NEWS.md
problem() and eval_feature_representation_summary() to avoid needlessly converting sparse matrices to regular matrices (#252).add_cbc_solver() to throw a segfault when solving a problem wherein the rij_matrix(x) has a zero amount for the last feature in the last planning unit (#247).simulate_data(), simulate_cost() and simulate_species() functions to improve performance using the fields package.boundary_matrix() to use STR query trees by default.boundary_matrix() to use the geos package (#218).simulate_cost() and simulate_species() so that they no longer depend on the RandomFields package.presolve_check() function to (i) reduce chances of it incorrectly throwing an error when the input data won’t actually cause any issues, and (ii) provide recommendations for addressing issues.add_min_largest_shortfall_objective() so that examples complete in a shorter period of time.x that are numeric or matrix format, (ii) x that contain missing (NA) values, and (iii) rij_matrix that are in dgCMatrix format. This bug only occurred when all three of these specific conditions were met. When it occurred, the bug caused planning units with NA cost values to receive very high cost values (e.g., 1e+300). This bug meant that when attempting to solve the problem, the presolve checks (per presolve_check()) would throw an error complaining about very high cost values (#236).add_locked_in_constraints() and add_locked_out_constraints() to ensure that a meaningful error message is provided when no planing units are locked (#234).presolve_check() so that it does not throw a meaningless warning when the mathematical objective function only contains zeros.presolve_check() to help reduce chances of mis-attributing high connectivity/boundary values due to planning unit costs.add_connectivity_penalties() function and documentation so that it is designed specifically for symmetric connectivity data.add_asym_connectivity_penalties() function that is designed specifically for asymmetric connectivity data. This function has been created to help ensure that asymmetric connectivity data are handled correctly. For instance, using asymmetric connectivity data with add_connectivity_penalties() function in previous versions of the package sometimes resulted in the data being incorrectly treated as symmetric data. Additionally, this function uses an updated mathematical formulation for handling asymmetric connectivity so that it provides similar results to the Marxan software (#323).marxan_problem() function so that it can be used with asymmetric connectivity data. This is now possible because there are dedicated functions for symmetric and asymmetric connectivity.zones parameter of the add_connectivity_penalties() function.eval_ferrier_importance() (#220). Although this function is now recommended for general use, the documentation contained an outdated warning and so the warning has now been removed.eval_n_summary() function now returns a table with the column name "n" (instead of "cost") for the number of selected planning units (#219).marxan_problem() for importing Marxan data files.sim_pu_sf and sim_pu_zones_sf data given class updates to the sf package (compatible with version 1.0.3+).write_problem() function.eval_ferrier_importance() function with verified code.presolve_check() function to throw warning when really high values specified in add_neighbor_constraints().Update add_cbc_solver() function so that it can use a starting solution to reduce run time (via the start_solution parameter).
add_linear_constraint() function to add arbitrary constraints.add_min_shortfall_objective() and add_min_largest_shortfall_objective() functions to handle targets with a target threshold value of zero.eval_connectivity_summary() function, and tweaking the header in the README.problem() function.add_gurobi_solver() function so that it doesn’t print excess debugging information (accidentally introduced in previous version 7.0.1.1).add_gurobi_solver() function to support the node_file_start parameter for the Gurobi software. This functionality is useful solving large problems on systems with limited memory (#139).write_problem() function to save the mixed integer programming representation of a conservation planning problem to a file. This function is useful for manually executing optimization solvers.rij_matrix() function documentation (#189).add_gurobi_solver() function to allow specification of a starting solution (#187). This functionality is useful for conducting a boundary penalty parameter calibration exercise. Specifically, users can specify the starting solution for a given penalty value based on the solution obtained using a smaller penalty value.solve now assigns layer names based on zone names for solutions in format.time_limit and verbose parameters for add_cbc_solver() now work as expected.add_gurobi_solver() function to report timings following the same methods as the other solvers.add_lpsymphony_solver() function to be more memory efficient (#183).add_cbc_solver() is now preferred over all other open source solvers.add_cbc_solver() would sometimes return incorrect solutions to problems with equality constraints.add_cbc_solver() function to generate solutions using the open source CBC solver via