smmR: 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) <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:	1.0.5
Imports:	DiscreteWeibull, Rcpp, seqinr
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	utils, knitr, rmarkdown, testthat (≥ 3.0.0), roxytest
Published:	2025-11-07
DOI:	10.32614/CRAN.package.smmR
Author:	Vlad Stefan Barbu [aut], Florian Lecocq [aut], Corentin Lothode [aut], Nicolas Vergne [aut, cre]
Maintainer:	Nicolas Vergne <nicolas.vergne at univ-rouen.fr>
BugReports:	https://github.com/corentin-dev/smmR/issues
License:	GPL-3
URL:	https://plmlab.math.cnrs.fr/lmrs/statistique/smmR, https://lmrs.pages.math.cnrs.fr/statistique/smmR
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	smmR results

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