{
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  "Package": "hetGP",
  "Type": "Package",
  "Title": "Heteroskedastic Gaussian Process Modeling and Design under\nReplication",
  "Version": "1.1.9",
  "Date": "2026-04-17",
  "Authors@R": "c(person(given = \"Mickael\",\nfamily = \"Binois\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mickael.binois@inria.fr\"),\nperson(given = c(\"Robert\", \"B.\"),\nfamily = \"Gramacy\",\nrole = \"aut\"))",
  "Description": "Performs Gaussian process regression with heteroskedastic\nnoise following the model by Binois, M., Gramacy, R.,\nLudkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with\nimplementation details in Binois, M. & Gramacy, R. B. (2021)\n<doi:10.18637/jss.v098.i13>. The input dependent noise is\nmodeled as another Gaussian process. Replicated observations\nare encouraged as they yield computational savings. Sequential\ndesign procedures based on the integrated mean square\nprediction error and lookahead heuristics are provided, and\nnotably fast update functions when adding new observations.",
  "License": "LGPL",
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  "Author": "Mickael Binois [aut, cre], Robert B. Gramacy [aut]",
  "Maintainer": "Mickael Binois <mickael.binois@inria.fr>",
  "Repository": "https://mbinois.r-universe.dev",
  "Date/Publication": "2026-04-23 17:52:07 UTC",
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  "_published": "2026-05-23T09:30:44.715Z",
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  "_topics": [
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    "extra/citation.json",
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    "hyperSharperQ",
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    "logLikH",
    "LOO_preds",
    "mleCRNGP",
    "mleHetGP",
    "mleHetTP",
    "mleHomGP",
    "mleHomTP",
    "pred_noisy_input",
    "qEI_loop",
    "qHSRI_loop",
    "rebuild",
    "scores",
    "simul",
    "sirEval",
    "sirSimulate",
    "strip",
    "update_pred",
    "Wij"
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      "page": "allocate_mult",
      "title": "Allocation of replicates on existing designs",
      "topics": [
        "allocate_mult"
      ]
    },
    {
      "page": "allocq",
      "title": "Allocation",
      "topics": [
        "allocq"
      ]
    },
    {
      "page": "allocq_c",
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      "topics": [
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    },
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      "page": "ato",
      "title": "Assemble To Order (ATO) Data and Fits",
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        "ato.a",
        "kill",
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        "nc",
        "out",
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        "X",
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      "title": "Likelihood-based comparison of models",
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      "page": "crit_EI",
      "title": "Expected Improvement criterion",
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      "page": "crit_ICU",
      "title": "Integrated Contour Uncertainty criterion",
      "topics": [
        "crit_ICU"
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    },
    {
      "page": "crit_IMSPE",
      "title": "Sequential IMSPE criterion",
      "topics": [
        "crit_IMSPE"
      ]
    },
    {
      "page": "crit_logEI",
      "title": "Logarithm of Expected Improvement criterion",
      "topics": [
        "crit_logEI"
      ]
    },
    {
      "page": "crit_MCU",
      "title": "Maximum Contour Uncertainty criterion",
      "topics": [
        "crit_MCU"
      ]
    },
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      "page": "crit_MEE",
      "title": "Maximum Empirical Error criterion",
      "topics": [
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    },
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      "page": "crit_optim",
      "title": "Criterion optimization",
      "topics": [
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    },
    {
      "page": "crit_qEI",
      "title": "Parallel Expected improvement",
      "topics": [
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      ]
    },
    {
      "page": "crit_tMSE",
      "title": "t-MSE criterion",
      "topics": [
        "crit_tMSE"
      ]
    },
    {
      "page": "deriv_crit_EI",
      "title": "Derivative of EI criterion for GP models",
      "topics": [
        "deriv_crit_EI"
      ]
    },
    {
      "page": "deriv_crit_IMSPE",
      "title": "Derivative of crit_IMSPE",
      "topics": [
        "deriv_crit_IMSPE"
      ]
    },
    {
      "page": "deriv_crit_logEI",
      "title": "Derivative of logEI criterion for GP models",
      "topics": [
        "deriv_crit_logEI"
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    },
    {
      "page": "f1d",
      "title": "1d test function (1)",
      "topics": [
        "f1d"
      ]
    },
    {
      "page": "f1d_n",
      "title": "Noisy 1d test function (1) Add Gaussian noise with variance r(x) = scale * (1.1 + sin(2 pi x))^2 to 'f1d'",
      "topics": [
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      ]
    },
    {
      "page": "f1d2",
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    },
    {
      "page": "f1d2_n",
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      "topics": [
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    {
      "page": "find_reps",
      "title": "Data preprocessing",
      "topics": [
        "find_reps"
      ]
    },
    {
      "page": "horizon",
      "title": "Adapt horizon",
      "topics": [
        "horizon"
      ]
    },
    {
      "page": "hyperSharpeMax",
      "title": "Hypervolume Sharpe ratio maximization",
      "topics": [