the rcbc R package (https://github.com/dirkschumacher/rcbc).add_rsymphony_solver() and add_lpsymphony_solver() functions to have a default time_limit argument set as the maximum machine integer for consistency.add_rsymphony_solver(), add_lpsymphony_solver(), and add_gurobi_solver() functions to require logical (TRUE/FALSE) arguments for the first_feasible parameter.add_default_solver() function so that it prefers add_lpsymphony_solver() over add_rsymphony_solver(), and add_cbc_solver() over all open source solvers.gap parameter for the add_rsymphony_solver() and add_lpsymphony_solver() corresponded to the maximum absolute difference from the optimal objective value. This was an error due to misunderstanding the SYMPHONY documentation. Under previous versions of the package, the gap parameter actually corresponded to a relative optimality gap expressed as a percentage (such thatgap = 10 indicates that solutions must be at least 10% from optimality). We have now fixed this error and the documentation described for the gap parameter is correct. We apologize for any inconvenience this may have caused.eval_) to mention that the argument to solution should only contain columns that correspond to the solution (#176).sf data to documentation for importance evaluation functions (#176).solution arguments are supplied to the evaluation functions (#176).sf planning unit data.add_manual_targets() documentation.add_min_largest_shortfall() objective function.eval_cost() function to calculate the cost of a solution.eval_boundary() function to calculate the exposed boundary length associated with a solution.eval_connectivity() function to calculate the connectivity associated with a solution.feature_representation() function. It is now superseded by the eval_feature_representation() function.eval_feature_representation() function to assess how well each feature is represented by a solution. This function is similar to the deprecated eval_feature_representation() function, except that it follows conventions for other evaluation functions (e.g. eval_cost).eval_target_representation() function to assess how well each target is met by a solution. This function is similar to the eval_feature_representation(), except that it corresponds to the targets in a conservation planning problem.ferrier_score function as eval_ferrier_importance() function for consistency.replacement_cost function as eval_replacement_importance() function for consistency.rarity_weighted_richness function as eval_rare_richness_importance() function for consistency.add_locked_out_constraints() function to enable a single planning unit from being locked out of multiple zones (when data are specified in raster format).problem() function to reduce memory consumption for sparse matrix arguments (#164).add_cplex_solver() function to generate solutions using IBM CPLEX (via the cplexAPI package).add_gap_portfolio() documentation to note that it only works for problems with binary decisions (#159).add_loglinear_targets() and loglinear_interpolation() functions. Previously they used a natural logarithm for log-linear interpolation. To follow target setting approaches outlined by Rodrigues et al. (2004), they now use the decadic logarithm (i.e. log10()).ferrier_score() function. It no longer incorrectly states that these scores can be calculated using CLUZ and now states that this functionality is experimental until the formulation can be double checked.--run-donttest).feature_representation() bug incorrectly throwing error with vector planning unit data (e.g. sf-class data).rij_matrix() to throw an error for large raster data (#151).add_linear_penalties() to add penalties that penalize planning units according to a linear metric.connectivity_matrix() documentation to provide an example of how to generate connectivity matrices that account for functional connectivity.solve() function.solve() function to the Salt Spring Island and Tasmania vignettes.compile() to throw warning when compiling problems that include feature weights and an objective function that does not use feature weights.add_gurobi_solver() function to provide more options for controlling the pre-solve step when solving a problem.ferrier_score() function to compute irreplaceability scores following Ferrier et al (2000).proximity_matrix() function to generate matrices indicating which planning units are within a certain distance of each other (#6).connected_matrix() function to adjacency_matrix() function to follow the naming conventions of other spatial association functions (#6).add_extra_portfolio(), add_top_portfolio(), add_gap_portfolio() functions to provide specific options for generating portfolios (#134).intersecting_units and fast_extract functions to use the exactextractr and fasterize packages to speed up raster data extraction (#130).boundary_matrix() function when handling SpatialPolygon planning unit data that contain multiple polygons (e.g. a single planning unit contains to two separate islands) (#132).set_number_of_threads(), get_number_of_threads(), and is.parallel() functions since they are no longer used with new data extraction methods.add_pool_portfolio() function because the new add_extra_portfolio() and add_top_portfolio() functions provide