GillespieSSA

https://github.com/rcannood/gillespiessa

Repository

README.Rmd

---
output: 
  github_document:
    toc: TRUE
editor_options: 
  chunk_output_type: console
---




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# `GillespieSSA`: Gillespie's Stochastic Simulation Algorithm (SSA)

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%",
  fig.path = "man/figures/",
  message = FALSE,
  dpi = 300
)
library(tidyverse)
set.seed(1)

submission_to_cran <- TRUE
```

**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.

## Install

You can install **GillespieSSA** from CRAN using

```R
install.packages("GillespieSSA")
```

Or, alternatively, you can install the development version of GillespieSSA from GitHub using

```R
devtools::install_github("rcannood/GillespieSSA", build_vignettes = TRUE)
```

## Examples

The following example models are available:

```{r vignettes, results='asis', echo=FALSE}
walk(
  list.files("vignettes", pattern = "*.Rmd"),
  function(file) {
    title <- 
      read_lines(paste0("vignettes/", file)) %>% 
      keep(~grepl("^title: ", .)) %>% 
      gsub("title: \"(.*)\"", "\\1", .)
    vignette_name <- gsub("\\.Rmd", "", file)
    markdown_name <- gsub("\\.Rmd", ".md", file)
    cat(
      "* ",
      ifelse(submission_to_cran, "", "["),
      title, 
      ifelse(submission_to_cran, "", paste0("](vignettes/", markdown_name, ")")),
      ": ",
      "`vignette(\"", vignette_name, "\", package=\"GillespieSSA\")`\n",
      sep = ""
    )
  }
)
```

```{r rerun_vignettes, include = FALSE}
rerun_vignettes <- FALSE
if (rerun_vignettes) {
  for (file in list.files("vignettes", pattern = "*.Rmd", full.names = TRUE)) {
    cat("Running '", file, "'\n", sep = "")
    rmarkdown::render(file, output_format = "github_document")
  }
}
```

## Latest changes
Check out `news(package = "GillespieSSA")` or [NEWS.md](NEWS.md) for a full list of changes.



```{r news, echo=FALSE, results="asis"}
cat(dynutils::recent_news())
```

## References

 * Brown D. and Rothery P. 1993. Models in biology: mathematics, statistics, and computing. John Wiley & Sons.
 * Cao Y., Li H., and Petzold L. 2004. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J. Chem. Phys. 121:4059-4067. [doi:10.1063/1.1778376](https://doi.org/10.1063/1.1778376)
 * Cao Y., Gillespie D.T., and Petzold L.R. 2006. Efficient step size selection for the tau-leaping method. J. Chem. Phys. 124:044109. [doi:10.1063/1.2159468](https://doi.org/10.1063/1.2159468)
 * Cao Y., Gillespie D.T., and Petzold L.R. 2007. Adaptive explicit tau-leap method with automatic tau selection. J. Chem. Phys. 126:224101. [doi:10.1063/1.2745299](https://doi.org/10.1063/1.2745299)
 * Chatterjee A., Vlachos D.G., and Katsoulakis M.A. 2005. Binomial distribution based tau-leap accelerated stochastic simulation. J. Chem. Phys. 122:024112. [doi:10.1063/1.1833357](https://doi.org/10.1063/1.1833357)
 * Gillespie D.T. 1977. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81:2340. [doi:10.1021/j100540a008](https://doi.org/10.1021/j100540a008)
 * Gillespie D.T. 2001. Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115:1716-1733. [doi:10.1063/1.1378322](https://doi.org/10.1063/1.1378322)
 * Gillespie D.T. 2007. Stochastic simulation of chemical kinetics. Annu. Rev. Chem. 58:35 [doi:10.1146/annurev.physchem.58.032806.104637](https://doi.org/10.1146/annurev.physchem.58.032806.104637)
 * Kot M. 2001. Elements of mathematical ecology. Cambridge University Press. [doi:10.1017/CBO9780511608520](https://doi.org/10.1017/CBO9780511608520)
 * Pineda-Krch M. 2008. Implementing the stochastic simulation algorithm in R. Journal of Statistical Software 25(12): 1-18. [doi: 	10.18637/jss.v025.i12](https://doi.org/10.18637/jss.v025.i12)
 * Pineda-Krch M., Blok H.J., Dieckmann U., and Doebeli M. 2007. A tale of two cycles --- distinguishing quasi-cycles and limit cycles in finite predator-prey populations. Oikos 116:53-64. [doi:10.1111/j.2006.0030-1299.14940.x](https://doi.org/10.1111/j.2006.0030-1299.14940.x)

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