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  "Package": "marp",
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  "Type": "Package",
  "Title": "Model-Averaged Renewal Process",
  "Authors@R": "c(person(\"Jie\", \"Kang\", role = c(\"aut\"),\nemail = \"7278212@gmail.com\"),\nperson(\"Chris\", \"Scott\", role = c(\"aut\")),\nperson(\"Vanessa\", \"Huang\", role = c(\"aut\")),\nperson(\"Veronica\", \"Tsou\", role = c(\"aut\", \"cre\"),\nemail = \"wantungtsou@gmail.com\"),\nperson(\"Albert\", \"Savary\", role = c(\"ctb\")))",
  "Maintainer": "Veronica Tsou <wantungtsou@gmail.com>",
  "Description": "To implement a model-averaging approach with different\nrenewal models, with a primary focus on forecasting large\nearthquakes. Based on six renewal models (i.e., Poisson, Gamma,\nLog-Logistics, Weibull, Log-Normal and BPT), model-averaged\npoint estimates are calculated using AIC weights. Additionally,\nboth percentile and studentized bootstrapped model-averaged\nconfidence intervals are constructed. In comparison, point and\ninterval estimation from the individual or \"best\" model\n(determined via model selection) can be retrieved.",
  "URL": "https://github.com/kanji709/marp",
  "BugReports": "https://github.com/kanji709/marp/issues",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Config/testthat/edition": "3",
  "Config/roxygen2/version": "8.0.0",
  "VignetteBuilder": "knitr",
  "Repository": "https://kanji709.r-universe.dev",
  "Date/Publication": "2026-08-22 00:16:30 UTC",
  "RemoteUrl": "https://github.com/kanji709/marp",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-09-21 09:44:40 UTC",
    "User": "root"
  },
  "Author": "Jie Kang [aut],\nChris Scott [aut],\nVanessa Huang [aut],\nVeronica Tsou [aut, cre],\nAlbert Savary [ctb]",
  "_user": "kanji709",
  "_type": "src",
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    "author": "Veronica Tsou <veronica.tsou@sydney.edu.au>",
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    "message": "Update for CRAN submission\n",
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  "_contributors": [
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    "name": "Jie Kang",
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  "_realowner": "kanji709",
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      "date": "2022-08-11"
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    "bpt_logl",
    "bpt_rp",
    "dllog",
    "gamma_bstrp",
    "gamma_logl",
    "gamma_rp",
    "loglogis_bstrp",
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    "loglogis_rp",
    "lognorm_bstrp",
    "lognorm_rp",
    "lowerT",
    "marp",
    "marp_bstrp",
    "marp_confint",
    "percent_confint",
    "pllog",
    "poisson_bstrp",
    "poisson_rp",
    "student_confint",
    "upperT",
    "weibull_bstrp",
    "weibull_logl",
    "weibull_rp"
  ],
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    {
      "page": "bpt_bstrp",
      "title": "A function to generate (double) bootstrap samples and fit BPT renewal model",
      "topics": [
        "bpt_bstrp"
      ]
    },
    {
      "page": "bpt_logl",
      "title": "A function to calculate the log-likelihood of BPT model",
      "topics": [
        "bpt_logl"
      ]
    },
    {
      "page": "bpt_rp",
      "title": "A function to fit BPT renewal model",
      "topics": [
        "bpt_rp"
      ]
    },
    {
      "page": "coef.marp_model_fit",
      "title": "Extract parameters from a model-specific fit",
      "topics": [
        "coef.marp_model_fit"
      ]
    },
    {
      "page": "confint.marp_fit",
      "title": "Confidence intervals for a model-averaged renewal-process fit",
      "topics": [
        "confint.marp_fit"
      ]
    },
    {
      "page": "dllog",
      "title": "Density function of Log-Logistics model",
      "topics": [
        "dllog"
      ]
    },
    {
      "page": "gamma_bstrp",
      "title": "A function to generate (double) bootstrap samples and fit Gamma renewal model",
      "topics": [
        "gamma_bstrp"
      ]
    },
    {
      "page": "gamma_logl",
      "title": "A function to calculate the log-likelihood of Gamma model",
      "topics": [
        "gamma_logl"
      ]
    },
    {
      "page": "gamma_rp",
      "title": "A function to fit Gamma renewal model",
      "topics": [
        "gamma_rp"
      ]
    },
    {
      "page": "logLik.marp_model_fit",
      "title": "Extract the log-likelihood from a model-specific fit",
      "topics": [
        "logLik.marp_model_fit"
      ]
    },
    {
      "page": "loglogis_bstrp",
      "title": "A function to generate (double) bootstrap samples and fit Log-Logistic renewal model",
      "topics": [
        "loglogis_bstrp"
      ]
    },
    {
      "page": "loglogis_logl",
      "title": "A function to calculate the log-likelihood of Log-Logistics model",
      "topics": [
        "loglogis_logl"
      ]
    },
    {
      "page": "loglogis_rp",
      "title": "A function to fit Log-Logistics renewal model",
      "topics": [
        "loglogis_rp"
      ]
    },
    {
      "page": "lognorm_bstrp",
      "title": "A function to generate (double) bootstrap samples and fit Log-Normal renewal model",
      "topics": [
        "lognorm_bstrp"
      ]
    },
    {
      "page": "lognorm_rp",
      "title": "A function to fit Log-Normal renewal model",
      "topics": [
        "lognorm_rp"
      ]
    },
    {
      "page": "lowerT",
      "title": "Evaluate the lower-limit equation for a model-averaged T statistic",
      "topics": [
        "lowerT"
      ]
    },
    {
      "page": "marp",
      "title": "A function to apply model-averaged renewal process",
      "topics": [
        "marp"
      ]
    },
    {
      "page": "marp_bstrp",
      "title": "A function to fit model-averaged renewal process",
      "topics": [
        "marp_bstrp"
      ]
    },
    {
      "page": "marp_confint",
      "title": "A function to apply model-averaged renewal process",
      "topics": [
        "marp_confint"
      ]
    },
    {
      "page": "nobs.marp_fit",
      "title": "Number of observations in a model-averaged fit",
      "topics": [
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      ]
    },
    {
      "page": "nobs.marp_model_fit",
      "title": "Number of observations in a model-specific fit",
      "topics": [
        "nobs.marp_model_fit"
      ]
    },
    {
      "page": "percent_confint",
      "title": "A function to calculate percentile bootstrap confidence interval",
      "topics": [
        "percent_confint"
      ]
    },
    {
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      "title": "Probability function of Log-Logistics model",
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      ]
    },
    {
      "page": "poisson_bstrp",
      "title": "A function to generate (double) bootstrap samples and fit Poisson renewal model",
      "topics": [
        "poisson_bstrp"
      ]
    },
    {
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      "title": "A function to fit Poisson renewal model",
      "topics": [
        "poisson_rp"
      ]
    },
    {
      "page": "print.marp_confint",
      "title": "Print a model-averaged confidence-interval result",
      "topics": [
        "print.marp_confint"
      ]
    },
    {
      "page": "print.marp_fit",
      "title": "Print a model-averaged renewal-process fit",
      "topics": [
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      ]
    },
    {
      "page": "print.marp_model_fit",
      "title": "Print a model-specific renewal-process fit",
      "topics": [
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      ]
    },
    {
      "page": "print.summary_marp_fit",
      "title": "Print a summary of a model-averaged renewal-process fit",
      "topics": [
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    },
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      "title": "A function to fit Weibull renewal model",
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