coef.hmm_mcmc_normal    Extract Model Estimates
conf_mat                Calculate and Visualise a Confusion Matrix
convert_to_ggmcmc       Converts MCMC Samples into 'ggmcmc' Format
eigen_system            Calculate Eigenvalues and Eigenvectors
example_hmm_mcmc_gamma_poisson
                        Example of a Simulated Gamma-Poisson Model
example_hmm_mcmc_normal
                        Example of a Simulated Normal Model
generate_random_T       Generate a Random Transition Matrix
get_pi                  Get the Prior Probability of States
hmm_mcmc_gamma_poisson
                        MCMC Sampler sampler for the Hidden Markov with
                        Gamma-Poisson emission densities
hmm_mcmc_normal         MCMC Sampler for the Hidden Markov Model with
                        Normal emission densities
hmm_simulate_gamma_poisson_data
                        Simulate data distributed according to oHMMed
                        with gamma-poisson emission densities
hmm_simulate_normal_data
                        Simulate data distributed according to oHMMed
                        with normal emission densities
kullback_leibler_cont_appr
                        Calculate a Continuous Approximation of the
                        Kullback-Leibler Divergence
kullback_leibler_disc   Calculate a Kullback-Leibler Divergence for a
                        Discrete Distribution
oHMMed-package          oHMMed: HMMs with Ordered Hidden States and
                        Emission Densities
plot.hmm_mcmc_gamma_poisson
                        Plot Diagnostics for 'hmm_mcmc_gamma_poisson'
                        Objects
plot.hmm_mcmc_normal    Plot Diagnostics for 'hmm_mcmc_normal' Objects
posterior_prob_gamma_poisson
                        Forward-Backward Algorithm to Calculate the
                        Posterior Probabilities of Hidden States in
                        Poisson-Gamma Model
posterior_prob_normal   Forward-Backward Algorithm to Calculate the
                        Posterior Probabilities of Hidden States in
                        Normal Model
