=1, the estimation procedure uses only the Baum-Welch algorithm.
HMM <- march.dcmm.construct(pewee,orderHC=1,orderVC=0,M=2,gen=1,popSize=1,iterBw=10,stopBw=0.0001)
```

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