Hidden Markov Models with Discrete Non-Parametric Observation Distributions
Anova for hmm.discnp models
Convert Rho between forms.
Fitted values of a discrete non-parametric hidden Markov model.
Internal hmm.discnp functions.
Fit a hidden Markov model to discrete data.
Canadian hydrological data sets.
Log likelihood of a hidden Markov model
Insert missing values.
Most probable states.
Calculate fractions of missing values.
Probability of state sequences.
Predicted values of a discrete non-parametric hidden Markov model.
Simulate discrete data from a non-parametric hidden Markov model.
Simulation based covariance matrix.
Calculate the conditional state probabilities.
Simulation-quantile based confidence intervals.
Update a fitted hmm.discnp model.
Most probable state sequence.
Data from An Introduction to Discrete-Valued Time Series
Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model. Auxiliary predictors are accommodated in the univariate setting.