Systematic conservation prioritization with robust optimization techniques. This is important because conservation prioritizations typically only consider the most likely outcome associated with a conservation action (e.g., establishing a protected area will safeguard a threatened species population) and fail to consider other outcomes and their consequences for meeting conservation objectives. By extending the 'prioritizr' package, this package can be used to generate conservation prioritizations that account of uncertainty in the climate change scenario projections, species distribution models, ecosystem service models, and measurement errors. In particular, prioritizations can be generated to be fully robust to uncertainty by minimizing (or maximizing) objectives under the worst possible outcome. Since reducing the associated with achieving conservation objectives may sacrifice other objectives (e.g., minimizing protected area implementation costs), prioritizations can also be generated to be partially robust based on a specified confidence level parameter. Partially robust prioritizations can be generated based on the chance constrained programming problem (Charnes & Cooper 1959, <doi:10.1287/mnsc.6.1.73>) and the conditional value-at-risk problem (Rockafellar & Uryasev 2000, <doi:10.21314/JOR.2000.038>).