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We consider a discrete-time semi-Markov process, with a finite state space. Taking a censored history, we obtain empirical estimators for the semi-Markov kernel, semi-Markov transition function, reliability and availability. We study the strong consistency and the asymptotic normality for each estimator.

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We consider the nonparametric robust estimation problem for regression models in continuous time with semi-Markov noises. An adaptive model selection procedure is proposed. Under general moment conditions on the noise distribution a sharp non-asymptotic oracle inequality for the robust risks is obtained and the robust efficiency is shown. It turns out that for semi-Markov models the robust minimax convergence rate may be faster or slower than the classical one.

Semi-Markov models, independently introduced by Lévy (1954), Smith (1955) and Takacs (1954), are a generalization of the well-known Markov models. For semi-Markov models, sojourn times can be arbitrarily distributed, while sojourn times of Markov models are constrained to be exponentially distributed (in continuous time) or geometrically distributed (in discrete time). The aim of this paper is to present the R package SMM, devoted to the simulation and estimation of discretetime multi-state semi-Markov and Markov models. For the semi-Markov case we have considered: parametric and non-parametric estimation; with and without censoring at the beginning and/or at the end of sample paths; one or several independent sample paths. Several discrete-time distributions are considered for the parametric estimation of sojourn time distributions of semi-Markov chains: Uniform, Geometric, Poisson, Discrete Weibull and Binomial Negative.Few R packages have been developed to handle semi-Markov models or hidden semi-Markov models. For semi-Markov models we have the recent semiMarkov R package (Król and Saint-Pierre, 2015) that performs maximum likelihood estimation for parametric continuous-time semi-Markov processes, where the distribution can be chosen between Exponential, Weibull or exponentiated Weibull. That package computes associated hazard rates; covariates can also be taken into account through the Cox proportional hazard model. Two R packages are also dedicated to hidden semi-Markov models, implementing estimation and prediction methods: the hsmm R package (Bulla et al., 2010) and the mhsmm R package (O'Connell and Højsgaard, 2011).Note that there is no R package developed for discrete-time multi-state semi-Markov models. Thus the purpose of this paper is to present an R package that we have developed, called SMM, which 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