Simulation, Estimation and Reliability of Semi-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. Reliability indicators such as reliability, maintainability, availability, BMP-failure rate, RG-failure rate, mean time to failure and mean time to repair are available as well. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) , Barbu, V.S., Limnios, N. (2008) and Trevezas, S., Limnios, N. (2011) . Estimation and simulation of discrete-time k-th order Markov chains are also considered.


Reference manual

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1.0.3 by Nicolas Vergne, 6 months ago

Browse source code at

Authors: Vlad Stefan Barbu [aut] , Caroline Berard [aut] , Dominique Cellier [aut] , Florian Lecocq [aut] , Corentin Lothode [aut] , Mathilde Sautreuil [aut] , Nicolas Vergne [aut, cre]

Documentation:   PDF Manual  

GPL license

Imports DiscreteWeibull, Rcpp, seqinr

Suggests utils, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

See at CRAN