These functions are deprecated and will ultimately be removed from the package. Please change to the object orientated versions: BaumWelch, residuals, simulate or Viterbi.
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)is a vector of length \(n\) containing the observed process.
length of process.
integer, being the initial hidden Markov state \((1, \cdots, m)\).
is the \(m \times m\) transition probability matrix of the hidden Markov chain.
is the marginal probability distribution of the \(m\) hidden states at the first time point.
is a list object containing the (Markov dependent) parameter values associated with the distribution of the observed process (see dthmm).
is a list object containing the observation dependent parameter values associated with the distribution of the observed process (see dthmm).
is logical, and is TRUE if distn is a discrete distribution.
is logical, TRUE if the homogeneous Markov chain is assumed to be non-stationary, default. See “Details” below.
is the maximum number of iterations, default is 500.
is the convergence criterion, being the difference between successive values of the log-likelihood; default is 0.00001.
is logical, and determines whether information is printed at each iteration; default is TRUE.
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.