{
  "_id": "6a116145acfb0bcc41cedbbe",
  "Package": "tidyabc",
  "Title": "Approximate Bayesian Computing with Tidy Data",
  "Version": "0.0.1",
  "Authors@R": "person(given = \"Robert\",\nfamily = \"Challen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"rob.challen@bristol.ac.uk\",\ncomment = c(ORCID = \"0000-0002-5504-7768\"))",
  "Description": "A flexible framework for Approximate Bayesian Computation\n(ABC) that integrates with the tidyverse. Define simulation\nmodels and summary statistics as standard R functions, use\n'dist_fns' to represent prior and posterior distributions, and\nperform inference via rejection sampling, Sequential Monte\nCarlo (SMC), or Adaptive ABC. The package provides tools for\ndiagnostics, visualization, and convergence assessment,\nenabling reproducible Bayesian inference for complex models\nwith intractable likelihoods.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE, packages = \"pkgtools\")",
  "RoxygenNote": "7.3.3.9007",
  "Config/Needs/build": "terminological/pkgtools",
  "Config/testthat/edition": "3",
  "Config/Needs/website": "rmarkdown",
  "VignetteBuilder": "knitr",
  "URL": "https://ai4ci.github.io/tidyabc, https://github.com/ai4ci/tidyabc",
  "BugReports": "https://github.com/ai4ci/tidyabc/issues",
  "LazyData": "true",
  "Config/pak/sysreqs": "libfontconfig1-dev libfreetype6-dev libfribidi-dev\nlibharfbuzz-dev libicu-dev libjpeg-dev libpng-dev libtiff-dev\nlibwebp-dev",
  "Repository": "https://ai4ci.r-universe.dev",
  "Date/Publication": "2025-11-24 20:25:01 UTC",
  "RemoteUrl": "https://github.com/ai4ci/tidyabc",
  "RemoteRef": "0.0.1",
  "RemoteSha": "6d673cfed37e9321fce896a079e64f928a6c67b8",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-23 08:07:39 UTC",
    "User": "root"
  },
  "Author": "Robert Challen [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-5504-7768>)",
  "Maintainer": "Robert Challen <rob.challen@bristol.ac.uk>",
  "MD5sum": "25ee126341496aee7f12ea76543e41ad",
  "_user": "ai4ci",
  "_type": "src",
  "_file": "tidyabc_0.0.1.tar.gz",
  "_fileid": "615889e5b1f84d8e41b15ad2cedf9468efdaa9acc6f4c2ca503a2fd6b255cd8c",
  "_filesize": 3273170,
  "_sha256": "615889e5b1f84d8e41b15ad2cedf9468efdaa9acc6f4c2ca503a2fd6b255cd8c",
  "_created": "2026-05-23T08:07:39.000Z",
  "_published": "2026-05-23T08:11:49.424Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 77508310856,
      "time": 212,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7175542634"
    },
    {
      "job": 77508310847,
      "time": 206,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7175542253"
    },
    {
      "job": 77508310822,
      "time": 117,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7175534101"
    },
    {
      "job": 77508310832,
      "time": 136,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7175535673"
    },
    {
      "job": 77508042098,
      "time": 307,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7175523071"
    },
    {
      "job": 77508310828,
      "time": 125,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7175534707"
    },
    {
      "job": 77508310838,
      "time": 196,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7175541314"
    },
    {
      "job": 77508310849,
      "time": 172,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7175538984"
    },
    {
      "job": 77508310841,
      "time": 159,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7175537776"
    }
  ],
  "_buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/ai4ci/tidyabc",
  "_commit": {
    "id": "6d673cfed37e9321fce896a079e64f928a6c67b8",
    "author": "robchallen <rob@terminological.co.uk>",
    "committer": "robchallen <rob@terminological.co.uk>",
    "message": "Analytical posteriors and ESS auto adjustment\n",
    "time": 1764015901
  },
  "_maintainer": {
    "name": "Robert Challen",
    "email": "rob.challen@bristol.ac.uk",
    "login": "robchallen",
    "orcid": "0000-0002-5504-7768",
    "description": "",
    "uuid": 16591648
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 3.5",
      "role": "Depends"
    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "furrr",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "role": "Imports"
