This project was financed by the National Agency of Research and Development, ANID, Chile, through the grant FONDECYT N.1180670 and through the Complex Engineering Systems Institute PIA/BASAL AFB180003. Also it has received funding from the European Union’s H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement N.691149 (SuFoRun).

The prioriactions package allows you to create and solve conservation planning problems that involve multiple threats and actions. This uses techniques of integer linear programming (ILP), obtaining optimal solutions or with a certain degree of guaranteed quality (gap). Due to its flexibility, the package offers the possibility of creating different mathematical models with multiple requirements (spatial, budget requirements, etc.). All the included models are presented in detail in Salgado-Rojas et al. (2020). The package has a variety of commercial and open-source exact algorithm solvers that guarantee to find optimal solutions.

Package prioriactions can be found at CRAN, where it is updated every few months. Installation from CRAN can be done via:

install.packages("prioriactions")

Also, the latest development version of prioriactions can be installed from GitHub using the following code (If you are using Windows, it is necessary to install Rtools beforehand).

if (!require(remotes)) install.packages("remotes")
remotes::install_github("prioriactions/prioriactions")

You can browse the package documentation online at https://prioriactions.github.io/prioriactions/.

If this is your first time using prioriactions, we strongly recommend reading the Introduction to prioriactions vignette.

If you believe you’ve found a bug in prioriactions, please file a bug (and, if possible, a reproducible example) at https://github.com/prioriactions/prioriactions/issues.

• Salgado-Rojas J, Alvarez-Miranda E, Hermoso V, Garcia-Gonzalo J, Weintraub A. A mixed integer programming approach for multi-action planning for threat management. Ecological Modelling 2020; 418:108901. DOI: https://doi.org/10.1016/j.ecolmodel.2019.108901.