# hmm.discnp v2.1-5

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## Hidden Markov Models with Discrete Non-Parametric Observation Distributions

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.

## Functions in hmm.discnp

 Name Description ccprSim Simulated monocyte counts and psychosis symptoms. SydColDisc Discretised version of coliform counts in sea-water samples lesionCount Multiple sclerosis lesion counts for three patients. predict.hmm.discnp Predicted values of a discrete non-parametric hidden Markov model. anova.hmm.discnp Anova for hmm.discnp models logLikHmm Log likelihood of a hidden Markov model scovmat Simulation based covariance matrix. misstify Insert missing values. mps Most probable states. rhmm Simulate discrete data from a non-parametric hidden Markov model. sp Calculate the conditional state probabilities. viterbi Most probable state sequence. hmm.discnp-internal Internal hmm.discnp functions. hydroDat Canadian hydrological data sets. squantCI Simulation-quantile based confidence intervals. weissData Data from “An Introduction to Discrete-Valued Time Series” update.hmm.discnp Update a fitted hmm.discnp model. fitted.hmm.discnp Fitted values of a discrete non-parametric hidden Markov model. hmm Fit a hidden Markov model to discrete data. nafracCalc Calculate fractions of missing values. pr Probability of state sequences. cnvrtRho Convert Rho between forms. No Results!