Function reference • prioritizr

Overview Overview of the package
`prioritizr`	prioritizr: Systematic Conservation Prioritization in R
Create and solve problems Functions for creating new problems and solving them
`problem()`	Conservation planning problem
`marxan_problem()`	Marxan conservation problem
`solve(<OptimizationProblem>,<Solver>)` `solve(<ConservationProblem>,<missing>)`	Solve
Data Simulated datasets distributed with the package
`sim_pu_polygons` `sim_pu_zones_polygons` `sim_pu_points` `sim_pu_lines` `sim_pu_sf` `sim_pu_zones_sf` `sim_pu_raster` `sim_locked_in_raster` `sim_locked_out_raster` `sim_pu_zones_stack` `sim_features` `sim_features_zones` `sim_phylogeny`	Simulated conservation planning data
Objectives Functions for adding an objective to a problem
`objectives`	Add an 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_largest_shortfall_objective()`	Add minimum largest shortfall 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`	Add representation targets
`add_absolute_targets()`	Add absolute targets
`add_loglinear_targets()`	Add targets using log-linear scaling
`add_manual_targets()`	Add manual targets
`add_relative_targets()`	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_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<character>)` `add_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<numeric>)` `add_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<matrix>)` `add_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<Matrix>)` `add_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<Raster>)` `add_linear_constraints(<ConservationProblem>,<ANY>,<ANY>,<dgCMatrix>)`	Add linear 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_bounded_constraints()`	Add manually specified bound 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`	Add a penalty
`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>,<data.frame>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<dgCMatrix>)` `add_connectivity_penalties(<ConservationProblem>,<ANY>,<ANY>,<array>)`	Add connectivity penalties
`add_linear_penalties(<ConservationProblem>,<ANY>,<character>)` `add_linear_penalties(<ConservationProblem>,<ANY>,<numeric>)` `add_linear_penalties(<ConservationProblem>,<ANY>,<matrix>)` `add_linear_penalties(<ConservationProblem>,<ANY>,<Matrix>)` `add_linear_penalties(<ConservationProblem>,<ANY>,<Raster>)` `add_linear_penalties(<ConservationProblem>,<ANY>,<dgCMatrix>)`	Add linear 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`	Add decision types
`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_cbc_solver()`	Add a CBC solver
`add_cplex_solver()`	Add a CPLEX solver
`add_default_solver()`	Add a 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_extra_portfolio()`	Add an extra portfolio
`add_gap_portfolio()`	Add a gap portfolio
`add_shuffle_portfolio()`	Add a shuffle portfolio
`add_top_portfolio()`	Add a top portfolio
Summary statistics Functions for summarizing the performance of solutions
`summaries`	Evaluate solutions using summary statistics
`eval_boundary_summary()`	Evaluate solution boundary length
`eval_connectivity_summary(<ConservationProblem>,<ANY>,<ANY>,<matrix>)` `eval_connectivity_summary(<ConservationProblem>,<ANY>,<ANY>,<Matrix>)` `eval_connectivity_summary(<ConservationProblem>,<ANY>,<ANY>,<data.frame>)` `eval_connectivity_summary(<ConservationProblem>,<ANY>,<ANY>,<dgCMatrix>)` `eval_connectivity_summary(<ConservationProblem>,<ANY>,<ANY>,<array>)`	Evaluate solution connectivity
`eval_cost_summary()`	Evaluate solution cost
`eval_feature_representation_summary()`	Evaluate feature representation
`eval_n_summary()`	Evaluate number of planning units selected
`eval_target_coverage_summary()`	Evaluate target coverage
Importance Functions for calculating importance scores for a solution
`importance`	Evaluate solution importance
`eval_ferrier_importance()`	Evaluate solution importance using Ferrier scores
`eval_rare_richness_importance()`	Evaluate solution importance using rarity weighted richness scores
`eval_replacement_importance()`	Evaluate solution importance using replacement cost scores
Data simulation Functions for simulating new datasets
`simulate_cost()`	Simulate cost data
`simulate_data()`	Simulate data
`simulate_species()`	Simulate species habitat suitability data
Geoprocessing Functions for manipulating spatial datasets
`fast_extract()`	Fast extract
`intersecting_units()`	Find intersecting units
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
`adjacency_matrix()`	Adjacency matrix
`boundary_matrix()`	Boundary matrix
`branch_matrix()`	Branch matrix
`connectivity_matrix()`	Connectivity matrix
`marxan_boundary_data_to_matrix()`	Convert Marxan boundary data to a matrix format
`proximity_matrix()`	Proximity matrix
`rij_matrix()`	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
`feature_abundances()`	Feature abundances
`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
`distribute_load()`	Distribute load
`write_problem()`	Write problem
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
Deprecated functions
`add_connected_constraints()` `add_corridor_constraints()` `set_number_of_threads()` `get_number_of_threads()` `is.parallel()` `add_pool_portfolio()` `connected_matrix()` `feature_representation()` `replacement_cost()` `rarity_weighted_richness()` `ferrier_score()`	Deprecation notice

Reference

Overview

Create and solve problems

Data

Objectives

Targets

Constraints

Penalties

Decisions

Solvers

Portfolios

Summary statistics

Importance

Data simulation

Geoprocessing

Processing multi-zone data

Matrix functions

Problem manipulation functions

Miscellaneous functions

Class definitions and methods

Parameter definitions

Deprecated functions