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]

prioriactions_0.5.0.tar.gz
prioriactions_0.5.0.zip(r-4.7)prioriactions_0.5.0.zip(r-4.6)prioriactions_0.5.0.zip(r-4.5)
prioriactions_0.5.0.tgz(r-4.6-x86_64)prioriactions_0.5.0.tgz(r-4.6-arm64)prioriactions_0.5.0.tgz(r-4.5-x86_64)prioriactions_0.5.0.tgz(r-4.5-arm64)
prioriactions_0.5.0.tar.gz(r-4.7-arm64)prioriactions_0.5.0.tar.gz(r-4.7-x86_64)prioriactions_0.5.0.tar.gz(r-4.6-arm64)prioriactions_0.5.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
prioriactions/json (API)

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

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

Pkgdown/docs site:https://prioriactions.github.io

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

On CRAN:

Conda:

conservationconservation-planoptimizationprioritizationthreatscpp

5.44 score 11 stars 6 scripts 210 downloads 19 exports 27 dependencies

Last updated from:16bc97420a. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK279
linux-devel-x86_64OK289
source / vignettesOK427
linux-release-arm64OK276
linux-release-x86_64OK265
macos-release-arm64OK271
macos-release-x86_64OK459
macos-oldrel-arm64OK261
macos-oldrel-x86_64OK426
windows-develOK339
windows-releaseOK321
windows-oldrelOK285
wasm-releaseFAIL152

Exports:%>%DataevalBlmevalBudgetevalTargetgetActionsgetConnectivityPenaltygetCostgetModelInfogetPerformancegetPotentialBenefitgetSolutionBenefitinputDataOptimizationProblemPortfolioprioriactionsproblemSolutionsolve

Dependencies:assertthatBHclicpp11dplyrgenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigprotopurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Benefits and sensitivities
1) Calculating benefits when threats are discrete (presence/absence) | Not proportional probability of persistence | 2) Calculating benefits when threats are continuous | Example with continuous intensities of threats | References

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

Introduction to prioriactions
Overview | Workflow | Usage | Planning units data | Features data | Threats data | Boundary data | Step 1: Initialize the problem | Step 2: Create the mathematical model | Step 3: Solve the model | Getting information about solutions | getActions() | getSolutionBenefit() | getCost() | getConnectivityPenalty() | getPerformance() | Sensitivity analyses on blm, budget and target parameters | References

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

Mitchell River
1) Preparing and analyzing input data | 2) Base model | 3) Model with different curve param | 4) Model with connectivity requirements | 5) Conclusions

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

Planning objectives
1) Model I: Only recovery targets | 2) Model II: Recovery and conservation targets | 3) Model III: Only recovery targets and connectivity | 4) Model IV: Recovery and conservation targets and connectivity

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

Solver benchmarks
Experimental settings | Gurobi | CPLEX | CBC | Symphony | Results | Academic solvers: gurobi vs CPLEX | Non-academic solvers: CBC vs symphony | Conclusions

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