Function reference • prioritizr

Summary Summary of the package
`prioritizr`	prioritizr
Data Simulated data sets distributed with the package
`sim_pu_polygons` `sim_features` `sim_features_zones` `sim_pu_polygons` `sim_pu_zones_polygons` `sim_pu_zones_polygons` `sim_pu_lines` `sim_pu_points` `sim_pu_raster` `sim_pu_zones_stack` `sim_phylogeny` `sim_locked_in_raster` `sim_locked_out_raster`	Simulated conservation planning data
Create and solve problems Functions for creating new problems and solving them
`problem(<Raster>,<Raster>)` `problem(<Raster>,<ZonesRaster>)` `problem(<Spatial>,<Raster>)` `problem(<Spatial>,<ZonesRaster>)` `problem(<Spatial>,<character>)` `problem(<Spatial>,<ZonesCharacter>)` `problem(<data.frame>,<character>)` `problem(<data.frame>,<ZonesCharacter>)` `problem(<data.frame>,<data.frame>)` `problem(<numeric>,<data.frame>)` `problem(<matrix>,<data.frame>)`	Conservation planning problem
`marxan_problem()`	Marxan conservation problem
`solve`	Solve
Objectives Functions for adding an objective to a problem
`objectives`	Problem objective
`add_max_cover_objective()`	Add maximum coverage objective
`add_max_features_objective()`	Add maximum feature representation objective
`add_max_phylo_div_objective()`	Add maximum phylogenetic diversity objective
`add_max_phylo_end_objective()`	Add maximum phylogenetic endemism objective
`add_max_utility_objective()`	Add maximum utility objective
`add_min_set_objective()`	Add minimum set objective
`add_min_shortfall_objective()`	Add minimum shortfall objective
Targets Functions for adding targets to a problem
`targets`	Targets
`add_absolute_targets(<ConservationProblem>,<numeric>)` `add_absolute_targets(<ConservationProblem>,<matrix>)` `add_absolute_targets(<ConservationProblem>,<character>)`	Add absolute targets
`add_loglinear_targets()`	Add targets using log-linear scaling
`add_manual_targets(<ConservationProblem>,<data.frame>)` `add_manual_targets(<ConservationProblem>,<tbl_df>)`	Add manual targets
`add_relative_targets(<ConservationProblem>,<numeric>)` `add_relative_targets(<ConservationProblem>,<matrix>)` `add_relative_targets(<ConservationProblem>,<character>)`	Add relative targets
Constraints Functions for adding constraints to a problem
`constraints`	Conservation problem constraints
`add_contiguity_constraints(<ConservationProblem>,<ANY>,<ANY>)` `add_contiguity_constraints(<ConservationProblem>,<ANY>,<data.frame>)` `add_contiguity_constraints(<ConservationProblem>,<ANY>,<matrix>)`	Add contiguity constraints
`add_feature_contiguity_constraints(<ConservationProblem>,<ANY>,<Matrix>)` `add_feature_contiguity_constraints(<ConservationProblem>,<ANY>,<data.frame>)` `add_feature_contiguity_constraints(<ConservationProblem>,<ANY>,<matrix>)` `add_feature_contiguity_constraints(<ConservationProblem>,<ANY>,<ANY>)`	Add feature contiguity constraints
`add_locked_in_constraints()`	Add locked in constraints
`add_locked_out_constraints()`	Add locked out constraints
`add_mandatory_allocation_constraints(<ConservationProblem>)`	Add mandatory allocation constraints
`add_manual_locked_constraints()`	Add manually specified locked constraints
`add_neighbor_constraints(<ConservationProblem>,<ANY>,<ANY>,<ANY>)` `add_neighbor_constraints(<ConservationProblem>,<ANY>,<ANY>,<data.frame>)` `add_neighbor_constraints(<ConservationProblem>,<ANY>,<ANY>,<matrix>)` `add_neighbor_constraints(<ConservationProblem>,<ANY>,<ANY>,<array>)`	Add neighbor constraints
Penalties Functions for adding penalties to a problem
`penalties`	Conservation problem penalties
`add_boundary_penalties()`	Add boundary penalties
