DESCRIPTION

Package: SimInf
Title: A Framework for Data-Driven Stochastic Disease Spread Simulations
Version: 9.8.1.9000
Authors@R: c(person("Stefan", "Widgren", role = c("aut", "cre"),
                    email = "stefan.widgren@gmail.com",
                    comment = c(ORCID = "0000-0001-5745-2284")),
             person("Robin", "Eriksson", role = "aut",
                    comment = c(ORCID = "0000-0002-4291-712X")),
             person("Stefan", "Engblom", role = "aut",
                    comment = c(ORCID = "0000-0002-3614-1732")),
             person("Pavol", "Bauer", role = "aut",
                    comment = c(ORCID = "0000-0003-4328-7171")),
             person("Thomas", "Rosendal", role = "ctb",
                    comment = c(ORCID = "0000-0002-6576-9668")),
             person("Ivana", "Rodriguez Ewerlöf", role = "ctb",
                    comment = c(ORCID = "0000-0002-9678-9813")),
             person("Attractive Chaos", role = "cph",
                    comment = "Author of 'kvec.h'."))
Description: 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>.
Acknowledgements: This software has been made possible by support from
    the Swedish Research Council within the UPMARC Linnaeus center of
    Excellence (Pavol Bauer, Robin Eriksson, and Stefan Engblom), the
    Swedish Research Council Formas (Stefan Engblom and Stefan
    Widgren), the Swedish Board of Agriculture (Stefan Widgren), the
    Swedish strategic research program eSSENCE (Stefan Widgren), and
    in the framework of the Full Force project, supported by funding
    from the European Union’s Horizon 2020 Research and Innovation
    programme under grant agreement No 773830: One Health European
    Joint Programme (Stefan Widgren).
License: GPL-3
URL: https://github.com/stewid/SimInf, http://stewid.github.io/SimInf/
BugReports: https://github.com/stewid/SimInf/issues
Type: Package
LazyData: true
Biarch: true
NeedsCompilation: yes
SystemRequirements: GNU Scientific Library (GSL)
Depends: R (>= 4.0)
Imports:
    digest,
    graphics,
    grDevices,
    MASS,
    methods,
    mvtnorm,
    stats,
    utils,
    Matrix (>= 1.3-0)
Suggests:
    knitr,
    rmarkdown
Collate:
    'C-generator.R'
    'check_arguments.R'
    'init.R'
    'valid.R'
    'classes.R'
    'SimInf_model.R'
    'SEIR.R'
    'SIR.R'
    'SIS.R'
    'SISe.R'
    'SISe3.R'
    'SISe3_sp.R'
    'SISe_sp.R'
    'SimInf-package.R'
    'SimInf.R'
    'SimInf_events.R'
    'SimInf_indiv_events.R'
    'run.R'
    'density_ratio.R'
    'abc.R'
    'degree.R'
    'distance.R'
    'distributions.R'
    'edge_properties.R'
    'match_compartments.R'
    'mparse.R'
    'pmcmc.R'
    'pfilter.R'
    'n.R'
    'openmp.R'
    'package_skeleton.R'
    'plot.R'
    'prevalence.R'
    'print.R'
    'punchcard.R'
    'trajectory.R'
    'u0.R'
    'v0.R'
Encoding: UTF-8
RoxygenNote: 7.3.2
VignetteBuilder:
    utils,
    knitr