Overview {HiddenMarkov}R Documentation

Overview of Package

Description

In this topic we give an overview of the package.

Classes of Hidden Markov Models Analysed

The classes of models currently fitted by the package are listed below. Each are defined within an object that contains the data, current parameter values, and other model characteristics.

Discrete Time Hidden Markov Model:
is described under the topic dthmm. This model can be simulated or fitted to data by defining the required model structure within an object of class "dthmm".
Markov Modulated Generalised Linear Model:
is described under the topic mmglm. This model can be simulated or fitted to data by defining the required model structure within an object of class "mmglm".
Markov Modulated Poisson Process:
is described under the topic mmpp. This model can be simulated or fitted to data by defining the required model structure within an object of class "mmpp".

Main Tasks Performed by the Package

The main tasks performed by the package are listed below. These can be achieved by calling the appropriate generic function.

Simulation of HMMs:
can be performed by the function simulate.
Parameter Estimation:
can be performed by the functions BaumWelch (EM algorithm), or neglogLik together with nlm or optim (Newton type methods or grid searches).
Model Residuals:
can be extracted with the function residuals.
Model Summary:
can be extracted with the function summary.
Log-Likelihood:
can be calculated with the function logLik.
Prediction of the Markov States:
can be performed by the function Viterbi.

All other functions in the package are called from within the above generic functions, and only need to be used if their output is specifically required.

Acknowledgement

Many of the functions contained in the package are based on those of Walter Zucchini (2005).

References

Zucchini, W. (2005). Hidden Markov Models Short Course, 3–4 April 2005. Macquarie University, Sydney.


[Package HiddenMarkov version 1.2-4 Index]