{
  "_id": "6a1055b1acfb0bcc41ca25f3",
  "Package": "BayesianTools",
  "Title": "General-Purpose MCMC and SMC Samplers and Tools for Bayesian\nStatistics",
  "Version": "0.1.9",
  "Date": "2026-03-09",
  "Authors@R": "c(person(\"Florian\", \"Hartig\", email = \"florian.hartig@biologie.uni-regensburg.de\", role = c(\"aut\", \"cre\"), comment=c(ORCID=\"0000-0002-6255-9059\")), person(\"Francesco\", \"Minunno\", role = c(\"aut\")), person(\"Stefan\", \" Paul\", role = c(\"aut\") ),  person(\"David\", \"Cameron\", role = \"ctb\"), person(\"Tankred\", \"Ott\", role = \"ctb\"), person(\"Maximilian\", \"Pichler\", role = \"ctb\"))",
  "Description": "General-purpose MCMC and SMC samplers, as well as plots\nand diagnostic functions for Bayesian statistics, with a\nparticular focus on calibrating complex system models.\nImplemented samplers include various Metropolis MCMC variants\n(including adaptive and/or delayed rejection MH), the T-walk,\ntwo differential evolution MCMCs, two DREAM MCMCs, and a\nsequential Monte Carlo (SMC) particle filter.",
  "License": "GPL-3",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "URL": "https://github.com/florianhartig/BayesianTools,\nhttps://florianhartig.github.io/BayesianTools/",
  "BugReports": "https://github.com/florianhartig/BayesianTools/issues",
  "VignetteBuilder": "knitr",
  "Encoding": "UTF-8",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nzlib1g-dev",
  "Repository": "https://pecanproject.r-universe.dev",
  "Date/Publication": "2026-03-10 08:21:23 UTC",
  "RemoteUrl": "https://github.com/florianhartig/BayesianTools",
  "RemoteRef": "HEAD",
  "RemoteSha": "ffa631c30cd6f849acc083e757496d6d6cacf121",
  "RemoteSubdir": "BayesianTools",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-09 07:59:32 UTC",
    "User": "root"
  },
  "Author": "Florian Hartig [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6255-9059>),\nFrancesco Minunno [aut],\nStefan Paul [aut],\nDavid Cameron [ctb],\nTankred Ott [ctb],\nMaximilian Pichler [ctb]",
  "Maintainer": "Florian Hartig <florian.hartig@biologie.uni-regensburg.de>",
  "MD5sum": "5088fdcf7cf19e91febc834dbf17ebae",
  "_user": "pecanproject",
  "_type": "src",
  "_file": "BayesianTools_0.1.9.tar.gz",
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  "_created": "2026-05-09T07:59:32.000Z",
  "_published": "2026-05-22T13:10:09.891Z",
  "_distro": "noble",
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    "author": "Florian Hartig <florianhartig@users.noreply.github.com>",
    "committer": "Florian Hartig <florianhartig@users.noreply.github.com>",
    "message": "Delete Makevars.win\n\nDeleted again Makevars.win - I had introduced this as a fix to #280 but it turned out that this was a winbuilder problem.\n",
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    "email": "florian.hartig@biologie.uni-regensburg.de",
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  "_owner": "florianhartig",
  "_selfowned": false,
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  "_updates": [
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  ],
  "_tags": [
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      "name": "v0.1.9",
      "date": "2026-03-09"
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  ],
  "_topics": [
    "bayes",
    "ecological-models",
    "mcmc",
    "optimization",
    "smc",
    "systems-biology",
    "cpp"
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  "_stars": 129,
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesianTools"
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  "_devurl": "https://github.com/florianhartig/bayesiantools",
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  "_metadata": {
    "notes": "Imported by PEcAn.assim.batch"
  },
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/BayesianTools.html",
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/florianhartig/bayesiantools",
  "_realowner": "florianhartig",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.1.0",
      "date": "2017-02-05"
    },
    {
      "version": "0.1.1",
      "date": "2017-04-05"
    },
    {
      "version": "0.1.2",
      "date": "2017-05-27"
    },
    {