this functionality (#134).add_rsymphony_solve()r and add_lpsymphony_solver() throwing an an infeasible error message for feasible problems containing continuous or semi-continuous variables.presolve_check() function more informative (#124).rij_matrix() so that amounts are calculated correctly for vector-based planning unit data.fast_extract().add_locked_in_constraints() and add_locked_out_constraints() functions so that they no longer throw an unnecessary warning when when they are added to multi-zone problems using raster data with NA values.add_locked_in_constraints() and add_locked_out_constraints() functions to provide recommended practices for raster data.rarity_weighted_richness() returning incorrect scores when the feature data contains one feature that has zeros amounts in all planning units (e.g. the tas_features object in the prioritizrdata R package; #120).add_gurobi_solver() returning solution statuses that are slightly larger than one (e.g. 1+1.0e-10) when solving problems with proportion-type decisions (#118).replacement_cost() function to use parallel processing to speed up calculations (#119).add_manual_bounded_constraints() function to apply lower and upper bounds on planning units statuses in a solution (#118).add_gurobi_solver(), add_lpsymphony_solver(), and add_rsymphony_solver() functions so that they will not return solutions with values less than zero or greater than one when solving problems with proportion-type decisions. This issue is the result of inconsistent precision when performing floating point arithmetic (#117).add_locked_in_constraints() and add_locked_out_constraints() functions to provide a more helpful error message the locked_in/locked_out argument refers to a column with data that are not logical (i.e. TRUE/FALSE; #118).solve() function to throw a more accurate and helpful error message when no solutions are found (e.g. due to problem infeasibility or solver time limits).add_max_phylo_objective() function to add_max_phylo_div_objective().add_max_phylo_end_objective() function to maximize the phylogenetic endemism of species adequately represented in a prioritization (#113).add_max_phylo_end_objective(), replacement_cost(), and rarity_weighted_richness() functions to the Prioritizr vignette.sim_phylogeny).add_max_phylo_div_objective() function.irreplaceability manual entry to document functions for calculating irreproducibility scores.replacement_cost() function to calculate irreproducibility scores for each planning unit in a solution using the replacement cost method (#26).rarity_weighted_richness() function to calculate irreproducibility scores for each planning unit in a solution using rarity weighted richness scores (#26).add_min_shortfall_objective() function to find solutions that minimize target shortfalls.add_min_shortfall_objective() function to main vignette.problem() tests so that they work when no solvers are installed.feature_representation() function now requires missing (NA) values for planning unit statuses in a solution for planning units that have missing (NA) cost data.presolve_check() function to investigate potential sources of numerical instability before trying to solve a problem. The manual entry for this function discusses common sources of numerical instability and approaches for fixing them.solve() function will now use the presolve_check() function to verify that problems do not have obvious sources of numerical instability before trying to solve them. If a problem is likely to have numerical instability issues then this function will now throw an error (unless the solve(x, force = TRUE)).add_rsymphony_solver() function now uses sparse matrix formats so that attempts can be made to solve large problems with SYMPHONY—though it is unlikely that SYMPHONY will be able to solve such problems in a feasible period of time.tibble::as.tibble() instead of tibble::as_tibble().solve() (#110).add_boundary_penalties() and add_connectivity_penalties() function (#106).add_rsymphony_solver() and add_lpsymphony_solver() sometimes returned infeasible solutions when subjected to a time limit (#105).ConservationProblem-class objects. These methods were implemented to be used in future interactive applications and are not currently used in the package. As a consequence, these bugs do not affect the correctness of any results.bad error message error being thrown when input rasters are not comparable (i.e. same coordinate reference system, extent, resolutions, and dimensionality) (#104).add_max_features_objective() example code.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_max_utility_objective() and add_max_cover_objective() functions to make it clearer that they do not use targets (#94).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).problem() function.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 versions (#38).add_lpsymphony_solver() examples and tests to skip on Linux operating systems.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.add_cuts_portfolio() on Travis.add_cuts_portfolio() and add_shuffle_portfolio() tests on CRAN.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.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().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.