prioritizr - prioritizr 8.0.4

Notice

• New default portfolio method for problem() objects. This new default portfolio -- which can be manually specified using add_default_portfolio() -- involves simply generating a single solution. The reason why this new default portfolio method was chosen was because planning problems that contain insufficient data (e.g., feature and cost data) to identify meaningful priorities can sometimes result in solutions containing strange spatial artifacts (e.g., lines or bands of selected planning units, see #205 and #268). Since the presence of these spatial artifacts can indicate an under-specified problem and shuffling optimization problems can suppress them, we have decided to update the default portfolio so that it does not shuffle problems. If users wish to prevent spatial artifacts from appearing in solutions, then spatial penalties (e.g., add_boundary_penalties()), spatial constraints (e.g., add_neighbor_constraints()), or shuffle portfolios (e.g., add_shuffle_portfolio(number_solutions = 1)) can be used.

Minor improvements and bug fixes

• New add_default_portfolio() function for specifying the default behavior for generating a solution (see Notice above for further details).
• Update solve() so that it provides information on the optimality of solutions (#323). For example, you might specify a 10% optimality gap for the optimization process (e.g., using add_highs_solver(gap = 0.1)), and this might produce a solution that is at least 7% from optimality. The resulting output from solve() will now provide this information about the solution (i.e., the 7% from optimality), and can be accessed using the gap attribute (e.g., attr(x, "gap"), where x is the output from solve()). Note that this information is currently only available when using the Gurobi or HiGHS solvers.
• Fix bug in add_linear_constraints() and add_linear_penalties() that resulted in an incorrect error message being shown (#324).
• Fix bug in add_shuffle_portfolio() that prevented solvers from using a pre-specified starting solution (per the start parameter) correctly. Please note that this bug did not result in incorrect solutions, it only meant that any pre-specified starting solutions were not used properly.
• Fix bug in add_cplex_solver() that caused solutions to not provide runtime information for the optimization process.
• Fix bug in add_shuffle_portfolio() so that optimization problems are randomly shuffled when a single solution is requested. This update should help prevent "strange" solutions that contain long horizontal lines/bands of planning units (#205, #268).
• Update add_contiguity_constraints() and add_feature_contiguity_constraints() to be compatible with updates to the igraph package.
• Update write_problem() so that it can use the gurobi package to write problems (if desired). This substantially reduces run time, because writing problems using the Rsymphony packages also requires solving them.
• Update presolve_check() to throw warning if a problem has a single feature (#309). Thanks to Sandra Neubert (\@sandra-neubert) for code contribution.
• Update print() and summary() for problem() objects so that all text is printed at once (rather than sequentially).
• Fix write_problem() so that it works as expected (#312).
• Update problem(), add_linear_constraints(), add_linear_penalties(), add_locked_in_constraints(), add_locked_out_constraints(), adjacency_matrix(), binary_stack(), category_layer(), connectivity_matrix(),fast_extract(), intersecting_units(), proximity_matrix(), rij_matrix(), simulate_data(), simulate_species(), simulate_cost(), and zones() and other functions so that they will throw an error if a categorical terra::rast() object is provided as an argument (#313). This is because categorical rasters are not supported. Thanks to Martin Jung (\@Martin-Jung) for bug report.
• Fix NAMESPACE issues related to registration of internal S3 methods.
• Fix bug with problem() not throwing multiple warnings with unusual data (e.g., given cost and feature data with negative values, previously only a single warning about negative costs would be thrown). Thanks to Sandra Neubert (\@sandra-neubert) for bug report.
• Update problem() to be more memory efficient when using a sparse matrix (dgCMatrix) argument for the rij_matrix parameter.
• Update error messages for checking that objects have the same coordinate reference system and overlapping spatial extents to format argument names correctly.
• Update error messages for nested expressions to refer to expressions using Caused by error instead of Caused by NULL.
• Fix add_locked_in_constraints() and add_locked_in_constraints() error messages when supplying locked_in and locked_out objects that do not spatially intersect with the planning units.
• Update error message for checking if objects spatially overlap to improve clarity.

Documentation updates

• Update publication record.
• Update package-level manual entry.
• Update URLs.
• Fix aliasing for package manual entry (#301).

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Published by jeffreyhanson over 1 year ago

prioritizr - 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.

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Published by jeffreyhanson over 2 years ago

prioritizr - prioritizr 8.0.2

Notice

• The package has been updated to focus on using the sf and terra package for spatial vector and raster datasets. This is because the sf package is the successor to the sp package, and the terra package is the successor to the raster package. By leveraging these newer packages, the prioritizr package can provide better performance. Although sp and raster package classes (e.g., raster::stack() and sp::SpatialPolyonsDataFrame()) are still supported, the prioritizr package will now throw deprecation warnings. Since support for the sp and raster package classes will be fully deprecated and removed in a later version this year, we recommend updating code to use the sf and terra packages.

