Changes in version 0.12.0 (2026-09-24)                 

  - Limited parallel fitting and acquisition searches to two worker
    processes.

  - Added random and space-filling threshold-accepting options for the
    sequence portion of an initial design. The existing
    simulated-annealing method remains the default for backward
    compatibility.

  - Added C++ incremental scoring for the threshold-accepting search,
    including bounded initialization, threshold calibration, best-result
    retention, and detailed diagnostics.

  - Connected the new sequence choices to the full initial-design and
    from-scratch Bayesian-optimization workflows.

  - Added examples and regression tests for method selection, input
    validation, reproducibility, finite permutation spaces, and
    quantitative-design preservation.

  - Rewrote the Bayesian optimization help pages and examples to explain
    function choice, arguments, workflow steps, and returned objects
    more directly.

  - Added a pkgdown website with a getting-started guide, an
    optimization workflow, a reproducible covariance-engine benchmark,
    and a curated function reference.

  - Added package-level citation metadata for both the software and the
    associated methodology paper.

  - Added magp_bayes_optimize_from_scratch() to connect
    simulated-annealing initial design, initial objective evaluation,
    and sequential MaGP Bayesian optimization in one call.

  - Added reproducible handling of stochastic objective functions while
    preserving the caller's random-number state.

  - Included the complete initial design and its responses in the
    optimization result.

  - Added early validation for design, fitting, acquisition, and
    objective controls before the initial-design search begins.

  - Added expected improvement for both two-dimensional and full-mapping
    models.

  - Added mixed acquisition search across quantitative bounds and
    sequence permutations, with multi-start and optional parallel
    execution.

  - Added a sequential Bayesian optimization interface for expensive
    objective functions.

  - Added deterministic sequence sampling for cases where complete
    permutation enumeration would be too large.

  - Added duplicate-input protection, early stopping, complete search
    history, and final-model refitting.

  - Added multi-start optimization to magp2d_fit() and magpfull_fit().

  - Added optional local parallel execution through portable socket
    clusters.

  - Made seeded sequential and parallel fits use the same parameter
    starts.

  - Added start-level objective values, convergence results, warnings,
    and errors to each fitted object.

  - Completed the quantitative-sequence initial-design workflow.

  - Added magp_quantitative_criterion() and magp_quantitative_design()
    for maximin-style Latin hypercubes.

  - Added magp_joint_criterion() to evaluate the combined separation of
    quantitative and sequence inputs.

  - Added magp_initial_design() to construct and align both design
    portions while preserving their individual structures.

  - Added reproducible simulated-annealing searches and validation
    across several run sizes and component counts.

  - Added tools for constructing the sequence portion of a
    quantitative-sequence initial design.

  - Added magp_sequence_criterion() to assess pair balance and
    Hamming-distance space filling in a sequence design.

  - Added magp_sequence_design() to optimize the sequence portion with
    reproducible simulated annealing.

  - Added reproducible simulated-annealing search with configurable
    criterion weights and search settings.

                 Changes in version 0.8.0 (2026-09-03)                  

  - Prepared the two-dimensional and full-mapping models for the first
    CRAN release.
  - Added a consistent predict() interface, including plug-in predictive
    uncertainty, for both fitted model classes.
  - Moved the main covariance, gradient, and cross-covariance
    calculations to C++ with Rcpp.
  - Added automatic input checks and reusable scaling for quantitative
    inputs.
  - Set the default nugget variance to tau = 0.001.

                        Changes in version 0.7.0                        

  - Renamed the package to magp.
  - Added two-dimensional and full-mapping MaGP models.
  - Added matching predict() methods for both fitted model classes.
  - Used Rcpp for covariance, gradient, and cross-covariance
    calculations.
  - Added automatic input-role detection, sequence-permutation checks,
    and stored min-max scaling for quantitative inputs outside [0, 1].