NEWS.md
add_neighbor_constraints and add_contiguity_constraints functions used more memory than they actually needed (#102). This is because the argument validation code converted sparse matrix objects (i.e. dgCMatrix) to base objects (i.e. matrix) class temporarily. This bug only meant inefficient utilization of computer resources—it did not affect the correctness of any results.add_mandatory_allocation_constraints function. This function can be used to ensure that every planning unit is allocated to a management zone in the solution. It is useful when developing land-use plans where every single parcel of land must be assigned to a specific land-use zone.add_mandatory_allocation_constraints to the Management Zones and Prioritizr vignettes.$find(x) method for Collection prototypes that caused it to throw an error incorrectly. This method was not used in earlier versions of this package.feature_representation function that caused the “amount_held” column to have NA values instead of the correct values. This bug only affected problems with multiple zones.category_layer function that it this function to incorrectly throw an error claiming that the input argument to x was invalid when it was in fact valid. This bug is encountered when different layers the argument to x have non-NA values in different cells.add_contiguity_constraints function now uses sparse matrix formats internally for single-zone problems. This means that the constraints can be applied to single-zoned problem with many more planning units.add_connectivity_penalties function now uses sparse matrix formats internally for single-zone problems. This means that connectivity penalties can be applied to single-zoned problem with many more planning units.add_locked_in_constraints and add_locked_out_constraints that incorrectly threw an error when using logical locked data (i.e. TRUE/FALSE) because it incorrectly thought that valid inputs were invalid.add_locked_in_constraints, add_locked_out_constraints, and add_manual_locked_constraints where solving the same problem object twice resulted in incorrect planning units being locked in or out of the solution (#92).feature_abundances that caused the solve function to throw an error when attempting to solve problems with a single feature.add_cuts_portfolio that caused the portfolio to return solutions that were not within the specified optimality gap when using the Gurobi solver.add_pool_portfolio function.feature_representation function now allows numeric solutions with attributes (e.g. when output by the solve function) when calculating representation statistics for problems with numeric planning unit data (#91).add_manual_targets function threw a warning when some features had targets equal to zero. This resulted in an excessive amount of warnings. Now, warnings are thrown for targets that are less then zero.problem function sometimes incorrectly threw a warning that feature data had negative values when the data actually did not contain negative values. This has now been addressed.problem function now allows negative values in the cost and feature data (and throws a warning if such data are detected).add_absolute_targets and add_manual_targets functions now allow negative targets (but throw a warning if such targets are specified).compile function throws an error if a problem is compiled using the expanded formulation with negative feature data.add_absolute_targets function now throws an warning—instead of an error—if the specified targets are greater than the feature abundances in planning units to accommodate negative values in feature data.add_max_cover_objective in prioritizr vignette (#90).add_relative_targets documentation now makes it clear that locked out planning units are included in the calculations for setting targets (#89).add_loglinear_targets function now includes a feature_abundances parameter for specifying the total amount of each feature to use when calculating the targets (#89).feature_abundances function to calculate the total amount of each feature in the planning units (#86).add_cuts_portfolio function uses the Gurobi solution pool to generate unique solutions within a specified gap of optimality when tasked with solving problems with Gurobi (version 8.0.0+; #80).add_pool_portfolio function to generate a portfolio of solutions using the Gurobi solution pool (#77).boundary_matrix function now has the experimental functionality to use GEOS STR trees to speed up processing (#74).feature_representation function to how well features are represented in solutions (#73).solve function printing superfluous text (#75).sim_pu_zones_stack, sim_pu_zones_polygons, and sim_features_zones for exploring conservation problems with multiple management zones.zones function and Zones class to organize data with multiple zones.problem function now accepts Zone objects as arguments for feature to create problems with multiple zones.add_relative_targets and add_absolute_targets functions for adding targets to problems can be used to specify targets for each feature in each zone.add_manual_targets function for creating targets that pertain to multiple management zones.solve function now returns a list of solutions when generating a portfolio of solutions.add_locked_in_constraints and add_locked_out_constraints