{
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  "Package": "BSL",
  "Type": "Package",
  "Title": "Bayesian Synthetic Likelihood",
  "Version": "3.2.6",
  "Date": "2026-06-29",
  "Authors@R": "c(person(\"Ziwen\",\"An\",role=c(\"aut\"),email=\"ziwen.an@hdr.qut.edu.au\",comment = c(ORCID = \"0000-0002-9947-5182\")),\nperson(\"Leah F.\",\"South\",role=c(\"aut\",\"cre\"),email=\"l1.south@qut.edu.au\",comment = c(ORCID = \"0000-0002-5646-2963\")),\nperson(\"Christopher C.\",\"Drovandi\",role=c(\"aut\"),email=\"c.drovandi@qut.edu.au\",comment = c(ORCID = \"0000-0001-9222-8763\")))",
  "Description": "Bayesian synthetic likelihood (BSL, Price et al. (2018)\n<doi:10.1080/10618600.2017.1302882>) is an alternative to\nstandard, non-parametric approximate Bayesian computation\n(ABC). BSL assumes a multivariate normal distribution for the\nsummary statistic likelihood and it is suitable when the\ndistribution of the model summary statistics is sufficiently\nregular. This package provides a Metropolis Hastings Markov\nchain Monte Carlo implementation of four methods (BSL, uBSL,\nsemiBSL and BSLmisspec) and two shrinkage estimators (graphical\nlasso and Warton's estimator). uBSL (Price et al. (2018)\n<doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator\nto the normal density. A semi-parametric version of BSL\n(semiBSL, An et al. (2020) <doi:10.1007/s11222-019-09904-x>) is\nmore robust to non-normal summary statistics. BSLmisspec\n(Frazier et al. 2021 <doi:10.1080/10618600.2021.1875839>)\nestimates the Gaussian synthetic likelihood whilst\nacknowledging that there may be incompatibility between the\nmodel and the observed summary statistic. Shrinkage estimation\ncan help to decrease the number of model simulations when the\ndimension of the summary statistic is high (e.g., BSLasso, An\net al. (2019) <doi:10.1080/10618600.2018.1537928>). Extensions\nto this package are planned. For a journal article describing\nhow to use this package, see An et al. (2022)\n<doi:10.18637/jss.v101.i11>.",
  "License": "GPL (>= 2)",
  "LazyLoad": "yes",
  "LazyData": "true",
  "Encoding": "UTF-8",
  "Collate": "'BSL-package.R' 'RcppExports.R' 's4-MODEL.R' 's4-BSL.R'\n'bsl.R' 'cell.R' 'combinePlotsBSL.R' 'covWarton.R'\n'estimateLoglike.R' 'estimateWhiteningMatrix.R'\n'gaussianRankCorr.R' 'gaussianSynLike.R'\n'gaussianSynLikeGhuryeOlkin.R' 'imports.R' 'kernelCDF.R'\n'logitTransform.R' 'ma2.R' 'mgnk.R' 'myMiniProgressBar.R'\n's4-PENALTY.R' 'selectPenalty.R' 'semiparaKernelEstimate.R'\n'sliceGammaMean.R' 'sliceGammaVariance.R' 'synLikeMisspec.R'\n'toad.R'",
  "RdMacros": "Rdpack",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-09-27 07:19:36 UTC",
    "User": "root"
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  "Author": "Ziwen An [aut] (ORCID:\n<https://orcid.org/0000-0002-9947-5182>), Leah F. South [aut,\ncre] (ORCID: <https://orcid.org/0000-0002-5646-2963>),\nChristopher C. Drovandi [aut] (ORCID:\n<https://orcid.org/0000-0001-9222-8763>)",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-07-02 22:30:28 UTC",
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    "cell_sum",
    "combinePlotsBSL",
    "cor2cov",
    "estimateLoglike",
    "estimateWhiteningMatrix",
    "gaussianRankCorr",
    "gaussianSynLike",
    "gaussianSynLikeGhuryeOlkin",
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    "getLoglike",
    "getPenalty",
    "getTheta",
    "ma2_prior",
    "ma2_sim",
    "ma2_sim_vec",
    "ma2_sum",
    "mgnk_sim",
    "mgnk_sum",
    "MODEL",
    "newModel",
    "obsMat2deltax",
    "PENALTY",
    "plot",
    "selectPenalty",
    "semiparaKernelEstimate",
    "show",
    "sim_toad",
    "simulate_cell",
    "simulation",
    "summary",
    "summStat",
    "synLikeMisspec",
    "toad_prior",
    "toad_sim",
    "toad_sum"
  ],
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      "title": "Cell biology example",
      "object": "cell",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "name": "ma2",
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      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "title": "The multivariate G&K example",
      "object": "mgnk",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "name": "toad",
      "title": "Toad example",
      "object": "toad",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "BSL-package",
