# prioritizr

## Systematic Conservation Prioritization in R

The *prioritizr R* package uses mixed integer linear programming (MILP)
techniques to provide a flexible interface for building and solving
conservation planning problems. It supports a broad range of objectives,
constraints, and penalties that can be used to custom-tailor
conservation planning problems to the specific needs of a conservation
planning exercise. Once built, conservation planning problems can be
solved using a variety of commercial and open-source exact algorithm
solvers. In contrast to the algorithms conventionally used to solve
conservation problems, such as heuristics or simulated annealing, the
exact algorithms used here are guaranteed to find optimal solutions.
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. Finally, this package has the functionality to
read input data formatted for the *Marxan* conservation planning
program, and find much cheaper solutions in a much shorter period of
time than *Marxan*.

[![YouTube
video](reference/figures/youtube-thumbnail.png)](https://www.youtube.com/watch?v=c7XgODGr9lE)

## Installation

#### Official version

The latest official version of the *prioritizr R* package can be
installed from the [Comprehensive R Archive Network
(CRAN)](https://cran.r-project.org/) using the following *R* code.

``` r
install.packages("prioritizr", repos = "https://cran.rstudio.com/")
```

#### Developmental version

The latest development version can be installed to gain access to new
functionality that is not yet present in the latest official version.
Please note that the developmental version is more likely to contain
coding errors than the official version. To install the developmental
version, you can install it directly from the [GitHub online code
repository](https://github.com/prioritizr/prioritizr) or from the [R
Universe](https://prioritizr.r-universe.dev/prioritizr). In general, we
recommend installing the developmental version from the [R
Universe](https://prioritizr.r-universe.dev/prioritizr). This is because
installation via [R
Universe](https://prioritizr.r-universe.dev/prioritizr) does not require
any additional software (e.g.,
[RTools](https://cran.r-project.org/bin/windows/Rtools/) for Windows
systems, or [Xcode and gfortran](https://mac.r-project.org/tools/) for
macOS systems).

- To install the latest development version from [R
  Universe](https://prioritizr.r-universe.dev/prioritizr), use the
  following *R* code.

  ``` r
  install.packages(
    "prioritizr",
    repos = c(
      "https://prioritizr.r-universe.dev",
      "https://cloud.r-project.org"
    )
  )
  ```

- To install the latest development version from
  [GitHub](https://github.com/prioritizr/prioritizr), use the following
  *R* code.

  ``` r
  if (!require(remotes)) install.packages("remotes")
  remotes::install_github("prioritizr/prioritizr")
  ```

## Citation

Please cite the *prioritizr R* package when using it in publications. To
cite the package, please use:

> Hanson JO, Schuster R, Strimas‐Mackey M, Morrell N, Edwards BPM,
> Arcese P, Bennett JR, and Possingham HP (2025) Systematic conservation
> prioritization with the prioritizr R package. *Conservation Biology*,
> **39**: e14376.

Additionally, we keep a [record of
publications](https://prioritizr.net/articles/publication_record.html)
that use the *prioritizr R* package. If you use this package in any
reports or publications, please [file an issue on
GitHub](https://github.com/prioritizr/prioritizr/issues/new) so we can
add it to the record.

## Usage

Here we provide a short example showing how the *prioritizr R* package
can be used to build and solve conservation problems. Specifically, we
will use an example dataset available through the *prioritizrdata R*
package. Additionally, we will use the *terra R* package to perform
raster calculations. To begin with, we will load the packages.

``` r
# to install packages for this example, please use:
# install.packages(c("prioritizr", "prioritizrdata"))

# load packages
library(prioritizr)
library(prioritizrdata)
library(terra)
```

We will use the Washington dataset in this example. To import the
planning unit data, we will use the
[`get_wa_pu()`](http://prioritizr.github.io/prioritizrdata/reference/wa_data.md)
function. Although the *prioritizr R* package can support many different
types of planning unit data, here our planning units are represented as
a single-layer raster (i.e.,
[`terra::rast()`](https://rspatial.github.io/terra/reference/rast.html)
object). Each cell represents a different planning unit, and cell values
denote land acquisition costs. Specifically, there are 10757 planning
units in total (i.e., cells with non-missing values).

``` r
# import planning unit data
wa_pu <- get_wa_pu()

# preview data
print(wa_pu)
```

``` R
## class       : SpatRaster
## size        : 109, 147, 1  (nrow, ncol, nlyr)
## resolution  : 4000, 4000  (x, y)
## extent      : -1816382, -1228382, 247483.5, 683483.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs
## source      : wa_pu.tif
## name        :        cost
## min value   :    0.298665
## max value   : 1804.183838
```

``` r
# plot data
plot(wa_pu, main = "Costs", axes = FALSE)
```

![](reference/figures/README-planning_units-1.png)

Next, we will use the
[`get_wa_features()`](http://prioritizr.github.io/prioritizrdata/reference/wa_data.md)
function to import the conservation feature data. Although the
*prioritizr R* package can support many different types of feature data,
here our feature data are represented as a multi-layer raster (i.e.,
[`terra::rast()`](https://rspatial.github.io/terra/reference/rast.html)
object). Each layer describes the spatial distribution of a feature.
Here, our feature data correspond to different bird species. To account
for migratory patterns, the breeding and non-breeding distributions of
species are represented as different features. Specifically, the cell
values denote the relative abundance of individuals, with higher values
indicating greater abundance.

``` r
# import feature data
wa_features <- get_wa_features()

# preview data
print(wa_features)
```

``` R
## class       : SpatRaster
## size        : 109, 147, 396  (nrow, ncol, nlyr)
## resolution  : 4000, 4000  (x, y)
## extent      : -1816382, -1228382, 247483.5, 683483.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs
## source      : wa_features.tif
## names       : Recur~ding), Botau~ding), Botau~ding), Corvu~ding), Corvu~ding), Cincl~full), ...
## min values  :           0,           0,           0,           0,           0,           0, ...
## max values  :       0.514,       0.812,       3.129,       0.115,       0.296,        0.06, ...
```

``` r
# plot the first nine features
plot(wa_features[[1:9]], nr = 3, axes = FALSE)
```

