dsmmR: Estimation and Simulation of Drifting Semi-Markov Models
Performs parametric and non-parametric estimation and simulation of
drifting semi-Markov processes. The definition of parametric and non-parametric
model specifications is also possible. Furthermore, three different types of
drifting semi-Markov models are considered. These models differ in the number
of transition matrices and sojourn time distributions used for the computation
of a number of semi-Markov kernels, which in turn characterize the drifting
semi-Markov kernel. For the parametric model estimation and specification,
several discrete distributions are considered for the sojourn times: Uniform,
Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric
model specification makes no assumptions about the shape of the sojourn time
distributions. Semi-Markov models are described in:
Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>.
Drifting Markov models are described in:
Vergne, N. (2008) <doi:10.2202/1544-6115.1326>.
Reliability indicators of Drifting Markov models are described in:
Barbu, V. S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8>.
We acknowledge the DATALAB Project
<https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the
European Union with the European Regional Development fund (ERDF) and by
the Normandy Region) and the HSMM-INCA Project (financed by the French
Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005).
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
[aut],
Ioannis Mavrogiannis