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  "Title": "Joint Bayesian 4PL Models for Thermal Load Sensitivity",
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  "Authors@R": "c(\nperson(\"Daniel W. A.\", \"Noble\",\nemail = \"daniel.noble@anu.edu.au\",\nrole  = c(\"aut\", \"cre\")),\nperson(\"Pieter A.\", \"Arnold\", role = \"aut\"),\nperson(\"Shinichi\", \"Nakagawa\", role = \"aut\"),\nperson(\"Patrice\", \"Pottier\",\nemail = \"patrice.pottier@bioenv.gu.se\",\nrole  = \"aut\"))",
  "Description": "Fits joint Bayesian four-parameter logistic (4PL) models\nto thermal-tolerance proportion data, extracts the classical\nthermal load sensitivity quantities (z, CTmax at 1 hour,\nT_crit) with full posterior uncertainty, and predicts\nheat-injury accumulation and survival under fluctuating\ntemperature regimes with optional Sharpe-Schoolfield repair.\nModels are fitted with 'Stan' via the 'brms' package.\nImplements the framework described in Noble, Arnold, Nakagawa\nand Pottier (2026; bioRxiv). \\doi{10.64898/2026.07.16.738378}.",
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      "page": "plot_temperature_density",
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      "page": "plot_temperature_scenarios",
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    {
      "page": "predict_heat_injury",
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    {
      "page": "predict_survival_curves",
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    {
      "page": "repair_rate_schoolfield",
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      ]
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