Discrete Time HMM - Deprecated Functions
Baum.Welch(x, Pi, delta, distn, pm, pn = NULL, nonstat = TRUE, maxiter = 500, tol = 1e-05, prt = TRUE, posdiff = (distn[1]!="glm")) residualshmm(x, Pi, delta, distn, pm, pn = NULL, discrete = FALSE) sim.hmm(n, initial, Pi, distn, pm, pn = NULL) sim.hmm1(n, initial, Pi, distn, pm) sim.markov(n, initial, Pi) Viterbihmm(x, Pi, delta, distn, pm, pn = NULL)
x |
is a vector of length n containing the observed process. |
n |
length of process. |
initial |
integer, being the initial hidden Markov state (1, \cdots, m). |
Pi |
is the m*m transition probability matrix of the hidden Markov chain. |
delta |
is the marginal probability distribution of the m hidden states at the first time point. |
distn |
is a character string with the distribution name, e.g. |
pm |
is a list object containing the (Markov dependent) parameter values associated with the distribution of the observed process (see |
pn |
is a list object containing the observation dependent parameter values associated with the distribution of the observed process (see |
discrete |
is logical, and is |
nonstat |
is logical, |
maxiter |
is the maximum number of iterations, default is 500. |
tol |
is the convergence criterion, being the difference between successive values of the log-likelihood; default is 0.00001. |
prt |
is logical, and determines whether information is printed at each iteration; default is |
posdiff |
is logical, and determines whether the iterative process stops if a negative log-likelihood difference occurs. |
The function sim.hmm1
will run faster for cases where the argument pn
is NULL
.
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