Function reference • oHMMed

Simulate Data

Functions for Simulating Data

hmm_simulate_normal_data(): Simulate data distributed according to oHMMed with normal emission densities

hmm_simulate_gamma_poisson_data(): Simulate data distributed according to oHMMed with gamma-poisson emission densities

Simulate Hidden Markov Models

Functions for Simulating Hidden Markov Models

hmm_mcmc_normal(): MCMC Sampler for the Hidden Markov Model with Normal emission densities

hmm_mcmc_gamma_poisson(): MCMC Sampler sampler for the Hidden Markov with Gamma-Poisson emission densities

Inference

Functions for Inference on Hidden Markov Models

posterior_prob_normal(): Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Normal Model

posterior_prob_gamma_poisson(): Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Poisson-Gamma Model

Diagnostic Tools

plot(<hmm_mcmc_normal>): Plot Diagnostics for hmm_mcmc_normal Objects

plot(<hmm_mcmc_gamma_poisson>): Plot Diagnostics for hmm_mcmc_gamma_poisson Objects

conf_mat(): Calculate and Visualise a Confusion Matrix

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

Helper Functions

generate_random_T(): Generate a Random Transition Matrix

eigen_system(): Calculate Eigenvalues and Eigenvectors

get_pi(): Get the Prior Probability of States

convert_to_ggmcmc(): Converts MCMC Samples into ggmcmc Format

coef(<hmm_mcmc_normal>) coef(<hmm_mcmc_gamma_poisson>): Extract Model Estimates

Simulated Example Models

example_hmm_mcmc_normal: Example of a Simulated Normal Model

example_hmm_mcmc_gamma_poisson: Example of a Simulated Gamma-Poisson Model

Package Description

oHMMed-package: oHMMed: HMMs with Ordered Hidden States and Emission Densities