![](reference/figures/README-features-1.png)

Let’s make sure that you have a solver installed on your computer. This
is important so that you can use optimization algorithms to generate
spatial prioritizations. If this is your first time using the
*prioritizr R* package, please install the HiGHS solver using the
following *R* code. Although the HiGHS solver is relatively fast and
easy to install, please note that you’ll need to install the [Gurobi
software suite and the *gurobi* *R* package](https://www.gurobi.com/)
for best performance (see the [Gurobi Installation
Guide](https://prioritizr.net/articles/gurobi_installation_guide.html)
for details).

``` r
# if needed, install HiGHS solver
install.packages("highs", repos = "https://cran.rstudio.com/")
```

Now, let’s generate a spatial prioritization. To ensure feasibility, we
will set a budget. Specifically, the total cost of the prioritization
will represent a 5% of the total land value in the study area. Given
this budget, we want the prioritization to increase feature
representation, as much as possible, so that each feature would,
ideally, have 20% of its distribution covered by the prioritization. In
this scenario, we can either purchase all of the land inside a given
planning unit, or none of the land inside a given planning unit. Thus we
will create a new
[`problem()`](https://prioritizr.net/reference/problem.md) that will use
a minimum shortfall objective (via
[`add_min_shortfall_objective()`](https://prioritizr.net/reference/add_min_shortfall_objective.md)),
with relative targets of 20% (via
[`add_relative_targets()`](https://prioritizr.net/reference/add_relative_targets.md)),
binary decisions (via
[`add_binary_decisions()`](https://prioritizr.net/reference/add_binary_decisions.md)),
and specify that we want near-optimal solutions (i.e., 10% from
optimality) using the best solver installed on our computer (via
[`add_default_solver()`](https://prioritizr.net/reference/add_default_solver.md)).

``` r
# calculate budget
budget <- terra::global(wa_pu, "sum", na.rm = TRUE)[[1]] * 0.05

# create problem
p1 <-
  problem(wa_pu, features = wa_features) %>%
  add_min_shortfall_objective(budget) %>%
  add_relative_targets(0.2) %>%
  add_binary_decisions() %>%
  add_default_solver(gap = 0.1, verbose = FALSE)

# print problem
print(p1)
```

``` R
## A conservation problem (<ConservationProblem>)
## ├•data
## │├•features:    "Recurvirostra americana (breeding)", … (396 total)
## │└•planning units:
## │ ├•data:       <SpatRaster> (10757 total)
## │ ├•costs:      continuous values (between 0.2986647 and 1804.184)
## │ ├•extent:     -1816382, 247483.5, -1228382, 683483.5 (xmin, ymin, xmax, ymax)
## │ └•CRS:        +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs (projected)
## ├•formulation
## │├•objective:   minimum shortfall objective (`budget` = 8748.491)
## │├•penalties:   none specified
## │├•features:
## ││├•targets:    relative targets (all equal to 0.2)
## ││└•weights:    none specified
## │├•constraints: none specified
## │└•decisions:   binary decision
## └•optimization
##  ├•portfolio:   single portfolio
##  └•solver:      gurobi solver (`gap` = 0.1, `time_limit` = 2147483647, `presolve` = 2, `threads` = 1, …)
## # ℹ Use `summary(...)` to see further details.
```

After we have built a
[`problem()`](https://prioritizr.net/reference/problem.md), we can solve
it to obtain a solution.

``` r
# solve the problem
s1 <- solve(p1)

# extract the objective
print(attr(s1, "objective"))
```

``` R
## solution_1 
##    4.46285
```

``` r
# extract time spent solving the problem
print(attr(s1, "runtime"))
```

``` R
## solution_1 
##      3.857
```

``` r
# extract state message from the solver
print(attr(s1, "status"))
```

``` R
## solution_1 
##  "OPTIMAL"
```

``` r
# plot the solution
plot(s1, main = "Solution", axes = FALSE)
```

![](reference/figures/README-minimal_solution-1.png)

After generating a solution, it is important to evaluate it. Here, we
will calculate the number of planning units selected by the solution,
and the total cost of the solution. We can also check how many
representation targets are met by the solution.

``` r
# calculate number of selected planning units by solution
eval_n_summary(p1, s1)
```

``` R
## # A tibble: 1 × 2
##   summary     n
##   <chr>   <dbl>
## 1 overall  2348
```

``` r
# calculate total cost of solution
eval_cost_summary(p1, s1)
```

``` R
## # A tibble: 1 × 2
##   summary  cost
##   <chr>   <dbl>
## 1 overall 8748.
```

``` r
# calculate target coverage for the solution
p1_target_coverage <- eval_target_coverage_summary(p1, s1)
print(p1_target_coverage)
```

``` R
## # A tibble: 396 × 10
##    feature   met   total_amount absolute_target absolute_held absolute_shortfall
##    <chr>     <lgl>        <dbl>           <dbl>         <dbl>              <dbl>
##  1 Recurvir… TRUE         100.             20.0          23.4               0   
##  2 Botaurus… TRUE          99.9            20.0          29.2               0   
##  3 Botaurus… TRUE         100.             20.0          34.0               0   
##  4 Corvus b… TRUE          99.9            20.0          20.4               0   
##  5 Corvus b… FALSE         99.9            20.0          18.5               1.46
##  6 Cinclus … TRUE         100.             20.0          20.5               0   
##  7 Spinus t… TRUE          99.9            20.0          22.6               0   
##  8 Spinus t… TRUE          99.9            20.0          23.1               0   
##  9 Falco sp… TRUE          99.9            20.0          24.9               0   
## 10 Falco sp… TRUE         100.0            20.0          24.5               0   
## # ℹ 386 more rows
## # ℹ 4 more variables: relative_target <dbl>, relative_held <dbl>,
## #   relative_shortfall <dbl>, relative_met <dbl>
```

``` r
# check percentage of the features that have their target met given the solution
print(mean(p1_target_coverage$met) * 100)
```

``` R
## [1] 96.21212
```

