{
  "_id": "6a292840732311cd8759f961",
  "Package": "fmcmc",
  "Title": "A friendly MCMC framework",
  "Version": "0.6-0",
  "Date": "2025-12-03",
  "Authors@R": "c(person(\"George\", \"Vega Yon\", email = \"g.vegayon@gmail.com\", role =\nc(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-3171-0844\")),\nperson(\"Paul\", \"Marjoram\", email = \"pmarjora@usc.edu\", role = c(\"ctb\",\n\"ths\"), comment = c(ORCID = \"0000-0003-0824-7449\")), person(\"National\nCancer Institute (NCI)\", role = \"fnd\", comment = \"Grant Number\n5P01CA196569-02\"), person(\"Fabian\", \"Scheipl\", role = \"rev\", comment =\nc(what = \"JOSS reviewer\", ORCID=\"0000-0001-8172-3603\")) )",
  "Description": "Provides a friendly (flexible) Markov Chain Monte Carlo\n(MCMC) framework for implementing Metropolis-Hastings algorithm\nin a modular way allowing users to specify automatic\nconvergence checker, personalized transition kernels, and\nout-of-the-box multiple MCMC chains using parallel computing.\nMost of the methods implemented in this package can be found in\nBrooks et al. (2011, ISBN 9781420079425). Among the methods\nincluded, we have: Haario (2001)\n<doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola\n(2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive\nMetropolis, and Thawornwattana et al. (2018)\n<doi:10.1214/17-BA1084> Mirror transition kernels.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "LazyData": "true",
  "URL": "https://github.com/USCbiostats/fmcmc,\nhttps://uscbiostats.github.io/fmcmc/",
  "BugReports": "https://github.com/USCbiostats/fmcmc/issues",
  "RoxygenNote": "7.3.3",
  "Roxygen": "list(markdown = TRUE)",
  "VignetteBuilder": "knitr",
  "Repository": "https://uscbiostats.r-universe.dev",
  "Date/Publication": "2025-12-12 15:50:38 UTC",
  "RemoteUrl": "https://github.com/uscbiostats/fmcmc",
  "RemoteRef": "HEAD",
  "RemoteSha": "d7330a7d899b210f912f2e5313b11587bc108675",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-10 08:29:58 UTC",
    "User": "root"
  },
  "Author": "George Vega Yon [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-3171-0844>),\nPaul Marjoram [ctb, ths] (ORCID:\n<https://orcid.org/0000-0003-0824-7449>),\nNational Cancer Institute (NCI) [fnd] (Grant Number 5P01CA196569-02),\nFabian Scheipl [rev] (what: JOSS reviewer, ORCID:\n<https://orcid.org/0000-0001-8172-3603>)",
  "Maintainer": "George Vega Yon <g.vegayon@gmail.com>",
  "MD5sum": "855ea10c0b7ec749ddd0031cd0c7bd4a",
  "_user": "uscbiostats",
  "_type": "src",
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  "_filesize": 2857073,
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  "_created": "2026-06-10T08:29:58.000Z",
  "_published": "2026-06-10T09:02:56.681Z",
  "_distro": "noble",
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    "author": "George G. Vega Yon <g.vegayon@gmail.com>",
    "committer": "GitHub <noreply@github.com>",
    "message": "Enhance CI to include Windows package check (#35)\n\n* Enhance CI to include Windows package check\n\nAdded a Windows-specific check for the R package in CI.\n\n* Rename artifact upload for fmcmc package",
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    "name": "George Vega Yon",
    "email": "g.vegayon@gmail.com",
    "login": "gvegayon",
    "bluesky": "@gvegayon.bsky.social",
    "linkedin": "in/georgevegayon",
    "description": "Scientist at @UofUEpiBio modeling complex systems using statistical computing. Lots of time at @CDCgov.",
    "uuid": 893619,
    "orcid": "0000-0002-3171-0844"
  },
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  "_dependencies": [
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      "package": "R",
      "version": ">= 3.3.0",
      "role": "Depends"
    },
    {
      "package": "parallel",
      "role": "Imports"
    },
    {
      "package": "coda",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "methods",
      "role": "Imports"
    },
    {
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      "role": "Imports"
    },
    {
      "package": "Matrix",
      "role": "Imports"
    },
    {
      "package": "covr",
      "role": "Suggests"
    },
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      "role": "Suggests"
    },
    {
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      "role": "Suggests"
    },
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    },
    {
      "package": "tinytest",
      "role": "Suggests"
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      "package": "mvtnorm",
