Systematic Conservation Prioritization in R
Add a default portfolio
Add default solver
Add an extra portfolio
Add feature contiguity constraints
Add feature weights
Add a gap portfolio
Add targets based on feature groups
Add a Gurobi solver
Add a HiGHS solver
Add linear constraints
Add linear penalties
Add locked in constraints
Add locked out constraints
Add a SYMPHONY solver with lpsymphony
Find intersecting units
Print an object for knitr package.
Linear interpolation
Log-linear interpolation
Convert Marxan boundary data to matrix format
Convert Marxan connectivity data to matrix format
Marxan conservation problem
Waiver
Number of features
Number of planning units
Number of total units
Number of zones
Portfolio class
Add portfolios
Presolve check
Simulate species habitat suitability data
Solve
Solver class
Add solvers
Specify absolute targets
Specify targets based on area units
Specify targets following Durán et al. (2020)
Specify targets based on interpolating absolute thresholds
Specify targets based on interpolating area-based thresholds
Specify targets following Jung et al. (2021)
Specify targets based on maxima
Specify targets based on minima
Specify targets following Polak et al. (2015)
Specify targets based on population size
Specify relative targets
Specify targets based on the IUCN Red List of Ecosystems
Specify targets based on the IUCN Red List of Threatened Species
Specify targets following Rodrigues et al. (2004)
Specify targets following a set of rules
Specify targets following Ward et al. (2025)
Specify targets following Watson et al. (2010)
Specify targets following Wilson et al. (2010)
Evaluate solutions using summary statistics
Target class
Target setting method class
Add representation targets
Manipulate tibbles
Weight class
Write problem
Zone names
Management zones
Add connectivity penalties
Add contiguity constraints
Add a CPLEX solver
Add Bender's cuts portfolio
Add minimum shortfall objective
Add neighbor constraints
Add neighbor penalties
Add proportion decisions
Add relative targets
Add a SYMPHONY solver with Rsymphony
Add semi-continuous decisions
Add absolute targets
Add asymmetric connectivity penalties
Add targets automatically
Add binary decisions
Add boundary penalties
Add a CBC solver
Add mandatory allocation constraints
Add manually specified bound constraints
Add manually specified locked constraints
Add manual targets
Add maximum coverage objective
Add maximum feature representation objective
Add maximum phylogenetic diversity objective
Add maximum phylogenetic endemism objective
Add maximum utility objective
Add minimum largest shortfall objective
Add minimum penalties objective
Add minimum set objective
Add a shuffle portfolio
Add a top portfolio
Adjacency matrix
Standardize unit to
Standardize unit to density per
Binary stack
Boundary matrix
Branch matrix
Calibrate penalties with Cohon's method
Category layer
Category vector
Feature names
Compile a problem
Connectivity matrix
Conservation problem modifier class
Conservation problem class
Constraint class
Conservation problem constraints
Decision class
Evaluate solution importance
Add decision types
Evaluate asymmetric connectivity of solution
Evaluate boundary length of solution
Evaluate connectivity of solution
Evaluate cost of solution
Evaluate feature representation by solution
Evaluate solution importance using Ferrier scores
Evaluate number of planning units selected by solution
Evaluate solution importance using incremental ranks
Evaluate solution importance using rarity weighted richness scores
Evaluate solution importance using replacement cost scores
Evaluate target coverage by solution
Fast extract
Feature abundances
Objective class
Add an objective
Optimization problem
Optimization problem class
Optimization problem methods
Add a penalty
Penalty class
Deprecation notice
prioritizr: Systematic Conservation Prioritization in R
Conservation planning problem
Proximity matrix
Objects exported from other packages
Rescale a matrix
Feature by planning unit matrix
Run calculations
Show
Get simulated conservation planning data
Simulate cost data
Simulate data
Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.
Useful links