oppr {oppr}

oppr: Optimal Project Prioritization

Description

The oppr R package a decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the 'Gurobi' optimizer (available from https://www.gurobi.com) because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits.

Installation

To make the most of this package, the ggtree and gurobi R packages will need to be installed. Since the ggtree package is exclusively available at Bioconductor—and is not available on The Comprehensive R Archive Network—please execute the following command to install it: ⁠source("https://bioconductor.org/biocLite.R");biocLite("ggtree")⁠. If the installation process fails, please consult the package's online documentation. To install the gurobi package, the Gurobi optimization suite will first need to be installed (see instructions for Linux, Mac OSX, and Windows operating systems). Although Gurobi is a commercial software, academics can obtain a special license for no cost. After installing the Gurobi optimization suite, the gurobi package can then be installed (see instructions for Linux, Mac OSX, and Windows operating systems).

See Also

Please refer to the package vignette for more information and worked examples. This can be accessed using the code vignette("oppr").

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# print project data
print(sim_projects)

# print action data
print(sim_features)

# print feature data
print(sim_actions)

# build problem
p <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 400) %>%
     add_feature_weights("weight") %>%
     add_binary_decisions()

# print problem
print(p)

## Not run: 
# solve problem
s <- solve(p)

# print output
print(s)

# print which actions are funded in the solution
s[, sim_actions$name, drop = FALSE]

# print the expected probability of persistence for each feature
# if the solution were implemented
s[, sim_features$name, drop = FALSE]

# visualize solution
plot(p, s)

## End(Not run)