GillespieSSA2
: Gillespie’s Stochastic Simulation Algorithm for impatient people.
GillespieSSA2 is a fast, scalable, and versatile framework for simulating large systems with Gillespie’s Stochastic Simulation Algorithm (SSA) (Cannoodt et al. 2021). This package is the spiritual successor to the GillespieSSA package originally written by Mario Pineda-Krch (Pineda-Krch 2008).
GillespieSSA2 has the following added benefits:
The SSA methods currently implemented are: Exact (ssa_exact()
), Explicit tau-leaping (ssa_etl()
), and the Binomial tau-leaping (ssa_btl()
).
You can install:
the latest released version from CRAN with
install.packages("GillespieSSA2")
the latest development version from github with
devtools::install_github("rcannood/GillespieSSA2", build_vignettes = TRUE)
If you encounter a bug, please file a minimal reproducible example on the issues page.
The following example models are available:
vignette("an_introduction", package="GillespieSSA2")
vignette("converting_from_GillespieSSA", package="GillespieSSA2")
vignette("decaying_dimer", package="GillespieSSA2")
vignette("epi_chain", package="GillespieSSA2")
vignette("linear_chain", package="GillespieSSA2")
vignette("logistic_growth", package="GillespieSSA2")
vignette("lotka_predator_prey", package="GillespieSSA2")
vignette("radioactive_decay", package="GillespieSSA2")
vignette("rm_predator_prey", package="GillespieSSA2")
vignette("sir", package="GillespieSSA2")
Cannoodt, Robrecht, Wouter Saelens, Louise Deconinck, and Yvan Saeys. 2021. “Spearheading Future Omics Analyses Using Dyngen, a Multi-Modal Simulator of Single Cells.” Nature Communications 12 (1). https://doi.org/10.1038/s41467-021-24152-2.
Pineda-Krch, Mario. 2008. “GillespieSSA: Implementing the Stochastic Simulation Algorithm in r.” Journal of Statistical Software 25 (12). https://doi.org/10.18637/jss.v025.i12.
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