      "title": "Bayesian synthetic likelihood",
      "topics": [
        "BSL-package",
        "BSL"
      ]
    },
    {
      "page": "bsl",
      "title": "Performing BSL, uBSL, semiBSL and BSLmisspec",
      "topics": [
        "bsl"
      ]
    },
    {
      "page": "BSL-class",
      "title": "S4 class ``BSL''.",
      "topics": [
        "BSL-class",
        "BSLclass",
        "getGamma,BSL-method",
        "getLoglike,BSL-method",
        "getTheta,BSL-method",
        "plot,BSL,ANY-method",
        "show,BSL-method",
        "summary,BSL-method"
      ]
    },
    {
      "page": "cell",
      "title": "Cell biology example",
      "topics": [
        "cell",
        "cell_prior",
        "cell_sim",
        "cell_sum"
      ]
    },
    {
      "page": "combinePlotsBSL",
      "title": "Plot the densities of multiple ``bsl'' class objects.",
      "topics": [
        "combinePlotsBSL"
      ]
    },
    {
      "page": "cor2cov",
      "title": "Convert a correlation matrix to a covariance matrix",
      "topics": [
        "cor2cov"
      ]
    },
    {
      "page": "estimateLoglike",
      "title": "Estimate the synthetic likelihood",
      "topics": [
        "estimateLoglike"
      ]
    },
    {
      "page": "estimateWhiteningMatrix",
      "title": "Estimate the Whitening matrix to be used in the ``wBSL'' method of Priddle et al. (2022)",
      "topics": [
        "estimateWhiteningMatrix"
      ]
    },
    {
      "page": "gaussianRankCorr",
      "title": "Gaussian rank correlation",
      "topics": [
        "gaussianRankCorr"
      ]
    },
    {
      "page": "gaussianSynLike",
      "title": "Estimate the Gaussian synthetic (log) likelihood",
      "topics": [
        "gaussianSynLike"
      ]
    },
    {
      "page": "gaussianSynLikeGhuryeOlkin",
      "title": "Estimate the Gaussian synthetic (log) likelihood with an unbiased estimator",
      "topics": [
        "gaussianSynLikeGhuryeOlkin"
      ]
    },
    {
      "page": "getGamma",
      "title": "Obtain the gamma samples (the latent parameters for BSLmisspec method) from a \"BSL\" object",
      "topics": [
        "getGamma"
      ]
    },
    {
      "page": "getLoglike",
      "title": "Obtain the log-likelihoods from a \"BSL\" object",
      "topics": [
        "getLoglike"
      ]
    },
    {
      "page": "getPenalty",
      "title": "Obtain the selected penalty values from a \"PENALTY\" object",
      "topics": [
        "getPenalty"
      ]
    },
    {
      "page": "getTheta",
      "title": "Obtain the samples from a \"BSL\" object",
      "topics": [
        "getTheta"
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      "title": "An MA(2) model",
      "topics": [
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        "ma2_prior",
        "ma2_sim",
        "ma2_sim_vec",
        "ma2_sum"
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      "title": "The multivariate G&K example",
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        "mgnk_sim",
        "mgnk_sum"
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    {
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      "title": "S4 class ``MODEL''",
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        "MODEL",
        "MODEL-class",
        "newModel",
        "simulation,MODEL-method",
        "summStat,ANY,MODEL-method"
      ]
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        "obsMat2deltax"
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    },
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      "page": "PENALTY-class",
      "title": "S4 class ``PENALTY''",
      "topics": [
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        "PENALTY",
        "PENALTY-class",
        "PENALTYclass",
        "plot,PENALTY,ANY-method",
        "show,PENALTY-method"
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      "page": "synLikeMisspec",
      "title": "Estimate the Gaussian synthetic (log) likelihood whilst acknowledging model incompatibility",
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        "synLikeMisspec"
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    },
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        "toad_prior",
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        "toad_sum"
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