    },
    {
      "package": "knitr",
      "role": "Imports"
    },
    {
      "package": "pillar",
      "role": "Imports"
    },
    {
      "package": "purrr",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "scales",
      "role": "Imports"
    },
    {
      "package": "splines",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "stringr",
      "role": "Imports"
    },
    {
      "package": "systemfonts",
      "role": "Imports"
    },
    {
      "package": "utils",
      "role": "Imports"
    },
    {
      "package": "carrier",
      "role": "Imports"
    },
    {
      "package": "magrittr",
      "role": "Imports"
    },
    {
      "package": "Matrix",
      "role": "Imports"
    },
    {
      "package": "cli",
      "role": "Imports"
    },
    {
      "package": "locfit",
      "role": "Imports"
    },
    {
      "package": "mvtnorm",
      "role": "Imports"
    },
    {
      "package": "patchwork",
      "role": "Imports"
    },
    {
      "package": "signal",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "role": "Imports"
    },
    {
      "package": "tidyr",
      "role": "Imports"
    },
    {
      "package": "ragg",
      "role": "Imports"
    },
    {
      "package": "fitdistrplus",
      "role": "Imports"
    },
    {
      "package": "progressr",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "role": "Suggests"
    },
    {
      "package": "lifecycle",
      "role": "Suggests"
    },
    {
      "package": "withr",
      "role": "Suggests"
    },
    {
      "package": "parallel",
      "role": "Suggests"
    },
    {
      "package": "mirai",
      "version": ">= 2.5.1",
      "role": "Suggests"
    },
    {
      "package": "future",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    }
  ],
  "_owner": "ai4ci",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2025-43",
      "n": 3
    },
    {
      "week": "2025-45",
      "n": 1
    },
    {
      "week": "2025-47",
      "n": 23
    },
    {
      "week": "2025-48",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "0.0.0.9000",
      "date": "2025-11-17"
    },
    {
      "name": "0.0.0.9003",
      "date": "2025-11-18"
    },
    {
      "name": "0.0.1",
      "date": "2025-11-24"
    }
  ],
  "_stars": 0,
  "_contributors": [
    {
      "user": "robchallen",
      "count": 25,
      "uuid": 16591648
    }
  ],
  "_userbio": {
    "uuid": 153937059,
    "type": "organization",
    "name": "AI for Collective Intelligence (AI4CI) ",
    "description": "A UKRI AI Research Hub"
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/tidyabc"
  },
  "_devurl": "https://github.com/ai4ci/tidyabc",
  "_pkgdown": "https://ai4ci.github.io/tidyabc",
  "_searchresults": 25,
  "_rbuild": "4.6.0",
  "_assets": [
    "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",
    "extra/tidyabc.html",
    "manual.pdf"
  ],
  "_cranurl": false,
  "_exports": [
    "%>%",
    "abc_adaptive",
    "abc_rejection",
    "abc_smc",
    "as.abc_prior",
    "as.dist_fns",
    "as.dist_fns_list",
    "as.link_fns",
    "as.link_fns_list",
    "calculate_rmse",
    "calculate_wasserstein",
    "dbeta2",
    "dcgamma",
    "default_termination_fn",
    "dgamma2",
    "dist_fns",
    "dlnorm2",
    "dlogitnorm",
    "dlogitnorm2",
    "dnbinom2",
    "dnull",
    "dwedge",
    "empirical",
    "empirical_cdf",
    "empirical_data",
    "example_adaptive_fit",
    "example_obs",
    "example_obsdata",
    "example_priors_list",
    "example_rejection_fit",
    "example_scorer_fn",
    "example_sim_fn",
    "example_smc_fit",
    "example_truth",
    "fixed_wave_termination_fn",
    "is.abc_prior",
    "is.dist_fns",
    "is.dist_fns_list",
    "is.link_fns",
    "is.link_fns_list",
    "kurtosis",
    "link_fns",
    "map_dist_fns",
    "map_link_fns",
    "map2_dist_fns",
    "map2_link_fns",
    "mixture",
    "pbeta2",
    "pcgamma",
    "pgamma2",
    "plnorm2",
    "plogitnorm",
    "plogitnorm2",
    "plot_convergence",
    "plot_correlations",
    "plot_evolution",
    "plot_simulations",
    "pmap_dist_fns",
    "pmap_link_fns",
    "pnbinom2",
    "pnull",
    "posterior_distance_metrics",
    "posterior_fit_analytical",
