Package: prioriactions 0.5.0

Jose Salgado-Rojas

prioriactions: 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, 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.

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

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prioriactions.pdf |prioriactions.html
prioriactions/json (API)
NEWS

# Install 'prioriactions' in R:
install.packages('prioriactions', repos = c('https://prioriactions.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/prioriactions/prioriactions/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

conservationconservation-planoptimizationprioritizationthreats

5.40 score 10 stars 6 scripts 314 downloads 19 exports 28 dependencies

Last updated 1 years agofrom:16bc97420a. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 09 2024
R-4.5-win-x86_64OKNov 09 2024
R-4.5-linux-x86_64OKNov 09 2024
R-4.4-win-x86_64NOTENov 09 2024
R-4.4-mac-x86_64NOTENov 09 2024
R-4.4-mac-aarch64NOTENov 09 2024
R-4.3-win-x86_64NOTENov 09 2024
R-4.3-mac-x86_64NOTENov 09 2024
R-4.3-mac-aarch64NOTENov 09 2024

Exports:%>%DataevalBlmevalBudgetevalTargetgetActionsgetConnectivityPenaltygetCostgetModelInfogetPerformancegetPotentialBenefitgetSolutionBenefitinputDataOptimizationProblemPortfolioprioriactionsproblemSolutionsolve

Dependencies:assertthatBHclicpp11dplyrfansigenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigprotopurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Benefits and sensitivities

Rendered fromsensitivities.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-08-09
Started: 2021-08-11

Introduction to prioriactions

Rendered fromprioriactions.Rmdusingknitr::knitron Nov 09 2024.

Last update: 2023-08-09
Started: 2021-08-11

Mitchell River

Rendered fromMitchellRiver.Rmdusingknitr::knitron Nov 09 2024.

Last update: 2023-08-09
Started: 2021-06-05

Planning objectives

Rendered fromobjectives.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-08-09
Started: 2021-10-21

Solver benchmarks

Rendered frombenchmark.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-08-09
Started: 2023-08-09

Readme and manuals

Help Manual

Help pageTopics
Data classData data-class
Evaluate multiple blm valuesevalBlm
Evaluate multiple budget valuesevalBudget
Evaluate multiple target valuesevalTarget
Extract action informationgetActions
Extract connectivity penalty valuesgetConnectivityPenalty
Extract cost valuesgetCost
Extract general information about mathematical modelgetModelInfo
Extract general information about solutiongetPerformance
Extract potential benefit of featuresgetPotentialBenefit
Extract benefit valuesgetSolutionBenefit
Creates the multi-action planning probleminputData inputData,data.frame,data.frame,data.frame,data.frame,data.frame-method
Optimization problem classOptimizationProblem optimizationProblem-class
Portfolio classPortfolio portfolio-class
Printprint print.Data print.OptimizationProblem print.Portfolio print.Solution
Create and solve multi-actions planning problemsprioriactions
Create mathematical modelproblem
Showshow show,Data-method show,OptimizationProblem-method show,Portfolio-method show,Solution-method
Simulated multi-action planning datasimData sim_boundary_data sim_dist_features_data sim_dist_threats_data sim_features_data sim_pu_data sim_sensitivity_data sim_threats_data
Solution classSolution solution-class
Solve mathematical modelssolve