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>.
| Version: |
0.0.96 |
| Depends: |
R (≥ 2.10) |
| Imports: |
DiscreteWeibull |
| Suggests: |
utils, knitr, rmarkdown |
| Published: |
2022-11-16 |
| Author: |
Vlad Stefan Barbu 
[aut],
Ioannis Mavrogiannis [aut, cre],
Nicolas Vergne [aut] |
| Maintainer: |
Ioannis Mavrogiannis <mavrogiannis.ioa at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
dsmmR results |
Documentation:
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