    "posterior_fit_empirical",
    "posterior_resample",
    "posterior_summarise",
    "priors",
    "pwedge",
    "qbeta2",
    "qcgamma",
    "qgamma2",
    "qlnorm2",
    "qlogitnorm",
    "qlogitnorm2",
    "qnbinom2",
    "qnull",
    "qwedge",
    "rbern",
    "rbeta2",
    "rcategorical",
    "rcgamma",
    "rexpgrowth",
    "rexpgrowthI0",
    "rgamma2",
    "rlnorm2",
    "rlogitnorm",
    "rlogitnorm2",
    "rnbinom2",
    "rnull",
    "rwedge",
    "skew",
    "test_simulation",
    "tidy.abc_fit",
    "transform",
    "truncate",
    "wasserstein_calculator",
    "wbw.nrd",
    "widen",
    "wmean",
    "wquantile",
    "wsd"
  ],
  "_datasets": [
    {
      "name": "sim_outbreak",
      "title": "The 'sim_outbreak' dataset",
      "object": "sim_outbreak",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "abc_adaptive",
      "title": "Perform ABC sequential adaptive fitting",
      "concept": [
        "workflow"
      ],
      "topics": [
        "abc_adaptive"
      ]
    },
    {
      "page": "abc_fit",
      "title": "'abc_fit' S3 class",
      "concept": [
        "abc_fit_s3"
      ],
      "topics": [
        "abc_fit",
        "format.abc_fit",
        "new_abc_fit",
        "plot.abc_fit",
        "print.abc_fit",
        "summary.abc_fit",
        "tidy.abc_fit"
      ]
    },
    {
      "page": "abc_prior",
      "title": "'abc_prior' S3 class",
      "concept": [
        "abc_prior_s3"
      ],
      "topics": [
        "abc_prior",
        "as.abc_prior",
        "format.abc_prior",
        "is.abc_prior",
        "new_abc_prior",
        "plot.abc_prior",
        "print.abc_prior"
      ]
    },
    {
      "page": "abc_rejection",
      "title": "Perfom simple ABC rejection algorithm",
      "concept": [
        "workflow"
      ],
      "topics": [
        "abc_rejection"
      ]
    },
    {
      "page": "abc_smc",
      "title": "Perform ABC sequential Monte Carlo fitting",
      "concept": [
        "workflow"
      ],
      "topics": [
        "abc_smc"
      ]
    },
    {
      "page": "as.dist_fns",
      "title": "Create a 'dist_fns' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "as.dist_fns",
        "as.dist_fns.character",
        "as.dist_fns.fitdist",
        "as.dist_fns.function"
      ]
    },
    {
      "page": "as.link_fns",
      "title": "Create a 'link_fns' S3 object",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "as.link_fns",
        "as.link_fns.character",
        "as.link_fns.dist_fns",
        "as.link_fns.family",
        "as.link_fns.numeric"
      ]
    },
    {
      "page": "c.dist_fns",
      "title": "Concatenate a 'dist_fns' S3 object or 'dist_fns_list's",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "c.dist_fns"
      ]
    },
    {
      "page": "c.link_fns",
      "title": "Concatenate a 'link_fns' S3 object or 'link_fns_list's",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "c.link_fns"
      ]
    },
    {
      "page": "calculate_rmse",
      "title": "Generate a function to calculate a Root Mean Squared Error (RMSE)",
      "concept": [
        "workflow"
      ],
      "topics": [
        "calculate_rmse"
      ]
    },
    {
      "page": "calculate_wasserstein",
      "title": "Calculate a Wasserstein distance",
      "concept": [
        "workflow"
      ],
      "topics": [
        "calculate_wasserstein"
      ]
    },
    {
      "page": "dbeta2",
      "title": "The Beta Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dbeta2"
      ]
    },
    {
      "page": "dcgamma",
      "title": "Density: gamma distribution constrained to have mean > sd",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dcgamma"
      ]
    },
    {
      "page": "default_termination_fn",
      "title": "Set up default convergence criteria for SMC and adaptive ABC",
      "concept": [
        "workflow"
      ],
      "topics": [
        "default_termination_fn"
      ]
    },
    {
      "page": "dgamma2",
      "title": "The Gamma Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dgamma2"
      ]
    },
    {
      "page": "dist_fns",
      "title": "Create an empty 'dist_fns_list'",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "dist_fns"
      ]