Although this solution helps meet the representation targets, it does
not account for existing protected areas inside the study area. As such,
it does not account for the possibility that some features could be
partially – or even fully – represented by existing protected areas and,
in turn, might fail to identify meaningful priorities for new protected
areas. To address this issue, we will use the
[`get_wa_locked_in()`](http://prioritizr.github.io/prioritizrdata/reference/wa_data.md)
function to import spatial data for protected areas in the study area.
We will then add constraints to the
[`problem()`](https://prioritizr.net/reference/problem.md) to ensure
they are selected by the solution (via
[`add_locked_in_constraints()`](https://prioritizr.net/reference/add_locked_in_constraints.md)).

``` r
# import locked in data
wa_locked_in <- get_wa_locked_in()

# print data
print(wa_locked_in)
```

``` R
## class       : SpatRaster
## size        : 109, 147, 1  (nrow, ncol, nlyr)
## resolution  : 4000, 4000  (x, y)
## extent      : -1816382, -1228382, 247483.5, 683483.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs
## source      : wa_locked_in.tif
## name        : protected areas
## min value   :               0
## max value   :               1
```

``` r
# plot data
plot(wa_locked_in, main = "Existing protected areas", axes = FALSE)
```

![](reference/figures/README-locked_in_constraints-1.png)

``` r
# create new problem with locked in constraints added to it
p2 <-
  p1 %>%
  add_locked_in_constraints(wa_locked_in)

# solve the problem
s2 <- solve(p2)

# plot the solution
plot(s2, main = "Solution", axes = FALSE)
```

![](reference/figures/README-locked_in_constraints-2.png)

This solution is an improvement over the previous solution. However,
there are some places in the study area that are not available for
protected area establishment (e.g., due to land tenure). As a
consequence, the solution might not be practical for implementation,
because it might select some places that are not available for
protection. To address this issue, we will use the
[`get_wa_locked_out()`](http://prioritizr.github.io/prioritizrdata/reference/wa_data.md)
function to import spatial data describing which planning units are not
available for protection. We will then add constraints to the
[`problem()`](https://prioritizr.net/reference/problem.md) to ensure
they are not selected by the solution (via
[`add_locked_out_constraints()`](https://prioritizr.net/reference/add_locked_out_constraints.md)).

``` r
# import locked out data
wa_locked_out <- get_wa_locked_out()

# print data
print(wa_locked_out)
```

``` R
## class       : SpatRaster
## size        : 109, 147, 1  (nrow, ncol, nlyr)
## resolution  : 4000, 4000  (x, y)
## extent      : -1816382, -1228382, 247483.5, 683483.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs
## source      : wa_locked_out.tif
## name        : urban areas
## min value   :           0
## max value   :           1
```

``` r
# plot data
plot(wa_locked_out, main = "Areas not available for protection", axes = FALSE)
```

![](reference/figures/README-locked_out_constraints-1.png)

``` r
# create new problem with locked out constraints added to it
p3 <-
  p2 %>%
  add_locked_out_constraints(wa_locked_out)

# solve the problem
s3 <- solve(p3)

# plot the solution
plot(s3, main = "Solution", axes = FALSE)
```

![](reference/figures/README-locked_out_constraints-2.png)

This solution is even better then the previous solution. However, we are
not finished yet. The planning units selected by the solution are fairly
fragmented. This can cause issues because fragmentation increases
management costs and reduces conservation benefits through edge effects.
To address this issue, we can further modify the problem by adding
penalties that punish overly fragmented solutions (via
[`add_boundary_penalties()`](https://prioritizr.net/reference/add_boundary_penalties.md)).
Here we will use a penalty factor (i.e., boundary length modifier) of
0.003, and an edge factor of 50% so that planning units that occur on
the outer edge of the study area are not overly penalized.

``` r
# create new problem with boundary penalties added to it
p4 <-
  p3 %>%
  add_boundary_penalties(penalty = 0.003, edge_factor = 0.5)

# solve the problem
s4 <- solve(p4)

# plot the solution
plot(s4, main = "Solution", axes = FALSE)
```

![](reference/figures/README-boundary_penalties-1.png)

Now, let’s explore which planning units selected by the solution are
most important for cost-effectively meeting the targets. To achieve
this, we will calculate importance (irreplaceability) scores using an
incremental rank approach. Briefly, the optimization problem is solved
multiple times in an incremental process with increasing budgets, and
planning units are assigned ranks based on which increment they are
selected in. Planning units selected earlier on in the process are
considered more important.

``` r
# calculate importance scores
imp <-
  p4 %>%
  eval_rank_importance(s4, n = 5)

# print scores
print(imp)
```

``` R
## class       : SpatRaster
## size        : 109, 147, 1  (nrow, ncol, nlyr)
## resolution  : 4000, 4000  (x, y)
## extent      : -1816382, -1228382, 247483.5, 683483.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere +units=m +no_defs
## source(s)   : memory
## varname     : wa_pu
## name        : rs
## min value   :  0
## max value   :  1
```

``` r
# set planning units that are locked in to -1 so we can easily
# see importance scores for priority areas
imp <- terra::mask(imp, s4, maskvalues = 0, updatevalue = -1)

# plot the total importance scores
## planning units shown in purple were not selected in solution s4
## planning units shown in blue are less important
## planning units shown in yellow are highly important
## note that locked in planning units are also shown in yellow
plot(imp, axes = FALSE,  main = "Importance scores")
```

![](reference/figures/README-importance-1.png)

This short example demonstrates how the *prioritizr R* package can be
used to build and customize conservation problems, and then solve them
to generate solutions. Although we explored just a few different
functions for modifying a conservation problem, the package provides
many functions for specifying objectives, constraints, penalties, and
decision variables, so that you can build and custom-tailor conservation
planning problems to suit your planning scenario.

## Learning resources

The [package website](https://prioritizr.net/index.html) contains
information on the *prioritizr R* package. Here you can find
[documentation for every function and built-in
dataset](https://prioritizr.net/reference/index.html), and [news
describing the updates in each package
version](https://prioritizr.net/news/index.html). It also contains the
following articles and tutorials.

