SMM: Simulation and Estimation of Multi-State Discrete-Time Semi-Markov and Markov Models

Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Version: 1.0.1
Depends: seqinr, DiscreteWeibull
Suggests: utils
Published: 2017-09-08
Author: Vlad Stefan Barbu, Caroline Berard, Dominique Cellier, Mathilde Sautreuil and Nicolas Vergne
Maintainer: Nicolas Vergne <nicolas.vergne at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: SMM results


Reference manual: SMM.pdf
Vignettes: SMM Vignette
Package source: SMM_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: SMM_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: SMM_1.0.1.tgz
Old sources: SMM archive


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