Estimation and Simulation of Drifting Semi-Markov Models R package dsmmR version 1.0.7 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>. We acknowledge the DATALAB Project <https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). Software Depends: R (>= 3.5.0) Imports: DiscreteWeibull Suggests: utils, knitr, rmarkdown, testthat (>= 3.0.0) Ioannis Mavrogiannis <mavrogiannis.ioa@gmail.com> Comprehensive R Archive Network (CRAN) Vlad Stefan Barbu [aut] (ORCID: <https://orcid.org/0000-0002-0840-016X>), Ioannis Mavrogiannis [aut, cre] (ORCID: <https://orcid.org/0000-0002-2948-0648>), Nicolas Vergne [aut] GPL 2025-07-08 application/tgz https://CRAN.R-project.org/package=dsmmR doi:10.32614/CRAN.package.dsmmR