{ "_id": "6a1ee75ab401979e7341172c", "Package": "smsncut", "Type": "Package", "Title": "Optimal Diagnostic Cutoff Selection under Scale Mixtures of\nSkew-Normal Distributions", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Renato\", \"de Paula\",\nemail = \"rrpaula@ciencias.ulisboa.pt\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-5835-5918\")),\nperson(\"Helena\", \"Mouriño\",\nrole = \"aut\"),\nperson(\"Tiago\", \"Dias Domingues\",\nrole = \"aut\"))", "Description": "Implements a parametric decision-theoretic framework for\noptimal diagnostic cutoff selection under the family of scale\nmixtures of skew-normal (SMSN) distributions, including the\nskew-normal (SN) and skew-t (ST) models as special cases. The\noptimal cutoff is defined by minimising a weighted\nmisclassification risk that incorporates disease prevalence and\nasymmetric costs, leading to a likelihood-ratio equation that\ngeneralises the Youden criterion. Under a monotone likelihood\nratio condition, existence, uniqueness, and global optimality\nof the cutoff are established. Asymptotic normality and a\nclosed-form plug-in variance estimator are provided via the\nimplicit function theorem and the multivariate delta method.\nTools for model fitting, cutoff estimation, confidence\nintervals, the local identifiability diagnostic, and Monte\nCarlo simulation are included. The methodology is described in\nde Paula, Mouriño, and Dias Domingues (2026)\n.", "License": "GPL-3", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 14:14:03 UTC", "User": "root" }, "Author": "Renato de Paula [aut, cre] (ORCID:\n), Helena Mouriño [aut],\nTiago Dias Domingues [aut]", "Maintainer": "Renato de Paula ", "Config/roxygen2/version": "8.0.0", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-05-19 08:50:02 UTC", "RemoteUrl": "https://github.com/cran/smsncut", "RemoteRef": "HEAD", "RemoteSha": "df95c3f1321c3d9a33b2a0582fae0f6b1bc5c401", "MD5sum": "89a08e97c2d00d4ca20f76384661d20b", "_user": "cran", "_type": "src", "_file": "smsncut_0.1.0.tar.gz", "_fileid": "894f27d8ff54f52f6800e16be71ab2db907fb4522e33d569bc1355a8e7588518", "_filesize": 284860, "_sha256": "894f27d8ff54f52f6800e16be71ab2db907fb4522e33d569bc1355a8e7588518", "_created": "2026-05-19T14:14:03.000Z", "_published": "2026-06-02T14:23:22.037Z", "_distro": "noble", "_jobs": [ { "job": 79093481918, "time": 129, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7086030684" }, { "job": 79093480732, "time": 138, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7086034104" }, { "job": 79093479836, "time": 167, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7085972422" }, { "job": 79093479605, "time": 124, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7360468476" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/26102325806", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/smsncut", "_commit": { "id": "df95c3f1321c3d9a33b2a0582fae0f6b1bc5c401", "author": "Renato de Paula ", "committer": "cran-robot ", "message": "version 0.1.0\n", "time": 1779180602 }, "_maintainer": { "name": "Renato de Paula", "email": "rrpaula@ciencias.ulisboa.pt", "orcid": "0000-0002-5835-5918" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "sn", "version": ">= 2.0.0", "role": "Imports" }, { "package": "numDeriv", "version": ">= 2016.8-1", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-21", "n": 1 } ], "_tags": [ { "name": "0.1.0", "date": "2026-05-19" } ], "_stars": 0, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/smsncut" }, "_searchresults": 0, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", 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"cutoff_build_interval" ] }, { "page": "cutoff_ci", "title": "Wald confidence interval for the optimal cutoff", "topics": [ "cutoff_ci" ] }, { "page": "cutoff_expand_interval_until_valid", "title": "Expand the search interval until boundary conditions hold", "topics": [ "cutoff_expand_interval_until_valid" ] }, { "page": "cutoff_identifiability", "title": "Local identifiability diagnostic", "topics": [ "cutoff_identifiability" ] }, { "page": "cutoff_optimal", "title": "Optimal diagnostic cutoff under SMSN distributions", "topics": [ "cutoff_optimal" ] }, { "page": "cutoff_phi", "title": "Estimating equation phi(c, theta)", "topics": [ "cutoff_phi" ] }, { "page": "cutoff_risk", "title": "Weighted misclassification risk", "topics": [ "cutoff_risk" ] }, { "page": "cutoff_variance", "title": "Asymptotic variance of the plug-in cutoff estimator", "topics": [ "cutoff_variance" ] }, { "page": "cutoff_youden", "title": "Parametric Youden cutoff under SMSN distributions", "topics": [ "cutoff_youden" ] }, { "page": "mc_one_rep", "title": "Run one Monte Carlo replication", "topics": [ "mc_one_rep" ] }, { "page": "mc_run_all", "title": "Run all scenarios", "topics": [ "mc_run_all" ] }, { "page": "mc_simulate_scenario", "title": "Monte Carlo simulation for one scenario", "topics": [ "mc_simulate_scenario" ] }, { "page": "smsn_auc", "title": "Plug-in AUC estimator under SMSN distributions", "topics": [ "smsn_auc" ] }, { "page": "smsn_cdf", "title": "CDF of an SMSN distribution", "topics": [ "smsn_cdf" ] }, { "page": "smsn_density", "title": "Density of an SMSN distribution", "topics": [ "smsn_density" ] }, { "page": "smsn_fit", "title": "Fit an SMSN model by maximum likelihood", "topics": [ "smsn_fit" ] }, { "page": "smsn_quantile", "title": "Quantile function of an SMSN distribution", "topics": [ "smsn_quantile" ] }, { "page": "smsn_rdist", "title": "Random generation from an SMSN distribution", "topics": [ "smsn_rdist" ] }, { "page": "smsn_roc", "title": "Parametric ROC curve under SMSN distributions", "topics": [ "smsn_roc" ] } ], "_rundeps": [ "lattice", "MASS", "Matrix", "MatrixModels", "mnormt", "numDeriv", "quantreg", "sn", "SparseM", "survival" ], "_vignettes": [ { "source": "smsncut-intro.Rmd", "filename": "smsncut-intro.html", "title": "Getting Started with smsncut", "author": "Renato de Paula, Helena Mouriño, Tiago Dias Domingues", "engine": "knitr::rmarkdown", "headings": [ "Overview", "1. Unconstrained parametrisation", "2. Model fitting", "3. Admissible interval and boundary conditions", "4. Optimal cutoff", "5. Local identifiability diagnostic", "6. Asymptotic variance and Wald confidence interval", "7. ROC curve and AUC", "8. 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