functions for specifying which planning units are locked in or out now accept matrix arguments for specifying which zones are locked in or out.add_manual_locked_constraints function to manually specify which planning units should or shouldn’t be allocated to specific zones in solutions.zones parameter) and specify how they they should be applied (using the data parameter. All of these functions have default arguments that mean that problems with a single zone should have the same optimal solution as problems created in the earlier version of the package.add_feature_weights function can be used to weight different the representation of each feature in each zone.binary_stack, category_layer, and category_vector functions have been provided to help work with data for multiple management zones.?prioritizr), and README.marxan_problem has been updated with more comprehensive documentation and to provide more helpful error messages. For clarity, it will now only work with tabular data in the standard Marxan format.add_boundary_penalties (#62).add_locked_in_constraints and add_locked_out_constraints throw an exception when used with semi-continuous-type decisions (#59).compile thrown when the same planning unit is locked in and locked out now prints the planning unit indices in a readable format.add_locked_in_constraints and add_locked_out_constraints are ignored when using proportion-type decisions (#58).predefined_optimization_problem which incorrectly recognized some inputs as invalid when they were in fact valid.R CMD check related to proto in Depends.add_lpsymphony_solver now throws warnings to alert users to potentially incorrect solutions (partially addressing #40).add_max_cover_objective function has been renamed to the add_max_utility_objective, because the formulation does not follow the historical formulation of the maximum coverage reserve selection problem (#38).add_max_cover_objective function now follows the historical maximum coverage objective. This fundamentally changes add_max_cover_objective function and breaks compatibility with previous version of prioritizr (#38).add_lpsymphony_solver causing error when attempting to solve problems.numeric vector data that caused an error.numeric vector input with rij data containing NA values.apply_boundary_penalties and add_connectivity_penalties causing the function to throw an error when the number of boundaries/edges is less than the number of planning units.boundary_matrix calculations (#30).add_max_phylo_objective (#24).ScalarParameter and ArrayParameter prototypes to check t that functions for generating widgets have their dependencies installed.numeric planning unit data and portfolios that caused the solve to throw an error.Spatial*DataFrame input to marxan_problem would always use the first column in the attribute table for the cost data. This bug is serious so analysis that used Spatial*DataFrame inputs in marxan_problem should be rerun.problem objects.data.frame and Spatial*DataFrame objects are now stored in columns named “solution_*" (e.g. “solution_1”) to store multiple solutions.verbose argument to all solvers. This replaces the verbosity argument in add_lpsymphony_solver and add_rsymphony_solver.add_lpsymphony_solver and add_rsymphony_solver is reduced.ConservationProblem$print() now only prints the first three species names and a count of the total number of features. This update means that ConservationProblem objects with lots of features can now safely be printed without polluting the R console.time_limit.devtools::build_vignettes(). Earlier versions needed the vignettes to be compiled using the Makefile to copy files around to avoid tangled R code causing failures during R CMD CHECK. Although no longer needed, the vignettes can still be compiled using the shell command make vigns if desired.rmarkdown::render("README.Rmd") or using the shell command make readme. Note that the figures for README.md can be found in the directory man/figures.prshiny will now only be run if executed during an interactive R session. Prior to this R CMD CHECK would hang.marxan_problem using input data.frame objects.priortizr::compile function.problem.data.frame that meant that it did not check for missing values in rij$pu.add_absolute_targets and add_relative_targets` related to their standardGeneric being incorrectly definedadd_corridor_targets when argument connectivities is a list. The elements in the list are assumed to be dsCMatrix objects (aka symmetric sparse matrices in a compressed format) and are coerced to dgCMatrix objects to reduce computational burden. There was a typo, however, and so the objects were coerced to dgCmatrix and not dgCMatrix. This evidently was ok in earlier versions of the RcppArmadillo and/or Matrix packages but not in the most recent versions.parallel::detectCores() returns NA on some systems preventing users from using the Gurobi solver–even when one thread is specified.structure(NULL, ...) with structure(list(), ...).new_waiver from internal.R.add_default_decisions and add_default_solver to own help fileadd_default_objectives and add_default_targets private functionsadd_corridor_constraints that fails to actually add the constraints with argument to connectivity is a list.make install command so that it now actually installs the package.