Function reference • oppr

Summary Summary of the package
`oppr`	oppr: Optimal Project Prioritization
Data Simulated datasets and data simulation functions
`sim_actions` `sim_projects` `sim_features` `sim_tree`	Simulated data
`simulate_ppp_data()`	Simulate data for the 'Project Prioritization Protocol'
`simulate_ptm_data()`	Simulate data for 'Priority threat management'
Create and solve problems Functions for creating new problems and solving them
`problem()`	Project prioritization problem
`solve(<OptimizationProblem>,<Solver>)` `solve(<ProjectProblem>,<missing>)`	Solve
Evaluate solutions Functions for evaluating and visualizing solutions to a problem
`plot(<ProjectProblem>)`	Plot a solution to a project prioritization problem
`plot_feature_persistence()`	Plot a bar plot to visualize a project prioritization
`plot_phylo_persistence()`	Plot a phylogram to visualize a project prioritization
`solution_statistics()`	Solution statistics
`replacement_costs()`	Replacement cost
`project_cost_effectiveness()`	Project cost effectiveness
Objectives Functions for adding an objective to a problem
`objectives`	Problem objective
`add_max_phylo_div_objective()`	Add maximum phylogenetic diversity objective
`add_max_richness_objective()`	Add maximum richness objective
`add_max_targets_met_objective()`	Add maximum targets met objective
`add_min_set_objective()`	Add minimum set objective
Weights Functions for adding weights to a problem
`weights`	Weights
`add_feature_weights()`	Add feature weights
Targets Functions for adding targets to a problem
`targets`	Targets
`add_absolute_targets()`	Add absolute targets
`add_manual_targets()`	Add manual targets
`add_relative_targets()`	Add relative targets
Constraints Functions for adding constraints to a problem
`constraints`	Project prioritization problem constraints
`add_locked_in_constraints()`	Add locked in constraints
`add_locked_out_constraints()`	Add locked out constraints
`add_manual_locked_constraints()`	Add manually specified locked constraints
Decisions Functions for specifying the type of decisions in a problem
`decisions`	Specify the type of decisions
`add_binary_decisions()`	Add binary decisions
Solvers Functions for specifying how a problem should be solved
`solvers`	Problem solvers
`add_default_solver()`	Add a default solver
`add_gurobi_solver()`	Add a Gurobi solver
`add_heuristic_solver()`	Add a heuristic solver
`add_lpsolveapi_solver()`	Add a lp_solve solver with lpSolveAPI
`add_lpsymphony_solver()`	Add a SYMPHONY solver with lpsymphony
`add_random_solver()`	Add a random solver
`add_rsymphony_solver()`	Add a SYMPHONY solver with Rsymphony
Problem manipulation functions Functions for extracting information from problems
`feature_names()`	Feature names
`action_names()`	Action names
`project_names()`	Project names
`number_of_features()`	Number of features
`number_of_actions()`	Number of actions
`number_of_projects()`	Number of projects
Miscellaneous functions Assorted functions distributed with the package
`print(<ProjectProblem>)` `print(<ProjectModifier>)` `print(<Id>)` `print(<Id>)` `print(<OptimizationProblem>)` `print(<ScalarParameter>)` `print(<ArrayParameter>)` `print(<Solver>)`	Print
`show(<ProjectModifier>)` `show(<ProjectProblem>)` `show(<Id>)` `show(<OptimizationProblem>)` `show(<Parameter>)` `show(<Solver>)`	Show
`%>%` `pipe`	Pipe operator
`%T>%` `tee`	Tee operator
`is.Id()` `is.Waiver()`	Is it?
`as.Id()` `as.list(<Parameters>)`	Coerce object to another object
`compile()`	Compile a problem
`branch_matrix()`	Branch matrix
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
`as.list(<OptimizationProblem>)`	Convert `OptimizationProblem` to list
`ArrayParameter-class` `ArrayParameter`	Array parameter prototype
`Collection-class` `Collection`	Collection prototype
`Constraint-class` `Constraint`	Constraint prototype
`Decision-class` `Decision`	Decision prototype
`MiscParameter-class` `MiscParameter`	Miscellaneous parameter prototype
`Objective-class` `Objective`	Objective prototype
`OptimizationProblem-class` `OptimizationProblem`	Optimization problem class
`Parameter-class` `Parameter`	Parameter class
`Parameters-class` `Parameters`	Parameters class
`ProjectModifier-class` `ProjectModifier`	Conservation problem modifier prototype
`ProjectProblem-class` `ProjectProblem`	Project problem class
`ScalarParameter-class` `ScalarParameter`	Scalar parameter prototype
`Solver-class` `Solver` `GurobiSolver-class` `HeuristicSolver-class` `LpsolveapiSolver-class` `LpsymphonySolver-class` `RandomSolver-class` `RsymphonySolver-class`	Solver prototype
`Target-class` `Target`	Target prototype
`Weight-class` `Weight`	Weight prototype
`nrow()` `ncol()` `ncell()` `modelsense()` `vtype()` `obj()` `pwlobj()` `A()` `rhs()` `sense()` `lb()` `ub()` `col_ids()` `row_ids()` `number_of_branches()` `get_data()`	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

Evaluate solutions

Objectives

Weights

Targets

Constraints

Decisions

Solvers

Problem manipulation functions

Miscellaneous functions

Class definitions and methods

Parameter definitions