      "version": "0.1.3",
      "date": "2017-08-02"
    },
    {
      "version": "0.1.4",
      "date": "2017-12-20"
    },
    {
      "version": "0.1.5",
      "date": "2018-07-16"
    },
    {
      "version": "0.1.6",
      "date": "2019-01-21"
    },
    {
      "version": "0.1.7",
      "date": "2019-12-09"
    },
    {
      "version": "0.1.8",
      "date": "2023-01-30"
    },
    {
      "version": "0.1.9",
      "date": "2026-03-10"
    }
  ],
  "_exports": [
    "applySettingsDefault",
    "bridgesample",
    "calibrationTest",
    "checkBayesianSetup",
    "convertCoda",
    "correlationPlot",
    "createBayesianSetup",
    "createBetaPrior",
    "createLikelihood",
    "createMcmcSamplerList",
    "createMixWithDefaults",
    "createPosterior",
    "createPrior",
    "createPriorDensity",
    "createProposalGenerator",
    "createSmcSamplerList",
    "createTruncatedNormalPrior",
    "createUniformPrior",
    "DE",
    "DEzs",
    "DIC",
    "DREAM",
    "DREAMzs",
    "gelmanDiagnostics",
    "generateParallelExecuter",
    "generateTestDensityMultiNormal",
    "getCredibleIntervals",
    "getPanels",
    "getPossibleSamplerTypes",
    "getPredictiveDistribution",
    "getPredictiveIntervals",
    "getSample",
    "getVolume",
    "GOF",
    "likelihoodAR1",
    "likelihoodIidNormal",
    "MAP",
    "marginalLikelihood",
    "marginalPlot",
    "mergeChains",
    "Metropolis",
    "plotDiagnostic",
    "plotSensitivity",
    "plotTimeSeries",
    "plotTimeSeriesResiduals",
    "plotTimeSeriesResults",
    "runMCMC",
    "sampleMetropolis",
    "smcSampler",
    "stopParallel",
    "testDensityBanana",
    "testDensityInfinity",
    "testDensityMultiNormal",
    "testDensityNormal",
    "testLinearModel",
    "tracePlot",
    "Twalk",
    "updateProposalGenerator",
    "VSEM",
    "vsemC",
    "VSEMcreateLikelihood",
    "VSEMcreatePAR",
    "VSEMgetDefaults",
    "WAIC"
  ],
  "_help": [
    {
      "page": "applySettingsDefault",
      "title": "Provides the default settings for the different samplers in runMCMC",
      "topics": [
        "applySettingsDefault"
      ]
    },
    {
      "page": "calibrationTest",
      "title": "Simulation-based calibration tests",
      "topics": [
        "calibrationTest"
      ]
    },
    {
      "page": "checkBayesianSetup",
      "title": "Checks if an object is of class 'BayesianSetup'",
      "topics": [
        "checkBayesianSetup"
      ]
    },
    {
      "page": "convertCoda",
      "title": "Convert coda::mcmc objects to BayesianTools::mcmcSampler",
      "topics": [
        "convertCoda"
      ]
    },
    {
      "page": "correlationPlot",
      "title": "Flexible function to create correlation density plots",
      "topics": [
        "correlationPlot"
      ]
    },
    {
      "page": "createBayesianSetup",
      "title": "Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.",
      "topics": [
        "createBayesianSetup"
      ]
    },
    {
      "page": "createBetaPrior",
      "title": "Convenience function to create a beta prior",
      "topics": [
        "createBetaPrior"
      ]
    },
    {
      "page": "createLikelihood",
      "title": "Creates a standardized likelihood class",
      "topics": [
        "createLikelihood"
      ]
    },
    {
      "page": "createMcmcSamplerList",
      "title": "Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers",
      "topics": [
        "createMcmcSamplerList"
      ]
    },
    {
      "page": "package-deprecated",
      "title": "Allows to mix a given parameter vector with a default parameter vector",
      "topics": [
        "createMixWithDefaults"
      ]
    },
    {
      "page": "createPosterior",
      "title": "Creates a standardized posterior class",
      "topics": [
        "createPosterior"
      ]
    },
    {
      "page": "createPrior",
      "title": "Creates a user-defined prior class",
      "topics": [
        "createPrior"
      ]
    },
    {
      "page": "createPriorDensity",
      "title": "Fits a density function to a multivariate sample",
      "topics": [
        "createPriorDensity"
      ]
    },
    {
      "page": "createProposalGenerator",