    },
    {
      "page": "dlnorm2",
      "title": "The Log Normal Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dlnorm2"
      ]
    },
    {
      "page": "dlogitnorm",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dlogitnorm"
      ]
    },
    {
      "page": "dlogitnorm2",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dlogitnorm2"
      ]
    },
    {
      "page": "dnbinom2",
      "title": "The Negative Binomial Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dnbinom2"
      ]
    },
    {
      "page": "dnull",
      "title": "Null distributions always returns NA",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dnull"
      ]
    },
    {
      "page": "dwedge",
      "title": "Wedge distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "dwedge"
      ]
    },
    {
      "page": "empirical",
      "title": "Fit a piecewise logit transformed linear model to cumulative data",
      "concept": [
        "empirical"
      ],
      "topics": [
        "empirical"
      ]
    },
    {
      "page": "empirical_cdf",
      "title": "Fit a piecewise logit transformed linear model to a CDF",
      "concept": [
        "empirical"
      ],
      "topics": [
        "empirical_cdf"
      ]
    },
    {
      "page": "empirical_data",
      "title": "Fit a piecewise logit transformed linear model to weighted data",
      "concept": [
        "empirical"
      ],
      "topics": [
        "empirical_data"
      ]
    },
    {
      "page": "fixed_wave_termination_fn",
      "title": "Run the SMC or adaptive algorithm for a set number of waves",
      "concept": [
        "workflow"
      ],
      "topics": [
        "fixed_wave_termination_fn"
      ]
    },
    {
      "page": "format.dist_fns",
      "title": "Format a 'dist_fns' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "format.dist_fns"
      ]
    },
    {
      "page": "format.link_fns",
      "title": "Format a 'link_fns' S3 object",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "format.link_fns"
      ]
    },
    {
      "page": "is.dist_fns",
      "title": "Check if this is a 'dist_fns' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "is.dist_fns"
      ]
    },
    {
      "page": "is.dist_fns_list",
      "title": "Check if this is a 'dist_fns_list' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "is.dist_fns_list"
      ]
    },
    {
      "page": "is.link_fns",
      "title": "Check if this is a 'link_fns' S3 object",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "is.link_fns"
      ]
    },
    {
      "page": "is.link_fns_list",
      "title": "Check if this is a 'link_fns_list' S3 object",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "is.link_fns_list"
      ]
    },
    {
      "page": "kurtosis",
      "title": "Calculate the excess kurtosis of a set of data",
      "concept": [
        "empirical"
      ],
      "topics": [
        "kurtosis"
      ]
    },
    {
      "page": "link_fns",
      "title": "Create an empty 'link_fns_list'",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "link_fns"
      ]
    },
    {
      "page": "map_dist_fns",
      "title": "Apply a function to each element of a vector returning a 'dist_fns_list'",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "map_dist_fns"
      ]
    },
    {
      "page": "map_link_fns",
      "title": "Apply a function to each element of a vector returning a 'link_fns_list'",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "map_link_fns"
      ]
    },
    {
      "page": "map2_dist_fns",
      "title": "Map over two inputs returning a 'dist_fns_list'",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "map2_dist_fns"
      ]
    },
    {
      "page": "map2_link_fns",
      "title": "Map over two inputs returning a 'link_fns_list'",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "map2_link_fns"
      ]
    },
    {
      "page": "mixture",
      "title": "Construct a mixture distribution",
      "concept": [
        "empirical"
      ],
      "topics": [
        "mixture"
      ]
    },
    {
      "page": "pbeta2",
      "title": "The Beta Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pbeta2"
      ]
    },
    {
