Package: prioriactions 0.5.0
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:
prioriactions_0.5.0.tar.gz
prioriactions_0.5.0.zip(r-4.5)prioriactions_0.5.0.zip(r-4.4)prioriactions_0.5.0.zip(r-4.3)
prioriactions_0.5.0.tgz(r-4.4-x86_64)prioriactions_0.5.0.tgz(r-4.4-arm64)prioriactions_0.5.0.tgz(r-4.3-x86_64)prioriactions_0.5.0.tgz(r-4.3-arm64)
prioriactions_0.5.0.tar.gz(r-4.5-noble)prioriactions_0.5.0.tar.gz(r-4.4-noble)
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')) |
Bug tracker:https://github.com/prioriactions/prioriactions/issues
- sim_boundary_data - Simulated multi-action planning data
- sim_dist_features_data - Simulated multi-action planning data
- sim_dist_threats_data - Simulated multi-action planning data
- sim_features_data - Simulated multi-action planning data
- sim_pu_data - Simulated multi-action planning data
- sim_sensitivity_data - Simulated multi-action planning data
- sim_threats_data - Simulated multi-action planning data
conservationconservation-planoptimizationprioritizationthreats
Last updated 1 years agofrom:16bc97420a. Checks:OK: 3 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 09 2024 |
R-4.5-win-x86_64 | OK | Nov 09 2024 |
R-4.5-linux-x86_64 | OK | Nov 09 2024 |
R-4.4-win-x86_64 | NOTE | Nov 09 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 09 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 09 2024 |
R-4.3-win-x86_64 | NOTE | Nov 09 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 09 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 09 2024 |
Exports:%>%DataevalBlmevalBudgetevalTargetgetActionsgetConnectivityPenaltygetCostgetModelInfogetPerformancegetPotentialBenefitgetSolutionBenefitinputDataOptimizationProblemPortfolioprioriactionsproblemSolutionsolve
Dependencies:assertthatBHclicpp11dplyrfansigenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigprotopurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr
Benefits and sensitivities
Rendered fromsensitivities.Rmd
usingknitr::rmarkdown
on Nov 09 2024.Last update: 2023-08-09
Started: 2021-08-11
Introduction to prioriactions
Rendered fromprioriactions.Rmd
usingknitr::knitr
on Nov 09 2024.Last update: 2023-08-09
Started: 2021-08-11
Mitchell River
Rendered fromMitchellRiver.Rmd
usingknitr::knitr
on Nov 09 2024.Last update: 2023-08-09
Started: 2021-06-05
Planning objectives
Rendered fromobjectives.Rmd
usingknitr::rmarkdown
on Nov 09 2024.Last update: 2023-08-09
Started: 2021-10-21
Solver benchmarks
Rendered frombenchmark.Rmd
usingknitr::rmarkdown
on Nov 09 2024.Last update: 2023-08-09
Started: 2023-08-09
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Data class | Data data-class |
Evaluate multiple blm values | evalBlm |
Evaluate multiple budget values | evalBudget |
Evaluate multiple target values | evalTarget |
Extract action information | getActions |
Extract connectivity penalty values | getConnectivityPenalty |
Extract cost values | getCost |
Extract general information about mathematical model | getModelInfo |
Extract general information about solution | getPerformance |
Extract potential benefit of features | getPotentialBenefit |
Extract benefit values | getSolutionBenefit |
Creates the multi-action planning problem | inputData inputData,data.frame,data.frame,data.frame,data.frame,data.frame-method |
Optimization problem class | OptimizationProblem optimizationProblem-class |
Portfolio class | Portfolio portfolio-class |
print print.Data print.OptimizationProblem print.Portfolio print.Solution | |
Create and solve multi-actions planning problems | prioriactions |
Create mathematical model | problem |
Show | show show,Data-method show,OptimizationProblem-method show,Portfolio-method show,Solution-method |
Simulated multi-action planning data | simData sim_boundary_data sim_dist_features_data sim_dist_threats_data sim_features_data sim_pu_data sim_sensitivity_data sim_threats_data |
Solution class | Solution solution-class |
Solve mathematical models | solve |