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>.
| Version: |
8.1.0 |
| Depends: |
R (≥ 3.3) |
| Imports: |
digest, graphics, grDevices, methods, stats, utils, Matrix |
| Published: |
2020-10-18 |
| Author: |
Stefan Widgren 
[aut, cre],
Robin Eriksson
[aut],
Stefan Engblom
[aut],
Pavol Bauer [aut],
Thomas Rosendal
[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 |
| CRAN checks: |
SimInf results |