      "title": "Factory that creates a proposal generator",
      "topics": [
        "createProposalGenerator"
      ]
    },
    {
      "page": "createSmcSamplerList",
      "title": "Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers",
      "topics": [
        "createSmcSamplerList"
      ]
    },
    {
      "page": "createTruncatedNormalPrior",
      "title": "Convenience function to create a truncated normal prior",
      "topics": [
        "createTruncatedNormalPrior"
      ]
    },
    {
      "page": "createUniformPrior",
      "title": "Convenience function to create a simple uniform prior distribution",
      "topics": [
        "createUniformPrior"
      ]
    },
    {
      "page": "DE",
      "title": "Differential-Evolution MCMC",
      "topics": [
        "DE"
      ]
    },
    {
      "page": "DEzs",
      "title": "Differential-Evolution MCMC zs",
      "topics": [
        "DEzs"
      ]
    },
    {
      "page": "DIC",
      "title": "Deviance information criterion",
      "topics": [
        "DIC"
      ]
    },
    {
      "page": "DREAM",
      "title": "DREAM",
      "topics": [
        "DREAM"
      ]
    },
    {
      "page": "DREAMzs",
      "title": "DREAMzs",
      "topics": [
        "DREAMzs"
      ]
    },
    {
      "page": "gelmanDiagnostics",
      "title": "Gelman Diagnostics",
      "topics": [
        "gelmanDiagnostics"
      ]
    },
    {
      "page": "generateParallelExecuter",
      "title": "Factory to generate a parallel executor of an existing function",
      "topics": [
        "generateParallelExecuter"
      ]
    },
    {
      "page": "generateTestDensityMultiNormal",
      "title": "Multivariate normal likelihood",
      "topics": [
        "generateTestDensityMultiNormal"
      ]
    },
    {
      "page": "getCredibleIntervals",
      "title": "Calculate confidence region from an MCMC or similar sample",
      "topics": [
        "getCredibleIntervals"
      ]
    },
    {
      "page": "getDharmaResiduals",
      "title": "Creates a DHARMa object",
      "topics": [
        "getDharmaResiduals"
      ]
    },
    {
      "page": "getPanels",
      "title": "getPanels",
      "topics": [
        "getPanels"
      ]
    },
    {
      "page": "getPossibleSamplerTypes",
      "title": "Returns possible sampler types",
      "topics": [
        "getPossibleSamplerTypes"
      ]
    },
    {
      "page": "getPredictiveDistribution",
      "title": "Calculates predictive distribution based on the parameters",
      "topics": [
        "getPredictiveDistribution"
      ]
    },
    {
      "page": "getPredictiveIntervals",
      "title": "Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample",
      "topics": [
        "getPredictiveIntervals"
      ]
    },
    {
      "page": "getSample",
      "title": "Extracts samples from a bayesianOutput",
      "topics": [
        "getSample",
        "getSample.data.frame",
        "getSample.double",
        "getSample.integer",
        "getSample.list",
        "getSample.matrix",
        "getSample.MCMC",
        "getSample.mcmc",
        "getSample.mcmc.list",
        "getSample.mcmcSampler",
        "getSample.mcmcSamplerList",
        "getSample.MCMC_refClass",
        "getSample.smcSampler"
      ]
    },
    {
      "page": "getVolume",
      "title": "Calculate posterior volume",
      "topics": [
        "getVolume"
      ]
    },
    {
      "page": "GOF",
      "title": "Standard GOF metrics Startvalues for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide a list",
      "topics": [
        "GOF"
      ]
    },
    {
      "page": "likelihoodAR1",
      "title": "AR1 type likelihood function",
      "topics": [
        "likelihoodAR1"
      ]
    },
    {
      "page": "likelihoodIidNormal",
      "title": "Normal / Gaussian Likelihood function",
      "topics": [
        "likelihoodIidNormal"
      ]
    },
    {
      "page": "MAP",
      "title": "Calculates the Maximum APosteriori value (MAP)",
      "topics": [
        "MAP"
      ]
    },
    {
      "page": "marginalLikelihood",
      "title": "Calculated the marginal likelihood from a set of MCMC samples",
      "topics": [
        "marginalLikelihood"
