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 ORCID iD [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


Reference manual: dsmmR.pdf


Package source: dsmmR_0.0.96.tar.gz
Windows binaries: r-devel: dsmmR_0.0.96.zip, r-release: dsmmR_0.0.96.zip, r-oldrel: dsmmR_0.0.96.zip
macOS binaries: r-release (arm64): dsmmR_0.0.96.tgz, r-oldrel (arm64): dsmmR_0.0.96.tgz, r-release (x86_64): dsmmR_0.0.96.tgz, r-oldrel (x86_64): dsmmR_0.0.96.tgz


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