- [**Getting
  started**](https://prioritizr.net/articles/prioritizr.html): Short
  tutorial on using the package.
- [**Package
  overview**](https://prioritizr.net/articles/package_overview.html):
  Introduction to systematic conservation planning and a comprehensive
  overview of the package.
- [**Connectivity
  tutorial**](https://prioritizr.net/articles/connectivity_tutorial.html):
  Tutorial on incorporating connectivity into prioritizations.
- [**Calibrating trade-offs
  tutorial**](https://prioritizr.net/articles/calibrating_trade-offs_tutorial.html):
  Tutorial on running calibration analyses to satisfy multiple criteria.
- [**Management zones
  tutorial**](https://prioritizr.net/articles/management_zones_tutorial.html):
  Tutorial on incorporating multiple management zones and actions into
  prioritizations.
- [**Gurobi installation
  guide**](https://prioritizr.net/articles/gurobi_installation_guide.html):
  Instructions for installing the *Gurobi* optimization suite for
  generating prioritizations.
- [**Solver
  benchmarks**](https://prioritizr.net/articles/solver_benchmarks.html):
  Performance comparison of optimization solvers for generating
  prioritizations.
- [**Publication
  record**](https://prioritizr.net/articles/publication_record.html):
  List of publications that have cited the package.

Additional resources can also be found in [online repositories under the
*prioritizr* organization](https://github.com/prioritizr). These
resources include [slides for talks and seminars about the
package](https://github.com/prioritizr/teaching). Additionally, workshop
materials are available too (e.g., [Carleton 2023
workshop](https://prioritizr.github.io/workshop/), [ECCB 2024
workshop](https://iiasa.github.io/eccb2024/), and [Statistical Methods
Webinar series 2025
workshop](https://github.com/eco4cast/Statistical-Methods-Seminar-Series/tree/main/schuster_prioritizr)).

## Getting help

If you have any questions about the *prioritizr R* package or
suggestions for improving it, please [post an issue on the code
repository](https://github.com/prioritizr/prioritizr/issues/new).

# Package index

## Overview

Overview of the package.

- [`prioritizr`](https://prioritizr.net/reference/prioritizr.md)
  [`prioritizr-package`](https://prioritizr.net/reference/prioritizr.md)
  : prioritizr: Systematic Conservation Prioritization in R

## Create and solve problems

Functions for creating new problems and solving them.

- [`problem()`](https://prioritizr.net/reference/problem.md) :
  Conservation planning problem
- [`solve(`*`<ConservationProblem>`*`)`](https://prioritizr.net/reference/solve.md)
  [`solve(`*`<MultiConservationProblem>`*`)`](https://prioritizr.net/reference/solve.md)
  : Solve
- [`zones()`](https://prioritizr.net/reference/zones.md) : Management
  zones

## Data

Simulated datasets distributed with the package.

- [`get_sim_pu_polygons()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_zones_pu_polygons()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_pu_lines()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_pu_points()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_pu_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_locked_in_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_locked_out_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_zones_pu_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_features()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_zones_features()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_phylogeny()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_complex_pu_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_complex_locked_in_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_complex_locked_out_raster()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_complex_features()`](https://prioritizr.net/reference/sim_data.md)
  [`get_sim_complex_historical_features()`](https://prioritizr.net/reference/sim_data.md)
  : Get simulated conservation planning data

## Objectives

Functions for adding an objective to a problem.

- [`objectives`](https://prioritizr.net/reference/objectives.md) : Add
  an objective
- [`add_max_cover_objective()`](https://prioritizr.net/reference/add_max_cover_objective.md)
  : Add maximum coverage objective
- [`add_max_n_targets_met_objective()`](https://prioritizr.net/reference/add_max_n_targets_met_objective.md)
  : Add maximum number of targets met objective
- [`add_max_phylo_div_objective()`](https://prioritizr.net/reference/add_max_phylo_div_objective.md)
  : Add maximum phylogenetic diversity objective
- [`add_max_phylo_end_objective()`](https://prioritizr.net/reference/add_max_phylo_end_objective.md)
  : Add maximum phylogenetic endemism objective
- [`add_max_wtd_sum_objective()`](https://prioritizr.net/reference/add_max_wtd_sum_objective.md)
  : Add maximum weighted sum objective
- [`add_min_largest_shortfall_objective()`](https://prioritizr.net/reference/add_min_largest_shortfall_objective.md)
  : Add minimum largest shortfall objective
- [`add_min_penalties_objective()`](https://prioritizr.net/reference/add_min_penalties_objective.md)
  : Add minimum penalties objective
- [`add_min_set_objective()`](https://prioritizr.net/reference/add_min_set_objective.md)
  : Add minimum set objective
- [`add_min_shortfall_objective()`](https://prioritizr.net/reference/add_min_shortfall_objective.md)
  : Add minimum shortfall objective

## Targets

Functions for adding targets to a problem.

- [`targets`](https://prioritizr.net/reference/targets.md) : Add
  representation targets

- [`add_absolute_targets()`](https://prioritizr.net/reference/add_absolute_targets.md)
  : Add absolute targets

- [`add_auto_targets(`*`<ConservationProblem>`*`,`*`<character>`*`)`](https://prioritizr.net/reference/add_auto_targets.md)
  [`add_auto_targets(`*`<ConservationProblem>`*`,`*`<list>`*`)`](https://prioritizr.net/reference/add_auto_targets.md)
  [`add_auto_targets(`*`<ConservationProblem>`*`,`*`<TargetMethod>`*`)`](https://prioritizr.net/reference/add_auto_targets.md)
  : Add targets automatically

- [`add_group_targets()`](https://prioritizr.net/reference/add_group_targets.md)
  : Add targets based on feature groups

- [`add_manual_targets()`](https://prioritizr.net/reference/add_manual_targets.md)
  : Add manual targets

- [`add_relative_targets()`](https://prioritizr.net/reference/add_relative_targets.md)
  : Add relative targets

