This uses a mixed integer mathematical programming (MIP)
        approach for building and solving multi-action planning problems, 
        where the goal is to find an optimal combination of management actions that
        abate threats, in an efficient way while accounting for spatial aspects. 
        Thus, optimizing the connectivity and conservation effectiveness of 
        the prioritized units and of the deployed actions. The package is capable of 
        handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. 
        Gurobi optimization solver can be installed using comprehensive instructions in 
        the 'gurobi' installation vignette of the prioritizr package (available in 
        <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX'
        optimization solver can be obtain from IBM CPLEX web page (available here 
        <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, 
        the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the 
        package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>,
        Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
        <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. 
        See the prioriactions website for more information, documentations and examples.

Systematic conservation prioritization using mixed integer linear
    programming (MILP). It provides a flexible interface for building and
    solving conservation planning problems. Once built, conservation planning
    problems can be solved using a variety of commercial and open-source exact
    algorithm solvers. By using exact algorithm solvers, solutions can be
    generated that are guaranteed to be optimal (or within a pre-specified
    optimality gap). Furthermore, conservation problems can be constructed to
    optimize the spatial allocation of different management actions or zones,
    meaning that conservation practitioners can identify solutions that benefit
    multiple stakeholders. To solve large-scale or complex conservation
    planning problems, users should install the Gurobi optimization software
    (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
    Gurobi Installation Guide vignette for details). Users can also install the
    IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and
    the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
    Additionally, the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). For
    further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.