These functions are deprecated and will ultimately be removed from the package. Please change to the revised versions: BaumWelch, Estep.mmpp, forwardback.mmpp, simulate or logLik.
backward0.mmpp(tau, Q, lambda)
forward0.mmpp(tau, Q, delta, lambda)logLikmmpp(tau, Q, delta, lambda)
Estep0.mmpp(tau, Q, delta, lambda)
Baum.Welch.mmpp(tau, Q, delta, lambda, nonstat = TRUE,
maxiter = 500, tol = 1e-05, prt = TRUE,
converge = expression(diff < tol))
Baum.Welch0.mmpp(tau, Q, delta, lambda, nonstat = TRUE,
maxiter = 500, tol = 1e-05, prt = TRUE,
converge = expression(diff < tol))
sim.mmpp(n, initial, Q, lambda)
vector containing the interevent times. Note that the first event is at time zero.
the infinitesimal generator matrix of the Markov process.
a vector containing the Poisson rates.
is the marginal probability distribution of the \(m\) hidden states at time zero.
number of Poisson events to be simulated.
integer, being the initial hidden Markov state \((1, \cdots, m)\).
is logical, TRUE if the homogeneous Markov chain is assumed to be non-stationary, default.
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 an expression giving the convergence criterion.
The functions with a suffix of zero are non-scaled, and hence will have numerical problems for series containing larger numbers of events; and are much slower.
These functions use the algorithm given by Ryden (1996) based on eigenvalue decompositions.