- [`spec_absolute_targets()`](https://prioritizr.net/reference/spec_absolute_targets.md)
  : Specify absolute targets

- [`spec_area_targets()`](https://prioritizr.net/reference/spec_area_targets.md)
  : Specify targets based on area units

- [`spec_duran_targets()`](https://prioritizr.net/reference/spec_duran_targets.md)
  :

  Specify targets following Durán *et al.* (2020)

- [`spec_interp_absolute_targets()`](https://prioritizr.net/reference/spec_interp_absolute_targets.md)
  : Specify targets based on interpolating absolute thresholds

- [`spec_interp_area_targets()`](https://prioritizr.net/reference/spec_interp_area_targets.md)
  : Specify targets based on interpolating area-based thresholds

- [`spec_jung_targets()`](https://prioritizr.net/reference/spec_jung_targets.md)
  :

  Specify targets following Jung *et al.* (2021)

- [`spec_max_targets()`](https://prioritizr.net/reference/spec_max_targets.md)
  : Specify targets based on maxima

- [`spec_min_targets()`](https://prioritizr.net/reference/spec_min_targets.md)
  : Specify targets based on minima

- [`spec_polak_targets()`](https://prioritizr.net/reference/spec_polak_targets.md)
  :

  Specify targets following Polak *et al.* (2015)

- [`spec_pop_size_targets()`](https://prioritizr.net/reference/spec_pop_size_targets.md)
  : Specify targets based on population size

- [`spec_relative_targets()`](https://prioritizr.net/reference/spec_relative_targets.md)
  : Specify relative targets

- [`spec_rl_ecosystem_targets()`](https://prioritizr.net/reference/spec_rl_ecosystem_targets.md)
  : Specify targets based on the IUCN Red List of Ecosystems

- [`spec_rl_species_targets()`](https://prioritizr.net/reference/spec_rl_species_targets.md)
  : Specify targets based on the IUCN Red List of Threatened Species

- [`spec_rodrigues_targets()`](https://prioritizr.net/reference/spec_rodrigues_targets.md)
  :

  Specify targets following Rodrigues *et al.* (2004)

- [`spec_rule_targets()`](https://prioritizr.net/reference/spec_rule_targets.md)
  : Specify targets following a set of rules

- [`spec_sreekar_targets()`](https://prioritizr.net/reference/spec_sreekar_targets.md)
  : Specify targets following Sreekar and Watson (2026)

- [`spec_ward_targets()`](https://prioritizr.net/reference/spec_ward_targets.md)
  :

  Specify targets following Ward *et al.* (2025)

- [`spec_watson_targets()`](https://prioritizr.net/reference/spec_watson_targets.md)
  :

  Specify targets following Watson *et al.* (2010)

- [`spec_wilson_targets()`](https://prioritizr.net/reference/spec_wilson_targets.md)
  :

  Specify targets following Wilson *et al.* (2010)

## Constraints

Functions for adding constraints to a problem.

- [`constraints`](https://prioritizr.net/reference/constraints.md) :
  Conservation problem constraints
- [`add_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`)`](https://prioritizr.net/reference/add_contiguity_constraints.md)
  [`add_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_contiguity_constraints.md)
  [`add_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_contiguity_constraints.md)
  : Add contiguity constraints
- [`add_cost_constraints()`](https://prioritizr.net/reference/add_cost_constraints.md)
  : Add cost constraints
- [`add_feature_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_feature_contiguity_constraints.md)
  [`add_feature_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_feature_contiguity_constraints.md)
  [`add_feature_contiguity_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`)`](https://prioritizr.net/reference/add_feature_contiguity_constraints.md)
  : Add feature contiguity constraints
- [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<character>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<numeric>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Raster>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<SpatRaster>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  [`add_linear_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/add_linear_constraints.md)
  : Add linear constraints
- [`add_locked_in_constraints()`](https://prioritizr.net/reference/add_locked_in_constraints.md)
  : Add locked in constraints
- [`add_locked_out_constraints()`](https://prioritizr.net/reference/add_locked_out_constraints.md)
  : Add locked out constraints
- [`add_mandatory_allocation_constraints()`](https://prioritizr.net/reference/add_mandatory_allocation_constraints.md)
  : Add mandatory allocation constraints
- [`add_manual_bounded_constraints()`](https://prioritizr.net/reference/add_manual_bounded_constraints.md)
  : Add manually specified bound constraints
- [`add_manual_locked_constraints()`](https://prioritizr.net/reference/add_manual_locked_constraints.md)
  : Add manually specified locked constraints
- [`add_neighbor_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`)`](https://prioritizr.net/reference/add_neighbor_constraints.md)
  [`add_neighbor_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_neighbor_constraints.md)
  [`add_neighbor_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_neighbor_constraints.md)
  [`add_neighbor_constraints(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/add_neighbor_constraints.md)
  : Add neighbor constraints

## Penalties

Functions for adding penalties to a problem.

- [`penalties`](https://prioritizr.net/reference/penalties.md) : Add a
  penalty
- [`add_asym_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_asym_connectivity_penalties.md)
  [`add_asym_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/add_asym_connectivity_penalties.md)
  [`add_asym_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_asym_connectivity_penalties.md)
  [`add_asym_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/add_asym_connectivity_penalties.md)
  [`add_asym_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/add_asym_connectivity_penalties.md)
  : Add asymmetric connectivity penalties
- [`add_boundary_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_boundary_penalties.md)
  [`add_boundary_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_boundary_penalties.md)
  [`add_boundary_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`)`](https://prioritizr.net/reference/add_boundary_penalties.md)
  : Add boundary penalties
- [`add_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_connectivity_penalties.md)
  [`add_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/add_connectivity_penalties.md)
  [`add_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_connectivity_penalties.md)
  [`add_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/add_connectivity_penalties.md)
  [`add_connectivity_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/add_connectivity_penalties.md)
  : Add connectivity penalties
- [`add_cost_penalties()`](https://prioritizr.net/reference/add_cost_penalties.md)
  : Add cost penalties
- [`add_feature_weights(`*`<ConservationProblem>`*`,`*`<numeric>`*`)`](https://prioritizr.net/reference/add_feature_weights.md)
  [`add_feature_weights(`*`<ConservationProblem>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_feature_weights.md)
  : Add feature weights
- [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<character>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<numeric>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<Raster>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<SpatRaster>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  [`add_linear_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/add_linear_penalties.md)
  : Add linear penalties
- [`add_neighbor_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/add_neighbor_penalties.md)
  [`add_neighbor_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/add_neighbor_penalties.md)
  [`add_neighbor_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<ANY>`*`)`](https://prioritizr.net/reference/add_neighbor_penalties.md)
  [`add_neighbor_penalties(`*`<ConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/add_neighbor_penalties.md)
  : Add neighbor penalties
- [`calibrate_cohon_penalty()`](https://prioritizr.net/reference/calibrate_cohon_penalty.md)
  : Calibrate penalties with Cohon's method

