{ "_id": "6a10401cacfb0bcc41c9d032", "Package": "optimalThreshold", "Type": "Package", "Title": "Bayesian Methods for Optimal Threshold Estimation", "Version": "1.0", "SystemRequirements": "JAGS 4.x.y", "Date": "2020-01-09", "Authors@R": "person(\"Yoann\", \"Blangero\", email = \"yblangero@gmail.com\", role = c(\"aut\", \"cre\"))", "Description": "Functions to estimate the optimal threshold of diagnostic\nmarkers or treatment selection markers. The optimal threshold\nis the marker value that maximizes the utility of the marker\nbased-strategy (for diagnostic or treatment selection) in a\ngiven population. The utility function depends on the type of\nmarker (diagnostic or treatment selection), but always takes\ninto account the preferences of the patients or the physician\nin the decision process. For estimating the optimal threshold,\nones must specify the distributions of the marker in different\ngroups (defined according to the type of marker, diagnostic or\ntreatment selection) and provides data to estimate the\nparameters of these distributions. Ones must also provide some\nfeatures of the target populations (disease prevalence or\ntreatment efficacies) as well as the preferences of patients or\nphysicians. The functions rely on Bayesian inference which\nhelps producing several indicators derived from the optimal\nthreshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and\nSubtil, F (2019) for the\noriginal article that describes the estimation method for\ntreatment selection markers and Subtil, F, and Rabilloud, M\n(2019) for diagnostic markers.", "License": "GPL (>= 2.0)", "Collate": "Package.R import_package.R scaledT.R ClassUnions.R\nClassFitNormalDist.R ClassFitLogNormalDist.R\nClassFitGammaDist.R ClassFitStudentDist.R\nClassFitLogisticDist.R ClassFitUserDefinedDist.R\nClassUndefined.R ClassNormalDist.R ClassLogNormalDist.R\nClassGammaDist.R ClassStudentDist.R ClassLogisticDist.R\nClassUnionsDist.R ClassCompoundDist.R ClassTrtSelOptThresh.R\nClassTrtSelRelUtility.R ClassDiagOptThresh.R\nClassDiagRelUtility.R cdf.R gradient.R hessian.R ARS.R\nsamplePosteriorDist.R global.R", "RoxygenNote": "6.1.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 08:25:02 UTC", "User": "root" }, "Author": "Yoann Blangero [aut, cre]", "Maintainer": "Yoann Blangero ", "Config/pak/sysreqs": "jags", "Repository": "https://yblan.r-universe.dev", "Date/Publication": "2020-01-13 15:10:05 UTC", "RemoteUrl": "https://github.com/cran/optimalThreshold", "RemoteRef": "HEAD", "RemoteSha": "66de5c78d76bb344ea4846c90fe1532216e71e75", "MD5sum": "587ddc0aff6b8a0c92fa136721b533c9", "_user": "yblan", "_type": "src", "_file": "optimalThreshold_1.0.tar.gz", "_fileid": "82f75523710df404bc3c6317025aa338ed69bd5dd1957cf826cc1ab007d69f7f", "_filesize": 332623, "_sha256": "82f75523710df404bc3c6317025aa338ed69bd5dd1957cf826cc1ab007d69f7f", "_created": "2026-05-11T08:25:02.000Z", "_published": "2026-05-22T11:38:04.362Z", "_distro": "noble", "_jobs": [ { "job": 77371958376, "time": 168, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6913713112" }, { "job": 77371958000, "time": 125, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6913697720" }, { "job": 77371958152, "time": 164, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": 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