Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.

Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.

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 uncertainty
    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>).