      "page": "pcgamma",
      "title": "Cumulative probability: gamma distribution constrained to have mean > sd",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pcgamma"
      ]
    },
    {
      "page": "pgamma2",
      "title": "The Gamma Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pgamma2"
      ]
    },
    {
      "page": "plnorm2",
      "title": "The Log Normal Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "plnorm2"
      ]
    },
    {
      "page": "plogitnorm",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "plogitnorm"
      ]
    },
    {
      "page": "plogitnorm2",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "plogitnorm2"
      ]
    },
    {
      "page": "plot_convergence",
      "title": "Plot convergence metrics by wave for SMC and adaptive ABC",
      "concept": [
        "workflow"
      ],
      "topics": [
        "plot_convergence"
      ]
    },
    {
      "page": "plot_correlations",
      "title": "A parameter posterior correlation plot",
      "concept": [
        "workflow"
      ],
      "topics": [
        "plot_correlations"
      ]
    },
    {
      "page": "plot_evolution",
      "title": "Plot the evolution of the density function by wave for SMC and adaptive ABC",
      "concept": [
        "workflow"
      ],
      "topics": [
        "plot_evolution"
      ]
    },
    {
      "page": "plot_simulations",
      "title": "Spaghetti plot of resampled posterior fits",
      "concept": [
        "workflow"
      ],
      "topics": [
        "plot_simulations"
      ]
    },
    {
      "page": "plot.dist_fns",
      "title": "Plot a 'dist_fns' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "plot.dist_fns"
      ]
    },
    {
      "page": "plot.dist_fns_list",
      "title": "Plot a 'dist_fns_list' S3 object",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "plot.dist_fns_list"
      ]
    },
    {
      "page": "pmap_dist_fns",
      "title": "Map over multiple inputs returning a 'dist_fns_list'",
      "concept": [
        "dist_fns_s3"
      ],
      "topics": [
        "pmap_dist_fns"
      ]
    },
    {
      "page": "pmap_link_fns",
      "title": "Map over multiple inputs returning a 'link_fns_list'",
      "concept": [
        "link_fns_s3"
      ],
      "topics": [
        "pmap_link_fns"
      ]
    },
    {
      "page": "pnbinom2",
      "title": "The Negative Binomial Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pnbinom2"
      ]
    },
    {
      "page": "pnull",
      "title": "Null distributions always returns NA",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pnull"
      ]
    },
    {
      "page": "posterior_distance_metrics",
      "title": "Generate a set of metrics from component scores",
      "concept": [
        "workflow"
      ],
      "topics": [
        "posterior_distance_metrics"
      ]
    },
    {
      "page": "posterior_fit_analytical",
      "title": "Fit analytical distribution to posterior samples for generating more waves",
      "concept": [
        "workflow"
      ],
      "topics": [
        "posterior_fit_analytical"
      ]
    },
    {
      "page": "posterior_fit_empirical",
      "title": "Fit empirical distribution to posterior samples for generating more waves",
      "concept": [
        "workflow"
      ],
      "topics": [
        "posterior_fit_empirical"
      ]
    },
    {
      "page": "posterior_resample",
      "title": "Generate a set of samples from selected posteriors",
      "concept": [
        "workflow"
      ],
      "topics": [
        "posterior_resample"
      ]
    },
    {
      "page": "posterior_summarise",
      "title": "Calculate a basket of summaries from a weighted list of posterior samples",
      "concept": [
        "workflow"
      ],
      "topics": [
        "posterior_summarise"
      ]
    },
    {
      "page": "priors",
      "title": "Construct a set of priors",
      "concept": [
        "workflow"
      ],
      "topics": [
        "priors"
      ]
    },
    {
      "page": "pwedge",
      "title": "Wedge distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "pwedge"
      ]
    },
    {
      "page": "qbeta2",
      "title": "The Beta Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qbeta2"