## Decisions

Functions for specifying the type of decisions in a problem.

- [`decisions`](https://prioritizr.net/reference/decisions.md) : Add
  decision types
- [`add_binary_decisions()`](https://prioritizr.net/reference/add_binary_decisions.md)
  : Add binary decisions
- [`add_proportion_decisions()`](https://prioritizr.net/reference/add_proportion_decisions.md)
  : Add proportion decisions
- [`add_semicontinuous_decisions()`](https://prioritizr.net/reference/add_semicontinuous_decisions.md)
  : Add semi-continuous decisions

## Solvers

Functions for specifying how a problem should be solved.

- [`solvers`](https://prioritizr.net/reference/solvers.md) : Add solvers

- [`add_cbc_solver()`](https://prioritizr.net/reference/add_cbc_solver.md)
  :

  Add a *CBC* solver

- [`add_cplex_solver()`](https://prioritizr.net/reference/add_cplex_solver.md)
  :

  Add a *CPLEX* solver

- [`add_default_solver()`](https://prioritizr.net/reference/add_default_solver.md)
  : Add default solver

- [`add_gurobi_solver()`](https://prioritizr.net/reference/add_gurobi_solver.md)
  :

  Add a *Gurobi* solver

- [`add_highs_solver()`](https://prioritizr.net/reference/add_highs_solver.md)
  :

  Add a *HiGHS* solver

- [`add_lpsymphony_solver()`](https://prioritizr.net/reference/add_lsymphony_solver.md)
  :

  Add a *SYMPHONY* solver with *lpsymphony*

- [`add_rsymphony_solver()`](https://prioritizr.net/reference/add_rsymphony_solver.md)
  :

  Add a *SYMPHONY* solver with *Rsymphony*

## Portfolios

Functions for generating a portfolio of solutions.

- [`portfolios`](https://prioritizr.net/reference/portfolios.md) : Add
  portfolios
- [`add_cuts_portfolio()`](https://prioritizr.net/reference/add_cuts_portfolio.md)
  : Add Bender's cuts portfolio
- [`add_default_portfolio()`](https://prioritizr.net/reference/add_default_portfolio.md)
  : Add a default portfolio
- [`add_extra_portfolio()`](https://prioritizr.net/reference/add_extra_portfolio.md)
  : Add an extra portfolio
- [`add_gap_portfolio()`](https://prioritizr.net/reference/add_gap_portfolio.md)
  : Add a gap portfolio
- [`add_shuffle_portfolio()`](https://prioritizr.net/reference/add_shuffle_portfolio.md)
  : Add a shuffle portfolio
- [`add_single_portfolio()`](https://prioritizr.net/reference/add_single_portfolio.md)
  : Add a single portfolio
- [`add_top_portfolio()`](https://prioritizr.net/reference/add_top_portfolio.md)
  : Add a top portfolio

## Summary statistics

Functions for summarizing the performance of solutions.

- [`summaries`](https://prioritizr.net/reference/summaries.md) :
  Evaluate solutions using summary statistics
- [`eval_asym_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/eval_asym_connectivity_summary.md)
  [`eval_asym_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/eval_asym_connectivity_summary.md)
  [`eval_asym_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/eval_asym_connectivity_summary.md)
  [`eval_asym_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/eval_asym_connectivity_summary.md)
  [`eval_asym_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/eval_asym_connectivity_summary.md)
  : Evaluate asymmetric connectivity of solution
- [`eval_boundary_summary()`](https://prioritizr.net/reference/eval_boundary_summary.md)
  : Evaluate boundary length of solution
- [`eval_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<matrix>`*`)`](https://prioritizr.net/reference/eval_connectivity_summary.md)
  [`eval_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<Matrix>`*`)`](https://prioritizr.net/reference/eval_connectivity_summary.md)
  [`eval_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<data.frame>`*`)`](https://prioritizr.net/reference/eval_connectivity_summary.md)
  [`eval_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<dgCMatrix>`*`)`](https://prioritizr.net/reference/eval_connectivity_summary.md)
  [`eval_connectivity_summary(`*`<GenericConservationProblem>`*`,`*`<ANY>`*`,`*`<ANY>`*`,`*`<array>`*`)`](https://prioritizr.net/reference/eval_connectivity_summary.md)
  : Evaluate connectivity of solution
- [`eval_cost_summary()`](https://prioritizr.net/reference/eval_cost_summary.md)
  : Evaluate cost of solution
- [`eval_feature_representation_summary()`](https://prioritizr.net/reference/eval_feature_representation_summary.md)
  : Evaluate feature representation by solution
- [`eval_n_summary()`](https://prioritizr.net/reference/eval_n_summary.md)
  : Evaluate number of planning units selected by solution
- [`eval_objective_summary()`](https://prioritizr.net/reference/eval_objective_summary.md)
  : Evaluate objective value of solution
- [`eval_target_coverage_summary()`](https://prioritizr.net/reference/eval_target_coverage_summary.md)
  : Evaluate target coverage by solution

## Importance

Functions for calculating importance scores for a solution.

