HMMs with Ordered Hidden States and Emission Densities

[Up] [Top]

Documentation for package ‘oHMMed’ version 1.0.2

Help Pages

oHMMed-package oHMMed: HMMs with Ordered Hidden States and Emission Densities
coef.hmm_mcmc_gamma_poisson Extract Model Estimates
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
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