      "role": "Suggests"
    }
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  "_owner": "uscbiostats",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [
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      "week": "2025-47",
      "n": 4
    },
    {
      "week": "2025-50",
      "n": 4
    }
  ],
  "_tags": [],
  "_topics": [
    "adaptive",
    "bayesian-inference",
    "markov-chain-monte-carlo",
    "mcmc",
    "metropolis-hastings",
    "parallel-computing"
  ],
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    "type": "organization",
    "name": "USC Division of Biostatistics",
    "description": "Organization for USC Division of Biostatistics"
  },
  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/fmcmc"
  },
  "_devurl": "https://github.com/uscbiostats/fmcmc",
  "_pkgdown": "https://uscbiostats.github.io/fmcmc/",
  "_searchresults": 61,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/fmcmc.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/uscbiostats/fmcmc",
  "_realowner": "uscbiostats",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.2-0",
      "date": "2019-08-27"
    },
    {
      "version": "0.3-0",
      "date": "2020-04-23"
    },
    {
      "version": "0.4-0",
      "date": "2020-09-01"
    },
    {
      "version": "0.5-0",
      "date": "2021-09-03"
    },
    {
      "version": "0.5-1",
      "date": "2022-01-14"
    },
    {
      "version": "0.5-2",
      "date": "2023-08-30"
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  "_exports": [
    "add_userdata",
    "append_chains",
    "check_initial",
    "convergence_auto",
    "convergence_data_get",
    "convergence_data_set",
    "convergence_gelman",
    "convergence_geweke",
    "convergence_heildel",
    "convergence_msg_get",
    "convergence_msg_set",
    "cov_recursive",
    "get_",
    "get_burnin",
    "get_chain_id",
    "get_cl",
    "get_conv_checker",
    "get_draws",
    "get_elapsed",
    "get_fun",
    "get_initial",
    "get_kernel",
    "get_logpost",
    "get_multicore",
    "get_nchains",
    "get_nsteps",
    "get_progress",
    "get_seed",
    "get_thin",
    "get_userdata",
    "ith_step",
    "kernel_adapt",
    "kernel_am",
    "kernel_new",
    "kernel_nmirror",
    "kernel_normal",
    "kernel_normal_reflective",
    "kernel_ram",
    "kernel_umirror",
    "kernel_unif",
    "kernel_unif_reflective",
    "last_",
    "LAST_CONV_CHECK",
    "last_conv_checker",
    "last_elapsed",
    "last_kernel",
    "LAST_MCMC",
    "last_nchains",
    "last_nsteps",
    "MCMC",
    "MCMC_OUTPUT",
    "MCMC_without_conv_checker",
    "mean_recursive",
    "new_progress_bar",
    "plan_update_sequence",
    "reflect_on_boundaries",
    "set_userdata"
  ],
  "_datasets": [
    {
      "name": "lifeexpect",
      "title": "Life expectancy in the US (2020)",
      "object": "lifeexpect",
      "class": [
        "data.frame"
      ],
      "fields": [
        "smoke",
        "female",
        "age"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "append_chains",
      "title": "Append MCMC chains (objects of class coda::mcmc)",
      "topics": [
        "append_chains"
      ]
    },
    {
      "page": "check_initial",
      "title": "Checks the initial values of the MCMC",
      "topics": [
        "check_initial"
      ]
    },
    {
      "page": "convergence-checker",
      "title": "Convergence Monitoring",
      "topics": [
        "automatic-stop",
        "convergence-checker",
        "convergence_auto",
        "convergence_data_get",
        "convergence_data_set",
        "convergence_gelman",
        "convergence_geweke",
        "convergence_heildel",
        "convergence_msg_get",
        "convergence_msg_set",
        "LAST_CONV_CHECK"
      ]
    },
    {
      "page": "cov_recursive",
      "title": "Recursive algorithms for computing variance and mean",
      "topics": [
        "cov_recursive",
        "mean_recursive"
      ]
    },
    {
      "page": "fmcmc",
      "title": "A friendly MCMC framework",
      "topics": [
        "fmcmc-package",
        "fmcmc"
      ]
    },
    {
      "page": "fmcmc-deprecated",
      "title": "Deprecated methods in fmcmc",
      "topics": [
        "fmcmc-deprecated",
        "last_",
        "last_conv_checker",
        "last_elapsed",
        "last_kernel",
        "LAST_MCMC",
        "last_nchains",
        "last_nsteps"
      ]
    },
    {
      "page": "kernel_adapt",
      "title": "Adaptive Metropolis (AM) Transition Kernel",
