Overview {HiddenMarkov} | R Documentation |
In this topic we give an overview of the package.
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.
dthmm
. This model can be simulated or fitted to data by defining the required model structure within an object of class "dthmm"
.mmglm
. This model can be simulated or fitted to data by defining the required model structure within an object of class "mmglm"
.mmpp
. This model can be simulated or fitted to data by defining the required model structure within an object of class "mmpp"
.The main tasks performed by the package are listed below. These can be achieved by calling the appropriate generic function.
simulate
.BaumWelch
(EM algorithm), or neglogLik
together with nlm
or optim
(Newton type methods or grid searches).residuals
.summary
.logLik
.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.
Many of the functions contained in the package are based on those of Walter Zucchini (2005).
Zucchini, W. (2005). Hidden Markov Models Short Course, 3–4 April 2005. Macquarie University, Sydney.