Multi-Action Conservation Planning

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) and non-commercial (symphony) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the gurobi installation vignette of the prioritizr package (available in <>). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples.


Reference manual

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0.3.3 by Jose Salgado-Rojas, 5 days ago,

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Authors: Jose Salgado-Rojas [aut, cre] , Irlanda Ceballos-Fuentealba [aut] , Virgilio Hermoso [aut] , Eduardo Alvarez-Miranda [aut] , Jordi Garcia-Gonzalo [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports assertthat, Matrix, proto, magrittr, Rsymphony, tidyr, dplyr, Rcpp, rlang

Suggests knitr, gurobi, roxygen2, rmarkdown, testthat, raster, tmap, sp, viridis, markdown, data.table, purrr, readr, slam, tibble, methods

Linking to Rcpp, RcppArmadillo, BH

System requirements: C++11

See at CRAN