Skip to contents

Changelog

Source: NEWS.md

magp 0.12.0

CRAN release: 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.

magp 0.8.0

CRAN release: 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.

magp 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].