1\name{asymptoticCov}
2\alias{asymptoticCov}
3\title{Asymptotic covariance matrix of the HMM parameters}
4\description{This function calculates the empirical asymptotic covariance matrix of the HMM parameters}
5\usage{
6asymptoticCov(HMM, obs)
7}
8\arguments{
9    \item{HMM}{A HMMClass or a HMMFitClass object}
10    \item{obs}{The vector, matrix, data frame, list of vectors or list of matrices of observations}
11}
12\value{A matrix}
13
14\section{Numerical computations}{
15   The Information matrix (of the independent parameters) is computed using the Lystig and Hugues's algorithm. Then the covariance matrix is computed by inversion of this information matrix.
16}
17
18\examples{
19  data(n1d_3s)
20  Res_n1d_3s<-HMMFit(obs_n1d_3s, nStates=3)
21  covMat <- asymptoticCov(Res_n1d_3s, obs_n1d_3s)
22}
23
24\references{
25     Lystig Theodore C. and Hugues James P. (2002) \emph{Exact Computation of the Observed Information Matrix for Hidden Markov Models}, Journal of Computational and Graphical Statistics, Vol. 11, No 3, 678-689.
26
27}
28
29\seealso{HMMFit}
30