Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based 
	on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall 
	welfare, defined as the sum  of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by  
	several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>),
        Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (2021, <doi:10.1080/13504851.2020.1820939>) 
	and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).

Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.

A decision support tool for prioritizing conservation projects.
    Prioritizations can be developed by maximizing expected feature richness,
    expected phylogenetic diversity, the number of features that meet
    persistence targets, or identifying a set of projects that meet persistence
    targets for minimal cost. Constraints (e.g. lock in specific actions) and
    feature weights can also be specified to further customize prioritizations.
    After defining a project prioritization problem, solutions can be obtained
    using exact algorithms, heuristic algorithms, or random processes. In
    particular, it is recommended to install the 'Gurobi' optimizer (available
    from <https://www.gurobi.com>) because it can identify optimal solutions
    very quickly. The 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can also be used to generate
    solutions using the CBC optimization software
    (<https://github.com/coin-or/Cbc>). Finally, methods are provided for
    comparing different prioritizations and evaluating their benefits.
    For more information, see Hanson et al. (2019)
    <doi:10.1111/2041-210X.13264>.