      ]
    },
    {
      "page": "qcgamma",
      "title": "Quantile: gamma distribution constrained to have mean > sd",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qcgamma"
      ]
    },
    {
      "page": "qgamma2",
      "title": "The Gamma Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qgamma2"
      ]
    },
    {
      "page": "qlnorm2",
      "title": "The Log Normal Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qlnorm2"
      ]
    },
    {
      "page": "qlogitnorm",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qlogitnorm"
      ]
    },
    {
      "page": "qlogitnorm2",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qlogitnorm2"
      ]
    },
    {
      "page": "qnbinom2",
      "title": "The Negative Binomial Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qnbinom2"
      ]
    },
    {
      "page": "qnull",
      "title": "Null distributions always returns NA",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qnull"
      ]
    },
    {
      "page": "qwedge",
      "title": "Wedge distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "qwedge"
      ]
    },
    {
      "page": "rbern",
      "title": "A random Bernoulli sample as a logical value",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rbern"
      ]
    },
    {
      "page": "rbeta2",
      "title": "The Beta Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rbeta2"
      ]
    },
    {
      "page": "rcategorical",
      "title": "Sampling from the multinomial equivalent of the Bernoulli distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rcategorical"
      ]
    },
    {
      "page": "rcgamma",
      "title": "Sampling: gamma distribution constrained to have mean > sd",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rcgamma"
      ]
    },
    {
      "page": "rexpgrowth",
      "title": "Randomly sample incident times in an exponentially growing process",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rexpgrowth"
      ]
    },
    {
      "page": "rexpgrowthI0",
      "title": "Randomly sample incident times in an exponentially growing process with initial case load",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rexpgrowthI0"
      ]
    },
    {
      "page": "rgamma2",
      "title": "The Gamma Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rgamma2"
      ]
    },
    {
      "page": "rlnorm2",
      "title": "The Log Normal Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rlnorm2"
      ]
    },
    {
      "page": "rlogitnorm",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rlogitnorm"
      ]
    },
    {
      "page": "rlogitnorm2",
      "title": "Logit-normal distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rlogitnorm2"
      ]
    },
    {
      "page": "rnbinom2",
      "title": "The Negative Binomial Distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rnbinom2"
      ]
    },
    {
      "page": "rnull",
      "title": "Null distributions always returns NA",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rnull"
      ]
    },
    {
      "page": "rwedge",
      "title": "Wedge distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "rwedge"
      ]
    },
    {
      "page": "sim_outbreak",
      "title": "The 'sim_outbreak' dataset",
      "topics": [
        "sim_outbreak"
      ]
    },
    {
      "page": "skew",
      "title": "Calculate the skew of a set of data",
      "concept": [
        "empirical"
      ],
      "topics": [
        "skew"
      ]
    },
    {
      "page": "test_simulation",
      "title": "Run the simulation for one set of parameters",
      "concept": [
        "workflow"
      ],
      "topics": [
        "test_simulation"
      ]
    },
    {
      "page": "transform",
      "title": "Generate a distribution from a link transform of another",
      "concept": [
        "empirical"
      ],
      "topics": [
        "transform"
      ]
    },
    {
      "page": "truncate",
      "title": "Generate a distribution from a truncation of another",
      "concept": [
        "empirical"
      ],
      "topics": [
        "truncate"
      ]
    },
    {
      "page": "wasserstein_calculator",