`add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<matrix>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<Matrix>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<dgCMatrix>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<data.frame>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<array>)`	Add connectivity penalties
`add_feature_weights(<ConservationProblem>,<numeric>)` `add_feature_weights(<ConservationProblem>,<matrix>)`	Add feature weights
Decisions Functions for specifying the type of decisions in a problem
`decisions`	Specify the type of decisions
`add_binary_decisions()`	Add binary decisions
`add_default_decisions()`	Add default decisions
`add_proportion_decisions()`	Add proportion decisions
`add_semicontinuous_decisions()`	Add semi-continuous decisions
Solvers Functions for specifying how a problem should be solved
`solvers`	Problem solvers
`add_default_solver()`	Default solver
`add_gurobi_solver()`	Add a Gurobi solver
`add_lpsymphony_solver()`	Add a SYMPHONY solver with lpsymphony
`add_rsymphony_solver()`	Add a SYMPHONY solver with Rsymphony
Portfolios Functions for generating a portfolio of solutions
`portfolios`	Solution portfolios
`add_cuts_portfolio()`	Add Bender's cuts portfolio
`add_pool_portfolio()`	Add a pool portfolio
`add_shuffle_portfolio()`	Add a shuffle portfolio
Irreplaceability Functions for calculating irreplaceability scores for a solution
`irreplaceability`	Irreplaceability
`replacement_cost(<ConservationProblem>,<numeric>)` `replacement_cost(<ConservationProblem>,<matrix>)` `replacement_cost(<ConservationProblem>,<data.frame>)` `replacement_cost(<ConservationProblem>,<Spatial>)` `replacement_cost(<ConservationProblem>,<Raster>)`	Replacement cost
`rarity_weighted_richness(<ConservationProblem>,<numeric>)` `rarity_weighted_richness(<ConservationProblem>,<matrix>)` `rarity_weighted_richness(<ConservationProblem>,<data.frame>)` `rarity_weighted_richness(<ConservationProblem>,<Spatial>)` `rarity_weighted_richness(<ConservationProblem>,<Raster>)`	Rarity weighted richness
Data simulation Functions for simulating new data sets
`simulate_cost()`	Simulate cost data
`simulate_data()`	Simulate data
`simulate_species()`	Simulate species habitat suitability data
Geoprocessing Functions for manipulating spatial data sets
`fast_extract(<Raster>,<SpatialPolygons>)` `fast_extract(<Raster>,<SpatialLines>)` `fast_extract(<Raster>,<SpatialPoints>)`	Fast extract
`intersecting_units(<Raster>,<Raster>)` `intersecting_units(<Spatial>,<Spatial>)` `intersecting_units(<Raster>,<Spatial>)` `intersecting_units(<Spatial>,<Raster>)` `intersecting_units(<data.frame>,<ANY>)`	Find intersecting units
Parallel processing Functions for controlling multi-threaded processing
`distribute_load()`	Distribute load
`is.parallel()`	Is parallel?
`set_number_of_threads()` `get_number_of_threads()`	Number of threads for data processing
Processing multi-zone data Functions for manipulating data that pertain to multiple zones
`category_layer()`	Category layer
`category_vector()`	Category vector
`binary_stack()`	Binary stack
Matrix functions Functions for creating matrices that are used in conservation planning problems for use in planning problems
`boundary_matrix()`	Boundary matrix
`branch_matrix()`	Branch matrix
`connected_matrix()`	Connected matrix
`connectivity_matrix(<Spatial>,<character>)` `connectivity_matrix(<Spatial>,<Raster>)` `connectivity_matrix(<Raster>,<Raster>)`	Connectivity matrix
`marxan_boundary_data_to_matrix()`	Convert Marxan boundary data to a matrix format
`rij_matrix(<Raster>,<Raster>)` `rij_matrix(<Spatial>,<Raster>)`	Feature by planning unit matrix
Problem manipulation functions Functions for extracting information from problems
`number_of_features()`	Number of features
`number_of_planning_units()`	Number of planning units
