GillespieSSA
:
Gillespie’s Stochastic Simulation Algorithm (SSA)GillespieSSA provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie’s exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap).
The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, decaying-dimerization reaction set, linear chain system, logistic growth model, Lotka predator-prey model, Rosenzweig-MacArthur predator-prey model, Kermack-McKendrick SIR model, and a metapopulation SIRS model.
You can install GillespieSSA from CRAN using
install.packages("GillespieSSA")
Or, alternatively, you can install the development version of GillespieSSA from GitHub using
::install_github("rcannood/GillespieSSA", build_vignettes = TRUE) devtools
The following example models are available:
vignette("decaying_dimer", package="GillespieSSA")
vignette("epi_chain", package="GillespieSSA")
vignette("linear_chain", package="GillespieSSA")
vignette("logistic_growth", package="GillespieSSA")
vignette("lotka_predator_prey", package="GillespieSSA")
vignette("radioactive_decay", package="GillespieSSA")
vignette("rm_predator_prey", package="GillespieSSA")
vignette("sir", package="GillespieSSA")
Check out news(package = "GillespieSSA")
or NEWS.md for a full list of changes.
.t
as parameter in the
propensity functions.This release contains a major rewrite of the internal code, to make sure the code is readable and that the algorithm doesn’t continuously update the local environment.
MAJOR CHANGE: Instead of passing "D"
,
"ETL"
, "OTL"
, or "BTL"
to
ssa()
, it is expected to pass ssa.d()
,
ssa.etl()
, ssa.otl()
, or
ssa.btl()
. This cleans up parameter setting clutter in the
ssa()
function.
MAJOR CHANGE: Rewrite ssa.*()
and
ssa.*.diag()
as ssa_step.ssa_*()
and
ssa_step_diag.ssa_*()
S3 functions.
MAJOR CHANGE: Do not save the current state in the function environment. Instead, simply save it in a local variable.
MAJOR CHANGE: Precompile propensity functions instead of evaluating them as R code at each iteration.
MAJOR CHANGE: Clean up and merge ssa.run()
,
ssa.terminate()
, ssa.check.args()
and
ssa.check.method()
into ssa()
.