{"id":22731074,"url":"https://github.com/genentech/stats4phc","last_synced_at":"2026-02-12T15:03:12.167Z","repository":{"id":229226745,"uuid":"760344314","full_name":"Genentech/stats4phc","owner":"Genentech","description":"Performance evaluation for the prognostic value of predictive models intended to support    personalized healthcare through predictiveness curves and positive / negative predictive values.","archived":false,"fork":false,"pushed_at":"2025-02-17T08:28:32.000Z","size":20877,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-23T16:25:41.666Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://genentech.github.io/stats4phc/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Genentech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-02-20T08:45:16.000Z","updated_at":"2025-02-17T08:13:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"24744282-d3f6-48a4-aeef-b778665f9d4e","html_url":"https://github.com/Genentech/stats4phc","commit_stats":null,"previous_names":["genentech/stats4phc"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/Genentech/stats4phc","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2Fstats4phc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2Fstats4phc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2Fstats4phc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2Fstats4phc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Genentech","download_url":"https://codeload.github.com/Genentech/stats4phc/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2Fstats4phc/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29369382,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-12T08:51:36.827Z","status":"ssl_error","status_checked_at":"2026-02-12T08:51:26.849Z","response_time":55,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-10T19:19:21.080Z","updated_at":"2026-02-12T15:03:12.129Z","avatar_url":"https://github.com/Genentech.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# stats4phc \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"260px\" height=\"300px\" /\u003e\n\n\u003c!-- start badges --\u003e\n[![Check 🛠](https://github.com/genentech/stats4phc/actions/workflows/check.yaml/badge.svg)](https://genentech.github.io/stats4phc/unit-test-report/)\n[![Docs 📚](https://github.com/genentech/stats4phc/actions/workflows/docs.yaml/badge.svg)](https://genentech.github.io/stats4phc/)\n[![Code Coverage 📔](https://raw.githubusercontent.com/genentech/stats4phc/_xml_coverage_reports/data/main/badge.svg)](https://genentech.github.io/stats4phc/coverage-report/)\n\u003c!-- end badges --\u003e\n\nPerformance evaluation for the prognostic value of predictive models intended to \nsupport personalized healthcare (phc) when the outcomes of interest are binary. \n\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/17982157/\" target=\"_blank\"\u003ePredictiveness curves\u003c/a\u003e\nare an insightful visualization to assess the inherent ability of such \nmodels to provide predictions to individual patients. Cumulative versions of predictiveness \ncurves represent positive predictive values and 1 - negative predictive values and are also \ninformative if the eventual goal is to use a cutoff for clinical decision making. \nIn addition, predictiveness curves and their cumulative versions are naturally related to \n\u003ca href=\"https://www.bmj.com/content/352/bmj.i6\" target=\"_blank\"\u003enet benefit\u003c/a\u003e\nperformance metrics to assess clinical utility for phc. Finally, some authors have \nproposed a visualization that assesses both the prognostic value of predictive models and \ntheir performance as a classifier. This package provides a variety of functions for estimation \nand plotting of these performance evaluation curves and metrics.\n\n\n## Installation\n\n``` r\nremotes::install_github(repo = \"genentech/stats4phc\")\n```\n\nFor reproducibility, refer to a specific version tag, for example\n\n``` r\nremotes::install_github(repo = \"genentech/stats4phc\", ref = \"v0.1.2\")\n```\n\n\n## Documentation\n\nPlease refer to https://genentech.github.io/stats4phc \nwhere you can see function reference as well as introduction vignette.\n\n## Example\n\nThis is a basic example which demonstrates `riskProfile` function (i.e. Predictiveness curve):\n\n``` r\nlibrary(stats4phc)\n\n# Read in example data\nauroc \u003c- read.csv(system.file(\"extdata\", \"sample.csv\", package = \"stats4phc\"))\nrscore \u003c- auroc$predicted_calibrated\ntruth \u003c- as.numeric(auroc$actual)\n\n# For clarity, show just PPV and 1-NPV. Or display Predictiveness curve by using \"PC\"\np1 \u003c- riskProfile(outcome = truth, score = rscore, include = c(\"PPV\", \"1-NPV\"))\np1$plot\n# You can also access the underlying data with `p1$data`\n```\n\n![](man/figures/readme_p1.png)\n\n\u003cbr/\u003e\n\nand `sensSpec` function (Sensitivity and Specificity Plot):\n\n```r\np2 \u003c- sensSpec(outcome = truth, score = rscore)\np2$plot\n```\n\n![](man/figures/readme_p2.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgenentech%2Fstats4phc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgenentech%2Fstats4phc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgenentech%2Fstats4phc/lists"}