        "hyperSharpeMax"
      ]
    },
    {
      "page": "hyperSharperQ",
      "title": "Hypervolume Sharpe ratio return and covariance",
      "topics": [
        "hyperSharperQ"
      ]
    },
    {
      "page": "IMSPE",
      "title": "Integrated Mean Square Prediction Error",
      "topics": [
        "IMSPE"
      ]
    },
    {
      "page": "IMSPE_optim",
      "title": "IMSPE optimization",
      "topics": [
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    },
    {
      "page": "LOO_preds",
      "title": "Leave one out predictions",
      "topics": [
        "LOO_preds"
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    },
    {
      "page": "mleCRNGP",
      "title": "Gaussian process modeling with correlated noise",
      "topics": [
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    },
    {
      "page": "mleHetGP",
      "title": "Gaussian process modeling with heteroskedastic noise",
      "topics": [
        "mleHetGP"
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    },
    {
      "page": "mleHetTP",
      "title": "Student-t process modeling with heteroskedastic noise",
      "topics": [
        "mleHetTP"
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    },
    {
      "page": "mleHomGP",
      "title": "Gaussian process modeling with homoskedastic noise",
      "topics": [
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    },
    {
      "page": "mleHomTP",
      "title": "Student-T process modeling with homoskedastic noise",
      "topics": [
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    },
    {
      "page": "pred_noisy_input",
      "title": "Gaussian process prediction prediction at a noisy input 'x', with centered Gaussian noise of variance 'sigma_x'.  Several options are available, with different efficiency/accuracy tradeoffs.",
      "topics": [
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    },
    {
      "page": "predict.CRNGP",
      "title": "Gaussian process predictions using a GP object for correlated noise (of class 'CRNGP')",
      "topics": [
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    },
    {
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      "title": "Gaussian process predictions using a heterogeneous noise GP object (of class 'hetGP')",
      "topics": [
        "predict.hetGP"
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    },
    {
      "page": "predict.hetTP",
      "title": "Student-t process predictions using a heterogeneous noise TP object (of class 'hetTP')",
      "topics": [
        "predict.hetTP"
      ]
    },
    {
      "page": "predict.homGP",
      "title": "Gaussian process predictions using a homoskedastic noise GP object (of class 'homGP')",
      "topics": [
        "predict.homGP"
      ]
    },
    {
      "page": "predict.homTP",
      "title": "Student-t process predictions using a homoskedastic noise GP object (of class 'homGP')",
      "topics": [
        "predict.homTP"
      ]
    },
    {
      "page": "qEI_loop",
      "title": "BO loop with qEI",
      "topics": [
        "qEI_loop"
      ]
    },
    {
      "page": "qHSRI_loop",
      "title": "BO loop with massive batches",
      "topics": [
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    },
    {
      "page": "ExpImp",
      "title": "Import and export of hetGP objects",
      "topics": [
        "rebuild",
        "rebuild.hetGP",
        "rebuild.hetTP",
        "rebuild.homGP",
        "rebuild.homTP",
        "strip"
      ]
    },
    {
      "page": "scores",
      "title": "Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).",
      "topics": [
        "scores"
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    },
    {
      "page": "simul",
      "title": "Conditional simulation for CRNGP",
      "topics": [
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    },
    {
      "page": "simul.CRNGP",
      "title": "Fast conditional simulation for a CRNGP model",
      "topics": [
        "simul.CRNGP"
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    },
    {
      "page": "SIR",
      "title": "SIR test problem",
      "topics": [
        "sirEval",
        "sirSimulate"
      ]
    },
    {
      "page": "update_pred",
      "title": "Prediction update with new designs and observations",
      "topics": [
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    },
    {
      "page": "update.hetGP",
      "title": "Update '\"hetGP\"'-class model fit with new observations",
      "topics": [
        "update.hetGP"
      ]
    },
    {
      "page": "update.hetTP",
      "title": "Update '\"hetTP\"'-class model fit with new observations",
      "topics": [
        "update.hetTP"
      ]
    },
    {
      "page": "update.homGP",
      "title": "Fast 'homGP'-update",
      "topics": [
        "update.homGP"
      ]
    },
    {
      "page": "update.homTP",
      "title": "Fast 'homTP'-update",
      "topics": [
        "update.homTP"
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    },
    {
      "page": "Wij",
      "title": "Compute double integral of the covariance kernel over a [0,1]^d domain",
      "topics": [
        "Wij"
      ]
    }
  ],
  "_rundeps": [
    "DiceDesign",
    "MASS",
    "mco",
    "quadprog",
    "Rcpp"
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  "_sysdeps": [
    {
      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "14.2.0-4ubuntu2~24.04.1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
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    {
      "source": "hetGP_vignette.Rnw",
      "filename": "hetGP_vignette.pdf",
      "title": "a guide to the hetGP package",
      "engine": "knitr::knitr",
      "headings": [],
      "created": "2019-01-10 13:00:16",
      "modified": "2026-04-23 17:52:07",
      "commits": 4
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