`number_of_total_units()`	Number of total units
`number_of_zones()`	Number of zones
`feature_names()`	Feature names
`zone_names()`	Zone names
Miscellaneous functions Assorted functions distributed with the package
`print(<ConservationProblem>)` `print(<ConservationModifier>)` `print(<Id>)` `print(<Id>)` `print(<OptimizationProblem>)` `print(<ScalarParameter>)` `print(<ArrayParameter>)` `print(<Solver>)` `print(<Zones>)` `print(<tbl_df>)`	Print
`show(<ConservationModifier>)` `show(<ConservationProblem>)` `show(<Id>)` `show(<OptimizationProblem>)` `show(<Parameter>)` `show(<Solver>)`	Show
`%>%`	Pipe operator
`%T>%`	Tee operator
`is.Id()` `is.Waiver()`	Is it?
`as.Id()` `as.list(<Parameters>)` `as.list(<Zones>)`	Coerce object to another object
`compile()`	Compile a problem
`presolve_check()`	Presolve check
`loglinear_interpolation()`	Log-linear interpolation
`run_calculations()`	Run calculations
`feature_representation(<ConservationProblem>,<numeric>)` `feature_representation(<ConservationProblem>,<matrix>)` `feature_representation(<ConservationProblem>,<data.frame>)` `feature_representation(<ConservationProblem>,<Spatial>)` `feature_representation(<ConservationProblem>,<Raster>)`	Feature representation
`feature_abundances()`	Feature abundances
Class definitions and methods These pages document the package’s internal data structures and functions for manipulating them—they contain information that is really only useful when adding new functionality to the package
`new_id()`	Identifier
`new_waiver()`	Waiver
`pproto()`	Create a new `pproto` object
`new_optimization_problem()`	Optimization problem
`predefined_optimization_problem()`	Predefined optimization problem
`as.list(<OptimizationProblem>)`	Convert `OptimizationProblem` to list
`ArrayParameter-class`	Array parameter prototype
`Collection-class`	Collection prototype
`ConservationModifier-class`	Conservation problem modifier prototype
`ConservationProblem-class`	Conservation problem class
`Constraint-class`	Constraint prototype
`Decision-class`	Decision prototype
`MiscParameter-class`	Miscellaneous parameter prototype
`Objective-class`	Objective prototype
`OptimizationProblem-class`	Optimization problem class
`Parameter-class`	Parameter class
`Parameters-class`	Parameters class
`Penalty-class`	Penalty prototype
`Portfolio-class`	Portfolio prototype
`ScalarParameter-class`	Scalar parameter prototype
`Solver-class`	Solver prototype
`Target-class`	Target prototype
`zones()`	Management zones
`nrow()` `ncol()` `ncell()` `modelsense()` `vtype()` `obj()` `A()` `rhs()` `sense()` `lb()` `ub()` `col_ids()` `row_ids()` `compressed_formulation()`	Optimization problem methods
`nrow(<tbl_df>)` `ncol(<tbl_df>)` `as.list(<tbl_df>)`	Manipulate tibbles
Parameter definitions These pages document the package’s internal data structures for representing different types of variables—they contain information that is really only useful when adding new functionality to the package
`proportion_parameter_array()` `binary_parameter_array()` `integer_parameter_array()` `numeric_parameter_array()`	Array parameters
`numeric_matrix_parameter()` `binary_matrix_parameter()`	Matrix parameters
`misc_parameter()`	Miscellaneous parameter
`proportion_parameter()` `binary_parameter()` `integer_parameter()` `numeric_parameter()`	Scalar parameters
`parameters()`	Parameters

Reference

Summary

Data

Create and solve problems

Objectives

Targets

Constraints

Penalties

Decisions

Solvers

Portfolios

Irreplaceability

Data simulation

Geoprocessing

Parallel processing

Processing multi-zone data

Matrix functions

Problem manipulation functions

Miscellaneous functions

Class definitions and methods

Parameter definitions

Contents