move.HMM.simulate: Simulate data from a hidden markov model
In benaug/move.HMM: Fit HMM and HSMM animal movement models

Description Usage Arguments Value Examples

This function simulates data from a user-specified hidden markov model

1	move.HMM.simulate(dists, params, n, delta = NULL)

`dists`	A length ndist vector of distributions from the following list: weibull, gamma, exponential, normal, lognormal, lnorm3, posnorm, invgamma, rayleigh, f, ncf, dagum, frechet, beta, binom, poisson, nbinom, zapois, wrpcauchy, wrpnorm
`params`	a list of length ndist+1 containing matrices of starting parameter values. The first element of the list must be the starting values for the transition matrix. If any distributions only have 1 parameter, the list entry must be a nstates x 1 matrix.
`n`	The length of the hidden markov model to be simulated
`delta`	an optional vector of starting state probabilities. If no vector is supplied, the stationary distribution is used.

The observations from the simulated HMM and the true state sequence

## Not run: 
#2 states, 2 dist-lognorm, wrapped normal
lmean=c(-3,-1) #meanlog parameters
sd=c(1,1) #sdlog parameters
rho<-c(0.2,0.3) # wrapped normal concentration parameters
mu<-c(pi,0) # wrapped normal mean parameters
gamma0=matrix(c(0.6,0.4,0.2,0.8),byrow=T,nrow=2)

dists=c("lognormal","wrpnorm")
nstates=2
turn=c(1,2)
params=vector("list",3)
params[[1]]=gamma0
params[[2]]=cbind(lmean,sd)
params[[3]]=cbind(mu,rho)
obs=move.HMM.simulate(dists,params,1000)
#see more examples in move.HMM.mle

## End(Not run)