rdata.hmm<-function(NUM, pii, A, f0, pc, f1)
{

## USAGE
 # rdata.hmm(n, pi, A, ...)

## ARGUEMENTS
 # NUM: sample size
 # pii=(pii[1], pii[2]): initial state distribution 
 # A=(a00, a01 \\ a10 a11): transition matrix
 # f0: parameter set for the null distribution
 # f1: parameter set for the non-null mixture distribution
 # pc: probabilities weights for the elements in the mixture

## DETAILS
 # xi are independent given si, which constitute a markov chain characterized
 # --by initial state distribution pi0 and transition matrix A

## VALUES
 # hmm.nmxdata generates random variables 
 # from a normal mixture via a hidden markov model
 # x: continuous observed data
 # theta: binary unobserved states

theta<-rep(0, NUM)
x<-rep(0, NUM)
nc<-length(pc)

if (nc==1)
{ 
  data<-rdata1.hmm (NUM, pii, A, f0, f1)
}    

else
{
## generating the states
 # initial state
theta[1]<-rbinom(1, 1, pii[2])
 # other states
for (i in 2:NUM)
{
  if (theta[i-1]==0)
     theta[i]<-rbinom(1, 1, A[1, 2])
  else
     theta[i]<-rbinom(1, 1, A[2, 2])
}

## generating the observations
for (i in 1:NUM)
{
  if (theta[i]==0)
  {
    mu0<-f0[1]
    sd0<-f0[2]
    x[i]<-rnorm(1, mean=mu0, sd=sd0)
  }
  else
  { 
    c<-sample(1:nc, 1, prob=pc)
    mu1<-f1[c, 1]
    sd1<-f1[c, 2]
    x[i]<-rnorm(1, mean=mu1, sd=sd1)
  }
}
data<-list(s=theta, o=x)
}

return (data)

}