{
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  "Title": "Concentration-Response Data Analysis using Curvep",
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  "Authors@R": "c(\nperson(\"Jui-Hua\", \"Hsieh\", email = \"juihua.hsieh@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1620-1571\")),\nperson(\"Alexander\", \"Sedykh\", role = c(\"aut\")),\nperson(\"Fred\", \"Parham\", role = c(\"ctb\")),\nperson(\"Yuhong\", \"Wang\", role = c(\"ctb\")),\nperson(\"Tongan\", \"Zhao\", role = c(\"aut\")),\nperson(\"Ruili\", \"Huang\", role = c(\"ctb\"))\n)",
  "Description": "An R interface for processing concentration-response\ndatasets using Curvep, a response noise filtering algorithm.\nThe algorithm was described in the publications (Sedykh A et\nal. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016)\n<doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting\napproaches (e.g., Hill equation) are also adopted for ease of\ncomparison. 3-parameter Hill equation from 'tcpl' package\n(Filer D et al., <doi:10.1093/bioinformatics/btw680>) and\n4-parameter Hill equation from Curve Class2 approach (Wang Y et\nal., <doi:10.2174/1875397301004010057>) are available. Also,\nmethods for calculating the confidence interval around the\nactivity metrics are also provided. The methods are based on\nthe bootstrap approach to simulate the datasets (Hsieh J-H et\nal. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be\nused to derive the baseline noise threshold in an assay\nendpoint. This threshold is critical in the toxicological\nstudies to derive the point-of-departure (POD).",
  "Language": "en-US",
  "BugReports": "https://github.com/moggces/Rcurvep/issues",
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      "object": "zfishdev_all",
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    {
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      "title": "Create concentration-response datasets that can be applied in the 'run_rcurvep()'",
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      "page": "curvep",
      "title": "The Curvep function to process one set of concentration-response data",
      "topics": [
        "curvep"
      ]
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    {
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      "topics": [
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      "title": "Estimate benchmark response (BMR) for each dataset",
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