prioritizr 8.0.3

Notice

• We have developed a better approach for rescaling boundary data to
  avoid numerical issues during optimization (#297). Earlier versions of the
  package recommended the use of the scales::rescale() to rescale such data.
  However, we now realize that this approach can produce inconsistencies for
  boundary length data (e.g., the total perimeter of a planning unit might not
  necessarily equal the sum of the edge lengths). In some cases, these
  inconsistencies can cause solutions generated with high boundary
  penalties (i.e., using add_boundary_penalties() with a high penalty
  value) to contain a large reserve (i.e., a spatial cluster of selected of
  planning units) with a single unselected planning unit in the middle of the
  reserve. In the the worst case, these inconsistencies produce a situation
  where increasing boundary penalties (i.e., generating multiple solutions with
  add_boundary_penalties() and increasing penalty values)
  does not alter the spatial configuration of solutions. Although use of
  scales::rescale() did not produce such behavior prior to version 8.0.0,
  changes to the output format for boundary_matrix() in subsequent versions
  now mean that scales::rescale() can cause these issues. We now recommend
  using the new rescale_matrix() function to rescale boundary length data to
  avoid numerical issues, whilst also avoid such inconsistencies.

New features

• New rescale_matrix() function to help with rescaling boundary length
  (e.g., generated using boundary_matrix()) and connectivity
  (e.g., generated using connectivity_matrix()) data so avoid
  numerical issues during optimization (#297).
• Update add_neighbors_constraints() so that it has an additional
  clamp argument so the minimum number of neighbors permitted for
  each planning unit in the solution is clamped to the number of neighbors that
  each planning unit has. For example, if a planning unit has 2 neighbors,
  k = 3, and clamp = FALSE, then the planning unit could not
  ever be selected in the solution. However, if clamp = TRUE, then
  the planning unit could potentially be selected in the solution if both of
  its 2 neighbors were also selected.

Minor improvements and bug fixes

• Update examples and vignettes to use the rescale_matrix() function
  instead of the scales::rescale() function for rescaling boundary
  length and connectivity data (#297).
• Update the print() and summary() methods for problem() objects
  so that they will now better describe situations when the planning cost
  data all contain a constant value (e.g., all costs equal to 1).
• Fix issue with problem() that prevents features being supplied as
  a data.frame that contains feature names stored as a factor (#295).
• Fix broken URLs in documentation.
• Fix compatibility with updates to terra package.
• Fix rij_matrix() so that it works when none of the raster layers being
  processed fit into memory (#290).
• Fix spatial extent of built-in raster datasets so that extents are between
  0 and 1 (i.e., get_sim_pu_raster(), get_sim_locked_in_raster(),
  get_sim_locked_out_raster(), get_sim_zones_pu_raster(),
  get_sim_features(), get_sim_zones_features()).
• Update add_manual_locked_constraints() and
  add_manual_bounded_constraints() so that the indices in the
  specified in the argument data$pu should consistently refer to the total
  units. In other words, the indices in data$pu should refer to the row
  numbers (for planning units in sf or data.frame format) or cell numbers
  (for planning units in Raster or SpatRaster format) of the planning units
  that should be locked.
• Fix warnings thrown due to package version comparisons.
• Update problem() so that it will throw a meaningful error message if the
  user accidentally specifies the geometry column for sf planning unit data
  as a feature.
• Export solve.ConservationProblem() so that it can be called directly (#283).
• Fix compatibility with highs package (version 0.1-10) (#281).
• Update problem() so that an error will be thrown if argument to features
  contains only missing (NA) values (e.g., an sf object is supplied that
  has NA values in all rows for a feature's column).
• Update publication record.