      "concept": [
        "kernels"
      ],
      "topics": [
        "kernel_adapt",
        "kernel_am"
      ]
    },
    {
      "page": "kernel_mirror",
      "title": "Mirror Transition Kernels",
      "concept": [
        "kernels"
      ],
      "topics": [
        "kernel_mirror",
        "kernel_nmirror",
        "kernel_umirror"
      ]
    },
    {
      "page": "kernel_new",
      "title": "Transition Kernels for MCMC",
      "concept": [
        "kernels"
      ],
      "topics": [
        "fmcmc_kernel",
        "kernels",
        "kernel_new"
      ]
    },
    {
      "page": "kernel_normal",
      "title": "Gaussian Transition Kernel",
      "concept": [
        "kernels"
      ],
      "topics": [
        "kernel_normal",
        "kernel_normal_reflective"
      ]
    },
    {
      "page": "kernel_ram",
      "title": "Robust Adaptive Metropolis (RAM) Transition Kernel",
      "concept": [
        "kernels"
      ],
      "topics": [
        "kernel_ram"
      ]
    },
    {
      "page": "kernel_unif",
      "title": "Uniform Transition Kernel",
      "concept": [
        "kernels"
      ],
      "topics": [
        "kernel_unif",
        "kernel_unif_reflective"
      ]
    },
    {
      "page": "lifeexpect",
      "title": "Life expectancy in the US (2020)",
      "topics": [
        "lifeexpect"
      ]
    },
    {
      "page": "MCMC",
      "title": "Markov Chain Monte Carlo",
      "topics": [
        "MCMC",
        "MCMC_OUTPUT",
        "MCMC_without_conv_checker",
        "Metropolis-Hastings"
      ]
    },
    {
      "page": "mcmc-loop",
      "title": "Functions to interact with the main loop",
      "topics": [
        "add_userdata",
        "get_userdata",
        "ith_step",
        "mcmc-loop",
        "set_userdata"
      ]
    },
    {
      "page": "mcmc-output",
      "title": "Information about the last 'MCMC' call",
      "topics": [
        "get_",
        "get_burnin",
        "get_chain_id",
        "get_cl",
        "get_conv_checker",
        "get_draws",
        "get_elapsed",
        "get_fun",
        "get_initial",
        "get_kernel",
        "get_logpost",
        "get_multicore",
        "get_nchains",
        "get_nsteps",
        "get_progress",
        "get_seed",
        "get_thin",
        "mcmc-output"
      ]
    },
    {
      "page": "new_progress_bar",
      "title": "Progress bar",
      "topics": [
        "new_progress_bar"
      ]
    },
    {
      "page": "plan_update_sequence",
      "title": "Parameters' update sequence",
      "topics": [
        "plan_update_sequence"
      ]
    },
    {
      "page": "reflect_on_boundaries",
      "title": "Reflective Boundaries",
      "topics": [
        "reflect_on_boundaries"
      ]
    }
  ],
  "_pkglogo": "https://github.com/uscbiostats/fmcmc/raw/HEAD/man/figures/logo.svg",
  "_readme": "https://github.com/uscbiostats/fmcmc/raw/HEAD/README.md",
  "_rundeps": [
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      "source": "advanced-features.Rmd",
      "filename": "advanced-features.html",
      "title": "Advanced features",
      "author": "George G. Vega Yon",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Life expectancy in the US",
        "Operating within the loop",
        "Using ith_step() with multiple chains",
        "Saving information as it happens"
      ],
      "created": "2021-07-20 00:46:34",
      "modified": "2025-12-09 05:28:56",
      "commits": 6
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      "filename": "user-defined-kernels.html",
      "title": "User-defined kernels",
      "author": "George G. Vega Yon",
      "engine": "knitr::rmarkdown",
      "headings": [
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        "Size parameter in a binomial distribution"
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    },
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      "filename": "workflow-with-fmcmc.html",
      "title": "Workflow with fmcmc",
      "author": "George G. Vega Yon",
      "engine": "knitr::rmarkdown",
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        "Motivating example",
        "Reusing fmcmc_kernel objects",
        "Accessing other elements of the chain"
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      "created": "2019-08-13 18:51:19",
      "modified": "2021-07-21 00:44:26",
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  "_indexed": true,
  "_nocasepkg": "fmcmc",
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