- [`importance`](https://prioritizr.net/reference/importance.md)
  [`irreplaceability`](https://prioritizr.net/reference/importance.md) :
  Evaluate solution importance
- [`eval_ferrier_importance()`](https://prioritizr.net/reference/eval_ferrier_importance.md)
  : Evaluate solution importance using Ferrier scores
- [`eval_rank_importance()`](https://prioritizr.net/reference/eval_rank_importance.md)
  : Evaluate solution importance using incremental ranks
- [`eval_rare_richness_importance()`](https://prioritizr.net/reference/eval_rare_richness_importance.md)
  : Evaluate solution importance using rarity weighted richness scores
- [`eval_replacement_importance()`](https://prioritizr.net/reference/eval_replacement_importance.md)
  : Evaluate solution importance using replacement cost scores

## Multi-objective optimization

Functions for multi-objective optimization.

- [`multi_problem()`](https://prioritizr.net/reference/multi_problem.md)
  : Multi-objective conservation planning problem
- [`approaches`](https://prioritizr.net/reference/approaches.md) : Add
  an approach
- [`add_hier_approach()`](https://prioritizr.net/reference/add_hier_approach.md)
  : Add a hierarchical approach
- [`add_ref_point_approach()`](https://prioritizr.net/reference/add_ref_point_approach.md)
  : Add a reference point approach
- [`add_wtd_sum_approach()`](https://prioritizr.net/reference/add_wtd_sum_approach.md)
  : Add a weighted sum approach
- [`approach_weights_matrix()`](https://prioritizr.net/reference/approach_weights_matrix.md)
  : Create weight values for a multi-objective approach
- [`approach_rel_tol_matrix()`](https://prioritizr.net/reference/approach_rel_tol_matrix.md)
  : Create relative tolerance values for a multi-objective approach

## Data simulation

Functions for simulating new datasets.

- [`simulate_cost()`](https://prioritizr.net/reference/simulate_cost.md)
  : Simulate cost data
- [`simulate_data()`](https://prioritizr.net/reference/simulate_data.md)
  : Simulate data
- [`simulate_species()`](https://prioritizr.net/reference/simulate_species.md)
  : Simulate species habitat suitability data

## Geoprocessing

Functions for manipulating spatial datasets.

- [`fast_extract()`](https://prioritizr.net/reference/fast_extract.md) :
  Fast extract
- [`intersecting_units()`](https://prioritizr.net/reference/intersecting_units.md)
  : Find intersecting units

## Marxan functions

Functions for importing and converting *Marxan* data.

- [`marxan_problem()`](https://prioritizr.net/reference/marxan_problem.md)
  :

  *Marxan* conservation problem

- [`marxan_boundary_data_to_matrix()`](https://prioritizr.net/reference/marxan_boundary_data_to_matrix.md)
  :

  Convert *Marxan* boundary data to matrix format

- [`marxan_connectivity_data_to_matrix()`](https://prioritizr.net/reference/marxan_connectivity_data_to_matrix.md)
  :

  Convert *Marxan* connectivity data to matrix format

## Matrix functions

Functions for creating matrices that are used in conservation planning
problems.

- [`adjacency_matrix()`](https://prioritizr.net/reference/adjacency_matrix.md)
  : Adjacency matrix
- [`boundary_matrix()`](https://prioritizr.net/reference/boundary_matrix.md)
  : Boundary matrix
- [`branch_matrix()`](https://prioritizr.net/reference/branch_matrix.md)
  : Branch matrix
- [`connectivity_matrix()`](https://prioritizr.net/reference/connectivity_matrix.md)
  : Connectivity matrix
- [`proximity_matrix()`](https://prioritizr.net/reference/proximity_matrix.md)
  : Proximity matrix
- [`rij_matrix()`](https://prioritizr.net/reference/rij_matrix.md) :
  Feature by planning unit matrix
- [`rescale_matrix()`](https://prioritizr.net/reference/rescale_matrix.md)
  : Rescale a matrix

## Processing multi-zone data

Functions for manipulating data that pertain to multiple zones.

- [`category_layer()`](https://prioritizr.net/reference/category_layer.md)
  : Category layer
- [`category_vector()`](https://prioritizr.net/reference/category_vector.md)
  : Category vector
- [`binary_stack()`](https://prioritizr.net/reference/binary_stack.md) :
  Binary stack

## Problem manipulation functions

Functions for working with problems.

- [`compile()`](https://prioritizr.net/reference/compile.md) : Compile a
  problem
- [`feature_abundances()`](https://prioritizr.net/reference/feature_abundances.md)
  : Feature abundances
- [`feature_names()`](https://prioritizr.net/reference/feature_names.md)
  [`problem_names(`*`<MultiConservationProblem>`*`)`](https://prioritizr.net/reference/feature_names.md)
  : Feature names
- [`multi_compile()`](https://prioritizr.net/reference/multi_compile.md)
  : Compile a multi-objective optimization problem
- [`number_of_features()`](https://prioritizr.net/reference/number_of_features.md)
  [`number_of_problems(`*`<MultiConservationProblem>`*`)`](https://prioritizr.net/reference/number_of_features.md)
  : Number of features
- [`number_of_planning_units()`](https://prioritizr.net/reference/number_of_planning_units.md)
  : Number of planning units
- [`number_of_problems()`](https://prioritizr.net/reference/number_of_problems.md)
  : Number of problems
- [`number_of_total_units()`](https://prioritizr.net/reference/number_of_total_units.md)
  : Number of total units
- [`number_of_zones()`](https://prioritizr.net/reference/number_of_zones.md)
  : Number of zones
- [`presolve_check()`](https://prioritizr.net/reference/presolve_check.md)
  : Presolve check
- [`problem_names()`](https://prioritizr.net/reference/problem_names.md)
  : Problem names
- [`run_calculations()`](https://prioritizr.net/reference/run_calculations.md)
  : Run calculations
- [`write_problem()`](https://prioritizr.net/reference/write_problem.md)
  : Write problem
- [`zone_names()`](https://prioritizr.net/reference/zone_names.md) :
  Zone names

## Class definitions and methods

Documentation for internal classes and associated functions.