      "title": "Generate a function to calculate a wasserstein distance",
      "concept": [
        "workflow"
      ],
      "topics": [
        "wasserstein_calculator"
      ]
    },
    {
      "page": "wbw.nrd",
      "title": "Weighted bandwidth selector",
      "concept": [
        "empirical"
      ],
      "topics": [
        "wbw.nrd"
      ]
    },
    {
      "page": "wedge",
      "title": "Wedge distribution",
      "concept": [
        "distributions"
      ],
      "topics": [
        "wedge"
      ]
    },
    {
      "page": "widen",
      "title": "Increase the dispersion of a distribution",
      "concept": [
        "empirical"
      ],
      "topics": [
        "widen"
      ]
    },
    {
      "page": "wmean",
      "title": "Weighted mean",
      "concept": [
        "empirical"
      ],
      "topics": [
        "wmean"
      ]
    },
    {
      "page": "wquantile",
      "title": "Quantile from weighted data with link function support",
      "concept": [
        "empirical"
      ],
      "topics": [
        "wquantile"
      ]
    },
    {
      "page": "wsd",
      "title": "Weighted standard deviation",
      "concept": [
        "empirical"
      ],
      "topics": [
        "wsd"
      ]
    }
  ],
  "_pkglogo": "https://github.com/ai4ci/tidyabc/raw/0.0.1/man/figures/logo.png",
  "_readme": "https://github.com/ai4ci/tidyabc/raw/0.0.1/README.md",
  "_rundeps": [
    "base64enc",
    "carrier",
    "cli",
    "codetools",
    "cpp11",
    "crayon",
    "digest",
    "dplyr",
    "evaluate",
    "farver",
    "fitdistrplus",
    "furrr",
    "future",
    "generics",
    "ggplot2",
    "globals",
    "glue",
    "gtable",
    "highr",
    "isoband",
    "jsonlite",
    "knitr",
    "labeling",
    "lattice",
    "lifecycle",
    "listenv",
    "lobstr",
    "locfit",
    "magrittr",
    "MASS",
    "Matrix",
    "mvtnorm",
    "parallelly",
    "patchwork",
    "pillar",
    "pkgconfig",
    "prettyunits",
    "purrr",
    "R6",
    "ragg",
    "RColorBrewer",
    "rlang",
    "S7",
    "scales",
    "signal",
    "stringi",
    "stringr",
    "survival",
    "systemfonts",
    "textshaping",
    "tibble",
    "tidyr",
    "tidyselect",
    "utf8",
    "vctrs",
    "viridisLite",
    "withr",
    "xfun",
    "yaml"
  ],
  "_vignettes": [
    {
      "source": "abc-adaptive.Rmd",
      "filename": "abc-adaptive.html",
      "title": "ABC Adaptive",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Step 1: Define the Model, Data, and Priors (Recap)",
        "Step 2: Run ABC-Adaptive",
        "Step 3: Visualize the Results",
        "Step 4: Diagnose Convergence",
        "Step 5: Visualize Parameter Evolution",
        "Step 6: Check Parameter Correlations",
        "Conclusion"
      ],
      "created": "2025-11-17 23:01:23",
      "modified": "2025-11-24 20:25:01",
      "commits": 6
    },
    {
      "source": "abc-smc.Rmd",
      "filename": "abc-smc.html",
      "title": "ABC Sequential Monte-Carlo",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Step 1: Define the Model and Data (Recap)",
        "Step 2: Inform Scoring Weights (Optional but Recommended)",
        "Step 3: Run ABC-SMC",
        "Step 4: Visualize the Results",
        "Step 5: Diagnose Convergence",
        "Step 6: Visualize Parameter Evolution",
        "Step 7: Check Parameter Correlations",
        "Conclusion"
      ],
      "created": "2025-11-17 23:01:23",
      "modified": "2025-11-20 22:06:14",
      "commits": 5
    },
    {
      "source": "tidyabc.Rmd",
      "filename": "tidyabc.html",
      "title": "Getting started with tidyabc",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Step 1: Define the Simulation Function",
        "Step 2: Define the Scoring Function",
        "Step 3: Generate Observed Data (Ground Truth)",
        "Step 4: Visualize the Observed Data",
        "Step 5: Define Prior Distributions",
        "Step 6: Run ABC Rejection Sampling",
        "Step 7: Plot the Results",
        "Conclusion"
      ],
      "created": "2025-11-06 10:44:18",
      "modified": "2025-11-24 20:25:01",
      "commits": 7
    },
    {
      "source": "scorer-functions.Rmd",
      "filename": "scorer-functions.html",
