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) 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>). 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.
c(person(c('Jose'), 'Salgado-Rojas',
email='jose.salgroj@gmail.com', role = c('aut','cre')),
person(c('Irlanda'), 'Ceballos-Fuentealba',
email='iceballosfuentealba@gmail.com', role = c('aut')),
person(c('Virgilio'), 'Hermoso',
email='virgilio.hermoso@gmail.com', role = c('aut')),
person(c('Eduardo'), 'Alvarez-Miranda',
email='eduardo.alvmir@gmail.com', role = c('aut')),
person(c('Jordi'), 'Garcia-Gonzalo',
email='j.garcia@ctfc.es', role = c('aut')))
assertthat (>= 0.2.0), Matrix, proto, magrittr, tidyr, dplyr,
Rcpp, rlang
knitr, gurobi (>= 9.0), Rcplex (>= 0.3.5), roxygen2,
Rsymphony (>= 0.1-31), rmarkdown, testthat (>= 3.0.0), raster,
tmap, sp, viridis, markdown, data.table, purrr, readr, slam,
tibble, methods

Systematic Conservation Prioritization in R
person("Matthew", "Strimas-Mackey", email="mstrimas@gmail.com", role = c("aut", "cre"))
Solve systematic reserve design problems using integer programming
techniques. To solve problems most efficiently users can install three
optional packages not available on CRAN: the 'gurobi' optimizer (available
from <http://www.gurobi.com/>) and the conservation prioritization package
'marxan' (available from <https://github.com/jeffreyhanson/marxan>).
assertthat, sp, raster, slam, dplyr, gstat, rgeos, rasterVis,
methods, tidyr
marxan, gurobi, lpsymphony, Rsymphony, knitr, rmarkdown,
glpkAPI, testthat