Breaking changes

• All proto classes have been migrated to R6 classes. This update reduces memory usage (#238), so problem() objects can now contain many more constraints and penalties. Note that any problem() objects that were produced using earlier versions of the package are no longer compatible.
• The proto, raster, sf, sp packages are no longer automatically loaded alongside prioritizr. As such, users will need to load them manually (e.g., using library(sf)).
• The built-in datasets have been removed and replaced with functions to import them as needed (i.e., get_sim_pu_raster(), get_sim_pu_polygons(), get_sim_pu_lines(), get_sim_pu_points(),, get_sim_locked_in_raster(), get_sim_locked_out_raster(), get_sim_zones_pu_raster(), get_sim_zones_pu_polygons(), get_sim_phylogeny(), get_sim_features(), get_sim_zones_features()). These functions now return sf::st_sf(), terra::rast(), ape::read.tree() and zones() objects. Note that these functions are provided because data(...) cannot be used with terra::rast() objects. See ?data for more information.
• The boundary_matrix() output format has been updated. This means that users will not be able to use boundary data generated using previous versions of the package.
• The add_lpsymphony_solver() now throws an error, instead of a warning, if an old version of the lpsymphony R package is installed that is known to produce incorrect results.
• The marxan_boundary_data_to_matrix() function is no longer compatible with boundary data for multiple zones.
• The distribute_load() function has been deprecated, because it is no longer used. For equivalent functionality, See parallel::splitIndices().
• The new_optimization_problem() and predefined_optimization_problem() functions have been superseded by the new optimization_problem() function.
• To simplify package documentation and functionality, the following functions are no longer exported: is.Waiver(), add_default_decisions() new_id(), is.Id(), print.Id(), pproto().

New features

• The print() function for problem(), optimization_problem(), and zones() objects has been updated to provide more information.
• New summary() function to provide extensive detail on problem() objects.
• Updates to improve the error messages and error message handling. Hopefully, users should no longer see "bad error message"!

Minor improvements and bug fixes

• Fix bug for add_feature_weights() when applied to problems with an add_max_phylo_div_objective() or add_max_phylo_end_objectve(). Specifically, the bug meant that weights weren't being applied to problems with these particular objectives.
• Fix instructions in add_gurobi_solver() documentation for opening vignette.
• Update solver functions to provide instructions for installing dependencies in error messages when their dependencies are not available.
• To ensure consistency among the portfolio functions, all of them (except for add_extra_portfolio()) default to generating 10 solutions.
• Update publication record.
• The solve() function will now output tibble::tibble() objects (instead of data.frame() objects), when the planning unit data are tibble::tibble() objects.
• The boundary_matrix() function now uses terra::sharedPaths() for calculations, providing greater performance (#257).
• The eval_ferrier_importance() function can now be used with any objective function that uses targets and a single zone.
• Fix CRAN note regarding C++ standards (#263).
• Remove doParallel and plyr packages as dependencies by simplifying the add_shuffle_portfolio() and eval_replacement_importance() functions.
• Assorted tweaks to improve writing in the vignettes and documentation. Many thanks to Marc Edwards (@edwardsmarc)!

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Published by jeffreyhanson almost 3 years ago

prioritizr - prioritizr 7.2.2

• Fix compiler warnings.
• Update tests to skip long-running tests on CRAN.
• Update examples to minimize overall package check timings for CRAN.
• Fix compatibility with upcoming Matrix package version (version 1.5-0).
• Update package documentation to provide details for obtaining and installing the cplexAPI package since it has been archived on CRAN (#214).
• Fix bug that caused the 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).
• Update simulate_data(), simulate_cost() and simulate_species() functions to improve performance using the fields package.
• Update boundary_matrix() to use STR query trees by default.
• Remove maptools, PBSmapping, and rgeos packages as dependencies. This involved updating the unit tests to hard-code correct results, updating examples to use the sf package, and updating the boundary_matrix() to use the geos package (#218).
• Fix broken URLs in package documentation.
• Update publication record.
• Update the 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.
• Update documentation for add_min_largest_shortfall_objective() so that examples complete in a shorter period of time.
• Fix bug in processing planning unit data when a problem is constructed using arguments to (i) 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).
• Fix 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).
• Fix presolve_check() so that it does not throw a meaningless warning when the mathematical objective function only contains zeros.
• Update presolve_check() to help reduce chances of mis-attributing high connectivity/boundary values due to planning unit costs.
• Update add_connectivity_penalties() function and documentation so that it is designed specifically for symmetric connectivity data.
• New 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).
• Update 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.
• Improve documentation for the zones parameter of the add_connectivity_penalties() function.
• Update documentation for 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.
• Fix bug so that the eval_n_summary() function now returns a table with the column name "n" (instead of "cost") for the number of selected planning units (#219).
• Update reference index for package website.
• Fix minor typos in vignettes.
• Minimum version numbers are now provided for all R package dependencies (excepting base R packages) (#217).
• The data.table package is now listed as a suggested (optional) dependency. This is because it is only used by the marxan_problem() for importing Marxan data files.
• The Tasmania tutorial has been reworked into the Getting started tutorial. This tutorial now provides short introduction to using the package.
• The Salt Spring Island tutorial has been reworked into the Connectivity tutorial. This tutorial now explores different approaches for incorporating connectivity.
• The prioritizr vignette has been renamed to the Package overview vignette.
• New Calibrating trade-offs tutorial showcasing methods for running calibration analyses. It outlines blended and hierarchical approaches for generating a set of different prioritizations based on different parameters. It also covers different approaches for selecting a candidate prioritization based on different trade-offs.
• Update tests to reduce run time and pass given slightly different results with new Gurobi version (9.5.0).
• Update built-in sim_pu_sf and sim_pu_zones_sf data given class updates to the sf package (compatible with version 1.0.3+).
• Update example for write_problem() function.

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Published by jeffreyhanson over 3 years ago

prioritizr - prioritizr 7.1.1

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Published by jeffreyhanson over 4 years ago

prioritizr - prioritizr 7.0.1

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Published by jeffreyhanson over 4 years ago

prioritizr - prioritizr 3.0.4

This release contains version 3.0.4 of the prioritizr R package. It is significant because this version is the most the most up-to-date and best-working version of the package before it was over-hauled to manage problems with multiple management zones.

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Published by jeffreyhanson almost 8 years ago