{ "_id": "6a213cdecd65a98ecbd286bc", "Package": "cutpointr", "Type": "Package", "Title": "Determine and Evaluate Optimal Cutpoints in Binary\nClassification Tasks", "Version": "1.2.0", "Date": "2024-12-10", "Authors@R": "person(given = \"Christian\",\nfamily = \"Thiele\",\nrole = c(\"cre\", \"aut\"),\nemail = \"c.thiele@gmx-topmail.de\",\ncomment = c(ORCID = \"0000-0002-1156-5117\"))", "Description": "Estimate cutpoints that optimize a specified metric in\nbinary classification tasks and validate performance using\nbootstrapping. Some methods for more robust cutpoint estimation\nare supported, e.g. a parametric method assuming normal\ndistributions, bootstrapped cutpoints, and smoothing of the\nmetric values per cutpoint using Generalized Additive Models.\nVarious plotting functions are included. For an overview of the\npackage see Thiele and Hirschfeld (2021)\n.", "License": "GPL-3", "URL": "https://github.com/thie1e/cutpointr", "BugReports": "https://github.com/thie1e/cutpointr/issues", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.1", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://thie1e.r-universe.dev", "Date/Publication": "2024-12-10 22:22:16 UTC", "RemoteUrl": "https://github.com/thie1e/cutpointr", "RemoteRef": "HEAD", "RemoteSha": "0a044b9448d95e5d4a465c4b0ad5ed4fe796e2c9", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-04 08:41:26 UTC", "User": "root" }, "Author": "Christian Thiele [cre, aut] (ORCID:\n)", "Maintainer": "Christian Thiele ", "MD5sum": "a771aed05b2747137e284383ef4dc8ac", "_user": "thie1e", "_type": "src", "_file": "cutpointr_1.2.0.tar.gz", "_fileid": "075c66a0cbf07559a48e76c6a2500102c3f9fcdc6eb0d0d1a875a736326dd318", "_filesize": 3492014, "_sha256": 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"role": "Suggests" } ], "_owner": "thie1e", "_selfowned": true, "_usedby": 2, "_updates": [], "_tags": [], "_topics": [ "bootstrapping", "cutpoint-optimization", "roc-curve", "cpp" ], "_stars": 92, "_contributors": [ { "user": "thie1e", "count": 350, "uuid": 7810625 }, { "user": "xrobin", "count": 11, "uuid": 1047170 }, { "user": "muschellij2", "count": 4, "uuid": 1075118 }, { "user": "kapsner", "count": 3, "uuid": 44973495 }, { "user": "hadley", "count": 2, "uuid": 4196 }, { "user": "jnshsrs", "count": 1, "uuid": 7271839 } ], "_userbio": { "uuid": 7810625, "type": "user", "name": "Christian Thiele" }, "_downloads": { "count": 1424, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/cutpointr" }, "_mentions": 11, "_devurl": "https://github.com/thie1e/cutpointr", "_searchresults": 432, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/cutpointr.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/thie1e/cutpointr", "_realowner": "thie1e", "_cranurl": true, "_releases": [ { "version": "0.7.2", "date": "2018-03-21" }, { "version": "0.7.3", "date": "2018-04-13" }, { "version": "0.7.4", "date": "2018-08-31" }, { "version": "0.7.6", "date": "2019-03-27" }, { "version": "1.0.0", "date": "2019-09-17" }, { "version": "1.0.1", "date": "2019-12-18" }, { "version": "1.0.2", "date": "2020-04-14" }, { "version": "1.0.32", "date": "2020-06-19" }, { "version": "1.1.0", "date": "2021-02-15" }, { "version": "1.1.1", "date": "2021-06-29" }, { "version": "1.1.2", "date": "2022-04-13" }, { "version": "1.2.0", "date": "2024-12-10" }, { "version": "1.2.1", "date": "2025-06-13" } ], "_exports": [ "abs_d_ppv_npv", "abs_d_sens_spec", "acc_constrain", "accuracy", "add_metric", "auc", "boot_ci", "boot_test", "cohens_kappa", "cutpoint", "cutpointr", "cutpointr_", "cutpoints", "F1_score", "false_discovery_rate", "false_omission_rate", "fn", "fnr", "fp", "fpr", "Jaccard", "maximize_boot_metric", "maximize_gam_metric", "maximize_loess_metric", "maximize_metric", "metric_constrain", "minimize_boot_metric", "minimize_gam_metric", "minimize_loess_metric", "minimize_metric", "misclassification_cost", "multi_cutpointr", "nlr", "npv", "oc_manual", "oc_mean", "oc_median", "oc_youden_kernel", "oc_youden_normal", "odds_ratio", "p_chisquared", "plot_cut_boot", "plot_cutpointr", "plot_metric", "plot_metric_boot", "plot_precision_recall", "plot_roc", "plot_sensitivity_specificity", "plot_x", "plr", "ppv", "precision", "prod_ppv_npv", "prod_sens_spec", "recall", "risk_ratio", "roc", "roc01", "sens_constrain", "sensitivity", "spec_constrain", "specificity", "sum_ppv_npv", "sum_sens_spec", "tn", "tnr", "total_utility", "tp", "tpr", "youden" ], "_datasets": [ { "name": "prostate_nodal", "title": "Nodal involvement and acid phosphatase levels in 53 prostate cancer patients", "object": "prostate_nodal", "class": [ "data.frame" ], "fields": [ "acid_phosphatase", "nodal_involvement" ], "rows": 53, "table": true, "tojson": true }, { "name": "suicide", "title": "Suicide attempts and DSI sum scores of 532 subjects", "object": "suicide", "class": [ "data.frame" ], "fields": [ "age", "gender", "dsi", "suicide" ], "rows": 532, "table": true, "tojson": true } ], "_help": [ { "page": "abs_d_ppv_npv", "title": "Calculate the absolute difference of positive and negative predictive value", "concept": [ "metric functions" ], "topics": [ "abs_d_ppv_npv" ] }, { "page": "abs_d_sens_spec", "title": "Calculate the absolute difference of sensitivity and specificity", "concept": [ "metric functions" ], "topics": [ "abs_d_sens_spec" ] }, { "page": "accuracy", "title": "Calculate accuracy", "concept": [ "metric functions" ], "topics": [ "accuracy" ] }, { "page": "add_metric", "title": "Add metrics to a cutpointr or roc_cutpointr object", "concept": [ "main cutpointr functions" ], "topics": [ "add_metric", "add_metric.cutpointr", "add_metric.multi_cutpointr", "add_metric.roc_cutpointr" ] }, { "page": "auc", "title": "Calculate AUC from a roc_cutpointr or cutpointr object", "topics": [ "auc", "auc.cutpointr", "auc.roc_cutpointr" ] }, { "page": "boot_ci", "title": "Calculate bootstrap confidence intervals from a cutpointr object", "concept": [ "main cutpointr functions" ], "topics": [ "boot_ci" ] }, { "page": "boot_test", "title": "Test for equivalence of a metric", "concept": [ "main cutpointr functions" ], "topics": [ "boot_test" ] }, { "page": "cohens_kappa", "title": "Calculate Cohen's Kappa", "concept": [ "metric functions" ], "topics": [ "cohens_kappa" ] }, { "page": "cutpoint", "title": "Extract the cutpoints from a ROC curve generated by cutpointr", "concept": [ "metric functions" ], "topics": [ "cutpoint", "cutpoints" ] }, { "page": "cutpointr", "title": "Determine and evaluate optimal cutpoints", "concept": [ "main cutpointr functions" ], "topics": [ "cutpointr", "cutpointr.default", "cutpointr.numeric" ] }, { "page": "cutpointr_", "title": "The standard evaluation version of cutpointr (deprecated)", "topics": [ "cutpointr_" ] }, { "page": "F1_score", "title": "Calculate the F1-score", "concept": [ "metric functions" ], "topics": [ "F1_score" ] }, { "page": "false_omission_rate", "title": "Calculate the false omission and false discovery rate", "concept": [ "metric functions" ], "topics": [ "false_discovery_rate", "false_omission_rate" ] }, { "page": "Jaccard", "title": "Calculate the Jaccard Index", "concept": [ "metric functions" ], "topics": [ "Jaccard" ] }, { "page": "maximize_boot_metric", "title": "Optimize a metric function in binary classification after bootstrapping", "concept": [ "method functions" ], "topics": [ "maximize_boot_metric", "minimize_boot_metric" ] }, { "page": "maximize_gam_metric", "title": "Optimize a metric function in binary classification after smoothing via generalized additive models", "concept": [ "method functions" ], "topics": [ "maximize_gam_metric", "minimize_gam_metric" ] }, { "page": "maximize_loess_metric", "title": "Optimize a metric function in binary classification after LOESS smoothing", "concept": [ "method functions" ], "topics": [ "maximize_loess_metric", "minimize_loess_metric" ] }, { "page": "maximize_metric", "title": "Optimize a metric function in binary classification", "concept": [ "method functions" ], "topics": [ "maximize_metric", "minimize_metric" ] }, { "page": "metric_constrain", "title": "Metrics that are constrained by another metric", "concept": [ "metric functions" ], "topics": [ "acc_constrain", "metric_constrain", "sens_constrain", "spec_constrain" ] }, { "page": "misclassification_cost", "title": "Calculate the misclassification cost", "concept": [ "metric functions" ], "topics": [ "misclassification_cost" ] }, { "page": "multi_cutpointr", "title": "Calculate optimal cutpoints and further statistics for multiple predictors", "concept": [ "main cutpointr functions" ], "topics": [ "multi_cutpointr" ] }, { "page": "npv", "title": "Calculate the negative predictive value", "concept": [ "metric functions" ], "topics": [ "npv" ] }, { "page": "oc_manual", "title": "Set a manual cutpoint for use with cutpointr", "concept": [ "method functions" ], "topics": [ "oc_manual" ] }, { "page": "oc_mean", "title": "Use the sample mean as cutpoint", "concept": [ "method functions" ], "topics": [ "oc_mean" ] }, { "page": "oc_median", "title": "Use the sample median as cutpoint", "concept": [ "method functions" ], "topics": [ "oc_median" ] }, { "page": "oc_youden_kernel", "title": "Determine an optimal cutpoint maximizing the Youden-Index based on kernel smoothed densities", "concept": [ "method functions" ], "topics": [ "oc_youden_kernel" ] }, { "page": "oc_youden_normal", "title": "Determine an optimal cutpoint for the Youden-Index assuming normal distributions", "concept": [ "method functions" ], "topics": [ "oc_youden_normal" ] }, { "page": "odds_ratio", "title": "Calculate the odds ratio", "concept": [ "metric functions" ], "topics": [ "odds_ratio" ] }, { "page": "p_chisquared", "title": "Calculate the p-value of a chi-squared test", "concept": [ "metric functions" ], "topics": [ "p_chisquared" ] }, { "page": "plot_cut_boot", "title": "Plot the bootstrapped distribution of optimal cutpoints from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_cut_boot" ] }, { "page": "plot_cutpointr", "title": "General purpose plotting function for cutpointr or roc_cutpointr objects", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_cutpointr" ] }, { "page": "plot_metric", "title": "Plot a metric over all possible cutoffs from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_metric" ] }, { "page": "plot_metric_boot", "title": "Plot the bootstrapped metric distribution from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_metric_boot" ] }, { "page": "plot_precision_recall", "title": "Precision recall plot from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_precision_recall" ] }, { "page": "plot_roc", "title": "Plot ROC curve from a cutpointr or roc_cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_roc", "plot_roc.cutpointr", "plot_roc.roc_cutpointr" ] }, { "page": "plot_sensitivity_specificity", "title": "Sensitivity and specificity plot from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_sensitivity_specificity" ] }, { "page": "plot_x", "title": "Plot the distribution of the independent variable per class from a cutpointr object", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot_x" ] }, { "page": "plot.cutpointr", "title": "Plot cutpointr objects", "concept": [ "cutpointr plotting functions" ], "topics": [ "plot.cutpointr" ] }, { "page": "plot.multi_cutpointr", "title": "Plotting multi_cutpointr objects is currently not supported", "topics": [ "plot.multi_cutpointr" ] }, { "page": "plot.roc_cutpointr", "title": "Plot ROC curve from a cutpointr or roc_cutpointr object", "topics": [ "plot.roc_cutpointr" ] }, { "page": "plr", "title": "Calculate the positive or negative likelihood ratio", "concept": [ "metric functions" ], "topics": [ "nlr", "plr" ] }, { "page": "ppv", "title": "Calculate the positive predictive value", "concept": [ "metric functions" ], "topics": [ "ppv" ] }, { "page": "precision", "title": "Calculate precision", "concept": [ "metric functions" ], "topics": [ "precision" ] }, { "page": "predict.cutpointr", "title": "Predict using a cutpointr object", "concept": [ "main cutpointr functions" ], "topics": [ "predict.cutpointr" ] }, { "page": "print.cutpointr", "title": "Print cutpointr objects", "topics": [ "print.cutpointr" ] }, { "page": "print.multi_cutpointr", "title": "Print multi_cutpointr objects", "topics": [ "print.multi_cutpointr" ] }, { "page": "prod_ppv_npv", "title": "Calculate the product of positive and negative predictive value", "concept": [ "metric functions" ], "topics": [ "prod_ppv_npv" ] }, { "page": "prod_sens_spec", "title": "Calculate the product of sensitivity and specificity", "concept": [ "metric functions" ], "topics": [ "prod_sens_spec" ] }, { "page": "prostate_nodal", "title": "Nodal involvement and acid phosphatase levels in 53 prostate cancer patients", "topics": [ "prostate_nodal" ] }, { "page": "recall", "title": "Calculate recall", "concept": [ "metric functions" ], "topics": [ "recall" ] }, { "page": "risk_ratio", "title": "Calculate the risk ratio (relative risk)", "concept": [ "metric functions" ], "topics": [ "risk_ratio" ] }, { "page": "roc", "title": "Calculate a ROC curve", "concept": [ "main cutpointr functions" ], "topics": [ "roc" ] }, { "page": "roc01", "title": "Calculate the distance between points on the ROC curve and (0,1)", "concept": [ "metric functions" ], "topics": [ "roc01" ] }, { "page": "sensitivity", "title": "Calculate sensitivity", "concept": [ "metric functions" ], "topics": [ "sensitivity" ] }, { "page": "specificity", "title": "Calculate specificity", "concept": [ "metric functions" ], "topics": [ "specificity" ] }, { "page": "suicide", "title": "Suicide attempts and DSI sum scores of 532 subjects", "topics": [ "suicide" ] }, { "page": "sum_ppv_npv", "title": "Calculate the sum of positive and negative predictive value", "concept": [ "metric functions" ], "topics": [ "sum_ppv_npv" ] }, { "page": "sum_sens_spec", "title": "Calculate the sum of sensitivity and specificity", "concept": [ "metric functions" ], "topics": [ "sum_sens_spec" ] }, { "page": "total_utility", "title": "Calculate the total utility", "concept": [ "metric functions" ], "topics": [ "total_utility" ] }, { "page": "tp", "title": "Extract number true / false positives / negatives", "concept": [ "metric functions" ], "topics": [ "fn", "fp", "tn", "tp" ] }, { "page": "tpr", "title": "Calculate true / false positive / negative rate", "concept": [ "metric functions" ], "topics": [ "fnr", "fpr", "tnr", "tpr" ] }, { "page": "user_span_cutpointr", "title": "Calculate bandwidth for LOESS smoothing of metric functions by rule of thumb", "topics": [ "user_span_cutpointr" ] }, { "page": "youden", "title": "Calculate the Youden-Index", "concept": [ "metric functions" ], "topics": [ "youden" ] } ], "_readme": "https://github.com/thie1e/cutpointr/raw/HEAD/README.md", "_rundeps": [ "cli", "codetools", "cpp11", "dplyr", "farver", "foreach", "generics", "ggplot2", "glue", "gridExtra", "gtable", "isoband", "iterators", "labeling", "lifecycle", "magrittr", "pillar", "pkgconfig", "purrr", "R6", "RColorBrewer", "Rcpp", "rlang", "S7", "scales", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": 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