      ]
    },
    {
      "page": "marginalPlot",
      "title": "Plot MCMC marginals",
      "topics": [
        "marginalPlot"
      ]
    },
    {
      "page": "mergeChains",
      "title": "Merge Chains",
      "topics": [
        "mergeChains"
      ]
    },
    {
      "page": "Metropolis",
      "title": "Creates a Metropolis-type MCMC with options for covariance adaptation, delayed rejection, Metropolis-within-Gibbs, and tempering",
      "topics": [
        "Metropolis"
      ]
    },
    {
      "page": "plot.mcmcSampler",
      "title": "Plots of MCMC output",
      "topics": [
        "plot.mcmcSampler",
        "plot.mcmcSamplerList"
      ]
    },
    {
      "page": "plot.smcSampler",
      "title": "Plots of smcSampler output",
      "topics": [
        "plot.smcSampler",
        "plot.smcSamplerList"
      ]
    },
    {
      "page": "plotDiagnostic",
      "title": "Diagnostic Plot",
      "topics": [
        "plotDiagnostic"
      ]
    },
    {
      "page": "plotSensitivity",
      "title": "One-factor-at-a-time sensitivity plot",
      "topics": [
        "plotSensitivity"
      ]
    },
    {
      "page": "plotTimeSeries",
      "title": "Plots a time series, with the option to include confidence and prediction band",
      "topics": [
        "plotTimeSeries"
      ]
    },
    {
      "page": "plotTimeSeriesResiduals",
      "title": "Plots residuals of a time series",
      "topics": [
        "plotTimeSeriesResiduals"
      ]
    },
    {
      "page": "plotTimeSeriesResults",
      "title": "Creates a time series plot typical for an MCMC / SMC fit",
      "topics": [
        "plotTimeSeriesResults"
      ]
    },
    {
      "page": "print.BayesianSetup",
      "title": "Print an object of BayesianSetup",
      "topics": [
        "print.BayesianSetup"
      ]
    },
    {
      "page": "print.mcmcSampler",
      "title": "Prints MCMC output",
      "topics": [
        "print.mcmcSampler",
        "print.mcmcSamplerList"
      ]
    },
    {
      "page": "print.prior",
      "title": "Print object of prior class",
      "topics": [
        "print.prior"
      ]
    },
    {
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      "headings": [
        "Interfacing a model with BT - step-by-step guide",
        "Step 1: Create a runModel(par) function",
        "Case 1 - model programmed in R",
        "Case 2 - compiled dll, parameters are set via dll interface",
        "Case 3 - model programmed in C / C++, interfaced with RCPP",
        "Case 4 - compiled executable, parameters set via command line (std I/O)",
        "Case 5 - compiled model, parameters set via parameter file or in any other method",
        "Case 6 - compiled model, parameters cannot be changed",
        "Step 2: Read back data",
        "Step 3 (optional) - creating an R package from your code",
        "Speed optimization and parallelization",
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        "Install, load and cite the package",
        "The Bayesian Setup",
        "Run MCMC and SMC functions",
        "Convergence checks for MCMCs",
        "Summarize the outputs",
        "BayesianSetup Reference",
        "Reference on creating likelihoods",
        "Parallelization of the likelihood evaluations",
        "1. In-build parallelization:",
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        "No prior set",
        "Set min/max values",
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        "MCMC sampler reference",
        "The different MCMC samplers",
        "The Metropolis MCMC class",
        "Standard MH MCMC",
        "Standard MH MCMC, prior optimization",
        "Adaptive MCMC, prior optimization",
        "Standard MCMC, prior optimization, delayed rejection",
        "Adaptive MCMC, prior optimization, delayed rejection",
        "Standard MCMC, prior optimization, Gibbs updating",
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        "Sequential Monte Carlo (SMC)",
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        "Example",
        "Model comparison via Bayes factor",
        "Model Comparison via WAIC"
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