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  "Title": "Mapping-Based Additive Gaussian Process Models",
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  "Authors@R": "c(\nperson(\"Tony\", \"Wang\", email = \"wangtony883@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Qian\", \"Xiao\", role = c(\"aut\", \"cph\")),\nperson(\"Yaping\", \"Wang\", role = \"cph\"),\nperson(\"Abhyuday\", \"Mandal\", role = \"cph\"),\nperson(\"Xinwei\", \"Deng\", role = \"cph\"))",
  "Description": "Fits mapping-based additive Gaussian process models for\nexperiments in which each component has both a quantitative\nlevel and a position in an ordered sequence. Two model\nstructures are available: a compact two-dimensional mapping and\na full mapping with one fewer dimension than the number of\ncomponents. Both models support parameter estimation, point\nprediction, and plug-in predictive uncertainty. Input checks\nvalidate the sequence data and apply consistent scaling to the\nquantitative inputs. Computationally intensive covariance and\ngradient calculations are implemented in C++ with 'Rcpp'.\nInitial-design functions combine a space-filling Latin\nhypercube with sequence permutations. The sequence portion can\nbe generated randomly or optimized with simulated annealing or\nspace-filling threshold accepting. Expected improvement can be\noptimized over both parts of the input, and a sequential\ninterface supports Bayesian optimization of an expensive\nuser-supplied objective. An integrated workflow can generate\nthe initial design, evaluate the objective, and continue the\nsequential search in one call. The model was introduced by Xiao\net al. (2024) <doi:10.1080/01621459.2022.2123335>.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/twskr/magp,\nhttps://CRAN.R-project.org/package=magp,\nhttps://twskr.github.io/magp/",
  "BugReports": "https://github.com/twskr/magp/issues",
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  "Date/Publication": "2026-10-06 02:58:04 UTC",
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    },
    {
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    },
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      "topics": [
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    {
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