# Executing the EM algorithm:
fit <- hsmm(sim$obs, od = "norm", rd = "log",
pi.par = pipar, tpm.par = tpmpar,
od.par = odpar, rd.par = rdpar)
# The log-likelihood:
fit$logl
# Ehe estimated parameters:
fit$para
# For comparison, the estimated parameters seperately together with the true parameter values
# ar given below.
# Transition probability matrix:
tpmpar
fit$para$tpm
# Observation distribution:
odpar
fit$para$od
# Runlength distribution:
rdpar
fit$para$rd
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