      "title": "Simulation, scoring and convergence functions",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "1. Writing the Simulation Function (sim_fn)",
        "Inputs",
        "Output",
        "Example",
        "2. Writing the Scoring Function (scorer_fn)",
        "Combining Scorer Outputs and Comparison to Observed Scores",
        "Score Weights (scoreweights)",
        "3. Debugging and Parallelisation",
        "4. The Convergence Function (converged_fn)",
        "Inputs to converged_fn",
        "Output of converged_fn",
        "The default_termination_fn",
        "5. Writing a Custom Convergence Function",
        "Structure",
        "Example: Convergence Based on ESS and Distance Reduction",
        "Example: Convergence Based on Parameter Variance",
        "Key Considerations for Custom Functions",
        "Conclusion"
      ],
      "created": "2025-11-17 23:01:23",
      "modified": "2025-11-20 22:06:14",
      "commits": 4
    },
    {
      "source": "s3-distributions.Rmd",
      "filename": "s3-distributions.html",
      "title": "Statistical distributions in tidyabc",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Reparameterised statistical distributions",
        "Core Functions",
        "Selected Examples",
        "List of Additional Families",
        "Specialized Random Number Generators",
        "Distribution family S3 class:",
        "Creating dist_fns Objects",
        "From Standard Families",
        "Creating Multiple Distributions with pmap_dist_fns",
        "Manipulating dist_fns Objects in Data Frames",
        "Applying Transformations: Truncation Example",
        "Combining dist_fns: Mixture Distributions",
        "Fitting Empirical Distributions",
        "From Quantiles using empirical_cdf",
        "Ensemble of Quantile-Based Distributions as a Mixture",
        "From Samples using empirical_data",
        "Weighted Sample Fitting",
        "Using wquantile for Quantiles from Weighted Data",
        "Conclusion"
      ],
      "created": "2025-11-17 23:01:23",
      "modified": "2025-11-24 20:25:01",
      "commits": 5
    }
  ],
  "_score": 4.3979400086720375,
  "_indexed": true,
  "_nocasepkg": "tidyabc",
  "_universes": [
    "ai4ci",
    "robchallen"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.0.1",
      "date": "2026-05-23T08:10:14.000Z",
      "distro": "noble",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "87018b13257ca4f28cb91a2f38d043d8e4de370944385ef6db470b38796cb87a",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.0.1",
      "date": "2026-05-23T08:10:12.000Z",
      "distro": "noble",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "998b82b38b72702d4e3ac6c83a88ca60b1efa847ae44fdc0d348d41eb555cc74",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "0.0.1",
      "date": "2026-05-23T08:09:10.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "811b54170662dea93713bdd1c412a8f0e06eadce37ab431701239ec4c041e004",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "0.0.1",
      "date": "2026-05-23T08:09:21.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "9e60eaa53ca38a1d3afa082dff8d091a4d241a025be9c8a1a66a3038670d47ff",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.0.1",
      "date": "2026-05-23T08:10:06.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "84606a86ae2cb58291f202f2a8fd5c484aa2e0106c5afcb82930e2163ac19089",
      "status": "success",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.0.1",
      "date": "2026-05-23T08:09:46.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "7be35802c73eba3be4ad5c6b0eb3a02c6290bea6860cc8428f675d302de63323",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "0.0.1",
      "date": "2026-05-23T08:09:08.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "2d59bfa54b6a553b03a2fba5424a5682a951f99c07495076cf3179f025c0eeb8",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "0.0.1",
      "date": "2026-05-23T08:09:03.000Z",
      "commit": "6d673cfed37e9321fce896a079e64f928a6c67b8",
      "fileid": "f0c97679f508567cb82f70332fcc69909d23bdfb4e1d312ef8dca759e0308dd7",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ai4ci/actions/runs/26327611743"
    }
  ]
}