Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.
Version: 9.8.1 Depends: R (≥ 4.0) Imports: digest, graphics, grDevices, MASS, methods, stats, utils, Matrix (≥ 1.3-0) Suggests: knitr, rmarkdown Published: 2024-06-21 DOI: 10.32614/CRAN.package.SimInf Author: Stefan Widgren [aut, cre], Robin Eriksson [aut], Stefan Engblom [aut], Pavol Bauer [aut], Thomas Rosendal [ctb], Ivana Rodriguez Ewerlöf [ctb], Attractive Chaos [cph] (Author of 'kvec.h'.) Maintainer: Stefan Widgren <stefan.widgren at gmail.com> BugReports: https://github.com/stewid/SimInf/issues License: GPL-3 URL: https://github.com/stewid/SimInf NeedsCompilation: yes SystemRequirements: GNU Scientific Library (GSL) Citation: SimInf citation info Materials: README NEWS In views: Epidemiology CRAN checks: SimInf results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SimInf to link to this page.
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