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