- [`new_waiver()`](https://prioritizr.net/reference/new_waiver.md) :
  Waiver
- [`optimization_problem()`](https://prioritizr.net/reference/optimization_problem.md)
  : Optimization problem
- [`ConservationModifier-class`](https://prioritizr.net/reference/ConservationModifier-class.md)
  [`ConservationModifier`](https://prioritizr.net/reference/ConservationModifier-class.md)
  : Conservation problem modifier class
- [`ConservationProblem-class`](https://prioritizr.net/reference/ConservationProblem-class.md)
  [`ConservationProblem`](https://prioritizr.net/reference/ConservationProblem-class.md)
  : Conservation problem class
- [`Constraint-class`](https://prioritizr.net/reference/Constraint-class.md)
  [`Constraint`](https://prioritizr.net/reference/Constraint-class.md) :
  Constraint class
- [`Decision-class`](https://prioritizr.net/reference/Decision-class.md)
  [`Decision`](https://prioritizr.net/reference/Decision-class.md) :
  Decision class
- [`MultiConservationProblem-class`](https://prioritizr.net/reference/MultiConservationProblem-class.md)
  [`MultiConservationProblem`](https://prioritizr.net/reference/MultiConservationProblem-class.md)
  : Multi-objective conservation problem class
- [`MultiObjApproach-class`](https://prioritizr.net/reference/MultiObjApproach-class.md)
  [`MultiObjApproach`](https://prioritizr.net/reference/MultiObjApproach-class.md)
  : Multi-objective approach class
- [`Objective-class`](https://prioritizr.net/reference/Objective-class.md)
  [`Objective`](https://prioritizr.net/reference/Objective-class.md) :
  Objective class
- [`OptimizationProblem-class`](https://prioritizr.net/reference/OptimizationProblem-class.md)
  [`OptimizationProblem`](https://prioritizr.net/reference/OptimizationProblem-class.md)
  : Optimization problem class
- [`Penalty-class`](https://prioritizr.net/reference/Penalty-class.md)
  [`Penalty`](https://prioritizr.net/reference/Penalty-class.md) :
  Penalty class
- [`Portfolio-class`](https://prioritizr.net/reference/Portfolio-class.md)
  [`Portfolio`](https://prioritizr.net/reference/Portfolio-class.md) :
  Portfolio class
- [`Solver-class`](https://prioritizr.net/reference/Solver-class.md)
  [`Solver`](https://prioritizr.net/reference/Solver-class.md) : Solver
  class
- [`Target-class`](https://prioritizr.net/reference/Target-class.md)
  [`Target`](https://prioritizr.net/reference/Target-class.md) : Target
  class
- [`TargetMethod-class`](https://prioritizr.net/reference/TargetMethod-class.md)
  [`TargetMethod`](https://prioritizr.net/reference/TargetMethod-class.md)
  : Target setting method class
- [`Weight-class`](https://prioritizr.net/reference/Weight-class.md)
  [`Weight`](https://prioritizr.net/reference/Weight-class.md) : Weight
  class
- [`nrow(`*`<tbl_df>`*`)`](https://prioritizr.net/reference/tibble-methods.md)
  [`ncol(`*`<tbl_df>`*`)`](https://prioritizr.net/reference/tibble-methods.md)
  [`as.list(`*`<tbl_df>`*`)`](https://prioritizr.net/reference/tibble-methods.md)
  : Manipulate tibbles

## Miscellaneous functions

Assorted functions distributed with the package.

- [`show(`*`<ConservationModifier>`*`)`](https://prioritizr.net/reference/show.md)
  [`show(`*`<ConservationProblem>`*`)`](https://prioritizr.net/reference/show.md)
  [`show(`*`<OptimizationProblem>`*`)`](https://prioritizr.net/reference/show.md)
  [`show(`*`<Solver>`*`)`](https://prioritizr.net/reference/show.md) :
  Show

- [`linear_interpolation()`](https://prioritizr.net/reference/linear_interpolation.md)
  : Linear interpolation

- [`loglinear_interpolation()`](https://prioritizr.net/reference/loglinear_interpolation.md)
  : Log-linear interpolation

- [`knit_print.ConservationProblem()`](https://prioritizr.net/reference/knit_print.md)
  [`knit_print.MultiConservationProblem()`](https://prioritizr.net/reference/knit_print.md)
  [`knit_print.OptimizationProblem()`](https://prioritizr.net/reference/knit_print.md)
  : Print an object for knitr package.

- [`as_km2()`](https://prioritizr.net/reference/as_km2.md) :

  Standardize unit to km²

- [`as_per_km2()`](https://prioritizr.net/reference/as_per_km2.md) :

  Standardize unit to density per km²

- [`do_run_example()`](https://prioritizr.net/reference/do_run_example.md)
  : Do run example?

## Deprecated functions

Documentation for functions that are no longer available.

- [`add_connected_constraints()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`add_corridor_constraints()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`set_number_of_threads()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`get_number_of_threads()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`is.parallel()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`add_pool_portfolio()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`connected_matrix()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`feature_representation()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`replacement_cost()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`rarity_weighted_richness()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`ferrier_score()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`distribute_load()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`new_optimization_problem()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`predefined_optimization_problem()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`add_loglinear_targets()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`add_max_phylo_objective()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  [`add_max_utility_objective()`](https://prioritizr.net/reference/prioritizr-deprecated.md)
  : Deprecation notice

# Articles

### All vignettes

- [Calibrating trade-offs
  tutorial](https://prioritizr.net/articles/calibrating_trade-offs_tutorial.md):
- [Connectivity
  tutorial](https://prioritizr.net/articles/connectivity_tutorial.md):
- [Gurobi installation
  guide](https://prioritizr.net/articles/gurobi_installation_guide.md):
- [Management zones
  tutorial](https://prioritizr.net/articles/management_zones_tutorial.md):
- [Package
  overview](https://prioritizr.net/articles/package_overview.md):
- [Getting started](https://prioritizr.net/articles/prioritizr.md):
- [Publication
  record](https://prioritizr.net/articles/publication_record.md):
- [Solver
  benchmarks](https://prioritizr.net/articles/solver_benchmarks.md):