NEWS

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multiscape 1.1.0

Solution architecture

• Simplified the public result architecture so that solve() consistently returns a SolutionSet, including for single-objective problems.
• Removed the internal Solution class from the public API and documentation. Individual run-level solutions remain available only as internal components of a SolutionSet.
• Added stable solution_id identifiers to distinguish stored solutions from attempted runs identified by run_id.
• Updated run, solution, and summary tables to preserve run_id and solution_id consistently across extraction and analysis functions.
• Improved internal finalization of solution metadata and identifiers after solving.
• Updated SolutionSet printing and documentation to reflect the distinction between run attempts and stored solutions.

Base conservation-planning workflow

• Added automatic support for base conservation-planning problems when no explicit actions or effects are supplied.
• Problems without explicit actions are now interpreted as binary conservation decisions, where each planning unit can be conserved or not conserved.
• Added an implicit conservation action and internally generated feature contributions based on the feature amounts stored in dist_features.
• The implicit conservation model uses amount_after to represent the feature amount obtained when a planning unit is conserved.

Multi-objective run design and controls

• Revised the multi-objective run-design resolver used by weighted-sum, epsilon-constraint, and AUGMECON methods.
• Improved support for automatic and manually specified run designs through run_grid() and run_manual().
• Added and documented common multi-objective execution controls through mo_control().
• Improved handling of infeasible runs, missing solutions, solver errors, and slack upper bounds.
• Standardized the storage of run-design parameters and objective values in the SolutionSet run table.
• Preserved infeasible runs in the run history while assigning solution_id only to runs that produced a stored solution.
• Improved objective evaluation and model preparation across multi-objective methods.
• Fixed weighted-sum objective preparation for implicit conservation benefit objectives.
• Improved validation of objective coefficient vectors and objective-specific error messages.

Result extraction

• Added get_runs() to extract run-level status, runtime, gap, design parameters, solution identifiers, and objective values.
• Added get_objectives() to extract objective values in long or wide format.
• Added get_objective_specs() to extract registered objective aliases, objective types, model types, optimization senses, and creation metadata.
• Updated objective extraction to include both run_id and solution_id.
• Updated existing extraction functions to work consistently with the unified SolutionSet architecture.
• Improved handling of infeasible runs and missing objective values during extraction.

Solution-set management

• Added solution_filter() to return a coherently filtered SolutionSet.
• solution_filter() can filter by run_id, solution_id, solver status, or feasibility.
• Added optional filtering of non-dominated solutions using moocore.
• Added support for selecting the objectives used to evaluate dominance.
• Ensured that filtering updates run tables, design tables, stored solutions, and summary tables consistently.
• Added cloning of SolutionSet objects before modification to prevent reference-based mutation of the original object.
• Added solution_append() to combine compatible SolutionSet objects generated from the same planning problem.
• solution_append() verifies compatibility of planning units, features, actions, effects, targets, constraints, locks, spatial relations, and objective definitions.
• Appended runs and solutions are automatically assigned unique run_id and solution_id values.
• Added support for combining solution sets obtained from different multi-objective methods or run designs applied to the same planning problem.
• Added solution_unique() to retain one representative from groups of equivalent solutions.
• solution_unique() can identify repeated solutions using either complete decision vectors or objective values.
• Added numerical tolerance controls for identifying equivalent points in objective space.
• Preserved runs without stored solutions when removing duplicated solutions.

Frontier analysis

• Added frontier_extremes() to identify the observed minimum and maximum values of each objective.
• Added classification of observed bounds as best or worst according to each objective's optimization sense.
• Added support for returning all tied extreme solutions or only the first representative.
• Added frontier_distances() to calculate normalized distances to observed ideal and nadir points.
• Added automatic transformation of maximization objectives into a common minimization space for frontier calculations.
• Added range normalization so that objectives measured in different units contribute comparably to distance calculations.
• Added Euclidean, Manhattan, and Chebyshev distance metrics.
• Added rankings based on proximity to the observed ideal point and distance from the observed nadir point.
• Added original-scale ideal, nadir, and objective-range metadata to the returned distance tables.
• Clarified that frontier reference points are calculated from the solutions contained in the supplied SolutionSet.

Selection analysis

• Added selection_frequency() to calculate how frequently each planning-unit/action assignment is selected across stored solutions.
• Standardized selection analysis around a canonical planning-unit/action representation.
• Simple conservation-planning problems are represented using the implicit conservation action.
• Added selection_similarity() to quantify structural similarity among solutions.
• Added Jaccard similarity for comparing selected planning-unit/action assignments.
• Added Hamming similarity for comparing complete binary assignment vectors, including shared non-selections.
• Added long-format and matrix-format similarity outputs.
• Added internal helpers to construct consistent long-format and matrix-format selection representations.

Documentation and website

• Reorganized the pkgdown reference index into dedicated sections for:
  • result extraction;
  • solution-set management;
  • frontier analysis;
  • selection analysis;
  • multi-objective workflow configuration.
• Removed public documentation references to the internal Solution class.
• Updated function documentation to use the unified SolutionSet terminology.
• Updated examples and cross-references for run_id, solution_id, objective extraction, filtering, frontier analysis, and selection analysis.
• Added moocore as an optional dependency for non-dominance filtering.
• Updated GitHub Actions configurations for current Codecov and Node.js runner requirements.
• Updated Codecov uploads to use the PyPI CLI and avoid binary signature-verification failures.
• Updated GitHub Actions versions for Node.js 24 compatibility.

multiscape 1.0.7 (2026-04-30)

• Updated native routine registration to resolve additional CRAN LTO/gcc-ASAN checks.
• Revised examples and package metadata for CRAN compliance.

multiscape 1.0.6 (2026-04-28)

• Fix CRAN submission issues
• Revise examples and DESCRIPTION for CRAN resubmission

multiscape 1.0.5

• Release candidate for CRAN.

multiscape 1.0.4

• First CRAN release of multiscape.
• Provides a modular workflow for exact multi-objective spatial planning based on mixed-integer programming (MIP).
• Introduces the core Problem, Solution, and SolutionSet classes. The public result architecture was later unified around SolutionSet in version 1.1.0.
• Adds support for modular problem construction through create_problem(), add_*(), set_*(), and solve().
• Supports atomic objective registration and multi-objective solution methods, including weighted-sum, epsilon-constraint, and AUGMECON.
• Includes support for spatial relations such as boundary, rook, queen, k-nearest neighbours, and distance-based relations.
• Supports commercial and open-source solvers, including Gurobi, CPLEX, CBC, and SYMPHONY.
• Adds user-facing extraction and visualization tools for planning units, actions, features, targets, spatial outputs, and trade-offs.
• Includes substantial updates to documentation, package structure, and contribution guidelines.